A World Bank Group
Flagship Report

JANUARY 2019

GlobalEconomic
Prospects
Darkening Skies

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ISSN: 1014-8906
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DOI: 10.1596/978-1-4648-1343-6
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The cutoff date for the data used in this report was December 19, 2018.

Summary of Contents
Chapter 1

Global Outlook: Darkening Skies..................................................................... 1

Chapter 2

Regional Outlooks ....................................................................................... 55

Chapter 3

Growing in the Shadow: Challenges of Informality........................................ 127

Chapter 4

Two Topical Essays .................................................................................... 197
Debt in Low-Income Countries: Evolution, Implications, and Remedies ........ 199
Poverty Impact of Food Price Shocks and Policies ........................................ 209

Boxes
Box 1.1

The great disinflation ...................................................................... 8

Box 1.2

Low-income countries: Recent developments and outlook................. 22

Box 1.3

Regional perspectives: Recent developments and outlook .................. 26

Box 2.1.1 Informality in East Asia and Pacific ................................................ 63
Box 2.2.1 Informality in Europe and Central Asia ........................................... 75
Box 2.3.1 Informality in Latin America and the Caribbean .............................. 85
Box 2.4.1 Informality in the Middle East and North Africa.............................. 95
Box 2.5.1 Informality in South Asia............................................................. 104
Box 2.6.1 Informality in Sub-Saharan Africa ................................................ 113
Box 3.1

Linkages between formal and informal sectors ................................ 132

Box 3.2

Regional dimensions of informality: An overview ............................140

Box 3.3

Casting a shadow: Productivity in formal and informal firms ......... 146

Box 3.4

Under the magnifying glass: How do policies affect informality? ...... 155

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

Global Outlook: Darkening Skies..........................................................................1
Summary ............................................................................................................3
Major economies: Recent developments and outlook................................................7
United States ................................................................................................ 7
Euro Area ....................................................................................................13
Japan ..........................................................................................................13
China..........................................................................................................14
Global trends ..................................................................................................... 15
Global trade ................................................................................................. 15
Financial markets ......................................................................................... 17
Commodities .............................................................................................. 18
Emerging market and developing economies: Recent developments and outlook ....... 19
Recent developments .................................................................................... 20
EMDE outlook ............................................................................................ 28
Risks to the outlook............................................................................................ 30
Disorderly financial market developments ....................................................... 31
Escalating trade restrictions ........................................................................... 32
Policy uncertainty and geopolitical tensions ..................................................... 33
Region-specific downside risks ....................................................................... 35
Simultaneous slowdown in the two largest economies ....................................... 35
Possible productivity revival........................................................................... 37
Policy challenges ................................................................................................ 37
Challenges in advanced economies ................................................................. 37
Challenges in emerging market and developing economies ................................ 39
Box 1.1 The great disinflation ...............................................................................8
Box 1.2 Low-income countries: Recent developments and outlook .......................... 22
Box 1.3 Regional perspectives: Recent developments and outlook ........................... 26
References ......................................................................................................... 47
VChapter 2

Regional Outlooks ............................................................................................ 55
East Asia and Pacific ......................................................................................... 57
Recent developments....................................................................................... 57
Outlook......................................................................................................... 59
Risks ............................................................................................................. 61
Box 2.1.1 Informality in East Asia and Pacific .................................................... 63
Europe and Central Asia ................................................................................... 69
Recent developments....................................................................................... 69
Outlook......................................................................................................... 70
Risks ............................................................................................................ 71
Box 2.2.1 Informality in Europe and Central Asia .............................................. 75
Latin America and the Caribbean ....................................................................... 79
Recent developments....................................................................................... 79
Outlook......................................................................................................... 80
Risks ............................................................................................................. 82
Box 2.3.1 Informality in Latin America and the Caribbean.................................. 85
Middle East and North Africa ............................................................................ 89
Recent developments....................................................................................... 89
Outlook......................................................................................................... 90
Risks ............................................................................................................. 91
Box 2.4.1 Informality in the Middle East and North Africa ................................. 95
South Asia ........................................................................................................ 99
Recent developments....................................................................................... 99
Outlook........................................................................................................100
Risks ............................................................................................................102
Box 2.5.1 Informality in South Asia ................................................................ 104
Sub-Saharan Africa ......................................................................................... 107
Recent developments......................................................................................107
Outlook........................................................................................................109
Risks ............................................................................................................110
Box 2.6.1 Informality in Sub-Saharan Africa.................................................... 113
References.....................................................................................................118

VI

Chapter 3

Growing in the Shadow: Challenges of Informality.............................................127
Introduction .................................................................................................... 129
Informality: Conceptual considerations and measurement..................................... 136
Main features of the informal economy ............................................................... 137
Causes and implications of informality................................................................ 143
Correlates of informality ................................................................................... 144
Informality, poverty, and income inequality ........................................................ 152
Worker earnings differentials ....................................................................... 152
Aggregate income inequality and poverty....................................................... 154
Informality and fiscal outcomes.......................................................................... 158
Policy options .................................................................................................. 158
Fiscal policy measures.................................................................................. 159
Business climate and governance measures ..................................................... 160
From a comprehensive strategy to implementation ......................................... 162
Emerging policy opportunities and challenges ................................................ 162
Annex 3.1 Measures of informality ..................................................................... 163
Annex 3.2 Characteristics of informal-economy business cycles.............................. 167
Annex 3.3 Informality and earnings inequality: A meta-analysis approach ............... 167
Annex 3.4 Pre-existing informality and changes in poverty and income inequality ... 169
Annex 3.5 Labor legislation and informality ........................................................ 169
References ....................................................................................................... 184
Box 3.1 Linkages between formal and informal sectors.......................................... 132
Box 3.2 Regional dimensions of informality: An overview ..................................... 140
Box 3.3 Casting a shadow: Productivity in formal and informal firms ..................... 146
Box 3.4 Under the magnifying glass: How do policies affect informality ................. 155

Chapter 4

Two Topical Essays ........................................................................................ 197
Debt in Low-Income Countries: Evolution, Implications, and Remedies ................ 199
Introduction .............................................................................................. 199
Key characteristics of the recent rise in LIC debt ............................................ 199
Other LIC vulnerabilities ............................................................................ 202
Role of better debt management................................................................... 204
Complementary policy measures .................................................................. 206
Conclusion ................................................................................................ 207
Annex 4.1 Comparison of LIDCs and LICs................................................... 208

VIIPoverty Impact of Food Price Shocks and Policies ................................................209
Introduction ..............................................................................................209
Food price shocks and their effects ................................................................211
Government interventions during food price shocks .......................................214
Poverty impact of the 2010-11 food price shock.............................................217
Conclusions ...............................................................................................220
Annex 4.2 Methodology ..............................................................................221
References........................................................................................................224

Statistical Appendix.............................................................................................................. 229
Selected Topics .................................................................................................................... 236

Figures

VIII

1.1

Summary - Global prospects ..................................................................... 5

1.2

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

1.3

Advanced economies................................................................................ 7

1.1.1

Global inflation ....................................................................................... 9

1.1.2

Disinflation and factors associated with disinflation................................... 10

1.1.3

Inflation synchronization........................................................................ 12

1.1.4

Low inflation episodes............................................................................ 12

1.4

United States ........................................................................................ 13

1.5Euro Area ............................................................................................. 14

1.6

Japan ................................................................................................... 14

1.7

China................................................................................................... 15

1.8

Global trade.......................................................................................... 16

1.9

Global finance....................................................................................... 17

1.10

Commodity markets .............................................................................. 18

1.11

Activity in EMDEs ................................................................................ 20

1.2.1

Recent developments in low-income countries .......................................... 23

1.2.2

Outlook ............................................................................................... 24

1.3.1

Regional growth .................................................................................... 27

1.12

EMDE growth prospects ........................................................................ 28

1.13

Poverty and per capita income growth ..................................................... 29

1.14

Risks: Tilted to the downside .................................................................. 30

1.15

Financial stress ...................................................................................... 32

1.16

Trade protectionism .............................................................................. 33

1.17

Policy uncertainty and other downside risks .........................................................34

1.18

Simultaneous slowdown in the two largest economies ...........................................35

1.19

Productivity revival ..........................................................................................37

1.20

Monetary and fiscal policy in advanced economies................................................38

1.21

Structural policies in advanced economies............................................................39

1.22

EMDE monetary policy .................................................................................... 40

1.23

EMDE fiscal policy........................................................................................... 41

1.24

EMDE structural policy .................................................................................... 43

2.1.1

EAP: Recent developments ................................................................................ 58

2.1.2

China: Recent developments .............................................................................. 59

2.1.3

EAP: Outlook and risks ..................................................................................... 60

2.1.1.1 Informality in East Asia and Pacific..................................................................... 64
2.1.1.2 Drivers and implications of informality in East Asia and Pacific ............................. 66
2.2.1

ECA: Recent developments................................................................................ 70

2.2.2

ECA: Outlook and risks ....................................................................................71

2.2.1.1 Informality in Europe and Central Asia .............................................................. 76
2.2.1.2 Correlates of informality in Europe and Central Asia ............................................ 78
2.3.1

LAC: Recent developments ................................................................................80

2.3.2

LAC: Outlook and risks ....................................................................................81

2.3.1.1 Informality in Latin America and the Caribbean................................................... 86
2.3.1.2 Evolution and correlates of informality in Latin America and the Caribbean............ 87
2.4.1

MENA: Recent developments ............................................................................90

2.4.2

MENA: Outlook and risks.................................................................................91

2.4.1.1 Informality in the Middle East and North Africa .................................................. 96
2.4.1.2 Correlates of informality and policy challenges in the Middle East and North Africa 97
2.5.1

SAR: Recent developments .............................................................................. 100

2.5.2

SAR: Outlook and risks ................................................................................... 101

2.5.1.1 Informality in South Asia................................................................................. 105
2.5.1.2 Drivers of informality in South Asia.................................................................. 106
2.6.1

SSA: Recent developments ............................................................................... 108

2.6.2

SSA: Outlook and risks ................................................................................... 109

2.6.1.1 Informality in Sub-Saharan Africa..................................................................... 114
2.6.1.2 Economic and institutional indicators in Sub-Saharan Africa ............................... 115
2.6.1.3 Correlates of informality in Sub-Saharan Africa.................................................. 116
2.6.1.4 Entrepreneurial conditions, entrepreneurship attitude, and informality indicators
in Sub-Saharan Africa.................................................................................................. 117
IX

3.1

Informality: Magnitude and correlates ................................................................. 131

3.1.1

Interaction between formal and informal economies ............................................. 135

3.2

Informality in advanced economies and EMDEs .................................................. 138

3.3

Informality by EMDE region .............................................................................. 139

3.2.1

Informality in EMDE regions ............................................................................. 141

3.2.2

Regional correlates of informality ........................................................................ 142

3.4

Changes in informality........................................................................................ 144

3.5

Characteristics of informal- and formal-economy business cycles ........................... 145

3.3.1

Characteristics of informal firms .......................................................................... 147

3.3.2

Formal firms facing informal competition ............................................................ 149

3.3.3

Productivity of formal firms facing informal competition ...................................... 150

3.6

Correlates of informality: Economic and institutional factors ............................... 151

3.7

EMDEs: Correlates of changes in informality ....................................................... 152

3.8

EMDEs: Estimates of informal-formal wage gap................................................... 153

3.9

Informality, poverty, and income inequality ......................................................... 154

3.4.1

Overview of policy changes ................................................................................. 157

3.10

EMDEs: Informality and fiscal outcomes ............................................................. 159

3.11

EMDEs: Policies to address challenges of informality............................................ 160

Annex Figure 3.5.1. Implications of relaxing the minimum wage restraint .......................... 171

X

4.1.1

LIC government finances ................................................................................... 200

4.1.2

Public debt in LICs............................................................................................. 201

4.1.3

Risks to LIC debt sustainability ........................................................................... 203

4.1.4

External positions in LICs ................................................................................... 205

4.1.5

LIC policy framework ......................................................................................... 206

4.2.1

Global food prices ............................................................................................. 210

4.2.2

Macroeconomic channels of transmission from global food prices ........................ 212

4.2.3

Microeconomic channels of transmission from global food prices ......................... 213

4.2.4

Food-related government policies ....................................................................... 215

4.2.5

Domestic and global food prices ......................................................................... 216

4.2.6

The extent of government interventions during the 2010-11 food price spike ....... 218

4.2.7

Poverty impact of policies implemented during the 2010-11 food price spike ........ 219

Tables

1.1

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

1.2.1

Low-income country forecasts ..............................................................................25

1.2

List of emerging market and developing economies.................................................46

2.1.1

East Asia and Pacific forecast summary ..................................................................62

2.1.2

East Asia and Pacific country forecasts...................................................................622.2.1

Europe and Central Asia forecast summary ............................................................73

2.2.2

Europe and Central Asia country forecasts .............................................................74

2.3.1

Latin America and the Caribbean forecast summary................................................83

2.3.2

Latin America and the Caribbean country forecasts.................................................84

2.4.1

Middle East and North Africa forecast summary ....................................................93

2.4.2

Middle East and North Africa economy forecasts ...................................................94

2.5.1

South Asia forecast summary .............................................................................. 103

2.5.2

South Asia country forecasts............................................................................... 103

2.6.1

Sub-Saharan Africa forecast summary.................................................................. 111

2.6.2

Sub-Saharan Africa country forecasts................................................................... 112

Annex Table 3.1 Labor productivity differential between types of firms (percent) ................. 172
Annex Table 3.2 Labor productivity of formal and informal firms...................................... 173
Annex Table 3.3 Labor productivity of formal firms facing informal competition ................ 174
Annex Table 3.4 Survey of policy changes ....................................................................... 175
Annex Table 3.1.1 Data coverage ................................................................................... 178
Annex Table 3.1.2 MIMIC model estimation results (1993-2015) ..................................... 179
Annex Table 3.3.1 Database of studies for meta regressions analysis ................................... 180
Annex Table 3.3.2 Meta regression analysis summary ....................................................... 181
Annex Table 3.4.1 Pre-existing informality and changes in poverty and income
inequality: OLS ............................................................................................................ 182
Annex Table 3.5.1 Sample............................................................................................. 183
Annex Table 4.1 List of countries in LIDCs and LICs ...................................................... 208

XI

Foreword
Achieving the Sustainable Development Goals, theWorld Bank’s twin goals—or any otherdevelopment goal for that matter—hinges onsustained economic growth in emerging market
and developing economies (EMDEs). This growthin turn depends on macroeconomic stability. The
semi-annual Global Economic Prospects (GEP)
report assesses the global outlook for growth andstability in these countries.
The January 2019 edition, Darkening Skies,
highlights how precarious the current economic
juncture is. In a nutshell, growth has weakened,
trade tensions remain high, several developing
economies have experienced financial stress, andrisks to the outlook have increased. As the report
points out, EMDEs face some of the greatest risks.
If a trade war between the United States andChina contributes to a global slowdown, thespillover effects on EMDEs could be profound.
Similarly, a sharp increase in global interest rates
would severely affect highly indebted EMDEs, as
Turkey and Argentina painfully discovered lastsummer.
Since the global outlook depends heavily on
advanced economies, the GEP also flags theimplications of advanced-economy policies for
EMDEs. In January 2018, when there was acelebratory mood about the synchronized recovery
among advanced economies and EMDEs, the
GEP questioned its duration, even in its title:
Broad-Based Upturn, but for How Long? That
skepticism came from a close study of potential
growth—the amount by which the economy
would grow if all factors were fully employed—
which found this to be wanting in many
economies because of the previous years’
slowdown in productivity and investment growth.

The GEP does not just analyze short-run growthforecasts; it embeds these forecasts in a longer-run
view of the economy.
This edition of the GEP continues that tradition
with a comprehensive study of the informal
economy, something which could in the short-run
be a shock absorber, but in the long-run isassociated with low productivity. The analysis
suggests that the informal sector’s role inabsorbing labor during downturns is limited.
However, the potential long-term gains fromincreasing productivity in the informal sector are
substantial. This edition also presents evidence
that debt in low-income countries is on the rise,
an issue that is being discussed extensively inpolicy circles.
The GEP tries to hit the sweet spot between
discussing topical policy issues and undertaking
rigorous analytical work. On the one hand,
analysts, especially those in internationalorganizations, are sometimes cautious about
speaking out on the global economy—especiallywhen the prospects are not good—lest they
exacerbate the pessimism. On the other hand, thepublic is subject to so much half-baked analysis,
half-truths, and fake news that an analyticallyrigorous report such as the GEP is critical to an
informed debate.
Public policy is not made by “whispering in thefinance minister’s ear.” Rather, it is made byarriving at a political consensus. That consensus is
more likely to improve people’s lives if the public
is informed with evidence. Like its predecessors,
this edition of the GEP is a contribution to that
evidence.
Shantayanan Devarajan
Senior Director
Development Economics Vice Presidency
The World Bank GroupXIII

Acknowledgments
This World Bank Group Flagship Report is a product of the Prospects Group in the Development Economics
Vice Presidency. The project was managed by M. Ayhan Kose and Franziska Ohnsorge, under the generalguidance of Shantayanan Devarajan.
Global and regional surveillance work was coordinatedby Carlos Arteta. Chapter 1 was prepared by Carlos
Arteta and Marc Stocker, with contributions from
Patrick Kirby, Ekaterine Vashakmadze, and Collette
M. Wheeler. Additional inputs were provided by John
Baffes, Alain Kabundi, Eung Ju Kim, Csilla Lakatos,
Peter Nagle, Rudi Steinbach, and Shu Yu.
Chapter 2 was coordinated by Marc Stocker andPatrick Kirby. The authors are Ekaterine Vashakmadze(East Asia and Pacific), Yoki Okawa (Europe andCentral Asia), Dana Vorisek (Latin America and the
Caribbean), Lei Sandy Ye (Middle East and North
Africa), Temel Taskin (South Asia), and Gerard
Kambou and Rudi Steinbach (Sub-Saharan Africa).
Chapter 3, Chapter 4, and the boxes in Chapters 1
and 2 were prepared by Sinem Kilic Celik, Jongrim
Ha, Patrick Kirby, Wee Chian Koh, M. Ayhan Kose,
David Laborde, Csilla Lakatos, Will Martin, Franziska
Ohnsorge, Yoki Okawa, Rudi Steinbach, Temel
Taskin, Ekaterine Vashakmadze, Dana Vorisek, Jinxin
Wu, Lei Sandy Ye, and Shu Yu, with contributions
from Mohammad Amin, Sinem Kilic Celik, SebastianEssl, Ergys Islamaj, Sergiy Kasyanenko, Gene
Kindberg-Hanlon, Cedric Okou, Andre Proite,
Naotaka Sugawara, and Collette M. Wheeler.
Research assistance was provided by Miyoko Asai,
Zhuo Chen, Liu Cui, Ishita Dugar, Brent Harrison,
Mengyi Li, Maria Hazel Macadangdang, Claudia
Marchini, Julia Roseman, Xinyue Wang, Jinxin Wu,
and Heqing Zhao. Modeling and data work were
provided by Rajesh Kumar Danda and Julia Roseman.
The online publication was produced by Graeme
Littler and Mikael Reventar. Mark Felsenthal managed
media relations and dissemination. Mark Felsenthal
and Graeme Littler provided editorial support, with
contributions from Adriana Maximiliano.
The print publication was produced by Maria Hazel
Macadangdang, Adriana Maximiliano, and Quinn
Sutton, in collaboration with Luiz H. Almeida,
Andrew Charles Berghauser, Adam Broadfoot, Aziz
Gökdemir, Michael Harrup, and Jewel McFadden.

Many reviewers offered extensive advice andcomments. These included: Kishan Abeygunawardana,
Issam Abousleiman, Abebe Adugna, Azamat
Agaidarov, Junaid Kamal Ahmad, Anna Carlotta Allen
Massingue, Sara Alnashar, Gallina Andronova
Vincelette, Paloma Anos Casero, Jorge Araujo, Rabah
Arezki, Kiatipong Ariyapruchya, Cindy Audiguier,
Rajul Awasthi, Dilek Aykut, Marina Bakanova, Daniel
Barco, Kevin Barnes, Rafael Barroso, Davaadalai
Batsuuri, Hans Anand Beck, John Beghin, Robert Carl
Michael Beyer, Mesfin Bezawagaw, Andrew
Blackman, Enrique Blanco Armas, Fernando Blanco,
Monika Blaszkiewicz-Schwartzman, Daniel Kwabena
Boakye, Zeljko Bogetic, Elena Bondarenko, Denis
Boskovski, Anne Brockmeyer, James Brumby, Cesar
Calderon, Salvatore Capasso, Kevin Carey, Bledi
Celiku, Derek Chen, Massarongo Chivulele, Yew KeatChong, Ajai Chopra, Ibrahim Chowdhury, Tamoya
Christie, Kevin Chua, Kevin Clinton, Fabiano
Colbano, Andrea Coppola, Barbara Cunha, Stefano
Curto, Anna Custers, Andrew Dabalen, Somneuk
Davading, Simon Davies, Annette De Kleine Feige,
Gabriel Demombynes, Agim Demukaj, Allen Dennis,
Sebastien Dessus, Mame Fatou Diagne, Caroline
Diaz-Bonilla, Tatiana Didier, Nora Carina Dihel, Viet
Tuan Dinh, Makhtar Diop, Ndiame Diop, Annette
Dixon, Mariam Dolidze, Jozef Draaisma, Bakyt
Dubashov, Victor Duggan, Mark Andrew Dutz, Etaki
Wa Dzon, Sebastian Eckardt, Kim Alan Edwards,
Khalid El Massnaoui, David Elmaleh, Olga
Emelyanova, Wilfried Engelke, Jorge Familiar
Calderon, Yuting Fan, Marianne Fay, Manuela Ferro,
Cornelius Fleischhaker, Manuela Francisco, Chisako
Fukuda, Josip Funda, Kevin Thomas Garcia Cruz,
Pedro Miguel Gaspar Martins, Michael Geiger,
Rangeet Ghosh, Adnan Ashraf Ghumman, Frederico
Gil Sander, Fernando Giuliano, Anastasia Golovach,Maria De los Angeles Cuqui Gonzalez Miranda, Errol
George Graham, Margaret Grosh, Poonam Gupta,
Gohar Gyulumyan, Kiryl Haiduk, Fiseha Haile
Gebregziabher, Michael Hamaide, Birgit Hansl,
Marek Hanusch, Wissam Harake, Zeina Hasra, Faya
Hayati, Johannes Herderschee, Sandra Hlivnjak, Bert
Hofman, Bingjie Hu, Sahar Sajjad Hussain, Elena
Ianchovichina, Stella Ilieva, Fernando Gabriel Im,
XV

Amina Iraqi, Yoichiro Ishihara, Ergys Islamaj, Ivailo
V. Izvorski, Evans Jadotte, Saroj Jha, Gloria Aitalohi
Joseph-Raji, Gerard Kambou, Bertine Kamphuis,
Soukeyna Kane, Lanre Kassim, Atsushi Kawamoto,
Majid Kazemi, Vera Kehayova, Zerihun Getachew
Kelbore, Nazmus Khan, Tehmina S. Khan, Ewa
Korczyc, Christos Kostopoulos, Megumi Kubota,
Chandana Kularatne, Aurelien Kruse, Jean Pierre
Lacombe, Emmanuel Lartey, Daniel Lederman, Ran
Li, John Litwack, Sodeth Ly, Julio Ricardo Loayza,
Norman Loayza, Julie Saty Lohi, J. Humberto Lopez,
José Roberto López-Cálix, Cal MacWilliam, Sanja
Madzarevic-Sujster, Sandeep Mahajan, Shireen
Mahdi, William Maloney, Shiva Makki, Pierre JeanClaude Mandon, Kemoh Mansaray, Marie FrancoiseMarie-Nelly, Daniela Marotta, Ashwaq Natiq Maseeh,
Andrew D. Mason, Hideki Matsunaga, Elitza Mileva,
Deepak K. Mishra, Aghassi Mkrtchyan, Lalita Moorty,
Lili Mottaghi, Rafael Munoz Moreno, Rinku Murgai,
Arvind Nair, Nur Nasser Eddin, Jean-Pascal Nganou,
Ha Nguyen, Francesca de Nicola, Eduardo Olaberria,
Harun Onder, Abdoulaye Ouedraogo, John Panzer,
Jim Parks, Catalin Pauna, Ceyla Pazarbasioglu,
Fernanda Ailina Pedro, Keomanivone Phimmahasay,
Samuel Jaime Pienknagura, Emmanuel Pinto Moreira,
Ruslan Pionkivsky, Abha Prasad, Mona Prasad, Rong
Qian, Habib Rab, Erick Rabemananoro, Wojciech
Pawel Rabiega, Martin Rama, Nadir Ramazanov,

XVI

Martin Raiser, Richard Record, Saadia Refaqat, Julio
E. Revilla, Alief Aulia Rezza, Daniel Riera-Crichton,
David Robinson, Alberto Rodriguez, Pedro Rodriguez,
Donato de Rosa, David Rosenblatt, Irina Rostovtseva,
Pablo Saavedra, James Sampi Bravo, Miguel Eduardo
Sanchez Martin, Apurva Sanghi, Ilyas Sarsenov,
Cristina Savescu, Tahseen Sayed, Kinnon Scott, Asli
Senkal, Claudia Paz Sepúlveda, Lazar Sestovic,
Natasha Sharma, Dhruv Sharma, Sudhir Shetty, Alta
Shiilegmaa, Emily Sinnott, Sophie Sirtaine, Karlis
Smits, Abdoulaye Sy, Fulbert Tchana Tchana, Shakira
Binti Teh Sharifuddin, Hans Timmer, Emilija
Timmis, Aby Toure, Christopher Towe, RobertTownsend, Melanie Simone Trost, Eskender Trushin,
Michal Tulwin, Angélique Umutesi, Cevdet Cagdas
Unal, Zainab Usman, Robert Utz, Ralph Van Doorn,
Axel van Trotsenburg, Sona Varma, Carlos Végh,
Julio Velasco, Marijn Verhoeven, Ekaterina
Vostroknutova, Muhammed Waheed, Georgia A.
Wallen, Marina Wes, Siti Budi Wardhani, Maria
Monica Wihardja, Christina Wood, Pinar Yasar, Pui
Shen Yoong, Hoda Youssef, Gabriel Zaourak, Albert
Zeufack, Yan Michelle Zhang, Luan Zhao, andBakhrom Ziyaev.
Regional projections and write-ups were produced incoordination with country teams, country directors,
and the offices of the regional chief economists.

Executive Summary
The outlook for the global economy has darkened. Global financing conditions have tightened, industrial
production has moderated, trade tensions remain elevated, and some large emerging market and developing
economies have experienced significant financial market stress. Faced with these headwinds, the recovery inemerging market and developing economies has lost momentum. Downside risks have become more acute andinclude the possibility of disorderly financial market movements and an escalation of trade disputes. Debt
vulnerabilities in emerging market and developing economies, particularly low-income countries, haveincreased. More frequent severe weather events would raise the possibility of large swings in international foodprices, which could deepen poverty. In this difficult environment, it is of paramount importance for emerging
market and developing economies to rebuild policy buffers while laying a stronger foundation for future growthby boosting human capital, promoting trade integration, and addressing the challenges associated withinformality.
Global Outlook. Moderating activity andheightened risks are clouding global economic
prospects. International trade and investment
have softened, trade tensions remain elevated, andsome large emerging market and developing
economies (EMDEs) have experienced substantial
financial market pressures. Against this lessfavorable backdrop, EMDE growth has lost
momentum, with a weaker-than-expected
recovery in commodity exporters accompanied bya deceleration in commodity importers.
Downside risks have become more acute.
Disorderly financial market developments could
disrupt activity in the affected economies andlead to contagion effects. Trade disputes could
escalate or become more widespread, denting
activity in the involved economies and leading tonegative global spillovers. To confront thisincreasingly challenging environment, animmediate priority for EMDE policymakers is tobrace for possible bouts of financial market stress,
rebuild macroeconomic policy buffers as
appropriate, and tackle adverse debt dynamics, allwhile sustaining historically low inflation. In thelonger run, the need to foster more robust
potential growth by boosting human capital,
removing barriers to investment, and promoting
trade integration remains.
Regional Perspectives. The rebound in EMDE
activity has stalled. The cyclical upswing inregions with many commodity exporters has lost
momentum, partly reflecting a substantial

slowdown in some large economies, and is
projected to plateau over the next couple of years.
Growth in regions with large numbers ofcommodity importers was solid but has
decelerated and is expected to stabilize around
potential. For all regions, risks to the outlook areincreasingly tilted to the downside.
This edition of Global Economic Prospects also
includes a chapter on the challenges associatedwith the presence of large informal sectors in
EMDEs and policy options to address
informality; a box on the prospects for continued
low inflation in EMDEs; and essays on risingdebt vulnerabilities in low-income countries andthe implications of large food price spikes forpoverty.
Growing in the Shadow: Challenges ofInformality. Informal sector output on average
accounts for about one-third of GDP and
informal employment constitutes about 70
percent of employment in EMDEs (of which selfemployment accounts for more than a half).
Informality is more widespread in less developed
economies with large agricultural sectors andhigher shares of unskilled workers. Whilesometimes providing the short-run advantage of
flexibility and employment, a larger informal
sector is associated with lower productivity,
reduced tax revenues, and greater poverty andinequality. Overcoming the adverse implicationsof informality will require a balanced mixture of

XVII

policies that carefully take into account countryspecific drivers of informality. A well-designed
policy framework should include measures aimed
at reducing regulatory and tax burdens, expanding
access to finance, improving education and otherpublic services, and strengthening public revenue
frameworks.
The Great Disinflation. Emerging market anddeveloping economies (EMDEs) have achieved aremarkable decline in inflation, from 17.3 percent
in 1974 to about 3.5 percent in 2018. This
achievement has coincided with an even sharper
decline in inflation in advanced economies. The
great disinflation in EMDEs has also beenaccompanied by growing inflation synchronization as evidenced by the emergence of a global
inflation cycle. It has been supported by longterm trends such as the widespread adoption ofrobust monetary policy frameworks andstrengthening of global trade and financial
integration. More recently, the disruptions causedby the global financial crisis also contributed tothe decline in inflation. However, a continuationof low and stable EMDE inflation is by no means
guaranteed. If the wave of structural and policyrelated factors that have driven disinflation sincethe 1970s loses momentum or is rolled back,elevated inflation could re-emerge. If the global
inflation cycle turns up, policymakers may findthat maintaining low inflation can be as great achallenge as achieving it.
Debt in Low-Income Countries: Evolution,
Implications, and Remedies. Debt vulnerabilities

XVIII

in low-income countries (LICs) have increased
substantially in recent years. Since 2013, mediangovernment debt has risen by about 20 percentage
points of GDP and increasingly comes from nonconcessional and private sources. As a result, inmost LICs, interest payments are absorbing an
increasing proportion of government revenues.
The majority of LICs would be hard hit by asudden weakening in trade or global financial
conditions given their high levels of external debt,
lack of fiscal space, low foreign currency reserves,and undiversified exports. Efforts to reduce debtrelated vulnerabilities are a policy priority formany LICs, and a key focus needs to be
improving debt management and developing
domestic financial systems.
Poverty Impact of Food Price Shocks andPolicies. In the event of large swings in world
food prices, governments sometimes intervene tosoften the impact on domestic prices and to lessen
the burden of adjustment for vulnerable groups.
While individual countries can succeed atinsulating their domestic markets from short-term
fluctuations in global food prices, the collective
intervention of many countries may exacerbate
the volatility of world prices. Policies introduced
during the 2010-11 food price spike may haveaccounted for 40 percent of the increase in theworld price of wheat and one-quarter of theincrease in the world price of maize. Combinedwith government policy responses, the 2010-11
food price spike tipped 8.3 million people (almost
1 percent of the world’s poor) into poverty.

Abbreviations
ADB

Asian Development BankAE

advanced economies

CEMAC

Central African Economic and Monetary Community

CES

constant elasticity of substitution

CET

constant elasticity of transformation

CGE

computable general equilibrium model

CPI

consumer price index

DB

Doing Business

DGE

dynamic general equilibrium

DTF

Distance to Frontier score

EAP

East Asia and Pacific

ECA

Europe and Central Asia

ECM

error correction model

EMDE

emerging market and developing economies

EU

European Union

FAO

Food and Agriculture Organization of the United Nations

FAOSTATFAO Statistical Databases

FCVfragility, conflict, and violence-affected economies

FICCI

Federation of Indian Chambers of Commerce

FewsnetFamine Early Warning Systems Network

G4

Euro Area, Japan, the United Kingdom, and the United States

GCC

Gulf Cooperation Council

GDPgross domestic product

GEP

Global Economic Prospects

GIEWS

Global Information and Early Warning System

GNFS

goods and nonfactor services

GTAP

Global Trade Analysis Project

HRW

hard red wheat

ICSE

International Classification of Status in Employment

ICT

information and communication technology

IEG

Independent Evaluation Group

ILO

International Labour Organization

IMF

International Monetary Fund

ISS

International Sector Survey

LAC

Latin America and the Caribbean

LES–CESlinear expenditure system–constant elasticity of substitution

LIC

low-income country

XIX

XX

MIC

middle-income country

MIMIC

Multiple Indicators Multiple Causes model

MMDA

Metro Manila Developments Authority

MENA

Middle East and North Africa

MSEsmicro and small enterprises

NBER

National Bureau of Economic Research

NEET

not in employment, education, or training

NPAs

non-performing assets

NRP

nominal rate of protection

OECD

Organisation for Economic Co-operation and Development

OLS

ordinary least squares

PPPpurchasing power parity

RHS

right-hand side (in figures)

SAR

South Asia Region

SDGs

Sustainable Development Goals

SEMP

self-employment rate

SMEs

small and medium-sized enterprises

SSA

Sub-Saharan Africa

SSRN

Social Science Research Network

UNCTAD

United Nations Conference on Trade and Development

UNDP

United Nations Development Programme

UNICEF

United Nations International Children’s Emergency Fund

USDA

United States Department of Agriculture

VAT

Value-added taxation

WDI

World Development Indicators

WEF

World Economic Forum

WGIWorld Governance Indicators

WVS

World Value Survey

WHOWorld Health Organization

WTO

World Trade Organization

CHAPTER 1

GLOBAL OUTLOOK

Darkening Skies

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

Moderating activity and heightened risks are clouding global economic prospects. International trade andinvestment have softened, trade tensions remain elevated, and some large emerging market and developing
economies (EMDEs) have experienced substantial financial market pressures. Against this challenging backdrop,
EMDE growth has stalled, with a sharply weaker-than-expected recovery in commodity exporters accompanied
by a deceleration in commodity importers. Downside risks have become more acute. Disorderly financial market
developments could disrupt activity in the affected economies and lead to contagion effects. Trade disputes could
escalate or become more widespread, denting activity in the economies involved and leading to negative global
spillovers. To confront this increasingly difficult environment, the most urgent priority is for EMDE
policymakers to prepare for possible bouts of financial market stress and rebuild macroeconomic policy buffers as
appropriate. Equally critically, policymakers need to foster stronger potential growth by boosting human capital,
removing barriers to investments, and promoting trade integration within a rules-based multilateral system.
Such efforts would also help address the challenges associated with informality.

Summary
Global growth is moderating as the recovery in
trade and manufacturing activity loses steam(Figure 1.1). Despite ongoing negotiations, tradetensions among major economies remain elevated.
These tensions, combined with concerns aboutsoftening global growth prospects, have weighedon investor sentiment and contributed to declines
in global equity prices. Borrowing costs for
emerging market and developing economies(EMDEs) have increased, in part as majoradvanced-economy central banks continue towithdraw policy accommodation in varying
degrees. A strengthening U.S. dollar, heightened
financial market volatility, and rising risk
premiums have intensified capital outflow andcurrency pressures in some large EMDEs, withsome vulnerable countries experiencing substantial
financial stress. Energy prices have fluctuatedmarkedly, mainly due to supply factors, withsharp falls toward the end of 2018. Other
commodity prices—particularly metals—have also
weakened, posing renewed headwinds forcommodity exporters.
Economic activity in advanced economies has
been diverging of late. Growth in the UnitedStates has remained solid, bolstered by fiscal

Note: This chapter was prepared by Carlos Arteta and Marc
Stocker, with contributions from Patrick Kirby, Ekaterine
Vashakmadze, and Collette M. Wheeler. Additional inputs were
provided by John Baffes, Alain Kabundi, Eung Ju Kim, Csilla
Lakatos, Peter Nagle, Rudi Steinbach, and Shu Yu. Research
assistance was provided by Liu Cui, Ishita Dugar, Brent Harrison,
Mengyi Li, Claudia Marchini, Julia Roseman, and Jinxin Wu.

stimulus. In contrast, activity in the Euro Area has
been somewhat weaker than previously expected,
owing to slowing net exports. While growth inadvanced economies is estimated to have slightly
decelerated to 2.2 percent last year, it is still above
potential and in line with previous forecasts.
EMDE growth edged down to an estimated 4.2
percent in 2018—0.3 percentage point slower
than previously projected—as a number ofcountries with elevated current account deficits
experienced substantial financial market pressuresand appreciable slowdowns in activity. Moregenerally, as suggested by recent high-frequency
indicators, the recovery among commodity
exporters has lost momentum significantly, largely
owing to country-specific challenges within thisgroup. Activity in commodity importers, whilestill robust, has slowed somewhat, reflecting
capacity constraints and decelerating export
growth. In low-income countries (LICs), growth isfirming as infrastructure investment continues andeasing drought conditions support a rebound in
agricultural output. However, LIC metals
exporters are struggling partly reflecting softer
metals prices. Central banks in many EMDEs
have tightened policy to varying degrees toconfront currency and inflation pressures.
In all, global growth is projected to moderate froma downwardly revised 3 percent in 2018 to 2.9
percent in 2019 and 2.8 percent in 2020-21, as
economic slack dissipates, monetary policy
accommodation in advanced economies isremoved, and global trade gradually slows.
Growth in the United States will continue to besupported by fiscal stimulus in the near term,

3

4

CHAPTER 1

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

TABLE 1.1 Real GDP1

Percentage point differencesfrom June 2018 projections(Percent change from previous year)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

2.4

3.1

3.0

2.9

2.8

2.8

-0.1

-0.1

-0.1

Advanced economies
United States
Euro Area

1.7
1.6
1.9

2.3
2.2
2.4

2.2
2.9
1.9

2.0
2.5
1.6

1.6
1.7
1.5

1.5
1.6
1.3

0.0
0.2
-0.2

0.0
0.0
-0.1

-0.1
-0.3
0.0

Japan
Emerging market and developing economies(EMDEs)Commodity-exporting EMDEs

0.6

1.9

0.8

0.9

0.7

0.6

-0.2

0.1

0.2

3.7

4.3

4.2

4.2

4.5

4.6

-0.3

-0.5

-0.2

0.8

1.7

1.7

2.3

2.9

2.9

-0.8

-0.7

-0.1

5.9

6.1

5.8

5.5

5.6

5.6

0.0

-0.3

-0.1

4.9

5.2

5.0

4.7

4.9

5.1

-0.1

-0.4

-0.2

6.3

6.6

6.3

6.0

6.0

5.8

0.0

-0.1

0.0

China

6.7

6.9

6.5

6.2

6.2

6.0

0.0

-0.1

0.0

Indonesia

5.0

5.1

5.2

5.2

5.3

5.3

0.0

-0.1

-0.1

3.3

3.9

4.1

3.8

3.9

3.9

0.0

0.0

0.1

1.7

4.0

3.1

2.3

2.7

2.9

-0.1

-0.8

-0.3

-0.2

1.5

1.6

1.5

1.8

1.8

0.1

-0.3

0.0

3.2
3.1
-1.5

7.4
4.8
0.8

3.5
5.0
0.6

1.6
4.0
1.7

3.0
3.6
2.4

4.2
3.3
2.5

-1.0
0.8
-1.1

-2.4
0.3
-0.6

-1.0
0.1
-0.1

World

Other EMDEs
Other EMDEs excluding China
East Asia and PacificThailandEurope and Central Asia
RussiaTurkeyPolandLatin America and the Caribbean
Brazil

-3.3

1.1

1.2

2.2

2.4

2.4

-1.2

-0.3

0.0

Mexico

2.9

2.1

2.1

2.0

2.4

2.4

-0.2

-0.5

-0.3

Argentina

-1.8

2.9

-2.8

-1.7

2.7

3.1

-4.5

-3.5

-0.1

5.1

1.2

1.7

1.9

2.7

2.7

-1.3

-1.4

-0.5

Saudi Arabia

1.7

-0.9

2.0

2.1

2.2

2.2

0.2

0.0

-0.1

Iran

13.4

3.8

-1.5

-3.6

1.1

1.1

-5.6

-7.7

-3.1

Egypt2

4.3

4.2

5.3

5.6

5.8

6.0

0.3

0.1

0.0

South Asia

7.5

6.2

6.9

7.1

7.1

7.1

0.0

0.0

-0.1

Middle East and North Africa

India3

7.1

6.7

7.3

7.5

7.5

7.5

0.0

0.0

0.0

Pakistan2

4.6

5.4

5.8

3.7

4.2

4.8

0.0

-1.3

-1.2

Bangladesh2

7.1

7.3

7.9

7.0

6.8

6.8

1.4

0.3

-0.2

Sub-Saharan Africa

1.3

2.6

2.7

3.4

3.6

3.7

-0.4

-0.1

-0.1

Nigeria

-1.6

0.8

1.9

2.2

2.4

2.4

-0.2

0.0

0.0

South Africa

0.6

1.3

0.9

1.3

1.7

1.8

-0.5

-0.5

-0.2

AngolaMemorandum items:
Real GDP1

-2.6

-0.1

-1.8

2.9

2.6

2.8

-3.5

0.7

0.2

High-income countries

1.7

2.3

2.2

2.0

1.7

1.6

0.0

0.0

-0.1

Developing countries

4.0

4.6

4.4

4.4

4.7

4.7

-0.3

-0.4

-0.1

4.8

5.5

5.6

5.9

6.2

6.3

-0.1

0.0

0.0

BRICS

4.4

5.2

5.3

5.2

5.3

5.3

-0.1

-0.2

-0.1

World (2010 PPP weights)

3.2

3.7

3.6

3.5

3.6

3.6

-0.2

-0.3

-0.1

2.6

5.4

3.8

3.6

3.5

3.4

-0.5

-0.6

-0.5

Oil price

-15.6

23.3

30.7

-2.9

0.0

0.0

-1.9

-1.5

-0.1

Non-energy commodity price index

-2.8

5.3

1.7

1.0

1.2

1.2

-3.4

0.8

0.7

Low-income countries

World trade volume4
Commodity prices5

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.
1. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights.
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 2017 refers to FY2016/17.
3. The column labeled 2016 refers to FY2016/17.
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 2019

which will likely lead to larger and more persistentfiscal deficits. Advanced-economy growth will
gradually decelerate toward potential, falling to
1.5 percent by the end of the forecast horizon, as
monetary policy is normalized and capacity
constraints become increasingly binding.
Softening global trade and tighter financing
conditions will result in a more challenging
external environment for EMDE economic
activity. EMDE growth is expected to stall at 4.2
percent in 2019—0.5 percentage point below
previous forecasts, partly reflecting the lingering
effects of recent financial stress in some large
economies (e.g., Argentina, Turkey), with asharply
weaker-than-expected
pickupincommodity exporters accompanied by adeceleration in commodity importers. EMDE
growth is projected to plateau at an average of 4.6
percent in 2020-21, as the recovery in commodity
exporters levels off. Per capita growth will remain
anemic in several EMDE regions—most notably,in those with a large number of commodity
exporters—likely impeding further poverty
alleviation.
The projected gradual deceleration of global
economic activity over the forecast horizon could
be more severe than currently expected given the
predominance of substantial downside risks(Figure 1.2). A sharper-than-expected tighteningof global financing conditions, or a renewed rapid
appreciation of the U.S. dollar, could exert further
downward pressure on activity in EMDEs,
including in those with large current accountdeficits financed by portfolio and bank flows.
Government and/or private sector debt has also
risen in a majority of EMDEs over the last few
years, including in many LICs, reducing the fiscalroom to respond to shocks and heightening theexposure to shifts in market sentiment and risingborrowing costs.
Escalating trade tensions are another major
downside risk to the global outlook. If all tariffs
currently under consideration were implemented,
they would affect about 5 percent of global tradeflows and could dampen growth in the economies
involved, leading to negative global spillovers.
While some countries could benefit from trade

CHAPTER 1

5

FIGURE 1.1 Summary – Global prospects
Global growth is moderating, as industrial activity and trade decelerate,
negatively impacting investor sentiment and equity prices. The recovery in
EMDEs has stalled, owing to softening external demand, tighter externalfinancing conditions, and heightened policy uncertainties. Many EMDE
central banks have raised interest rates to fend off currency pressures. Per
capita growth will remain anemic in several EMDE regions—most notably
in those with a large number of commodity exporters.
A. Global growth

B. Global industrial production andnew export orders

C. Global and EMDE equity prices

D. Growth in EMDEs

E. EMDE policy interest rates, byextent of currency depreciation
against the U.S. dollar

F. Per capita growth, by region

Source: Bloomberg, Haver Analytics, World Bank.
Note: EMDEs = emerging market and developing economies.
A.D.F. Shaded areas indicate forecasts. Data for 2018 are estimates. Aggregate growth rates
calculated using constant 2010 U.S. dollar GDP weights.
B. New export orders measured by Purchasing Managers’ Index (PMI). PMI readings above 50
indicate expansion in economic activity; readings below 50 indicate contraction. Last observationis November 2018 for new export orders and October 2018 for industrial production.
C. Figure shows MSCI Global and Emerging Markets Indexes. Last observation is December 19,
2018.
D. Data for 2015-17 are simple averages. Green diamonds denote forecasts in the June 2018 editionof the Global Economic Prospects report.
E. The aggregate policy interest rates are calculated using constant 2010 U.S. dollar GDP weights.
The above average and below average currency depreciation groups are defined by countries above
or below the sample average of the year-to-date percent change in the bilateral exchange rate
against the U.S. dollar. The sample average is -9.3 percent and includes 27 EMDEs, of which 12 areabove and 15 are below average. Last observation is November 2018.
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, and SSA = Sub-Saharan Africa.
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 2019

FIGURE 1.2 Global risks and policy challenges
Downside risks predominate, with the possibility of financial stress leading
to further deterioration in activity in EMDEs. Escalating trade tensions
involving major economies could spread globally. A simultaneous sharp
slowdown in both the United States and China could have severe effects
on the global outlook. Fiscal space is particularly limited in countries withhigh foreign-currency-denominated debt. Informality remains widespreadin EMDEs and is associated with large productivity gaps between formal
and informal firms.
A. Probability of 2020 global growth
being 1-percentage-point below/above
baseline

B. Growth forecast revisions andcurrent account position, 2019

C. Imports affected by new tariffs

D. Impact on global growth of 1percentage-point growth slowdowns
in the United States and China

E. Fiscal sustainability gaps in
EMDEs, by extent of reliance on
foreign-currency-denominated debt

F. Average productivity in formal andinformal firms

Source: Bloomberg; International Monetary Fund; Kose, Kurlat et al. (2017); Peterson Institute forInternational Economics; U.S. Census Bureau; World Bank.
A. Probabilities are computed from the distribution of 24-month-ahead oil price futures, S&P 500
equity price futures, and term spread forecasts. Each of the risk factor weights are derived from themodel described in Ohnsorge, Stocker, and Some (2016). Last observation is December 18, 2018.
B. Forecast revisions for GDP growth in 2019 relative to June 2018. Sample includes 23 EMDEs.
Current account position net of foreign direct investment in 2018.
C. Import tariffs implemented in the United States and the rest of the world in 2018, as well as thoseunder consideration, as a percent of global goods imports.
D. Blue and red bars show scenarios assuming a 1-percentage-point growth shock in China, theUnited States, and the combination of the two. Shocks are applied in the second half of 2019. Basedon the vector autoregression model presented in World Bank (2016). Deviations from baseline are all
significantly different from zero.
E. FC debt = foreign-currency-denominated debt. A negative sustainability gap indicates government
debt is rising along an accelerated trajectory. The sample includes 27 EMDEs. The above (below)
average foreign-currency-denominated debt groups are defined by countries above (below) the
sample average of external debt in foreign currency as a share of total external debt in 2017.
F. Blue bars represent estimates and orange vertical lines indicate two standard deviation error
bands. World Bank’s Enterprise Survey data for 135 countries (2008-18).
Click here to download data and charts.

diversion in the short run, rising tradeprotectionism would stifle investment and severely
disrupt global value chains, contributing to higher
prices and lower productivity. Other downside
risks—such as heightened political uncertainty,
escalating geopolitical tensions, and conflict—further cloud the outlook.
Even though the probability of a recession in the
United States is still low, and the slowdown in
China is projected to be gradual, markedly
weaker-than-expected activity in the world’s twolargest economies could have a severe impact on
global economic prospects. Stimulus measures
have bolstered the near-term outlook in these twocountries but could contribute to a more abrupt
slowdown later on. A simultaneous occurrence ofa severe U.S. downturn and a sharper-thanexpected deceleration in China would significantly
increase the probability of an abrupt global
slowdown and thus negatively impact the outlookof other EMDEs through trade, financial, andcommodity market channels. A global downturnwould be particularly detrimental for those
EMDEs with reduced policy space to respond toshocks.
The softening outlook and heightened downside
risks exacerbate various challenges faced bypolicymakers around the world. Advanced
economies should use this period of abovepotential growth to rebuild macroeconomic policy
buffers and lay the foundation for stronger growthwith reforms that bolster potential output. Careshould be taken to avoid shifts in trade andimmigration policies that could negatively affectlonger-term growth prospects, both domesticallyand abroad. A renewed commitment to a rulesbased international trading system would also help
bolster confidence, investment, and trade.
In a context of limited policy buffers, EMDE
policymakers need to bolster the capacity to copewith possible bouts of financial market volatility,
including sharp exchange rate movements—while
undertaking measures to sustain the ongoing
period of historically stable inflation (Box 1.1).
This immediate priority will require a credible
commitment to price stability from central
banks, underpinned by strong institutional

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019independence, as well as efforts by regulators andprudential authorities to reduce persistentfinancial fragilities. EMDEs also face substantial
fiscal challenges and the risk of worsening debt
dynamics as global financing conditions tighten.
For many EMDEs, it will be imperative to restore
fiscal space given cyclical conditions, as well as
address the vulnerabilities associated with elevatedforeign-currency-denominated debt.
Equally critically, amid a projected deceleration in
potential growth, EMDEs face the pressing
challenge of ensuring sustained improvements inliving standards. This will require investments inhuman capital and skills development to raise
productivity and take full advantage oftechnological changes. In the current environmentof limited fiscal resources, the urgency of theseinvestments highlights the critical need toprioritize effective public spending and increase
public sector efficiency.
Moreover, facilitating the expansion of small- andmedium-sized enterprises, including by improving
their access to international markets and finance,
would also spur productivity and stimulate growth-enhancing investments. For many EMDEs, thereis scope to further liberalize trade and improvethe extent to which they are integrated into global
value chains, which would foster a more efficient
allocation of resources, job creation, and export
diversification. Policies that help improveoutcomes in these areas would also contributeto address the challenges associated withinformality, thus reinforcing the basis for future
productivity growth.

Major economies: Recent
developments and outlook
Growth has moderated in most advanced economies,
with the notable exception of the United States,
where fiscal stimulus is boosting activity. Over theforecast horizon, growth in all major advanced
economies is projected to slow toward potential as
capacity constraints become increasingly binding andmonetary accommodation is withdrawn. In China,
activity remains robust, but headwinds are increasing
in a context of heightened trade tensions.

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7

FIGURE 1.3 Advanced economies
Activity has softened but still points to above-potential growth in majoradvanced economies. Growth is expected to continue to moderate overthe forecast period. Fiscal policy will boost U.S. activity in 2019 but will
become a drag thereafter.

A. GDP and demand component
growthB. Growth

Source: World Bank.
A.B Green diamonds correspond with the June 2018 edition of the Global Economic Prospects
report. Shaded areas indicate forecasts. Data for 2018 are estimates.
A. Aggregate growth rates and components calculated using constant 2010 U.S. dollar GDP weights.
Click here to download data and charts.

Incoming data in advanced economies havesoftened but still point to above-potential growth.
Unemployment rates have continued to decline,
and for many countries are below levels seenprior to the global financial crisis. After slightly
decelerating from 2.3 percent in 2017 to an
estimated 2.2 percent last year, advanced-economy
growth is expected to continue slowing over theforecast period, with a notable slowdown ininvestment and the eventual shift of U.S.
fiscal policy from stimulative to contractionary(Figure 1.3).
United States
U.S. growth in 2018 is estimated to have picked
up to 2.9 percent, up 0.2 percentage point fromprevious projections, mostly reflecting strongerthan-expected domestic demand (Figure 1.4).
Activity is being bolstered by procyclical fiscal
stimulus and still-accommodative monetary
policy.
The labor market remains robust, bolstering
consumption. The unemployment rate has fallen
to an almost 50-year low, despite an influx of newworkers—about
three-quartersoftheapproximately 200,000 jobs being added every
month are being filled by new entrants. Labor
productivity is showing signs of picking up.

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BOX 1.1 The great disinflation“The greatest threat to today’s low inflation, of course, would be a reversal of the modern trend towards enhanced central
bank independence, particularly if trend economic growth were to slow, owing, say, to a retreat in globalization andeconomic liberalization.” Kenneth Rogoff (2003)
Emerging market and developing economies (EMDEs) have achieved a remarkable decline in inflation, from 17.3 percent in1974 to about 3.5 percent in 2018. This achievement has coincided with an even sharper decline in inflation in advanced
economies. The great disinflation in EMDEs has also been accompanied by growing inflation synchronization as evidenced by the
emergence of a global inflation cycle. It has been supported by long-term trends such as the widespread adoption of robust
monetary policy frameworks and strengthening of global trade and financial integration. More recently, the disruptions caused by
the global financial crisis also contributed to the decline in inflation. However, a continuation of low and stable EMDE inflation
is by no means guaranteed. If the wave of structural and policy-related factors that have driven disinflation since the 1970s loses
momentum or is rolled back, elevated inflation could re-emerge. If the global inflation cycle turns up, policymakers may find thatmaintaining low inflation can be as great a challenge as achieving it.
Emerging market and developing economies (EMDEs)
have achieved a remarkable decline in inflation since themid-1970s (Ha, Kose, and Ohnsorge 2019).1 Medianannual national consumer price inflation in EMDEs fell
from stubbornly persistent double-digits during the 1970s
to about 3.5 percent in 2018 (Figure 1.1.1). By 2017,
inflation was within or below central bank target ranges inthree-quarters of the EMDEs that had adopted inflation
targeting. Inflation has also fallen around the world, froma peak of nearly 17 percent in 1974 to less than 2.5
percent in 2018. The decline in inflation began in the mid-1980s in advanced economies and in the mid-1990s in
EMDEs. By 2000, global inflation had stabilized athistorically low levels.

Evolution of EMDE inflation: A remarkableconquest

Low and stable inflation has historically been associatedwith greater output stability, higher growth and better
development outcomes. EMDEs can continue enjoying
the benefits of low inflation, but only if the confluence ofstructural and policy related factors that have fosteredglobal disinflation over the past decades is sustained.

EMDE disinflation was set against the backdrop of sharper
disinflation among advanced economies, where median
inflation dropped from its highest (15 percent in 1974) to
its lowest level (0.3 percent in 2015) in more than 60
years. Since then, it has risen somewhat to just over 1.5
percent in 2018 but remains below the median inflation
target of advanced-economy central banks. After 2008,
below-target inflation and, in some cases, deflation became
pervasive across advanced economies: for example, in
2015, inflation was negative in more than half of advanced
economies. Some advanced-economy central banks havestruggled to lift inflation back to their inflation targets overthe past decade.

Against this backdrop, this box addresses the following
questions:•

How has EMDE inflation evolved?

•

How important is global inflation in explaining
national inflation in EMDEs?

•

Can EMDEs sustain the era of low inflation?

Note: This box was prepared by Jongrim Ha, M. Ayhan Kose, andFranziska Ohnsorge.
1 The “near-universal” character of the decline in inflation since themid-1970s was recognized at an early stage by Rogoff (2003).

Disinflation. EMDEs have witnessed a significant decline
in inflation since the mid-1970s, with median annual
national consumer price inflation down from a peak of
17.3 percent in 1974 to about 3.5 percent in 2018.
Disinflation over recent decades has been broad-based
across regions and country groups.2 For example,
disinflation occurred across all EMDE regions, including
those with a history of persistently high inflation, such as
Latin America and Sub-Saharan Africa (Figure 1.1.2).3
Even among low-income countries (LICs), inflation fell by
two-thirds between the mid-1970s and 2017, to 5 percent.

Drivers of low inflation. While the global financial crisis
played a major role in pushing inflation down around the

2 Disinflation is a decline in inflation rates, regardless of inflation being
negative (deflation) or positive.
3 However, inflation remains in double-digits in some relatively large
EMDEs, in part reflecting currency depreciations.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

BOX 1.1 The great disinflation (continued)
world over the past decade, the longer-term trend of
disinflation has been supported by a wide range ofstructural changes. The most significant of these have been
the wide-spread adoption of more effective and moretransparent monetary, exchange rate, and fiscal policy
frameworks as well as globalization (Figure 1.1.2).4
Macroeconomic policies. In the second half of the
1980s and during the 1990s, many EMDEs
implemented macroeconomic stabilization programs
and structural reforms, and gave their central banks
clear mandates to control inflation. The adoption ofresilient policy frameworks has facilitated more
effective control of inflation (Taylor 2014; Fischer
2015). Twenty-four EMDEs have introduced
inflation targeting monetary policy frameworks since
the late 1990s and, in the median EMDE, the index
of central bank independence and transparency rose
more than one-and-a-half-fold between 1990 and2014. Inflation tends to be lower in countries that
employ an inflation targeting framework and thathave more independent and transparent central banks.
Changes in fiscal policy frameworks have also
contributed: fiscal rules have been adopted in 88
countries, including 49 EMDEs. Other reforms,
including labor market and product market
liberalization, and the removal or easing of foreignexchange market controls, also assisted the disinflation
process.

•

Trade and financial integration. Trade integration
has contributed to lower prices, as higher shares ofimports in consumption and production result incompetitive pressures from foreign producers (Figure
1.1.4). Financial integration has helped discipline
macroeconomic policies since more financiallyintegrated economies are more likely to implementmonetary policies targeting low and stable inflation(Kose et al. 2010). In the median EMDE, as in the
median advanced economy, the ratio of trade to GDP
increased by half between 1970 and 2017, to 75
percent of GDP, and international assets andliabilities tripled (although they remain only half thelevel of advanced economies). Inflation tends to belower in economies that are more open to trade andfinancial flows.

•

Other structural changes have also been important (Ha, Ivanova et
al. 2019). For example, technological advances, including thedigitalization of services and automation of manufacturing have alsotransformed production processes, attenuating inflation pressures.
Population aging may also have contributed.
4

FIGURE 1.1.1 Global inflation
EMDE inflation remains near historic lows despite arecent normalization of inflation in advanced economies.
Inflation is now within target ranges in the majority of
EMDEs.

A. Median CPI inflation, by country group

B. Share of advanced economies and EMDEs with inflation
below or within target range

Source: Bloomberg, Consensus Economics, Haver Analytics, World Bank.
A. Median year-on-year consumer price inflation for 29 advanced
economies and 123 EMDEs (including 28 LICs).
B. All inflation rates refer to year-on-year inflation. Share of 11 advanced
economies and 24 EMDEs with consumer price inflation below-target orwithin target range. Horizontal line indicates 50 percent.
Click here to download data and charts.

Global inflation cycle: Getting stronger
A critical feature of the international inflation experienceof the past five decades has been the emergence of a“global inflation cycle” (Ciccarelli and Mojon 2010). Thisis reflected in a growing contribution of a common global
factor to the variation in country-level inflation rates. To

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BOX 1.1 The great disinflation (continued)
FIGURE 1.1.2 Disinflation and factors associated with disinflation
EMDE inflation has declined in all EMDE regions and low-income countries. In most EMDEs, inflation is now below 5 percent.
Lower inflation is associated with greater trade and financial openness. Inflation also tends to be lower in countries that
employ an inflation targeting framework and that have more independent and transparent central banks.
A. Median CPI inflation, by region

B. Inflation in low-income countries

C. Distribution of inflation in EMDEs

D. Inflation, by trade and financial
openness

E. Inflation, by index of central bankindependence and transparency

F. Inflation, by monetary policy regime

Source: Ha, Kose, and Ohnsorge (2019); Haver Analytics; IMF International Financial Statistics and World Economic Outlook databases; OECDstat; World Bank.
Note: Median headline CPI (consumer price index) inflation of 29 advanced economies and 123 EMDEs.
A. All inflation rates refer to year-on-year inflation. EAP = East Asia and the Pacific, ECA = Eastern Europe and Central Asia, LAC = Latin America and the Caribbean,
MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.
B. Solid line shows median year-on-year headline inflation and dotted lines refer to interquartile range, based on 28 LICs.
C. Inflation refers to quarter-on-quarter annualized inflation. Sample includes 50 EMDEs.
D. Columns indicate median inflation in countries high trade-to-GDP ratios (“Trade”) or financial assets and liabilities relative to GDP (“Finance”) in the top quartile (“highopenness”) of 175 economies during 1970-2017. Horizontal bars indicate countries in the bottom quartile (“low openness”). Differences are statistically significant at the 5
percent level.
E.F. Columns indicate median inflation in country-year pairs with a central bank independence and transparency index in the top quartile of the sample (E) or with inflation
targeting monetary policy regimes (C). Horizontal bars denote medians in the bottom quartile (B) or with monetary policy regimes that are not inflation targeting (F).
Differences are statistically significant at the 5 percent level.
Click here to download data and charts.

analyze its importance, a dynamic factor model isestimated for annual consumer price inflation rates in 25
advanced economies and 74 EMDEs during 1970-2017(Ha, Kose et al. 2019). The model includes a common
global factor as well as group factors specific to advanced
economies and EMDEs. The presence of group factors
allows the model to account for the large differences incountry characteristics between advanced economies and
EMDEs.

time: since 2001, it has almost doubled, and now accounts
for 22 percent of inflation variation (Ha, Kose et al. 2019).
It has explained about one-fifth and one-quarter of EMDE
and advanced economy inflation variation, respectively,
since 2001. Over the past four decades, an EMDE-specificfactor has also become more prominent. The rising
importance of these global and group-specific factors
indicates that inflation synchronization has become more
broad-based over time.

Global inflation factor. Inflation has become increasingly
globally synchronized (Figure 1.1.3). The contribution of
the global factor to inflation variation has grown overGlobal inflation versus global business cycle. Inflationsynchronization is sizable by comparison with global
business cycle synchronization. The international businessG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

BOX 1.1 The great disinflation (continued)
cycle literature has established the presence of a welldefined global business cycle (Kose, Otrok, and Prasad2012). In the sample used here, the global business cycle,
as captured by a common global factor in output growth,
has accounted for 5 percent of national output growthfluctuations since 1970—less than half the degree of
inflation synchronization.
Tradables versus non-tradables. The role of the global
factor has been more prominent in price baskets with alarger tradables content. The global factor’s contribution
to inflation variation was largest for import prices (54percent in the median country) and smallest for core CPI
inflation (5 percent). Between these two extremes, theglobal factor’s contribution to variation in PPI inflation
was 42 percent and that for GDP deflator growth was 13
percent and comparable to that for headline CPI inflation.

Maintaining low inflation: A greater challenge
The achievement of low inflation cannot be taken for
granted (Rogoff 2014; Draghi 2016; Carstens 2018). If
cyclical and structural forces become less disinflationary
over the next decade than they have been over the past five
decades, inflation could rise globally. Through thestrengthening global inflation cycle, this may put upward
pressure on EMDE inflation. More importantly, structuraland policy related factors that have helped lower inflation
over the past several decades may lose momentum or berolled back amid mounting populist sentiment.
•

Slowing globalization. The rising protectionist
sentiment of recent years may slow or even reverse thepace of globalization. New tariffs and import
restrictions have been put in place in advanced
economies and EMDEs since 2017. The possibility offurther escalation in trade restrictions involving major
economies remains elevated.

•

Weakening monetary policy frameworks. A shift froma strong mandate of inflation control, to objectives
related to the financing of government, would
undermine the credibility of monetary policy
frameworks and raise inflation expectations. Among
EMDEs, a decline in central bank independence andtransparency has been associated with significantly lesswell-anchored inflation expectations and greater passthrough of exchange rate movements to inflation.

•

Weakening fiscal policy frameworks. Growing
populist sentiment could lead to a move away fromrule-based fiscal policies. Fiscal rules can become
ineffective once commitment to them falters(Wyplosz 2012). Mounting public and private debt in
EMDEs could also weaken commitment to strong
fiscal and monetary policy frameworks. Government
and/or private sector debt has risen in more than halfof EMDEs since 2012, including in many LICs
(World Bank 2018a). EMDE sovereign credit ratings
have continued to deteriorate, with some falling below
investment grade, reflecting concerns about risingdebt and deteriorating growth prospects.
If unwanted inflation makes a comeback, policy
frameworks may be tested in EMDEs: their inflation
expectations are less well-anchored, and the absence ofstrong monetary policy frameworks in many of these
economies means that inflation is sensitive to exchange
rate movements (Kose et al. 2019; Ha, Stocker andYilmazkuday 2019). Growing inflation synchronization
also increases the risk of policy errors when the appropriate
response differs depending on the origin of the underlying
inflation shock (IMF 2018a).5 EMDE central banks may
struggle to contain inflationary pressures and may notreceive adequate support from fiscal policy in stabilizing
the business cycle. For some EMDEs, a significant increase
in inflation could set back poverty reduction efforts.
The demise of previous periods of sustained low inflation
is a reminder that low EMDE inflation is by no means
guaranteed. Inflation has been low and stable before:
during the Bretton Woods fixed exchange rate system of
the post-war period up to 1971 and during the Gold
Standard of the early 1900s (Figure 1.1.4). Yet directly
following the low inflation period that ended in the early
1970s, the sharp increase in oil prices in 1973-74 led to a
rapid acceleration in global inflation and sharp declines ingrowth in many countries (Kose and Terrones 2015).
Global inflationary pressures also led to a significant
increase in domestic inflation in developing economies,
including those that experienced relatively low and stableinflation in the late 1960s and early 1970s (Cline 1981).
All three episodes of sustained low inflation arecharacterized by inflation below 5 percent for an extended
period. It is notable, however, that the two earlier episodes
were followed by sharply rising inflation. This illustrates

5 Major advanced-economy central banks have also acknowledged theneed to consider the global environment in setting monetary policy inlight of the highly synchronized nature of global inflation (Bernanke
2007; Draghi 2015; Carney 2015).

11

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BOX 1.1 The great disinflation (continued)
FIGURE 1.1.3 Inflation synchronization
Inflation has become increasingly globally synchronized. The global factor accounted for a greater share of the inflation
variance in advanced economies than in EMDEs. The global factor was more important in explaining the variance of priceindices with a greater tradable goods and services content. The synchronization of inflation has been stronger than thesynchronization of output growth, especially in EMDEs.
A. Contribution of global factor to
inflation variation

B. Contribution of global factors to
inflation and output growth variation

C. Contribution of global factors to
inflation variation, by inflation measure

Source: World Bank; Ha, Kose, and Ohnsorge (2019).
A.B. The results are based on a two-factor dynamic factor model with inflation (A,B) or output growth (B) using a sample of 99 economies (25 advanced economies and
74 EMDEs) for 1970-2017. The model includes global and group inflation factors. All numbers refer to median variance shares of total inflation (A,B) or output growth(B) variance accounted for by the global factor.
C. The global inflation factors are estimated with two-factor dynamic factor models for annual inflation for each measure in 38 countries (25 advanced economies and
13 EMDEs) for the period 1970-2016, the size of the sample being constrained by data availability. “IMP” = import price index, “PPI” = producer price index, “CPI” =headline consumer price index, “DEF” = GDP deflator, and “CORE” = core consumer price index.
Click here to download data and charts.

FIGURE 1.1.4 Low inflation episodes
Global inflation has been low and stable before: during the Bretton Woods fixed exchange rate system in the post-war
period up to the early 1970s, and during the gold standard of the early 1900s.
A. Global inflation

B. Global inflation

Source: World Bank; Ha, Kose, and Ohnsorge (2019).
A. Median of annual average inflation in a sample of 24 economies for which data are available across the full period.
B. Cross-country average of annual average inflation. 1900-13 spans the gold standard, and 1944-71 the Bretton Woods system.
Click here to download data and charts.

that maintaining low inflation can be as great a challenge
as achieving low inflation.
EMDE policymakers need to recognize the increasing roleof the global inflation cycle in driving domestic inflation.
Options to help insulate economies from the impact of

global shocks include strengthening institutions, including
central bank independence, and establishing fiscal
frameworks that can both assure long-run debt
sustainability and provide room for effective countercyclical policies. Low inflation in EMDEs in the past two
decades is no guarantee of low inflation in the future.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Nominal wage gains have been outpacing
inflation, resulting in modest real wage growth.
Long-term inflation expectations have edged up
but remain contained.
During 2018, the U.S. administration raised
tariffs on about $300 billion worth of imports,
mostly from China; other countries have retaliatedwith tariffs on about $150 billion worth of U.S.
exports. In all, new tariffs have been imposed on
about 12 percent of U.S. goods imports and may
expand further, resulting in higher prices andelevated policy uncertainty (Kutlina-Dimitrova
and Lakatos 2017; Lindé and Pescatori 2017).
During the forecast horizon, growth is expected todecelerate as monetary policy accommodation isremoved, and as fiscal stimulus fades andsubsequently begins to drag on growth. Higher
trade tariffs are expected to further weigh on
activity, especially exports and investment. In all,
U.S. growth is projected to slow to 2.5 percent in2019 and to an average of 1.7 in 2020-21—
roughly consistent with potential.

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13

FIGURE 1.4 United States
The U.S. economy is experiencing robust growth, with strength in domestic
demand. There are signs that productivity and labor participation are
increasing. Nominal wages have been outpacing inflation, resulting inmodest real wage gains. Fiscal and monetary policies will stimulate activity
in the near term but are likely to become a drag by 2020.
A. Domestic demand and investment
growthB. Additions to labor force andproductivity growth

C. Real and nominal wage growth

D. Stance of fiscal and monetary
policyEuro Area
Euro Area growth slowed notably in 2018 to an
estimated 1.9 percent, 0.2 percentage point below
previous projections. In particular, exports havesoftened, reflecting the earlier appreciation of theeuro and slowing external demand (Figure 1.5).
While unemployment has declined, inflation
remains stubbornly low. Headline inflation has
risen to target, but largely due to a temporary
acceleration in energy prices. Core inflation
remains around 1 percent, while long-term
inflation expectations continue to hover around
1.6 percent, as in the past three years. The
European Central Bank has stopped adding to its
balance sheet, although it is expected to maintainits negative interest rate policy until at least mid2019. Financial system lending and profitability
have continued to increase, though some
European banks may be exposed to financial stressin some EMDEs.
Across the Euro Area, the stance of fiscal policy is
expected to be mildly expansionary. Increased
German expenditures are envisioned to lead tosmaller surpluses, while deficits in France and Italy

Source: Bureau of Economic Analysis; Bureau of Labor Statistics; Federal Reserve Bank of St. Louis;
Haver Analytics; Holston, Laubach, and Williams (2016); International Monetary Fund; World Bank.
A. Investment is measured using gross fixed capital formation. Total domestic demand is GDP lessnet exports of goods and services. Last observation is 2018Q3.
B. Last observation is November 2018 for labor force data and 2018Q3 for productivity.
C. Wage growth is the average hourly earnings of private, non-farm production, and nonsupervisory
employees. Last observation is November 2018.
D. Policy rate is the mid-range of the federal funds target rates. Forecast for the policy rate andinflation are market expectations. The neutral rate is the nominal short-term interest rate consistent
with the economy operating at its full potential once transitory shocks have abated, and is estimatedaccording to Holston, Laubach, and Williams (2016). The neutral rate is assumed to remainunchanged at its latest value (November 28, 2018) until 2020. Shaded area indicates forecasts.
Click here to download data and charts.

are likely to rise amid public pressures for
additional spending and tax relief. Italy’s
borrowing costs have increased and remain
volatile, reflecting uncertainties about the outlook
for the country’s debt load.
In all, Euro Area growth is projected to further
decelerate toward potential over the forecasthorizon, to 1.6 percent in 2019 and an average of
1.4 percent in 2020-21, as monetary stimulus iswithdrawn and global trade growth moderates.
Japan
Japanese growth slowed to an estimated 0.8
percent in 2018, reflecting contractions in the first14

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FIGURE 1.5 Euro Area
A slowdown in exports has been the primary driver of cooling Euro Area
activity. While headline inflation has risen to target, it is largely due to atemporary acceleration in energy prices.
A. Export contribution to growth

to its balance sheet. It now holds about 40 percent
of government debt. The government continues torun a primary deficit, and it has announced atemporary stimulus package to offset the shortterm impact of a VAT hike in late 2019.

B. Inflation

Growth is projected to pick up to 0.9 percent in
2019, reflecting a recovery from last year’s
temporary disruptions. As employment growthslows and fiscal policy tightens, growth is expected
to moderate to 0.7 percent in 2020 and 0.6
percent in 2021.
China
Source: Bloomberg, European Central Bank, Eurostat, Haver Analytics, World Bank.
A. Last observation is 2018Q3.
B. Inflation expectations are derived from 5-year over 5-year forward inflation-linked swap rates,
averaged over the quarter. Horizontal line represents 1.9 percent, consistent with the ECB's inflation
target of close to, but below, 2 percent. Last observation is November 2018.
Click here to download data and charts.

FIGURE 1.6 JapanThe economy is still growing above potential, as solid growth in
employment offsets subdued productivity. The Bank of Japan is providing
exceptionally supportive monetary policy by keeping long-term rates nearzero and expanding its balance sheet, while the fiscal deficit is narrowing.

A. Employment and productivity
growthB. Gross government debt and longterm bond yields

Source: Bank of Japan; Cabinet Office of Japan; Haver Analytics; Japan Ministry of Finance; Japan
Ministry of Health, Labor, and Welfare.
A. Last observation is 2018Q3.
B. BoJ = Bank of Japan. Bond yield is the quarterly average. Yellow horizontal line indicates theorigin x-axis line corresponding to the right-hand scale (RHS). Last observation is 2018Q3.
Click here to download data and charts.

and third quarters due to bad weather and natural
disasters. Nevertheless, the labor market has been
robust, with the unemployment rate at 2.4
percent, rising earnings, and the participation rate
standing above 79 percent—up 1.5 percentage
points since the beginning of last year. Risinglabor force inputs, however, have been offset byweak productivity (Figure 1.6).
The Bank of Japan continues to provide stimulusby keeping long-term rates near zero and adding

Growth is estimated to have slowed to a stillrobust 6.5 percent in 2018, supported by resilient
consumption (Figure 1.7). A rebound in private
fixed investment helped offset a decline in public
infrastructure and other state spending. However,industrial production and export growth havedecelerated, reflecting easing global manufacturing
activity. Import growth continued to outpace
export growth, contributing to a shrinking current
account surplus. Net capital outflows haveresumed, and international reserves have been
edging down. Stock prices and the renminbi haveexperienced continued downward pressures, andsovereign bond spreads have risen amid ongoingtrade tensions and concerns about the growth
outlook.
New regulations on commercial bank exposures toshadow financing, together with stricter provisions
for off-budget borrowing by local governments,
have slowed credit growth to the non-financial
sector. However, in mid- and late 2018, theauthorities reiterated their intention to pursue
looser macroeconomic policies to counter the
potential economic impact of trade disputes with
the United States. Prices of newly constructed
residential buildings have rebounded, including inTier 1 cities, following several years of correction.
Consumer price inflation has generally moved up
since mid-2018, partly reflecting currency
depreciation and higher energy and food prices inmost of last year, but it remains below target.
Growth is projected to decelerate to 6.2 percent in
2019, slightly below previous projections as aresult of weaker exports, and to further moderateG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

to 6 percent by the end of the forecast horizon,
broadly in line with its potential pace. Domestic
demand is projected to remain robust aided bypolicies to boost consumption. Supportive fiscal
and monetary policies undertaken or announced
so far are expected to largely offset the negative
impact of higher tariffs; however, additional
stimulus may have the undesirable effect ofslowing the deleveraging and de-risking process(World Bank 2018b).

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15

FIGURE 1.7 China
Growth in China remains robust, in part reflecting resilient consumption.
However, industrial production and new export orders have moderated,asset prices have experienced downward pressures, and sovereign bondspreads have risen amid trade tensions. Prices of newly constructed
residential buildings have rebounded, including in Tier 1 cities following aperiod of correction.
A. Contribution to GDP growth

B. Industrial production and newexport orders

C. Bond spreads and equity prices

D. Housing price growth

Global trends
In 2018, global trade slowed more rapidly than
expected, alongside softening industrial activity.
Trade policy uncertainty remains elevated,dampening global investment and trade. Borrowing
costs have generally tightened in EMDEs following abroad-based appreciation of the U.S. dollar, bouts ofinvestor risk aversion, and increased focus oncountry-specific vulnerabilities. External financing
conditions are expected to continue deteriorating in
2019, as monetary policy accommodation inadvanced economies is unwound. Oil prices weremarkedly volatile in the second half of 2018, mainly
due to supply factors, with sharp falls toward the endof the year. Most other commodity prices—
particularly metals—also weakened, reflecting
heightened trade tensions.
Global trade
Following strong momentum in 2017, growth inglobal goods trade markedly slowed during thefirst half of 2018 and has only partially recovered
since then. The deceleration was more
pronounced than previously expected, as reflected
in decelerating export orders and global
manufacturing activity (Figure 1.8).
In particular, global capital goods production,
which is highly trade-intensive, has slowed notably
in Europe and developing Asia, two tightlyinterconnected global manufacturing hubs
(Raschen and Rehbock 2016). Nearly a third ofEuropean exports and more than half of German
exports to developing Asia are of machinery andvehicles, while capital goods and electronics
account for a third of exports from developing
Asia to Europe.

Source: National Bureau of Statistics of China, Haver Analytics, J.P. Morgan, World Bank.
A. Investment refers to gross capital formation, which includes the change in inventories.
Consumption refers to total consumption, which includes public consumption and private
consumption. Data for 2018 are estimates.
B. New export orders measured by Purchasing Managers’ Index (PMI). PMI readings above 50
indicate expansion in economic activity; readings below 50 indicate contraction. Last observation isNovember 2018.
C. Bond spread measures the average spread of China’s sovereign debt (as measured by J.P.
Morgan’s Emerging Market Bond Index) over its equivalent maturity U.S. Treasury bond. Equity index
is the Shanghai Stock Exchange (SSE) Composite. Last observation is December 18, 2018.
D. Prices of newly constructed residential buildings. The National Bureau of Statistics of China
surveys house prices in 70 cities and divides them into three tiers. The first tier includes Shanghai,
Beijing, Guangzhou, and Shenzhen. The second tier includes 31 provincial capital and sub-provincial
capital cities. The third tier includes 35 other cities. The green bars are the February 2011 toNovember 2018 averages. Data for 2017 reflect the average of monthly growth rates; 2018H2 covers
data through November. Last observation is November 2018.
Click here to download data and charts.

The softening of global goods trade comes against
the backdrop of ongoing trade tensions involving
major economies. New tariffs introduced since thebeginning of last year have affected about 12
percent of U.S. goods imports, 6.5 percent ofChina’s goods imports, and about 2.5 percent of
global goods trade. In the United States, tariffincreases were implemented citing national
security concerns and unfair trade practices.
Import restrictions and tariff increases were also
put in place in some EMDEs, as retaliatory actions
or as measures aimed at reducing current accountvulnerabilities in the face of intensifying capital

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FIGURE 1.8 Global trade
Global goods trade and industrial activity decelerated in 2018 amid tradetensions between major economies. A projected moderation of investment
growth in China and major advanced economies is expected to lead toslower trade growth in coming years. Technological changes could
continue to increase the share of services trade.
A. Global industrial production andnew export orders

B. Capital goods production, G20

C. Share of goods imports affected by
new tariffs, 2018

D. Global trade growth, volumes

E. Import demand growth, volumes

F. Global services trade, shares

Source: Haver Analytics, World Bank, World Trade Organization.
A. New export orders measured by Purchasing Managers’ Index (PMI). PMI readings above 50
indicate expansion in economic activity; readings below 50 indicate contraction. Last observation isNovember 2018 for new export orders and October 2018 for industrial production.
B. Industrial production indexes weighted by gross domestic product at constant 2010 U.S. dollars.
Sample includes the G20 countries for which capital goods data are available. Last observation is
October 2018.
C. Value of tariffs implemented as of December 19, 2018, as a share of total imports.
D.E. Shaded areas indicate forecasts. Aggregate growth rates calculated using constant 2010 U.S.
dollar GDP weights. Trade measured as the average of export and import volumes.
F. Trade measured as the average of export and import values. Trade and GDP measured in current
U.S. dollars. Data are 4-quarter moving averages. Last observation is 2018Q2.
Click here to download data and charts.

outflow pressures (e.g., Arab Republic of Egypt,Indonesia, Islamic Republic of Iran, Pakistan, Sri
Lanka, Turkey).
Combined with the rising prevalence of temporary
trade barriers (such as anti-dumping andcountervailing duties and safeguards), recent
protectionist measures have disproportionately
affected trade in parts and components, withnegative repercussions for international value
chains (Baldwin 2018; Bown 2018; Johnson andNoguera 2017). Increased tariffs on certain goods,
including on U.S. steel imports, is associated withan especially large negative effect on producers inpoorer and smaller EMDEs (Bown, Jung, andZhang 2018). In contrast, some EMDEs may bebenefiting in the short term from trade diversion,as rising tariffs increase the cost of targeted goodsin the United States and China.
The temporary pause in tariff hikes agreed by theUnited States and China during the G20 meetingin early December 2018 and the successful
negotiations of the new United States-MexicoCanada Agreement have somewhat tempered trade
policy uncertainties. However, the possibility ofescalating trade restrictions involving major
economies remains elevated. This uncertainty islikely to weigh on firms’ willingness to invest,
export, and engage in international value chains,with negative effects on the global trade outlook(Feng, Li, and Swenson 2017; Handley and Limão
2015; Osnago, Piermartini, and Rocha 2018). In
addition, rising interest rates in advanced
economies and economic rebalancing in China is
expected to contribute to slower global investment
and trade growth, with the latter projected todecelerate from 3.8 percent in 2018 to 3.4 percent
by the end of the forecast horizon (Ahuja andNabar 2012; Kose, Ohnsorge et al. 2017). Global
trade is still projected to grow somewhat fasterthan global GDP, but at a much weaker pace thanpreviously envisaged, reflecting a deterioration in
growth prospects in several large EMDEs and in
the Euro Area, as well as trade policy uncertainties.
Structural factors continue to weigh on themedium-term outlook for global trade, including
maturing
internationalvaluechains(Constantinescu et al. 2018; ECB 2016;
Hoekman 2015). However, technological change
and progress in liberalization efforts under the
Trade in Services Agreement (TiSA) should
continue to increase the relative importance ofservices in global trade flows (Lodefalk 2014;
Miroudot and Cadestin 2017).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

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17

Financial markets

FIGURE 1.9 Global finance

Borrowing costs in advanced economies crept up
during most of 2018, as inflation moved closer tocentral bank targets and monetary policy
accommodation continued to be withdrawn. Afternotable fluctuations, U.S. long-term yields ended
the year at 2.7 percent, up around 30 basis points
from the start of 2018 (Figure 1.9). Notwithstanding a scaling back of central bank asset
purchases in the Euro Area and Japan, negativeinterest rate policies in these economies havecontinued to keep a lid on global bond yields,with more than $7.5 trillion of outstanding debt
still trading at negative interest rates (15 percent of
all bonds). Investor concerns about softening
growth prospects and a search for higher-yielding
safe assets have led to a further compression of the
U.S. yield curve, despite higher inflation andballooning U.S. government deficits driven by
fiscal stimulus measures. Global equity markets
dropped in the final quarter of 2018, partly
reflecting a deterioration in market sentiment
regarding global activity and trade policy shifts.

Borrowing costs increased in the United States, as monetary policy
accommodation continued to be withdrawn, while softening global growthprospects weighed on equity markets. Tighter external financing conditions
contributed to significant capital outflows and more significant currencypressures in more vulnerable EMDEs. International bond issuances slowedmarkedly in some regions, with yields increasing at their fastest pace since
2013.

Divergent monetary policy among major
economies also contributed to a significant
appreciation of the U.S. dollar in 2018. This,
together with increased investor risk aversion andrenewed attention to external vulnerabilities,
contributed to significant capital outflows in many
EMDEs. Since the U.S. dollar started
strengthening in April 2018, EMDE currencies
fell by an average of about 10 percent—the mostsignificant episode of sustained depreciation since
early 2016. Cumulative portfolio outflows from
EMDEs also surpassed those seen after the 2013
Taper Tantrum, reflecting a broad-based sell-off
in both equity and bond funds.
While financial market stress was mostpronounced in Turkey and Argentina, many other
EMDEs also suffered from deteriorating market
sentiment. Countries with current account deficits
financed by volatile capital flows, as well as
countries with high short-term external debt, weremost severely impacted, pointing to heightened
investor focus on external vulnerabilities. Elevateddomestic debt, above-target inflation, andidiosyncratic factors such as policy uncertainty

A. U.S. sovereign bond yields

B. Global and EMDE equity prices

C. EMDE portfolio flows during recent
stress episodes

D. EMDE currency movements sinceApril 2018, by current accountbalance ex. FDI

E. EMDE new bond issuance, byregionF. Largest annual changes in EMDE
bond yields since 2000

Source: Bloomberg, Dealogic, Haver Analytics, Institute of International Finance, International
Monetary Fund, J.P. Morgan, World Bank.
A. Sovereign yields reflect the yield on U.S. Treasury bonds. Last observation is December 19, 2018.
B. Figure shows MSCI Global and Emerging Markets Indexes. Last observation is December 19,
2018.
C. Cumulative flows to major EMDEs, excluding China, for the 250 days following the start of thestress episode. The start dates for the stress episodes are: Taper Tantrum: May 23, 2013; China
concerns: June 12, 2015; Latest episode: April 15, 2018. Last observation is December 19, 2018.
D. FDI = foreign direct investment. Figure shows the median of cumulative changes in exchange
rates since April 15, 2018. Orange lines indicate interquartile ranges. Last observation is December
19, 2018.
E. 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.
Figure shows the total new bond issuance from January to November for each year. Last observationis November 2018.
F. EMDE bond yields are calculated as the sum of the J.P. Morgan Emerging Market Bond Index(EMBI) spread and the 10-year U.S. Treasury yield. Last observation is December 19, 2018.
Click here to download data and charts.

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FIGURE 1.10 Commodity markets
Commodity prices are expected to generally stabilize in 2019, following
sharp movements last year. Crude oil prices fluctuated markedly in thesecond half of 2018, mainly due to supply factors, with sharp declines
toward the end of the year. Trade tensions between the United States andChina, including the imposition of tariffs on a range of products, have hadvarying effects on metal and agricultural commodities. In particular, theimpact has depended on whether tariffs were broad-based or commodity
specific, such as in the case of steel and soybeans.
A. Commodity price forecasts,
nominalB. Crude oil prices, nominal

C. Change in oil supply in majoroil-producing economies

D. Metals price indexes, nominal

E. Benchmark steel price indexes,
nominalF. Soybean spot prices

Coulibaly, and Zlate 2015; Eichengreen and
Gupta 2014).
Bond issuance has slowed markedly since mid2018, particularly in Latin America and the
Caribbean and Eastern Europe and Central Asia,
amid worsening external financing conditions.
EMDE sovereign credit ratings have continued todeteriorate, with some falling below investment
grade, reflecting concerns about rising debt anddeteriorating growth prospects. Yields on EMDE
debt issued in international bond markets rose by
140 basis points in 2018—the third largest
increase over the last two decades. Demand forcross-border bank loans has also weakened, withthe appreciation of the U.S. dollar putting upward
pressure on dollar funding costs. Various EMDE
central banks have responded to currency andcapital outflow pressures with interest rate hikes,
leading to tighter domestic borrowing conditions
and, in some cases, slower credit and domestic
demand growth.
In contrast to the deceleration in portfolio andbank flows, foreign direct investment (FDI) into
EMDE is estimated to have stabilized in 2018,
while remittance flows continued to increase(World Bank 2018c). Outward FDI from China
remained robust, boosted by the Belt and Road
Initiative.
Looking forward, global interest rates are likely torise at a slower pace than previously expected,
reflecting increased headwinds to global growth.
Nevertheless, external financing conditions are
expected to tighten further in EMDEs, and capital
flows to remain moderate, particularly among
more vulnerable economies.

Source: Bloomberg, International Energy Agency, U.S. Department of Agriculture, World Bank.
A. Nominal price indexes. Shaded area indicates forecast.
B. WTI = West Texas Intermediate. Last observation is January 4, 2019.
C. Chart shows the change in oil production of five major oil-producing economies from January 2018
to November 2018. Blue bars indicate total oil production in January and November. Red bars
indicate a decline in an economy’s production over the period, and orange bars indicate an increasein production.
D. Last observation is December 18, 2018.
D.E. Indexes are based on nominal U.S. dollars.
E.F. Last observation is December 19, 2018.
Click here to download data and charts.

played a role as well. As in previous episodes,
EMDEs with more liquid currency and equity
markets were particularly affected by shifting
market sentiment and contagion effects (Ahmed,

Commodities
Energy prices fluctuated markedly in the second
half of 2018, mainly reflecting supply factors, withsharp falls toward the end of the year. Prices ofmost metals and, to a lesser extent, agriculturalcommodities also weakened, largely due toconcerns about the effects of tariffs on global
growth and trade. Prices of the three commodity
groups are expected to generally stabilize in 2019
(Figure 1.10).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Oil prices averaged $68 per barrel (bbl) in 2018, a
touch lower than June forecasts but about 30
percent higher than in 2017. While robust global
oil consumption contributed to this increase,
supply-side factors were the main drivers of pricemovements through the year. Continuing declines
in production in Venezuela and market concern
about the impact of U.S. sanctions on Iran
contributed to rising Brent crude oil prices, whichpeaked at $86/bbl in early October. However,prices fell sharply in November after the UnitedStates announced temporary waivers to the
sanctions on Iran for eight countries, including
China and India. The decline in prices also
reflected continued rapid growth in oil productionin the United States, as well as a substantial
increase in supply by the Organization of thePetroleum Exporting Countries (OPEC) and theRussian Federation.
Oil prices are expected to average $67/bbl in 2019and 2020, $2/bbl lower than June projections;however, uncertainty around the forecast is high.
While growth in oil demand is expected to remainrobust in 2019, the expected loss in momentum
across EMDEs could have a greater impact on oil
demand than expected. The outlook for supply is
uncertain and depends to a large extent on
production decisions by OPEC and its nonOPEC partners. While these producers haveagreed to cut output by 1.2mb/d for six months
starting January 2019, few details have been
forthcoming about the distribution of the cuts,and they may prove insufficient to reduce theoversupply of oil. Considerable uncertainty
remains about the full impact of Iranian sanctions
once the waivers end, as well as the outlook for
Venezuelan production. Meanwhile, crude oil
output in the United States is expected to rise by afurther 1mb/d in 2019, with capacity constraints
envisioned to ease in the second half of the year as
new pipelines come onstream.
Metals prices rose 6 percent, on average, in 2018,less than previously expected. After increasing in
the first half of last year, prices fell sharply in thesecond half following the imposition of broadbased tariffs by the United States on China’s
imports (World Bank 2018d). Heightened tradetensions involving these economies have raised

CHAPTER 1

market concerns about global trade andinvestment prospects; as a result, they haveclouded the outlook for demand for commodities.
Industrial metals have been particularly responsive
to these concerns given their many uses in the
manufacture of tradable goods, with some metals
such as nickel falling more than 20 percent. In
contrast, the price of steel and aluminum in the
United States rose following the announcement ofspecific tariffs on imports of those metals from awide range of countries. Metals prices are expected
to stabilize in 2019 and 2020.
While agricultural prices were roughly flat in 2018as a whole, they declined appreciably in the second
half of the year, with developments varying bycommodity. Soybean prices in the United Statesfell substantially following the announcement oftariffs by China on imports of U.S. soybeans,
while prices were higher in other countries,
particularly in Brazil. The imposition of tariffs has
led to trade diversion, with China’s imports ofsoybeans from the United States 25 percent lower
in 2018 relative to 2017, while those from Brazil
have risen 22 percent. More recently, the gap inprices has closed, as China has resumed purchasesof U.S. soybeans. Wheat prices were slightly
higher in 2018, as bad weather in Europe led tosmaller harvests. Estimates for the 2018-19 crop
forecast have been revised up for mostcommodities, and high stock-to-use ratios for rice
and wheat reduce the likelihood of a food pricespike. In all, agricultural prices are projected toremain broadly stable in 2019 and 2020.

Emerging market anddeveloping economies:
Recent developments
and outlook
EMDE growth is expected to stall at 4.2 percent in
2019, markedly below previous expectations. The
forecast reflects the lingering effects of recent financial
market pressure in some large economies, with asubstantially weaker-than-expected pickup incommodity exporters accompanied by a decelerationin commodity importers. Growth is projected toplateau at 4.6 percent toward the end of the forecasthorizon, as the recovery in commodity exporters levels

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.11 Activity in EMDEs
EMDE activity has stalled, in part reflecting the effect of financial stress insome large economies with sizable current account deficits and highexposure to volatile capital flows. Domestic demand across EMDEs has
generally moderated, and trade flows have softened. High-frequency
indicators suggest that the weakness continues, particularly in more
vulnerable economies.
A. Growth

B. Contribution to GDP growth

C. Import growth, volumes

D. Manufacturing PMIs

Source: Haver Analytics, International Monetary Fund, World Bank.
A.-C. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Data for 2018
are estimates. Data for 2015-16 are simple averages.
A.-D. High CA def. ex. FDI = high current account deficit excluding foreign direct investment, whichrefers to countries with zero or negative values of current account balances net of foreign direct
investment. Others refers to countries with positive values of current account balances net of foreign
direct investment.
A. Yellow diamonds correspond with the June 2018 edition of the Global Economic Prospects report.
B. Domestic demand includes government consumption, private consumption, and gross capitalformation, which includes the change in inventories. Net exports are calculated as the volume ofexports minus imports.
C. Figure shows imports of goods and services.
D. Figure shows average Purchasing Managers’ Index (PMI) for manufacturing output for countrygroups. Readings above 50 indicate expansion in economic activity; readings below 50 indicate
contraction.
Click here to download data and charts.

renewed market attention to country-specificvulnerabilities and financial stress in some large
economies with persistent macroeconomic
fragilities—most notably, Argentina and Turkey.
More generally, the weakness in activity was mostpronounced in EMDEs that suffered financial
market pressures in a context of elevated current
account deficits and high exposure to portfolio
and bank inflows (Figure 1.11). Many of these
economies faced sizable currency depreciation,
equity market declines, or foreign reserve losses(e.g., Angola, Argentina, Turkey, South Africa).
Domestic demand across EMDEs has generally
moderated , reflecting tighter domestic borrowing
conditions, softer confidence, and policy
tightening in some large economies to ward off
domestic price and capital outflow pressures. A
rebound in EMDE gross capital formation thatbegan in 2015 has slowed, and investor sentimenthas deteriorated. On the external front, import
growth has softened, partly due to sharp currency
depreciations in some large economies, while
export growth has also moderated, reflecting
weaker external demand—notably, moderating
global investment. Recent high-frequency
indicators confirm the weaker momentum among
EMDEs, particularly in those that have sizable
current account deficits and rely heavily on
portfolio and bank flows.
Commodity-exporting EMDEs

Recent developments

The pace of recovery in commodity exporters has
weakened significantly, and activity across thegroup has become more heterogenous. Investor
confidence has generally worsened, especiallytoward economies with external vulnerabilities andfragile domestic conditions (e.g., Angola,
Argentina, Nigeria, South Africa). Recent declines
in oil and other commodity prices have posited
additional headwinds to activity.

The recovery in EMDE activity has stagnated.
Aggregate growth in EMDEs edged down to an
estimated 4.2 percent in 2018—0.3 percentage
point below previous projections—against the
backdrop of a substantial strengthening of the
U.S. dollar, weakening capital flows, heightened
trade tensions,and moderating global
manufacturing and trade. This more challenging
international environment was accompanied byLong-standing challenges in several large
economies have resurfaced. In a number ofcountries, capital flows have softened, and asset
prices and currencies have come under significant
pressure amid weaker global trade, rising trade
restrictions, and renewed investor attention tocountry-specific factors including sizable current
account and fiscal deficits and elevated debt. As aconsequence, the rebound in domestic demandoff. In over 35 percent of EMDEs, per capita growth
will be too low to avoid widening income gaps withadvanced economies.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019has slowed and the recovery in investment hasstalled (e.g., Argentina, Iran, South Africa). Private
consumption growth has also cooled following
several years of continued recovery, partly
reflecting the dampening impact of higher
inflation and tighter lending conditions.
Among the largest commodity exporters, growthin Argentina plummeted following acute financial
market stress that resulted in sharp currency
depreciation and monetary policy tightening. In
South Africa, activity contracted in the first half of2018 and, despite a recovery in the second half, itremains subdued, reflecting challenges in mining
production, low business confidence, and policy
uncertainty. Growth in Brazil was lackluster in
2018, reflecting a truckers’ strike mid-year andheightened policy uncertainty. In Russia, growthhas been resilient, supported by private
consumption and exports; however, momentum
has slowed, reflecting policy uncertainty, recent oil
price declines, and renewed pressures on currency
and asset prices. Output has contracted in a
number of other commodity exporters thatexperienced declines in commodity production(e.g., Angola, Equatorial Guinea); social tensions(e.g., Nicaragua), or other idiosyncratic factors(e.g., sanctions in Iran).
In contrast, activity has firmed further in several
oil-exporting economies where oil production
rebounded in 2018 (e.g., Kuwait, United ArabEmirates). Recoveries have also continued, tovarying degrees, in some large energy exporters
where significant adjustments were introduced in
response to the 2014-16 oil price plunge (e.g.,
Azerbaijan, Colombia, Saudi Arabia; World Bank
2018e, 2018f). Despite recent declines inindustrial metals prices, growth among some large
metals exporters has continued to show resilience(e.g., Chile, Mongolia, Peru). In addition, activity
in a number of countries has been supported byinfrastructure spending and foreign direct
investment flows (e.g., Benin, Côte d’Ivoire,
Ethiopia, Lao People’s Democratic Republic,
Morocco, Senegal, Uganda; World Bank 2018g).
Commodity-importing EMDEs
Growth in commodity importers has decelerated,reflecting capacity constraints, moderating export

CHAPTER 1

growth, and deteriorated conditions in some large
economies with elevated vulnerabilities andheightened policy uncertainty. Inflation has
generally moved up, partly in response to higher
energy prices in most of 2018 and closed or
positive output gaps. Price pressures, widening
fiscal and current account deficits, or in some cases
currency and financial market volatility haveprompted a shift to less accommodative monetary
policy in some countries in this group (e.g., India,
Mexico, Pakistan, the Philippines, Romania).
The moderation in activity is most evident among
commodity importers with increasing capacity
constraint, high current account deficits, or sizablepublic debt. The slowdown in Turkey—which
faced a substantial deterioration in foreign investor
confidence—has been especially severe. Activity isalso slowing, and financial conditions havetightened, in a number of other commodity
importers that have experienced financial market
stress or continue to face widening fiscal andcurrent account deficits (e.g., Pakistan, thePhilippines, Romania).
Slowing Euro Area growth has diminished thepositive trade and financial spillovers that had
previously supported activity in several countries
in Europe and Central Asia (e.g., Bulgaria,
Croatia, Montenegro). However, in some
economies, moderate inflation and low interest
rates have supported a pickup in growth (e.g.,
Hungary, Poland, Serbia). Growth in Mexico
remains moderate, partly owing to tighter
financing conditions and domestic policy-related
uncertainty.
Although activity continues to be generally more
solid in Asia, external headwinds have increased.
In India, growth has accelerated, driven by an
upswing in consumption, and investment growthhas firmed as the effects of temporary factors
wane. However, rising interest rates and currency
volatility are weighing on activity (World Bank
2018h). Other Asian economies (e.g., Bhutan,
Cambodia, Vietnam) continue to benefit frompan-Asian infrastructure investment projects,
including the China-led Belt and Road Initiative(World Bank 2018b).

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BOX 1.2 Low-income countries: Recent developments and outlook
Growth in low-income countries increased only slightly in 2018, to 5.6 percent, but is expected to rise to 5.9 percent in 2019 andaverage about 6.3 in 2020-21. Oil producers are benefitting from higher oil prices and output, while softer metals prices areweighing on growth in the metals exporters. Higher agricultural production and continued infrastructure spending has supported
growth in non-resource-intensive countries. However, progress on poverty reduction across all low-income countries will remain
slow. Downside risks to the outlook include the possibility that commodity prices will soften as a result of trade disputes, global
financing conditions will tighten abruptly, fiscal policies will slip, or extreme weather-related or health crises will emerge.

Recent developments
Economic growth is gradually improving in most lowincome countries (LICs), even though the external
environment is becoming less favorable (Figure 1.2.1).
Robust growth in several non-resource-intensive countries
has been supported by agricultural production (e.g.,
Rwanda, Uganda) and services (e.g., Nepal, Uganda) on
the production side, and household consumption (e.g.,Togo, Tajikistan) and public investment (e.g., Benin, The
Gambia, Nepal, Tajikistan) on the demand side. However,in Ethiopia—the largest LIC—growth lost momentum as
weaker activity in the construction and manufacturingsectors was aggravated by foreign exchange shortages.
Among exporters of industrial commodities, Chademerged from two years of recession partly due to therecovery in oil prices from their 2016 trough, as well as
increased oil production. In contrast, the growth
performance of metals exporters was more subdued,reflecting weaker metals prices and external demand, as
well as mine closures (e.g., Sierra Leone), and heightenedpolitical uncertainty (e.g., Democratic Republic ofCongo).
Progress on poverty reduction in LICs continues to bedisappointing, with more than 40 per cent of the
population in these countries living in extreme poverty—
i.e., earning below $1.90 per day. And while this ratio has
remained broadly unchanged in recent years, insufficientper capita GDP growth, especially in economies affectedby fragility, conflict, and violence, means that the poverty
headcount is rising.
Current account deficits are estimated to have widened inseveral countries in 2018. Among non-resource-intensive
economies, as well as metals exporters, external balances
have deteriorated as exports declined in response to weaker
external demand and moderating metals prices and theeffect of rising fuel prices on import bills. In contrast, oil

Note: This box was prepared by Rudi Steinbach. Research assistance
was provided by Hazel Macadangdang.

exporters, such as Chad, recorded smaller deficits, helpedby higher oil export earnings.
The financing of current account deficits has become morechallenging amid a less supportive external environment,
as foreign direct investment (FDI) inflows slowed inalmost 40 percent of countries (e.g., Mozambique,Tanzania, Zimbabwe; UNCTAD 2018). FDI inflows, inparticular to LICs, are more vulnerable to fluctuations ininternational financial conditions(BurgerandIanchovichina 2017). However, in some countries,
reduced political uncertainty and improved investor
sentiment have supported stronger FDI inflows (e.g.,
Benin, The Gambia). In addition, remittance flows haverecovered in several countries as growth in selectedadvanced economies improved in recent years (e.g., Benin,
Guinea-Bissau, Haiti; World Bank 2018i). Nevertheless,for many LICs, the accumulation of sufficientinternational reserves remains difficult, leaving them below
the three-months-of-imports benchmark and highly
vulnerable to negative shocks.
Fiscal deficits generally widened among the LICs, with themedian deficit increasing from 3.3 percent of GDP in2017 to an estimated 3.5 percent in 2018. The
deterioration reflected rising fiscal deficits among several
industrial-commodity-exporting LICs as moderating
metals prices dampened revenues. However, in oilexporting countries (e.g., Chad), higher oil revenues
combined with improved non-oil revenue collection
yielded a fiscal surplus, and in some non-resourceintensive countries, fiscal consolidation delivered narrower
fiscal deficits (e.g., Benin, The Gambia).
Debt levels remain elevated in many countries andcontinue to rise. In Liberia and Sierra Leone, the debt-toGDP ratio has increased more than twofold over the last
five years, driven by a significant slowdown in growth andcontinually weak revenue collection (Liberia) and a
depreciating exchange rate coupled with new borrowings(Sierra Leone). In addition to the rise in debt ratios,changes in the composition of debt have made somecountries more vulnerable to shifts in international
financing conditions (Chapter 4). As countries have gainedG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

BOX 1.2 Low-income countries: Recent developments and outlook (continued)
FIGURE 1.2.1 Recent developments in low-income countries
Growth in LICs is gradually improving. Metals exporters are struggling owing to softer metals prices, while growth in nonresource-intensive countries is supported by higher agricultural production and infrastructure spending. However, the poverty
headcount is rising, especially in economies affected by fragility, conflict, and violence. Current account and fiscal deficits
have been widening, especially in metals exporters. Increased reliance on non-concessional debt is making LICs more
vulnerable to global financial conditions, and the number of countries in debt distress has continued to rise.
A. Growth

B. Poverty headcountC. Current account balance

D. Fiscal balance

E. Non-concessional debt

F. Debt distress

Source: International Monetary Fund, World Bank.
Note: LICs = low-income countries. Industrial-commodity-exporting countries include energy- and metal- exporting economies, and the sample includes 8 countries.
Non-resource-intensive countries include agricultural-exporting economies and commodity importers, and the sample includes 22 countries. Data for 2018 are estimates.
A. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights.
B. The number of people living on or below the international poverty line of $1.90 per day. Data for 2016-18 are estimates and calculated using data from World Bank
(2018h). FCV = fragility, conflict, and violence. Per capita GDP represents the average growth rate from 2016 to 2019.
C.D. Median of country groups.
E. Includes 30 low-income countries and excludes Somalia, South Sudan, and Syria due to data restrictions.
F. Percent of LICs eligible to access the IMF’s concessional lending facilities that are either at high risk of, or in, debt distress. The sample includes 30 low-income
countries.
Click here to download data and charts.

access to international capital markets and non-resident
participation in domestic debt markets expanded, nonconcessional debt has increased, reaching more than 30
percent of total public debt in several LICs (e.g., Ethiopia,
Mozambique, Senegal) and over half of total public debt
in Zimbabwe.
As a result, debt sustainability has deteriorated in several
LICs. By late 2018, The Gambia, Mozambique, South
Sudan, and Zimbabwe were classified as in debt distressunder the IMF–World Bank debt sustainability

framework. In addition, Ethiopia was downgraded during
the year from a moderate-risk to high-risk rating.

Outlook
Growth in LICs is expected to improve, rising to 5.9
percent in 2019 and an average of about 6.3 percent in
2020-21 (Figure 1.2.2). While the growth recovery among
the metals exporters is expected to be sluggish, as lower
revenues constrain fiscal spending, growth among oil
exporters is expected to be spurred by higher oil

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 1.2 Low-income countries: Recent developments and outlook
FIGURE 1.2.2 Outlook
Growth among the LICs is expected to improve. In nonresource-intensive economies, growth will be supported
by stronger agriculture production and continued
infrastructure investment, while oil exporters should
benefit from higher oil production. However, weaker
metals prices and subdued external demand imply asluggish recovery in metals exporters. Moreover,progress on poverty reduction in LICs is expected to be
slow, as per capita income growth still remains modest,
especially among fragility, conflict, and violenceaffected economies.
A. GDP growth forecasts

production and improving domestic demand. Economic
activity is also expected to remain robust in non-resourceintensive LICs. In fast-growing countries, such as Rwanda
and Tanzania, the expansion will be supported by public
investment in infrastructure and strong agricultural
growth. Similarly, infrastructure, agriculture, and energy
investments related to structural reforms should sustain
Senegal’s growth recovery. While growth in Ethiopia is
expected to remain strong, it will be weighed down by atighter fiscal stance, as the government aims to stabilize
public debt.
Per capita GDP growth in LICs is expected to increase
only modestly from 2.7 percent in 2018 to 3.1 percent in
2019, and to an average of 3.5 percent in 2020-21.
Moreover, among LICs affected by fragility, conflict, andviolence, growth in per capita GDP is expected to besignificantly lower—increasing from 0.5 percent in 2018
to an average of 1.6 per cent in 2020-21. In all, these rates
are not sufficient to generate a marked reduction inpoverty rates, and the number of people in LICs living
below the international poverty line of $1.90 per day is
expected to remain elevated.

Risks

B. Per capita GDP growth

Source: World Bank.
Note: Shaded area indicates forecasts. Industrial commodity countries
include energy- and metal- based economies, and the sample includes 8
countries. Non-resource intensive countries include agricultural exporters
and commodity importers, and the sample includes 22 countries.
A. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP
weights.
B. FCV = fragility, conflict, and violence. Aggregate per capita growth rates
calculated using the total GDP for each subgroup divided by its total
population. Afghanistan, Liberia, and Tajikistan are excluded due to data
limitations.
Click here to download data and charts.

The economic outlook is dominated by downside risks.
On the external front, slower-than-projected growth inmajor world economies—such as the United States, EuroArea, or China—would adversely affect export demandand investment in several LICs, specifically countries thatare heavily dependent on these large economies for tradeand investment flows. Moreover, escalating trade tensions
involving major economies (e.g., rising tariffs between the
United States and China) would be detrimental to LICs
that depend on extractive industries—specifically metals
producers, as metals prices are likely to fall faster thanother commodity prices in response (World Bank 2018j).
Furthermore, an unexpected deterioration in international
financial conditions could disrupt capital inflows (IMF
2018b), fuel disorderly exchange rate depreciations, andraise financing costs, especially in LICs with weaker
macroeconomic fundamentals or higher political risks.
Sharp increases in debt-servicing costs, specifically foreigncurrency-denominated debt, would undermine muchneeded fiscal consolidation efforts and crowd out povertyreducing expenditures.
Risks to debt sustainability are high, as several countries
are either already in debt distress or facing high risk
thereof, according to the IMF–World Bank debt
sustainability framework for LICs (Chapter 4). The recent
increased reliance on foreign currency borrowing has

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

BOX 1.2 Low-income countries: Recent developments and outlook (continued)
TABLE 1.2.1 Low-income country forecastsa

Percentage point differencesfrom June 2018 projectionsd(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021

2018e

2019f

4.8

5.5

5.6

5.9

6.2

6.3

-0.1

0.0

0.0

Afghanistan

2.4

2.7

2.4

2.7

3.2

3.2

0.2

0.2

-0.1

Low Income Country, GDP b

2020f

Benin

4.0

5.8

6.0

6.2

6.5

6.6

0.0

0.1

0.2

Burkina Faso

5.9

6.3

6.0

6.0

6.0

6.0

0.0

0.0

0.0

Burundi

-0.6

0.5

1.9

2.3

2.5

2.8

0.0

0.0

0.0

Chad

-6.3

-3.0

3.1

4.6

6.1

4.9

0.5

2.1

0.3

Comoros

2.2

2.7

2.7

3.1

3.1

3.1

-0.2

0.1

0.1

Congo, Dem. Rep.

2.4

3.4

4.1

4.6

5.5

5.9

0.3

0.5

1.1

Ethiopiac

8.0

10.1

7.7

8.8

8.9

8.9

-1.9

-0.9

-1.0
0.3

Gambia, The

0.4

4.6

5.3

5.4

5.2

5.2

-0.1

0.2

Guinea

10.5

8.2

5.8

5.9

6.0

6.0

-0.2

0.0

0.0

Guinea-Bissau

5.8

5.9

3.9

4.2

4.4

4.5

-1.2

-1.0

-1.0

Haitic

1.5

1.2

1.6

2.3

2.4

2.5

-0.2

-0.1

0.0

Liberia

-1.6

2.5

3.0

4.5

4.8

4.8

-0.2

-0.2

0.0

Madagascar

4.2

4.2

5.2

5.4

5.3

5.3

0.1

-0.2

0.0

Malawi

2.5

4.0

3.5

4.3

5.3

5.5

-0.2

0.2

0.4

Mali

5.8

5.4

4.9

5.0

4.9

4.8

-0.1

0.3

0.2

Mozambique

3.8

3.7

3.3

3.5

4.1

4.1

0.0

0.1

0.5

Nepalc

0.6

7.9

6.3

5.9

6.0

6.0

0.0

1.4

1.8

Niger

4.9

4.9

5.2

6.5

6.0

5.6

-0.1

1.1

0.2

Rwanda

6.0

6.1

7.2

7.8

8.0

8.0

0.4

0.7

0.5

Senegal

6.2

7.2

6.6

6.6

6.8

6.9

-0.2

-0.2

-0.2

Sierra Leone

6.3

3.7

3.7

5.1

6.3

6.3

-1.4

-0.6

-0.2

Tajikistan

6.9

7.1

6.0

6.0

6.0

6.0

-0.1

0.0

0.0

Tanzania

7.0

7.1

6.6

6.8

7.0

7.0

0.0

0.0

0.0

Togo

5.1

4.4

4.5

4.8

5.1

5.1

-0.3

-0.2

0.1

Ugandac

4.8

3.9

6.1

6.0

6.4

6.5

0.6

0.0

-0.1

Zimbabwe

0.6

3.2

3.0

3.7

4.0

4.0

0.3

-0.1

0.0

Source: World Bank.
World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here may differ fromthose 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, Democratic People’s Republic of Korea, Somalia, Syria, and Yemen are not forecast due to data limitations.
b. GDP at market prices and expenditure components are measured in constant 2010 U.S. dollars.
c. GDP growth based on fiscal year data. For Nepal, the year 2017 refers to FY2016/17.
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 2018. However, an
identical sample is used for the comparison of the aggregate LIC GDP projection.
To download this data, please visit www.worldbank.org/gep.
Click here to download data.

increased the extent to which debt sustainability isvulnerable to sharp currency depreciations.
Weather-related shocks, such as flooding or severe andprolonged drought episodes remain an important risk formany LICs. A return of the drought conditions
experienced in recent years would undermine the ongoingrecovery in agricultural production. In addition, lower
agricultural output, and the food price spikes that arelikely to follow, could adversely affect poverty rates inmany LICs, especially countries where agricultural activity
accounts for a dominant share of domestic value added(e.g., Chad, Sierra Leone), or is the prevailing sourceof employment (e.g., Burkina Faso, Burundi; Chapter 4).
Health crises are a continuous concern. The recent Ebola
outbreak in the Democratic Republic of Congo could havea detrimental impact on economic activity in the country
and the sub-region should it spread to major urban centers
and to neighboring countries.

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BOX 1.3 Regional perspectives: Recent developments and outlook
The cyclical upswing in regions with many commodity exporters (such as Latin America and the Caribbean, and the Middle Eastand North Africa) is proceeding at a more moderate pace than previously anticipated, partly reflecting a substantial slowdown insome large economies, and is expected to plateau toward the end of the forecast horizon. Growth in regions with large numbers ofcommodity importers (such as South Asia and East Asia and the Pacific) is projected to remain solid at around 6-7 percent. For
all regions, risks to the outlook are increasingly tilted to the downside.
East Asia and Pacific. Growth is projected to moderate toa still-robust pace of about 6 percent in 2019 and remain
near that level over the forecast period, in line with earlier
projections. In China, policies aimed at rebalancing the
economy and countering the impact of higher U.S. tariffs
will continue to tilt activity toward consumption and awayfrom exports. Excluding China, regional growth isexpected to remain steady at 5.2 percent over the forecasthorizon. Risks to regional growth are to the downside andhave intensified. They include a further escalation of trade
restrictions and a faster-than-expected tightening of global
financing conditions. Highly leveraged economies andcountries with sizable external financing needs are
particularly vulnerable to disruptions in real and financial
activity.
Europe and Central Asia: Growth fell to an estimated 3.1
percent in 2018, driven by a slowdown in Turkey and inCentral European economies. Turkish growth for this year
has been revised sharply down due to substantial financial
market stress and the associated economic effects,
contributing to a deceleration in regional growth in 2019
to 2.3 percent. Growth in the region is expected to pick up
to 2.7 percent in 2020, as a rebound in Turkey offsets amoderation in activity among other commodity importers.
Risks are tilted to the downside and growing. They
include the possibility of renewed stress in Turkey
alongside larger-than-expected spillovers to the rest of theregion, and unexpected shifts in policy.
Latin America and the Caribbean. Growth stalled at 0.6
percent in 2018, held back by a currency crisis anddrought in Argentina, a truckers’ strike in Brazil, andworsening conditions in Venezuela. Although regional
growth is projected to strengthen over the forecasthorizon, the improvement will be weaker than previously
expected, partly owing to the effects of financial market
tightening and trade policy uncertainty. However, firming
momentum in Brazil and Colombia, together with gradualimprovements in Argentina, will push regional growth to
1.7 percent in 2019 and 2.4 percent in 2020. Downside
Note: This box was prepared by Patrick Kirby, with contributions from
Yoki Okawa, Rudi Steinbach, Temel Taskin, Ekaterine Vashakmadze,
Dana Vorisek, and Lei Ye. Research assistance was provided by Hazel
Macadangdang.

risks dominate, including the possibility of an abrupt
further tightening of external financial conditions, afurther escalation of domestic or international trade policy
uncertainty, adverse market responses to fiscal conditions,and disruptions from natural disasters.
Middle East and North Africa. Growth in the region is
expected to pick up slightly to 1.9 percent in 2019, butprospects are uneven across countries. Accelerating activity
in Saudi Arabia and Egypt is expected to be offset by a
sharp contraction in Iran following the imposition of U.S.
sanctions. Increased oil production and fiscal easing aresupporting the recovery in some oil exporters, while oil
importers continue to benefit from policy reforms.
Regional growth is projected to rise to 2.7 percent in
2020-21, as domestic demand among both oil importers
and exporters shows a broad-based pickup, supported byreforms and diversification policies. Key downside risks
include the possibility of intensified geopolitical tensions,renewed volatility in oil prices, rising global trade
restrictions, an abrupt tightening of global financing
conditions, and delays in reform implementation.
South Asia. Growth is projected to accelerate to 7.1
percent in 2019. This mainly reflects strengthening
domestic demand in India, as the benefits of structuralreforms such as GST harmonization and bankrecapitalization take effect. Elsewhere in the region, theforecast is for a moderation in activity, notably in
Bangladesh and Pakistan. Over the medium term, growthis expected to remain at 7.1 percent, underpinned byrobust domestic demand in the region. External
vulnerabilities are rising, reflected in mounting external
debt, widening current account deficits, and eroding
foreign reserves. Risks to the outlook are to the downside.
On the domestic front, vulnerabilities are being
exacerbated by fiscal slippages and rising inflation, andthere is a risk of delays in structural reforms to addressbalance sheet issues in the banking and non-financial
corporate sectors. Key external risks include a further
deterioration in current accounts and a faster-thanexpected global financial tightening.
Sub-Saharan Africa. Regional growth reached an
estimated 2.7 percent in 2018—a downward revision fromprevious projections, reflecting a sluggish expansion in theG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

BOX 1.3 Regional perspectives: Recent developments and outlook (continued)
FIGURE 1.3.1 Regional growth
The cyclical upswing in regions with many commodity exporters is proceeding at a more moderate pace than previously
anticipated. Growth in regions with large numbers of commodity importers is projected to remain solid.
A. Regional growth, weighted average

B. Regional growth, unweighted average

Source: World Bank.
A.B. Bars denote latest forecast; diamonds correspond to June 2018 forecasts in the Global Economic Prospects report. Average for 1990-2017 is constructed
depending on data availability. For Europe and Central Asia, the long-term average uses data for 1995-2017 to exclude the immediate aftermath of the collapse of theSoviet Union.
A. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Since 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
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.

region’s largest economies amid moderate trade growth,tightening financial conditions, and weak prices for key
metals and agricultural commodities. Regional growth isexpected to pick up, reaching 3.4 percent in 2019 and an
average of 3.7 in 2020-21, predicated on diminishedpolicy uncertainty and improved investment in large
economies, together with continued robust growth in nonresource-intensive countries. Per capita income growth is

Low-income countries
Economic activity has continued to strengthen in
most low-income countries (LICs; Box 1.2).
Increased agricultural production in the wake ofeasing drought conditions is supporting robust
growth in several non-resource-intensive countries(e.g., Rwanda, Uganda), as well as infrastructure
investment related to reforms (e.g., Benin,
Senegal). However, in Ethiopia—the largest
LIC—growth lost momentum as weaker activity
in the construction and manufacturing sectors wasaggravated by foreign exchange shortages. Amongpredicted to remain well below its long-term average inmany countries, yielding little progress in poverty
reduction. Downside risks include the possibility ofslower-than-projected growth in China and the Euro Area,
further declines in commodity prices, a sharp tightening of
global financing conditions, fiscal slippage, stalled
structural reforms, and conflict.

exporters of industrial commodities, growth
performances have varied. Chad emerged fromtwo years of recession partly due to the recovery in
oil prices from their 2016 trough, as well as
increased oil production. However, for metal
exporters, growth was more subdued, reflecting
weaker metals prices and external demand. Lower
export growth, combined with higher fuel-related
imports, has caused current account deficits towiden in many LICs. In addition, the lessfavorable external environment is making thefinancing of these deficits more challenging.
Moreover, government debt has continued to rise,

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.12 EMDE growth prospects
EMDE growth is expected to remain at 4.2 percent in 2019, well below
previous forecasts, partly reflecting the lingering effects of financial stressin some large economies. EMDE growth is subsequently projected toplateau at 4.6 percent, as the recovery in commodity exporters levels off.
Growth is close to upper estimates of potential in commodity importers,
while slack remains in commodity exporters. A decreasing share of
EMDEs will see further acceleration in activity, in part reflecting a lessfavorable external environment. Drivers of long-term growth suggest
softening potential over the next decade.
A. Growth

B. Projected and potential growth in2019

C. Number of EMDEs with increasing,unchanged, or decreasing growth

D. Drivers of potential long-term
growthSource: International Monetary Fund, Organisation for Economic Co-operation and Development,
World Bank.
A.-D. Aggregate growth rates are calculated using constant 2010 U.S. dollar GDP weights.
A.C.D. Shaded areas indicate forecasts. Data for 2018 are estimates.
B.D. Potential growth estimates based on eight different methodologies (production function
approach; multivariate filter; three univariate filters, including Hodrick-Prescott filter, ChristianoFitzgerald filter, and Butterworth filter; IMF World Economic Outlook estimates; and OECD Economic
Outlook and Long-Term Baseline Projections estimates). For further details on potential growthestimates, refer to the January 2018 edition of the Global Economic Prospects report.
A. Data for 2015-17 are simple averages. Green diamonds correspond with the June 2018 edition of
the Global Economic Prospects report.
B. Blue bars refer to average projected growth for 2019. Vertical orange lines show minimummaximum range of potential growth.
C. Sample includes 50 largest EMDEs. Increasing/decreasing growth are changes of at least 0.1
percentage point from the previous year. Countries with a slower pace of contraction from one year
to the next are included in the increasing growth category.
D. TFP = total factor productivity. The sample includes 23 EMDEs (11 EMDE commodity exporters
and 12 EMDE commodity importers).
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as fiscal deficits remain elevated due to commodityrelated declines in revenue, as well as governancechallenges in some countries (Chapter 4).
EMDE outlook
Growth outlook
EMDE growth is expected to stall at 4.2 percent
in 2019—down 0.5 percentage point relative toprevious projections. This reflects the lingering
effects of recent financial market stress on several
large economies, a lackluster and notably softerthan-envisioned cyclical recovery in commodity
exporters, and a further deceleration incommodity importers (Figure 1.12). Growthacross EMDEs in 2019 is expected to be close to
the upper bound of estimates of its potential
pace—particularly among commodity importers,
where slack has largely been exhausted.
Growth in EMDEs is foreseen to increase to 4.5
percent in 2020, with a large part of thisacceleration reflecting the projected dissipation ofsevere headwinds in a few large economies (e.g.,
Argentina, Iran, Turkey). In 2021, EMDE growthis expected to plateau at 4.6 percent as therecovery in commodity exporters matures.
Throughout the forecast horizon, the international
context is expected to be increasingly lessfavorable, in light of a projected slowdown inadvanced-economy growth, weakening trade andinvestment, tighter financing conditions, andtrade policy uncertainty. These factors will impede
further acceleration in EMDE activity.
Growth in commodity exporters is projected topick up to 2.3 percent in 2019—sharply below
previous expectations—and plateau at 2.9 percent
in both 2020 and 2021. Some large economies
that experienced sizable contractions in activity in2018 are expected to gradually recover over theforecast horizon (e.g., Angola, Argentina, Iran).
The outlook for commodity exporters is uneven,however, partly owing to renewed market
attention to country-specific vulnerabilities.
Projections for about half of commodity exporters
have been downgraded for 2019. Downward
revisions reflect, to varying degrees, more adverse
financial conditions and the resulting policy
adjustment, softening confidence, lingering effectsof strikes and political uncertainty, and weaker
commodity prices and mining bottlenecks. These
downward revisions are also reflected in forecasts
for EMDE regions with a substantial number ofcommodity exporters (Box 1.3; Chapter 2).
Growth in commodity importers is expected tomoderate to 5.5 percent in 2019 and remain
steady at 5.6 percent in both 2020 and 2021—
broadly in line with its potential rate. A structural

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

slowdown in China is expected to be partly offset
by a moderate pickup in other large economies inthis group. In commodity importers excluding
China, a downgrade to growth projections of 0.4
percentage point this year partly reflects theworsened outlook for Turkey as a result of theeffects of recent financial market stress, and, to alesser degree, in some other large economies (e.g.,
Pakistan, Romania).
Growth in LICs is expected to improve, rising to
5.9 percent in 2019 and 6.3 percent in 2020-21.
However, for metals exporters, growth will be
more sluggish than previously envisioned, withlower revenues constraining fiscal spending. In
contrast, oil exporters should benefit from higher
oil production and improving domestic demand.
Economic activity is expected to remain robust in
non-resource-intensive LICs. In fast-growing
countries (e.g., Rwanda, Tanzania), the expansion
will be supported by public investment ininfrastructure and strong agricultural growth.
Similarly, infrastructure investment related tostructural reforms should sustain Senegal’s growthrecovery. While growth in Ethiopia is expected toremain strong, it will be weighed down by atighter fiscal stance, as the government aims tostabilize public debt.
In the longer run, the underlying potential growthof EMDEs has fallen considerably over the past
decade, reflecting softening productivity growthand, to a lesser degree, slowing capital
accumulation and less favorable demographic
trends (Vorisek et al. forthcoming; World Bank
2018k). Potential growth in EMDEs is expected
to further decline, as its fundamental drivers
continue to weaken. Moreover, tightening global
financing conditions, higher borrowing costs,
moderating capital flows, and lingering policy
uncertainty are likely to hamper investment
growth in coming years, further constraining
potential growth.
Outlook for per capita income and poverty
Per capita income growth in EMDEs is expected
to stabilize at 3 percent in 2019—insufficient tonarrow income gaps with advanced economies inover 35 percent of countries (Figure 1.13). The
share will be even greater among commodity

CHAPTER 1

29

FIGURE 1.13 Poverty and per capita income growth
Per capita income growth in the near term will be insufficient to restart thecatch-up with advanced economies in more than one-third of EMDEs.
Poverty is increasingly concentrated in a few large lower-middle-income
countries. Per capita GDP growth is expected to remain weak in EMDE
regions with a large number of commodity exporters.

A. Share of EMDEs with widening
income per capita gaps with advanced
economies

B. Poverty in EMDEs, in 2015

C. Poverty projections for India andNigeria

D. Per capita growth, by region

Source: United Nations, World Bank.
Note: 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.D. Shaded areas indicate forecasts. Data for 2018 are estimates.
A. EMDEs with per capita GDP growth of at least 0.1-percentage-point higher than advancedeconomy per capita GDP growth are those counted as converging. Advanced-economy growth rates
calculated using constant 2010 U.S. dollar GDP weights. Sample includes 117 EMDEs.
B. LICs = low-income countries, LMCs = lower-middle-income countries, and Other EMDEs = EMDEs
not classified in LICs or LMCs. Aggregate poverty rates are weighted by total population. Data as of2015, the latest available observation.
C. The number of poor are people living on or below the international poverty line of $1.90 per day.
Shaded area indicates forecasts. Data for 2016-18 are estimates and calculated using data fromWorld Bank (2018l).
D. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights.
Click here to download data and charts.

exporters (41 percent) and in countries affected byfragility, conflict, and violence (nearly 60 percent).
Although the extreme poverty rate—defined at athreshold of $1.90 per day—has fallen below 3
percent in more than half of the world’s
economies in recent years, nearly one-fifth ofcountries faced rates above 30 percent in 2015,
with the average for LICs standing above 40
percent. Poverty rates remain the highest among
LICs, but the majority of extreme poor currently
reside in large lower-middle-income countries,
including India and Nigeria. Current growth

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.14 Risks: Tilted to the downside
Past global recessions often came unexpectedly following periods ofhighly synchronized growth. Risks to global growth are predominantly tothe downside amid trade tensions, rising borrowing costs, anddeteriorating financial market sentiment.

A. Share of countries in recession

B. Probability distribution aroundglobal growth forecasts

C. Probability of 2020 global growth
being 1-percentage-point below/above
baseline

D. Volatility and skewness in equity
price futures

Source: Bloomberg, Kose and Terrones (2015), World Bank.
A. Recession is defined as a contraction in per capita GDP. Unbalanced sample includes 149
countries. Aggregate share is calculated using constant 2010 U.S. dollar GDP weights. Red bars
show the four global recessions in 1975, 1982, 1991, and 2009, and orange bars show the two global
slowdowns in 1998 and 2001.
B.C.D. The fan chart shows the forecast distribution of global growth using time-varying estimates ofthe standard deviation and skewness extracted from the forecast distribution of three underlying risk
factors (oil price futures, the S&P 500 equity price futures, and term spread forecasts). Each of therisk factor’s weight is derived from the model described in Ohnsorge, Stocker, and Some (2016).
Values for 2019 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 2020 are based on 24-month-ahead
forecast distributions. Last observation is December 18, 2018.
C. Bars show the probability that global growth is 1 percentage point above or below baseline
forecasts 24 months ahead.
D. The implied volatility and skewness are derived from 12-month S&P 500 equity price futures.
Click here to download data and charts.

projections suggest that the number of extreme
poor should continue to fall rapidly in India, but
remain broadly unchanged in some othercountries, including Nigeria. While extreme
poverty has declined notably, progress inalleviating poverty at higher income levels has beenslower, with nearly a quarter of the world’s
population still living with less than $3.20 per day.
Worryingly, per capita income growth in SubSaharan Africa is expected to average only 0.9
percent in 2019-21—insufficient to drive
significant progress toward poverty alleviation.
Indeed, if recent growth trends persist, the fractionof the global poor residing in Sub-Saharan Africa
could be as large as 87 percent by 2030 (WorldBank 2018l).

Risks to the outlook
The balance of risks is more firmly on the downside.
The risk of disorderly financial market developments
has increased and could spread through EMDEs,
amplified by elevated vulnerabilities in many
countries. A marked intensification of trade
restrictions remains possible, and its realization could
be highly disruptive in the presence of complex value
chains. Policy uncertainty and geopolitical risks
remain elevated and could negatively impact
confidence and investment both in the affected
countries and globally. Although unlikely in the nearterm, the simultaneous occurrence of a severe U.S.
downturn and a sharper-than-expected decelerationin China would trigger a marked slowdown in global
activity.
Global growth is projected to gradually slow overthe forecast horizon as economic slack dissipates,
major
centralbanks
removepolicyaccommodation, and the recovery in commodity
exporters matures. This moderation is somewhat
more pronounced than previously expected, amid
softer-than-expected global trade and industrial
activity and heightened financial market pressuresin some EMDEs. While an abrupt slowdown isonly expected in countries that faced severe
financial stress in 2018, the global outlook has
become more uncertain, with downside risks
becoming more predominant.
A faster-than-expected tightening of global
financing conditions, or disorderly exchange rate
movements, could have large adverse effects on
activity, particularly among more vulnerable
EMDEs. Escalating trade tensions represent
another key risk to the global outlook, as they
could significantly hamper cross-border trade andinvestment, with the impact amplified by complex
regional and global value chains. Loss ofconfidence in international trading rules could
inflict long-lasting damage, lowering opportunities
for future growth in EMDEs. Rising politicaluncertainty and polarization, geopolitical risks,and conflict could also depress sentiment andinvestment in the affected countries and globally.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

The materialization of one or several of thesedownside risks would result in a more abrupt
slowdown in global growth than currently
envisioned. In particular, a simultaneous
occurrence of a severe U.S. downturn and a
sharper-than-expected deceleration in China
would significantly increase the likelihood of an
abrupt global slowdown. Past experience illustrates
that global slowdowns and recessions often come
unexpectedly after spells of highly synchronized
growth and rapid debt build-ups (Figure 1.14;
Kose and Terrones 2015).
On the upside, a resolution of trade tensions
between major economies could lift sentiment andsupport global investment and trade. Furthermore,
the ongoing cyclical recovery in global
productivity growth could prove more durable
than expected, especially if the pickup in
intangible investments in recent years leads to abroader diffusion of productivity-enhancing
technologies. If so, this would help counter thedampening effect of population aging on potential
growth in the longer term.
A quantification of possible global growth
outcomes around the baseline provides additional
evidence of elevated forecast uncertainty and the
predominance of downside risks. At current
market conditions, the probability of global
growth being more than 1 percentage point below
baseline in 2020 is estimated at 21 percent, whilethat of growth being more than 1 percentage
point above baseline is 17 percent. This reflects
uncertainty embedded in the distribution of key
risk factors, including equity price futures.
Disorderly financial market developments
Risks of disorderly financial market developments
have intensified substantially, reflecting thepossibility of a faster-than-expected tightening of
global financing conditions, sharp movements inmajor currencies, and contagion from financial
stress in some EMDEs.
Despite bouts of volatility in bond and equity
markets, as well as ongoing uncertainty about
growth and inflation prospects, U.S. term
premiums are still negative, raising the risks ofsudden upward adjustments (Crump, Eusepi, andMoench 2018; Kopp and Williams 2018). While

CHAPTER 1

investors appear to foresee an end to thetightening cycle in U.S. policy interest rates, theFederal Reserve continues to signal additional
hikes, implying risks of disorderly market
reassessments (FOMC 2018). In this context, asharper-than-expected rise in U.S. borrowing costs
remains possible. This could be triggered, forinstance, by concerns about swelling fiscal deficits,
intensifying wage pressures, or slowing foreign
demand for U.S. government debt (Andolfatto
and Spewak 2018; Kopp and Williams 2018).
Following a decade of exceptionally low U.S.
interest rates and growing debt levels, the effects ofa sudden rise in borrowing costs could be
amplified by increased investor risk aversion andsudden stops in capital flows to EMDEs (Arteta etal. 2015; Buttiglione et al. 2014; Dobbs et al.
2015; Mai 2018). The dampening effect could beparticularly severe on cross-border bank loans to
EMDEs (Bräuning and Ivashina 2018).
A further appreciation of the U.S. dollar, possibly
triggered by diverging monetary policy andgrowth prospects among major economies, could
also impact the outlook for EMDEs. Periods ofdollar strength have been associated in the pastwith an increased frequency of disorderly currency
depreciations in EMDEs. If currency crises were tomaterialize, they would be associated with slowing
growth or outright contractions. In the past, alarge proportion of crises were accompanied by arecession in the same year (Figure 1.15). Whencurrency crises are accompanied by banking crises,
as is sometimes the case, the likelihood of large
output losses rises substantially (Laeven andValencia 2018). These “twin” crises can occur inthe presence of elevated foreign-currencydenominated debt or on the back of an abrupt end
to capital inflows and credit booms leading torising corporate defaults and large asset pricecorrections (Bordo and Meissner 2016; Caballero2014).
Financial stress could spread through contagion
effects. Excluding China and a few large regional
economies (e.g., Brazil, Russia), direct trade andfinancial sector spillovers from most other EMDEs
are limited (World Bank 2016). However,contagion across countries can result fromheightened investor risk aversion and shifts in
portfolio allocations between broad asset classes,

31

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.15 Financial stressMore than a third of currency crises in EMDEs are associated withnegative growth in the same year. Currency crises are sometimes
accompanied by banking crises, and their simultaneous occurrence canbe particularly damaging. Financial stress can be amplified by persistentexternal vulnerabilities, potentially leading to further forecast downgradesfor more exposed countries.
A. Currency crises and growth in
EMDEs

C. Share of EMDEs with negative
growth around currency crises

B. EMDE banking and sovereign debt
crises around currency crises

D. Growth forecast revisions andcurrent account position, 2019

increasingly relied on bond markets to financerising debt levels, and now face significant
refinancing needs amid rising interest rates (Lundet al. 2018). This could result in sudden increases
in corporate default rates and have a sustained
negative effect on investment and financial
stability (Borensztein and Ye 2018). On theexternal side, many EMDEs are faced with thechallenge of financing large current accountdeficits and rely heavily on volatile capital inflows.
Coupled with high levels of short-term external
debt and low foreign currency reserves, this leaves
them exposed to shifts in external financing
conditions, which could exert further downward
pressure on activity.
In low-income countries (LICs), public debt
burdens and vulnerabilities associated with agreater reliance on non-concessional financing arerising (Chapter 4). About 40 percent of LICs are
in debt distress or at high risk of debt distress—
roughly twice the share in 2015 (IMF 2018c;
World Bank 2018g). Most LICs also suffer from alack of transparency in public sector accounts,
further exacerbating vulnerabilities.
Escalating trade restrictions

Source: Bloomberg, Institute of International Finance, Laeven and Valencia (2018), World Bank.
A. Currency crises with negative or positive GDP growth during the year of the crisis. Currency crises
are defined as nominal depreciation of the currency vis-à-vis the U.S. dollar of at least 30 percent thatis also at least 10-percentage-points higher than the rate of depreciation in the year before.
B. The percent of EMDE currency crisis episodes that were preceded by, coincided with, or followed
by a banking or sovereign debt crisis, with t denoting the start of the currency crisis. Crises episodes
are as defined in Laeven and Valencia (2018).
C. Share of countries that experienced negative growth in the current or next year following acurrency crisis, a currency and banking crisis, or a currency, banking, and sovereign debt crisis
between 1975-2017.
D. Forecast revisions for GDP growth in 2019 relative to June 2018. Sample includes 23 EMDEs.
Current account position net of foreign direct investment in 2018.
Click here to download data and charts.

amplifying the effects of shocks (Gelos 2012).
Historically, the correlation across EMDE assets
has been high and tends to increase during stressepisodes (Eichengreen and Gupta 2016; Park andMercado 2014).
These risks are particularly salient in the current
context of persistent domestic and external
vulnerabilities in EMDEs, as these can both
amplify the impact of financial shocks and limit
policy options in response to financial stress. On
the domestic front, many countries have sizable
government debt and primary fiscal deficits,
elevated or rising private debt, and high nonperforming loans. Corporate borrowers have

The risk of rising trade protectionism remains
high. New U.S. tariffs and the retaliatory responseof trading partners now affect close to $430 billionof global imports—around 2.5 percent of global
goods trade (Figure 1.16). Despite a temporary
pause in tariff hikes agreed by the United States
and China in early December, unsuccessful
negotiations could lead to a renewed escalation intrade restrictions. These, along with previous
measures, would affect close to all goods trade
between the two countries. Additional tariffs on
U.S. imports of motor vehicles and parts are also
under consideration, which could cause serious
adverse effects given tightly integrated global
automotive value chains.
If all proposed tariffs increases were to be
implemented, the average U.S. tariff rate would
more than quadruple, rising to levels not seen
since the late 1960s. These new tariffs, and theassociated retaliatory actions, could substantiallydepress bilateral U.S.-China trade, increasedemand for costlier substitutes, and lead to lower

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

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33

growth in both the United States and China. It isalso likely to affect investment strategies bymultinational companies and lead to changes insome value chains. While some countries could
benefit from trade diversion in the short run,including those with comparative advantages inclose substitutes to the goods subject to U.S. or
China tariffs, adverse effects from weakening
growth and rising policy uncertainty involving theworld’s two largest economies would havepredominantly negative repercussions. In thiscontext, a further escalation of trade frictions
between the United States and China, coupledwith possible negative effects on confidence, could
reduce global exports by up to 3 percent andglobal income by 1.7 percent over the medium
term (Freund et al. 2018).

FIGURE 1.16 Trade protectionism

A. Imports affected by new tariffs

B. Average import tariffs in the UnitedStatesMore generally, a proliferation of trade barriers
across both advanced economies and EMDEs
could inflict lasting damage to the global
economy. In particular, if all WTO members wereto increase tariffs up to legally-allowed boundrates, this could translate into a decline in global
trade flows of about 9 percent, similar to thedrop seen during the global financial crisis in
2008-09 (Kutlina-Dimitrova and Lakatos 2017).
In the presence of regional and global value
chains, costs associated with increasing tariffs orother barriers to trade would cumulate throughdifferent stages of production (Koopman, Wang,
and Wei 2014; World Bank et al. 2017). Thisamplification effect of vertical specialization would
be particularly important for EMDEs, as the shareof domestic value added in manufactured exports
is usually lower and trade costs higher than inadvanced economies. In the automotive sector,
participation of EMDEs in global value chains has
proliferated in the past decade, intensifying risks
in the event of sudden pullbacks (Van Biesebroeck
and Sturgeon 2011).

C. Impact on nominal GDP of tariff
hikes on all U.S.-China trade flows

D. Magnification of tariffs due tovertical specialization

Intensifying trade disputes could eventually
threaten the stability of the rules-based global
trading system and undo the beneficial effects oftrade liberalization and global integration achieved
during decades of multilateral cooperation.
Uncertainty about future trade rules could
compound the negative effect of trade barriers on
investment and activity (IMF 2018d; Kose,
Ohnsorge et al. 2017).

If all new tariffs currently under consideration were to be implemented,
more than 5 percent of global goods trade would be affected, and average
U.S. tariff rates would increase to levels not seen since the late 1960s. The
dampening impact of trade tensions involving major economies could be
amplified by adverse confidence effects. The cost of protectionism can be
multiplied through global value linkages, particularly in EMDEs.

Source: Freund et al. (2018), Peterson Institute for International Economics, U.S. Census Bureau,
U.S. International Trade Commission, World Bank.
A. Import tariffs implemented in the United States and the rest of the world in 2018, as well as thoseunder consideration, as a percent of global goods imports.
B. Ratio of duties collected to the total value of imports. Data for 2018-20 are forecast from tariffs
under consideration, excluding car imports from Canada and Mexico, as of December 19, 2018.
C. Blue bars depict the impact of a 25-percentage-point increase of the tariff surcharge on all bilateral
U.S.-China trade flows. Red bars depict the additional impact from adverse investor confidence. The
additional confidence shock assumes a decline of the investment-to-GDP ratio of 0.5 percentage
point, similar to that observed during the global slowdown in 2001. The percent deviations from CGE
simulations are applied to GDP in current U.S. dollars.
D. The magnification ratio of vertical specialization is estimated by comparing a country’s standard
tariff on exports and the gross effective tariff rate faced in export markets. A country’s standard tariffon exports is defined as the trade-weighted tariff rate applied by a country’s trading partners(in ad-valorem equivalent). The gross effective tariff rate is defined as the standard tariff rate divided
by the domestic content share and weighted by trade. Higher magnification ratios in EMDEs
are partly driven by their lower domestic value-added share.
Click here to download data and charts.

Policy uncertainty and geopolitical tensions
Global policy uncertainty has increased since mid2018, reflecting heightened trade tensions andgeopolitical risks, as well as idiosyncratic
developments in a number of large advanced
economies and EMDEs. Elevated policy
uncertainty tends to encourage investors to require
higher risk premiums to hedge against negative
outcomes. Financial market volatility remained
exceptionally low in 2018, implying the risk of34

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.17 Policy uncertainty and other downsiderisks
Policy and geopolitical uncertainties remain above historical norms, which
could eventually lead to an increase in financial market volatility fromcurrent low levels. More frequent armed conflicts and extreme weather
patterns can significantly dampen prospects in the affected countries andglobally.
A. Global policy uncertainty,
geopolitical risks, and financial
market volatility, 2018

B. Long-term bond yield spread
between Italy and Germany

C. Number of armed conflicts

D. Number of extreme weather events

Source: Baker, Bloom, and Davis (2016); Bloomberg; Caldara and Iacoviello (2017); Centre for the
Study of Civil War at the Peace Research Institute Oslo (PRIO); EM-DAT, The CRED/OFDA
International Disaster Database; Uppsala University; World Bank.
A. Z-scores computed as the index value minus the sample mean divided by the sample standard
deviation. The global policy uncertainty index is computed by Baker, Bloom, and Davis (2016) and isbased on the frequency of words in domestic newspapers mentioning economic policy uncertainty.
The geopolitical risk index is computed by Caldara and Iacoviello (2017) and is based on thefrequency of words in domestic newspapers mentioning geopolitical tensions, including military,
nuclear, war, and terrorism. Financial market volatility is measured by the Chicago Board Options
Exchange (CBOE) VIX implied volatility index of option prices on the U.S. S&P 500 index. Lastobservation is November 2018.
B. Bond yield spread is the difference between 10-year government bond yields in Italy and Germany.
Last observation is December 19, 2018.
C. Conflicts are defined as when the use of armed force between two parties, of which at least one is
the government of a state, results in at least 25 battle-related deaths in one calendar year.
D. Decadal simple averages.
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disorderly repricing of policy-related risks (Figure1.17). A further escalation of policy uncertainty
could lead companies to delay or reconsider capitalspending, contributing to a more rapid
deceleration of global growth than currently
projected.
Political uncertainty is generally associated withlower growth in both advanced economies and
EMDEs (Aisen and Veiga 2013). It has increasedor remained elevated in a number of European
countries—including in Italy where fiscal slippages
have led to a market reassessment of country risk,
and in the United Kingdom as it transitions out of
the European Union (EU). In the absence of an
approved withdrawal agreement, the exit of theUnited Kingdom from the EU could beaccompanied by significant disruptions to activity
in the short term and lasting economic losses overthe medium term (Bank of England 2018; H.M.
Treasury 2018). A sustained period of financial
market stress and interruptions in cross-borderfinancial flows associated with a disorderly exit
process could cause significant adverse spillover
effects and become a source of financial stability
risks in systematically large economies (ECB 2018;
FSOC 2018). Electoral outcomes in a number of
EMDEs and advanced economies could result infurther polarization and political fragmentation,
making it harder to govern and formulate policies.
A backlash against trade and immigration could
also spur more inward-looking and populist
policies (Aksoy, Guriev, and Treisman 2018;
Moriconi, Peri, and Turati 2018).
Geopolitical risks intensified again in the Middle
East, and persist in Central Asia, East Asia, andAfrica. An intensification of these risks could
impact growth in the affected regions, and theirmain trading partners. In the case of the Middle
East, disruptions to global oil supplies could resultin higher-than-expected oil prices, with negative
impacts on aggregate demand and trade balances
in major oil importers (Baffes et al. 2015; Stocker
et al. 2018).
The number of armed conflicts also remains above
historical averages. In particular, security
conditions remain challenging in many countries
in Sub-Sahara Africa, the Middle East, and North
Africa. In the past, countries in conflict or infragile situations suffered from below average
growth in income per capita, delaying or derailingtheir catchup with advanced economy levels (UNand World Bank 2018). Beyond adverse shortterm effects on growth, conflict can also
substantially set back efforts to reduce poverty andchild mortality, and can hamper access to
education, implying longer lasting negative
repercussions on development (Gates et al. 2012).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Region-specific downside risks
These global risks are compounded by multiple
region-specific risks (Box 1.2; Chapter 2). Most
regions are vulnerable to sudden shifts in policy,
which could result in fiscal slippage, reduced
investment due to policy uncertainty, and weakerpotential growth resulting from insufficient
structural reforms. Security-related risks remainpresent, in varying degrees, in Europe and Central
Asia, the Middle East and North Africa, SouthAsia, and East Asia, and could rise in the face of
renewed geopolitical tensions. A flare-up inviolence would disrupt activity in various ways,
weigh on potential output, and drive up refugee
flows. A fall in the price of specific commodities
could disrupt activity in large regional commodity
exporters, with possible broader spillovers.
Severe weather events appear to be becoming more
frequent, with particularly serious consequencesfor vulnerable countries, such as island nations in
the Caribbean and East Asia and the Pacific.
Adverse weather patterns are also problematic for
countries with large agricultural sectors dependent
on rainfall, including many in Sub-Saharan Africa
and South Asia. In those countries, large foodprice increases could severely impact poverty(Chapter 4). For instance, the spike in food prices
in 2010-11 is estimated to have increased extreme
poverty by 8.3 million people. Other natural
disasters, such as earthquakes and hurricanes, caninflict severe damage in the affected countries.
These events are unpredictable and often force
countries to overly rely on aid for reconstruction,even though recent progress in disaster risk financehas created opportunities for preventive actions(Végh et al. 2018).
Simultaneous slowdown in the two largest
economies
Fiscal measures undertaken in the United States
and China are supporting their near-term growthprospects; however, they could exacerbate
imbalances and amplify risks of a more abrupt
downturn later on. A sharper-than-expected andsimultaneous slowdown in these two economies
could have severe consequences for the global
economy.

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35

FIGURE 1.18 Simultaneous slowdown in the two largest
economies
The U.S. expansion is on track to be the longest in more than a century.
The probability of a U.S. recession in the short term is still low, but has
increased, and corporate debt vulnerabilities are growing. Private debt in
China exceeds levels that gave rise to significant adjustments in other
EMDEs in the past. A simultaneous sharp slowdown in both the UnitedStates and China could bring the global economy closer to a global
recession.
A. Duration of U.S. expansions

B. Probability of U.S. recession

C. Private sector debt in the UnitedStatesD. Private sector debt in China
compared with previous peaks inother EMDEs

E. Share of EMDEs with growth
slowdowns or crises after reaching
debt peaks

F. Global per capita growth scenarios:
Impact of growth slowdowns in the
United States and China

Source: Bank for International Settlements, Federal Reserve Bank of New York, Haver Analytics,
Institute of International Finance, Kose and Terrones (2015), Laeven and Valencia (2018), National
Bureau of Economic Research, World Bank.
A. Shaded area indicates the number of months from January 2019 to December 2019.
B. Probability of a recession in 12 months derived from the U.S. yield curve model of the Federal
Reserve Bank of New York. Last observation is November 2018.
C. Shaded areas indicate recessions, as identified by the National Bureau of Economic Research(NBER). Last observation is 2018Q2.
D. Debt peaks are defined as the highest value of the private non-financial credit to GDP ratio overthe period 1960Q1 to 2018Q2. Sample includes 15 EMDEs. For China, values are the last
observations in 2018Q2.
E. Countries 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 ormore 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 2018Q2.
F. Blue and red bars show scenarios assuming a 1-percentage-point growth shock in China, theUnited States, and the combination of the two. Shocks are applied in the second half of 2019. Basedon the vector autoregression model presented in World Bank (2016). Deviations from baseline are all
significantly different from zero.
Click here to download data and charts.

36

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The policy mix in the United States will shift fromexpansionary to contractionary during the forecasthorizon, with monetary, fiscal, and trade policies
all expected to become a drag on activity within
the next couple of years. In this context, relatively
small negative shocks have the potential toabruptly end the current expansion, which is on
track to be the longest in more than century(Figure 1.18). Although the probability of a U.S.
recession in the short term is still low, at about half
its level prior to previous recessions, it has
increased throughout 2018.
Economic expansions do not end and give way torecessions only because they have lasted long
(Castro 2013; Diebold and Rudebusch 1999;
Rudebusch 2016). Instead, they tend to end as areflection of corrections from imbalances
accumulated over the business cycle. In particular,
recessions often follow periods of rapid increase in
debt levels and excessive asset price valuations(Claessens, Kose, and Terrones 2012; Mendoza
and Terrones 2012). These imbalances tend tosuddenly unwind, often during or shortly after theend of a monetary policy tightening cycle(Bernanke and Gertler 1995; Sims and Tao 2006).
In the United States, three of the last fourtightening cycles were indeed followed by arecession within a year and a half, with the most
severe contractions following unsustainablehousing market booms (Berkovec, Chang, andMcManus 2012; Gelain, Lansing, and Natvik
2018; Mian and Sufi 2009). The only exception
was the productivity-driven growth revival aroundmid-1990, which continued uninterrupted despite
interest rate hikes in 1994-95.
At the present juncture, the rise in U.S. private
debt is smaller than prior to past recessions, mostly
because of household and bank deleveraging since
the global financial crisis. U.S. corporate debt isstarting to accumulate, however, raising the riskthat corporate bond defaults could amplify thenext downturn (FSOC 2018). On balance, theU.S. economy has some of the characteristics thathave preceded relatively mild recessions, but some
corporate and non-bank financial sector risks are asource of concern (IMF 2018e).
In China, risks to the outlook are increasingly
tilted to the downside. Fiscal and monetary policy

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019stimulus measures could offset the adverse effect oftrade tensions with the United States but may
delay efforts to contain credit growth and limit thebuildup of balance sheet vulnerabilities ofcorporates, local governments, and financial
institutions (IMF 2017; World Bank 2018a,
2018k). Both the level and growth rate of private
debt stocks are well above those observed during
previous credit booms in other EMDEs—twothirds of which ended in significant growth
slowdowns and more than a third in financial
crises (Acharya et al. 2015; Alter and Elekdag2016). In the case of China, risks are somewhat
tempered by still low central government debt,
extensive capital controls, large foreign reserves,and a low reliance on external financing. Thatsaid, if financial stress were to materialize, it would
likely translate into a significantly sharper-thanexpected slowdown in activity (Beltran, Garud,
and Rosenblum 2017; Bernadini and Forni 2017;
Maliszewski et al. 2016).
The simultaneous occurrence of a severe downturnin the United States and a sharper-than-expected
deceleration in China, although still unlikely inthe near term, would substantially increase the riskof an abrupt global slowdown. These two
economies are, together with the Euro Area, themost important source of global spillovers, andcan impact the outlook for EMDEs through trade,
confidence, financial-market, and commoditymarket channels (World Bank 2016).
In all, a 1-percentage-point decline in U.S. growthis estimated to translate after one year into a
decline in other advanced economy and EMDE
growth of 0.6 percentage point for both groups.
The impact of slower growth in China is aroundhalf that of a U.S. slowdown for other advanced
economies (-0.3 percentage point), but it iscomparable for other EMDEs (-0.6 percentage
point)—and, among them, significantly larger forcommodity exporters (-1.2 percentage points).
Slower growth in China tends to dampen
commodity prices, as this country is a primary
driver of global demand for industrial
commodities, especially of metals (World Bank
2018d). Critically, a combined 1-percent negative
growth shock in China and the United States
would have severe consequences for global growth,
reducing it by almost 1 percentage point after one

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

year. Should such a risk materialize in the second
half of 2019, global per capita growth would drop
to around 1 percent in 2020, bringing the global
economy somewhat closer to a global recession.1
The probability of a global recession tends toincrease noticeably when one or several
systemically large economies decelerate (Kose andTerrones 2015). For instance, a recession in the
United States increases the probability of a global
recession from 7 percent on an average year to 50
percent. The risk of a sharp global downturn
could be magnified as policymakers’ ability torespond is constrained by a lack of fiscal andmonetary space and by a reduced appetite forcoordinated policy responses among major
economies. High levels of private and public debt
also make EMDEs particularly vulnerable toadverse shocks (World Bank 2018k). The
materialization of a global downturn could set
back efforts to alleviate extreme poverty—
including in Sub-Saharan Africa, where progresshas been slow in recent years.
Possible productivity revival
Although global downside risks predominate, asustained revival in productivity growth following
cyclical improvements in 2017-18 could lead tostronger-than-expected global activity in coming
years (Figure 1.19). An acceleration in patentapplications and growing investments inintangible assets could be tentative signs of such a
revival. Greater connectivity, falling computing
costs, and open software architectures could also
facilitate the adoption of digital technologies andenable less productive firms to catch up with thetechnological frontier (Andrews, Criscuolo, andGal 2016; OECD 2018). Over the medium term,
breakthroughs in data processing, artificial
intelligence, and manufacturing could drive
additional productivity-enhancing innovations(Brynjolfsson and McAfee 2014; Diamandis andKotler 2012).
Economies experiencing faster productivity
growth would benefit from additional policy
Global recessions are defined as a decline in world real GDP per
capita accompanied by a synchronized deceleration in multiple
measures of global activity, including industrial production, trade,
capital flows, employment, and energy consumption (Kose andTerrones 2015).
1

CHAPTER 1

37

FIGURE 1.19 Productivity revival
A sustained revival in productivity growth following cyclical improvements
in 2017-18 could lead to stronger-than-expected global activity. A
significant productivity gap between leading and lagging firms suggests
an untapped potential for existing technologies.

A. Global productivity growth

B. Productivity growth in frontier andnon-frontier firms

Source: Conference Board, Organisation for Economic Co-operation and Development, World Bank.
A. Labor productivity per person employed.
B. Productivity growth for frontier and non-frontier firms of the period 2001-13. The global frontier isdefined as the top 5 percent of firms in terms of labor productivity levels within each two-digit industry
at the start of the period, while non-frontier firms refer to all other firms. Aggregate productivity is
calculated as the unweighted average of log labor productivity across firms, which the initial year,
2001, is indexed to 0 and separately for the manufacturing and services sectors.
Click here to download data and charts.

room, as the recovery could continue without
generating overheating pressures. This could allow
for a more gradual pace of monetary policy
tightening than currently envisioned and facilitate
the necessary restoration of fiscal buffers given
higher revenues. A sustained pickup in
productivity could also spur additional
investments and trigger a virtuous cycle between
capital deepening and growth.

Policy challenges
Challenges in advanced economies
Advanced-economy monetary policy is expected to beless stimulative, especially in the United States, where
tightening is proceeding more quickly than elsewherepartly in response to pro-cyclical fiscal easing.
Advanced economies should use this period of abovepotential growth to create the room to respond to
future cyclical shocks. Longer-term prospects remain
subdued and could be further eroded by major shifts
in trade and immigration policies.
Monetary and financial policies
The U.S. Federal Reserve is gradually removing
stimulus in response to low unemployment andnear-target inflation amid pro-cyclical fiscal
stimulus. In contrast, the European Central Bank

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.20 Monetary and fiscal policy in advanced
economies
The U.S. policy rate is approaching the point where it may no longer be
providing stimulus and is expected to peak at a lower level than in previous
tightening cycles, with significantly less room to cut rates. Government
debt has become the largest component of debt for advanced economies,
which can lead to less effective fiscal stimulus during an economic
slowdown.

A. U.S. federal funds rate

B. Peak and trough of Federal
Reserve policy rates in previous
cyclesC. Debt in advanced economies

D. Fiscal multiplier, by level of fiscal
spacesuggests that the Federal Reserve has significantly
less room to cut rates before reaching the zero
lower bound should a new downturn occur—in
the last three downturns, the Federal Reserve cut
its policy rate by about 5 percentage points.
To varying degrees, central banks in otheradvanced economies currently have even lesspolicy space. While unconventional monetary
policies could again be deployed, theireffectiveness in returning inflation to target andsupporting growth is subject to debate (Bernanke
2017a; Engen, Laubach, and Reifschneider 2015;
Greenlaw et al. 2018). This lack of monetary
space highlights the importance of avoiding a
policy-driven downturn in activity, combinedwith research into alternative methods ofproviding monetary policy stimulus (Bernanke
2017b; Williams 2017).
Fiscal policy
In many advanced economies, government debt-to-GDP ratios have reached unprecedented levels,with government debt becoming the largest
component of total debt. This limits the capacityof countries to provide counter-cyclical fiscal
stimulus in response to economic slowdowns(Huidrom et al. 2016).

Source: Board of Governors of the Federal Reserve System; Federal Reserve Bank of St. Louis;
Haver Analytics; Holston, Laubach, and Williams (2016); Huidrom et al. (2016); Institute ofInternational Finance; National Bureau of Economic Research; World Bank.
A. The effective rate is the mid-range of the federal funds target rates. The long-run estimate is thefederal funds rate that would be expected to prevail in the absence of shocks to the economy, asassessed by members of the Federal Open Market Committee. The neutral rate is the nominal shortterm interest rate consistent with the economy operating at its full potential once transitory shocks
have abated, and is estimated according to Holston, Laubach, and Williams (2016). Shaded area
indicates forecasts. Last observation is 2018Q3 for the effective rate and long-run estimate, and2018Q2 for the neutral rate.
B. Bars represent the peak policy rates in the four months prior the official recession dates of the
National Bureau of Economic Research, and the trough policy rate in the subsequent expansion.
C. Aggregates calculated using current U.S. dollar GDP weights. Sample includes: Australia, Canada,
Denmark, Finland, the Euro Area, Japan, New Zealand, Norway, Sweden, Switzerland, the UnitedStates, and the United Kingdom. Last observation is 2018Q2.
D. Figure shows fiscal multipliers 1 year on at different levels of fiscal space. These are based on
estimates from the IPVAR model of Huidrom et al. (2016). A country is considered to have “wide”
fiscal space if it is in the lowest quartile of advanced economies for government debt, and to have“narrow” fiscal space if it is in the highest quartile. Orange lines represent the 16-84 percent
confidence bands.
Click here to download data and charts.

and the Bank of Japan have signaled that they will
be holding policy rates at current levels in the nearterm.
For the first time since the financial crisis, themain U.S. policy rate is approaching its neutrallevel (Figure 1.20). However, the policy rate is
expected to peak at about 3 percent, which

The United States has enacted significant fiscal
stimulus even though the economy is already at or
above full employment. This stimulus is expected
to result in persistent deficits equivalent to about 5
percent for most of the next decade (CBO 2018).
In these circumstances, the consequence of procyclical stimulus is likely to be inflation pressures,
higher domestic interest rates, a crowding out ofprivate sector activity, and a widening of the U.S.
current account deficit.
Structural policies
Potential growth remains subdued in advanced
economies and is likely to slow further in coming
years, partly due to aging populations anddeclining birth rates (Figure 1.21; World Bank
2018k). An increasing number of countries areraising barriers to immigration, which might
hasten this deceleration. Immigration is an
important reason for rising labor forces in many
advanced economies and may also contribute toG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

productivity growth; immigrants skew younger
than host populations, and younger populations
have been associated with faster labor productivity
growth for various industries and occupations(Maestas, Mullen, and Powell 2016; World Bank
2018m). Heightened restrictions on immigration
could also worsen fiscal positions, by dampening
growth and the net contribution that immigrants
typically provide to the government budget(Clements et al. 2015).
Recent trade disputes represent a critical headwind
to longer-term prospects. Rising tariffs may
already be contributing to weaker productivity byincreasing costs, disrupting global supply chains,stranding productive assets, and relocating activity
away from the most efficient locations (Melitz
2003). Lack of policy clarity also risks causingfirms to delay investment because of uncertainty
over market access. This highlights the criticalimportance of a continued commitment to a rulesbased international trading system.
Increasing restrictions to trade and immigration
could therefore result in weaker growth and lower
productivity. While international trade andimmigration can impose costs on some sectors of
the economy or vulnerable groups of workers, abetter course is to adopt policies that mitigate
these costs and redistribute more equitably thebenefits of globalization.
Challenges in emerging market anddeveloping economies
Recent financial market stress in some EMDEs
highlights the pressing need to strengthen buffers
against the risk of less favorable global financial
conditions. Fiscal positions remain fragile,
underscoring the urgency to improve domestic revenue
mobilization and to commit to or deepen fiscal
reforms aimed at controlling expenditures. In thelonger term, steps to enhance human capital,
encourage regional economic integration, and lower
barriers to investment for small- and medium-sizedenterprises would boost potential growth and help
tackle challenges associated with high informality.
China’s key policy challenge is to foster the transition
to more sustainable growth while dealing with traderelated headwinds without overstimulating the
economy and delaying the deleveraging process.

CHAPTER 1

39

FIGURE 1.21 Structural policies in advanced economies
Potential growth is expected to decelerate in advanced economies, partly
due to demographic factors. This deceleration is likely to be more severe if
government policies lead to heightened restrictions on immigration, as
immigrants tend to be younger than the native population.

A. Potential growth in advanced
economies

B. Immigrant versus native population
age distribution to OECD destinations

Source: Organisation for Economic Co-operation and Development, World Bank.
A. TFP = total factor productivity growth. Figure shows potential growth estimates based on the
production function approach. For further details on potential growth estimates, refer to the January
2018 edition of the Global Economic Prospects report. Aggregates calculated using constant 2010
U.S. dollar GDP weights. Sample includes 30 advanced economies.
B. "Working age" includes population aged 25-64 years, "dependents" includes population aged
0-24 and above 65 years. Country-level age proportions are weighted by size of the immigrant
population. Data are from the 2010-11 OECD Database on Immigrants in OECD and non-OECD
Countries (DIOC-E).
Click here to download data and charts.

Policy challenges in China
Authorities in China have shifted to looser
monetary and fiscal policies in response to a morechallenging external environment, includingheightened trade tensions. They have cut reserve
requirements, introduced new tax breaks forfinancial institutions lending to small firms, andencouraged banks to buy more local government
bonds. They have also reduced taxes and fees,
increased export tax rebates, and accelerated
issuance of special purpose local government
bonds to bolster infrastructure spending. In
addition, the authorities have stepped up theirstructural reform efforts to improve the businessenvironment, including for foreign firms, havestrengthened intellectual property protection, andhave lowered tariffs on imports—with the
exception of some tariffs on U.S. imports inretaliation to U.S. tariffs on China’s goods. The
authorities’ commitment to growth stability andstructural reforms was reaffirmed in late
December (CEWC 2018).
The trade disputes with the United States, as wellas the ongoing moderation of global trade,

40

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 1.22 EMDE monetary policy
Monetary and financial policy challenges have been compounded byrecent financial market pressures. Policy interest rates and inflation ticked
up in EMDEs facing above-average currency depreciation against the U.S.
dollar in 2018. The share of EMDEs hiking policy rates during U.S.
tightening cycles is markedly higher than the share of EMDEs cutting rates
during U.S. easing periods, suggesting that ongoing U.S. normalization
may constrain the room of maneuvering for many EMDE central banks.
Higher borrowing costs contributed to an increase in sovereign bondspreads, especially in EMDEs with large current account deficits.

A. Policy interest rates, by extent of
currency depreciation against the U.S.
dollarB. Inflation, by extent of currency
depreciation against the U.S. dollar

Additional efforts to enhance market competition,encourage a shift of capital and labor toward moreproductive firms and sectors, and bolster
household consumption would also be needed(World Bank 2018n). Advancing reforms that
boost innovation, including through stronger
intellectual property rights, would also help
alleviate trade frictions while enhancing China’s
competitiveness in the medium term. In addition,
productivity-enhancing investments in health,education, and research and development would
encourage a shift from growth that is dependenton physical capital and help offset the impact ofadverse demographic trends.
EMDE monetary and financial policies

C. Share of policy interest ratechanges following U.S. policy rate
hikes or cuts

D. Change in sovereign bond spreads,
by extent of current account deficit in2018

Source: Haver Analytics, International Monetary Fund, J.P. Morgan, Shambaugh (2004), World Bank.
A.B. The above average and below average currency depreciation groups are defined by countries
above or below the sample average of the year-to-date percent change in the bilateral exchange rate
against the U.S. dollar. The sample average is -9.3 percent and includes 27 EMDEs, of which 12 areabove and 15 are below average. Last observation is November 2018.
A. The aggregate policy interest rates are calculated using constant 2010 U.S. dollar GDP weights.
B. Median consumer price inflation for each group.
C. Pegged exchange rates are defined, based on a de facto classification, as exchange rates
fluctuating within a +/-2 percent band or, at most, a one-time devaluation over the preceding
11-month period relative to a country-specific reference currency. Refer to Shambaugh (2004) for
details. Unbalanced sample includes 108 non-LIC EMDEs and considers policy rate actions from1970 onwards. Last observation is November 2018.
D. The above average and below average current account deficit groups are defined by countries
above or below the 2018 sample average of the current account balance. The sample average is-1.6 percent of GDP. The sovereign bond spread is measured by J.P. Morgan Emerging Market Bond
Index (EMBI). Sample includes 38 EMDEs. Last observation is December 19, 2018.
Click here to download data and charts.

highlight China’s key policy challenge to foster thetransition to more sustainable growth while
dealing with trade-related headwinds without
overstimulating the economy and delaying thedeleveraging process (World Bank 2018b). This
will require continued reforms to reduce financial
vulnerabilities, including those associated with theaccumulation of non-financial enterprise debt.

Policy challenges across many EMDEs have been
compounded by recent financial market pressuresthat have been associated with sizable currency
depreciations and capital outflows. Among somekey EMDEs, currency and financial market
pressures were substantial (e.g., Argentina,
Turkey), leading to significant policy tightening
andmarkedly
cloudingthenear-term
macroeconomic and financial outlook. Moregenerally, monetary policy became lessaccommodative in EMDEs that faced aboveaverage currency depreciation, or that used up
reserves to stem it (Figure 1.22).
Weaker exchange rates have pushed up inflation
across many EMDEs, particularly in some key
commodity exporters, highlighting the role of theexchange rate pass-through to domestic prices(e.g., Argentina, Brazil, South Africa; World Bank
2018p). Among commodity importers, pricepressures also reflect their cyclical positions, assuggested by their positive output gaps. Higher
energy prices in most of 2018 also pushed up
inflation in a number of net oil importers (e.g.,
Egypt, Kenya, Mexico).
The ongoing normalization of advanced-economy
monetary policy will continue to pose challenges
for EMDEs (Arteta et al. 2015; Obstfeld 2015;
Sobrun and Turner 2015). In particular, U.S.
tightening cycles spillover to EMDEs mainly
through the availability of foreign credit, especiallythrough portfolio bond flows (Bräuning andIvashina 2018; Koepke 2018). Moreover, theG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

share of EMDEs hiking policy rates during U.S.
tightening cycles is markedly higher than the shareof EMDEs cutting rates during U.S. easing
periods,
suggestingthat
ongoingU.S.
normalization may constrain the roomof maneuvering for many EMDE central banks.
These challenges will be greater for those countries
with large external vulnerabilities, such as sizable
current account imbalances, weak foreign reserves,and high inflation or external debt (Iacoviello andNavarro 2018). In addition, higher borrowing
costs may cause balance sheet, debt service, androllover difficulties for some EMDE sovereigns
and corporates, which could undermine financial
stability (Borensztein and Ye 2018; World Bank
2018a). These may be particularly acute ineconomies facing currency mismatches on
corporate and household balance sheets (Davies etal. 2014).
To confront these challenges, EMDE authorities
need to urgently address persistent vulnerabilities
that render their countries more susceptible totighter financing conditions, capital flow reversals,
and financial market shocks. This includes shoringup the financing of current account deficits to
reduce the effects of net portfolio flow reversals,
improving public and corporate balance sheet
management, and implementing macroprudential
frameworks that bolster banking and corporate
sector resilience, such as countercyclical capital
buffers and stricter reserve ratio or leverage ratio
requirements (Cerutti, Claessens, and Laeven
2015; Forbes 2018). In addition, reducing
exposures to foreign currency borrowing andcurrency mismatches, as appropriate, can help
contain the financial system’s vulnerability todislocating exchange rate movements (Ahnert etal. 2018). For EMDEs that have made progress on
macroprudential reforms, enhancing financial
deepening and improving governance could
further boost resilience to shocks (Sahay et al.
2015).
EMDE policymakers also need to uphold acredible commitment to medium-term pricestability—one
thatissupportedbymacroeconomic frameworks that set attainableinflation targets where appropriate, as well as
maintain strong institutional independence andtransparency. This will be especially critical if the

CHAPTER 1

41

FIGURE 1.23 EMDE fiscal policy
For the first time in several years, fiscal deficits are projected to be wider incommodity importers than in commodity exporters. Government debt isrising among EMDEs with high foreign-currency-denominated debt or
persistent current account deficits. In low-income countries, the cost ofservicing debt has risen as the composition has moved fromconcessionary to market financing. Greater government effectiveness is
associated with stronger tax revenue collection.
A. Fiscal deficits

B. Fiscal sustainability gaps, byextent of reliance on foreigncurrency-denominated debt

C. Interest rate payments on debt in
LICs

D. Tax revenues, by extent ofgovernment effectivenessSource: Haver Analytics; International Monetary Fund; Kose, Kurlat et al. (2017); World Bank.
A.C.D. Figures show medians across groups.
A. Shaded area indicates forecasts. Sample includes 151 EMDEs.
B. FC debt = foreign-currency-denominated debt. The sustainability gap is measured as thedifference between the primary balance and the debt-stabilizing primary balance, assuming historical
median (1990-2016) interest rates and growth rates. A negative gap indicates government debt isrising along an accelerated trajectory. The aggregates are calculated using constant 2010 U.S. dollar
GDP weights. The sample includes 27 EMDEs. The above (below) average foreign-currencydenominated debt groups are defined by countries above (below) the sample average of external
debt in foreign currency as a share of total external debt in 2017. The sample average is 86.9
percent of GDP.
C. Interest rate payments include those made on government debt to domestic and foreign residents.
Solid line represents median and area between the dashed lines represents the interquartile range.
The sample includes 30 low-income countries and excludes Somalia, South Sudan, and Syria due
to data restrictions.
D. Government effectiveness measured by the Worldwide Governance Indicators. Higher
government effectiveness are EMDEs with 2000-17 averages above 0 (stronger governance); lower
are EMDEs with 2000-17 average government effectiveness below 0 (weaker governance).
Unbalanced sample includes 150 EMDEs.
Click here to download data and charts.

ongoing period of low and stable global inflation
comes to an end, perhaps driven by a slowdown or
rollback of the structural factors that have held
inflation at bay in recent decades—in particular,
trade and financial integration—or an erosion ofcentral bank independence. The reversals of theselong-term trends could coincide with cyclicalupward pressures on prices in some EMDEs,
reigniting inflation (Box 1.1).

42

CHAPTER 1

EMDE fiscal policy
Government finances in many EMDEs are in a
fragile position, with deteriorating debt dynamics
and limited fiscal space. In some cases, muchneeded reforms to improve fiscal space have either
stalled or not been fully implemented, while
funding new or increasing liabilities, such as
public-sector wage bills, have put further strain on
domestic revenues (e.g., Brazil, South Africa).
Oil exporters continue to face fiscal sustainability
challenges, with recent oil price volatility
highlighting the need for these countries tocontinue to reduce their reliance on oil revenues
by deepening reforms that bolster the nonresource budget (e.g., Ecuador, Russia; IMF
2018f). In metals and agricultural producers,
weaker-than-envisaged commodity prices could
put further pressure on already fragile publicfinances (e.g., South Africa, Zambia). Among
commodity importers, for the first time since theoil price collapse that began in 2014, fiscal deficits
are projected to be larger as a share of GDP thanthose in commodity exporters, as waning revenue
growth continues to be accompanied by strong
expenditure growth (Figure 1.23).
Across EMDEs, rising global interest rates will
make financing fiscal deficits through sovereign
debt issuance more costly, underscoring the needto realign government spending with revenue andto manage the composition of debt, particularly incountries
withelevated
foreign-currencydenominated debt or persistent current accountdeficits (Du and Schreger 2016). In a rising global
interest rate environment, employing expansionary
fiscal policy with limited fiscal space can amplify
vulnerabilities by increasing market perceptions ofsovereign credit risk, which may lead to higher
sovereign bond yields and borrowing costs (Bi,Shen, and Yang 2014; Corsetti et al. 2013). LICs
are more vulnerable to rising global financial costs
than in the past, as LIC debt has increasingly
shifted from concessionary to market financing(Chapter 4; World Bank 2018a). More generally,
EMDE governments with high private-sector debt
are exposed to contingent liabilities if banking
sector stress materializes in a rising interest rateenvironment (World Bank 2018k).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Regaining policy buffers is a key priority to be able
to use countercyclical fiscal policy to stabilize
growth (World Bank 2015). Efforts to build fiscal
space could include implementing credible
medium-term expenditure or deficit targets, better
managing contingent liabilities to contain fiscal
risks, stabilizing debt, and reforming the tax
system to improve domestic resource mobilizationand the investment climate—e.g., adjusting
statutory rates, broadening bases, eliminating
loopholes and exemptions, and improving tax
administration and compliance. Managing thecomposition of debt can also help address publicsector balance sheet vulnerabilities. For EMDEs
with elevated foreign-currency-denominated debt,
bolstering domestic-currency bond markets, if
feasible, could help stem rollover and currency
risks.
To complement these efforts, improving
government effectiveness and strengthening
institutions would support tax revenue collection(Ajaz and Ahmad 2010; Prichard 2010). If fiscal
adjustment remains necessary to ensure long-term
fiscal sustainability, policymakers need to evaluate
the efficacy of public expenditures, prioritizing
spending on quality investment and safeguarding
poverty-reducing social transfers, while reining inprograms that are unproductive or inefficient(World Bank 2018k, 2018p). EMDE fiscal
policymakers also need to confront the longerterm challenges posed by high informality, as its
prevalence in some regions reduces government
revenues through tax base erosion (Chapter 3).
EMDE structural policies
EMDEs also face substantial longer-term
challenges to ensure sustained improvements inincomes and living standards amid rapid
technological and demographic changes. Meetingthese challenges will require, among other actions,effective investments in human capital, efforts toaccelerate regional and global integration, andmeasures to free up a large untapped potential for
growth and productivity gains among small- andmedium-sized enterprises. Progress in these areas
would also help bring people and companies out
of informality.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 1

43

Improving human capital

FIGURE 1.24 EMDE structural policies

Under-investment in human capital has left large
parts of the workforce in EMDEs unprepared for
rapid technological changes and future skill
requirements (Flabbi and Gatti 2018). This
represents a significant bottleneck to growth inmany countries. Moreover, continued divergencesin the demand for high- and low-skilled labor
could exacerbate income inequality over time.
How education systems adapt to skills needs will
be a key determinant of the productivity anddistributional effects of technological change(Barro and Lee 2015).

Learning-adjusted years of schooling are a good predictor of growth
performance in EMDEs. Greater trade integration, as well as lower barriers
to investment in small- and medium-sized enterprises, could help boost
productivity in EMDEs. Informality remains prevalent in EMDEs and isgenerally associated with large productivity gaps between formal andinformal sectors.

Improving student learning is particularly
important, starting with an effective measurementof the performance of education systems.
Measures that capture both the quantity andquality of learning, such as learning-adjusted yearsof schooling, reflect relevant dimensions of successand are better predictors of subsequent growthacross EMDEs (Figure 1.24; Filmer et al. 2018). A
focus on both schooling participation and learning
results can more properly inform policy actions
and support effective investments in humancapital (World Bank forthcoming).
Beyond a heightened focus on learning outcomes,
a comprehensive approach to human capital
improvements in EMDEs should also address
other dimensions, including malnutrition andhealth throughout the life cycle. In this context, ahuman capital index has recently been developed
to assess productivity gains that could be achieved
by matching education and health outcomes tobest practices (Kraay 2018). This benchmarking
exercise helps to identify areas of intervention to
improve public spending and governance ineducation and health systems—and to raiseawareness of the costs of inaction (World Bank
2018q).
The urgency to bolster human capital comes in aperiod of constrained public-sector resources andelevated debt levels, creating a notable policy
challenge. Accordingly, more effective spending in
education and health will need to be accompanied
by renewed efforts to prioritize government
spending, improve efficiency of public
administrations and revenue mobilization, andencourage private sector participation.

A. Link between education measuresin 2000 and GDP growth in 2000-16

B. Average productivity growth since
2000, by gross exports to GDP ratio

C. Productivity level, by firm size

D. Share of fixed capital spending
financed by retained earnings

E. Share of informal activity

F. Average productivity in formal andinformal firms

Source: Ayyagari, Demirgüç-Kunt, and Maksimovic (2017); Borensztein and Ye (2018); Elgin et al.
(forthcoming); Filmer et al. (2018); Organisation for Economic Co-operation and Development; WorldBank.
A. LAYS = learning-adjusted years of schooling. Figure shows regression of three measures of
education in 2000—LAYS, average years of schooling, and average test score—on average GDP
growth over the period 2000-16 across EMDEs. Test scores are from the mathematics assessment of
the 1999 Trends in International Mathematics and Science Study (TIMSS) and 2000 Program forInternational Student Assessment (PISA). Regression conditioned on original GDP per capita levels.
B. Above average and below average indicate above and below average changes in gross exports to
GDP ratios over the period 2000-14.
C. Productivity is defined as the value added per person employed, measured in thousands of U.S.
dollars in current PPP terms. Data as of 2015, or latest available year. Advanced economies include
Austria, Denmark, Finland, France, Germany, Italy, Netherlands, Spain, Sweden, and the United
Kingdom. EMDEs include Brazil, Hungary, Mexico, Poland, and Turkey.
D. Proportion of the purchase of fixed assets financed by internal funds or retained earnings.
E. Unweighted averages, in percent of official GDP. Estimates of informal output derived from adeterministic dynamic general equilibrium model, as described in Chapter 3.
F. Blue bars represent estimates and orange vertical lines indicate two standard deviation error
bands. World Bank’s Enterprise Survey data for 135 countries (2008-18). Labor productivity is
proxied by annual sales per worker.
Click here to download data and charts.

44

CHAPTER 1

Boosting regional and international integration
If faced with growing protectionist measures,
policymakers in EMDEs may be tempted to resort
to retaliatory action or unilateral increases inbarriers to trade. While such measures could help
recapture some of their terms-of-trade losses, anincrease in trade barriers would likely lead tosignificant distortionary effects and efficiency
losses for EMDEs (Devarajan et al. 2018). Instead,continued commitment to regional andinternational
integration
through
tradeliberalization, properly designed free trade
agreements, and participation in global value
chains (GVCs) within an open and rules-based
multilateral trading system could yield significant,previously untapped benefits for EMDEs. The callof G20 members to consider additional reform of
the World Trade Organization could be a chance
to maximize development opportunities for
EMDEs.
International integration enables firms of all sizes
to increase their participation in international
trade. In particular, participation in GVCs helps
companies specialize in tasks closely aligned with
their comparative advantage and can contribute toa more efficient allocation of resources, job
creation, and export diversification. In turn,increased trade openness and GVC integration
boost productivity growth and helps the diffusionof technologies (Criscuolo and Timmis 2017;
Elms and Low 2013).
Many EMDEs and LICs, however, still face
important challenges in fostering an environment
conducive to greater GVC integration. Although
their participation has increased during the lasttwo decades, LICs still have little presence in
GVCs. Participation is hindered by domestic
capacity constraints and restrictive trade andinvestment regimes. Tariffs and other barriers totrade increase costs for firms, reduce their ability
to access foreign inputs, and can impede thedevelopment of downstream industries (Miroudot,
Rouzet, and Spinelli 2013). Reducing thesebarriers remains a key priority to support GVC
participation, and to increase trade gains for many
EMDEs (Kowalski at al. 2015). Closer physicalintegration of transport networks and other

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

regional infrastructure can also help reduce thecost of trade and support increased openness(Donaldson 2018).
That said, increased international integration andparticipation in GVCs does not guarantee positiveand sustainable development outcomes for
EMDEs. Targeted policies that encourage theupgrading of domestic production are crucial inensuring that the social and development impacts
of GVC activities are optimized (Fessehaie andMorris 2018; Taglioni and Winkler 2016).
Untapping SME growth potential
Supported with appropriate frameworks, smalland medium-sized enterprises (SMEs) can be key
drivers of growth and job creation in EMDEs(Ayyagari, Demirgüç-Kunt, and Maksimovic2017). They can play a central role in industrial
development and restructuring, support larger
firms with inputs and services, and allow increased
sectoral specialization. However, their growthpotential continues to be hindered by manyfactors, including insufficient access to finance; tax
and regulatory burdens; skills mismatch; limitedaccess to infrastructure, particularly electricity; andcorruption (Wang 2016). Alleviating thoseobstacles could lead to significant growth windfalls
for EMDEs, given that SMEs have the largest
untapped potential for productivity catch-up withadvanced economies (Cusolito, Safadi, andTaglioni 2017). Supporting SME development
could also help reduce high informality in someregions, which is most prevalent among microenterprises.
Limited access to finance is most often cited as a
key obstacle to SME growth in EMDEs, forcing
these companies to rely on retained earnings tofund investment. This leads to sub-optimal capitalspending and an unrealized potential for
expansion and job creation. Key obstacles include
the lack of reliable credit information, the absenceof suitable collateral, and weak legal institutions.
Increasing SME access to finance could help boost
their average size and support innovation and job
creation(Ayyagari,
Demirgüç-Kunt,and
Maksimovic 2017; Ayyagari et al. 2016).
Improved access to finance for womenG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

entrepreneurs could also lead to more investment,
while access to savings account for female-headedhouseholds could result in additional spending on
education (Demirgüç-Kunt et al. 2018; Sahay and
Cihak 2018). Bankruptcy protection laws also laginternational best practices in many EMDEs.
Historical experience suggests that strengthening
bankruptcy protection can boost investment
activity and facilitate responsible corporate risktaking, helping to relieve the costs of debt
overhang (Gopalan, Mukherjee, and Singh 2016;
World Bank 2014).
Beyond basic education, technological know-how,managerial capabilities, and tolerance for risk arealso
keyfactors
underlying
successful
entrepreneurship and vibrant firm dynamics(Cusolito and Maloney 2018). Conditions thatencourage experimentation and do not penalize
failure are crucial to support the upgrading of firm
capabilities and diffusion of technologicalprogress.
Tax,registration,
and
other
administrative simplifications can also be
successful tools to facilitate SME creation andexpansion (Bruhn 2011). Finally, restrained accessto infrastructure, particularly electricity, is often
mentioned as a major barrier to the developmentof SMEs and start-up companies in many
EMDEs, especially in Sub-Saharan Africa.
Improvements in both traditional power line
supplies and off-grid solutions such as solar energy
and micro grids need to be achieved in tandem,
supported by proper policy incentives and effective
regulations (World Bank 2017).
Growing out of informality
Informality remains widespread in many EMDEs(Chapter 3). It is particularly prevalent in lessdeveloped regions, with South Asia and SubSaharan Africa accounting for nearly 60 percent of
all informal workers in EMDEs. It is also elevatedin regions with weak institutions and high levels offiscal and regulatory burdens, such as Latin
America and the Caribbean and Europe andCentral Asia.

CHAPTER 1

While the informal economy provides animportant safety net to workers, particularly
during downturns, it can dampen growth byweighing on physical and human capitalformation (Docquier, Müller, and Naval 2017;
Oviedo, Komas, and Karakurum-Ozdemir 2009).
In particular, firms operating in the informal
economy tend to limit their size to avoid detection
and use less advanced production technologies(Dabla-Norris et al. 2018). Their lack ofcompliance with regulations and taxes may help
them stay in business despite low productivity (LaPorta and Shleifer 2014; Schneider and Enste
2000; Box 3.4).
High informality can also limit fiscal revenues andthus can constrain the ability of governments toprovide public services, conduct countercyclicalpolicies, and implement effective redistributive
measures (Besley and Persson 2014; Ordóñez
2014). Both government revenues andexpenditures are lower in EMDEs where
informality is widespread. High informality isoften associated with lack of development, limitedaccess to finance, low human capital, poor
governance, and heavy regulatory burdens. If
properly designed, policies that help improveoutcomes in those areas would bolster growthprospects and encourage workers to participate in
the formal economy, thus helping reduce
informality and its associated challenges.
Policies that are implemented with other purposesin mind also need to take into consideration theunintended consequences on informality. For
example, changes in labor market regulationaccompanying the decentralization of minimumwage-setting responsibilities or the liberalization of
trade have resulted in higher informality in somecountries (Attanasio, Goldberg, and Pavcnik
2004; Goldberg and Pavcnik 2003; Chapter 3;
Box 3.4). These experiences are a reminder of theneed to design comprehensive reform packages
that are calibrated to country circumstances.

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CHAPTER 1

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

TABLE 1.2 List of emerging market and developing economies1
Commodity exporters2

Commodity importers3

Albania*

Madagascar

Afghanistan

PanamaAlgeria*

Malawi

Antigua and Barbuda

Philippines

Angola*Malaysia*

Bahamas, The

PolandArgentina

Mali

Bangladesh

Romania

Armenia

Mauritania

Barbados

Samoa

Azerbaijan*

Mongolia

BelarusSerbiaBahrain*

MoroccoBhutanSeychelles

BelizeMozambique

Bosnia and Herzegovina

Solomon Islands

Benin

Myanmar*

Bulgaria

Sri Lanka

Bolivia*

Namibia

Cabo Verde

St. Kitts and Nevis

Botswana

Nicaragua

Cambodia

St. Lucia

Brazil

Niger

China

St. Vincent and the Grenadines

Burkina Faso

Nigeria*

Comoros

Thailand

BurundiOman*

Croatia

Tunisia

Cameroon*

Papua New Guinea

Djibouti

TurkeyChad*Paraguay

Dominica

Tuvalu

Chile

Peru

Dominican Republic

VanuatuColombia*

Qatar*

Egypt

VietnamCongo, Dem. Rep.

Russia*El Salvador

Congo, Rep.*

RwandaEritrea

Costa Rica

Saudi Arabia*

Eswatini

Côte d’Ivoire

SenegalFiji

Ecuador*

Sierra Leone

Georgia

Equatorial Guinea*

South Africa

GrenadaEthiopia

Sudan*

Haiti

Gabon*

Suriname

Hungary

Gambia, The

Tajikistan

India

Ghana*

Tanzania

Jamaica

GuatemalaTimor-Leste*

JordanGuineaTogo

Kiribati

Guinea-Bissau

Tonga

LebanonGuyanaTrinidad and Tobago*

LesothoHonduras

Turkmenistan*

Macedonia, FYR

Indonesia*

UgandaMaldives

Iran*

UkraineMarshall Islands

Iraq*

United Arab Emirates*

Mauritius

Kazakhstan*

Uruguay

MexicoKenya

Uzbekistan

Micronesia, Fed. Sts.

KosovoVenezuela*

Moldova, Rep.

Kuwait*West Bank and Gaza

Montenegro

Kyrgyz RepublicZambiaNepal

Lao PDR

Zimbabwe

Pakistan

Liberia

Palau

Energy exporters.
1 Emerging market and developing economies (EMDEs) include all those that are not classified as advanced economies. 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; theRepublic 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 notavailable, 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.
*

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CHAPTER 2REGIONALOUTLOOKS

Growth in the East Asia and Pacific region is expected to moderate to a still-robust 6 percent in 2019 and2020. In China, policies aimed at rebalancing and countering external headwinds will continue to tilt activity
toward domestic demand and away from exports. Risks to regional growth are to the downside and haveintensified, including the possibility of further escalation of trade restrictions and faster-than-expected tighteningof global financing conditions. Countries most vulnerable to disruptions in global trade and financial market
activity are those with high debt, elevated current account deficits and high exposure to portfolio and bankinflows, and significant reliance on exports.

Recent developments
Growth in the East Asia and Pacific (EAP) regionis estimated to have slowed to 6.3 percent in 2018
and momentum has weakened, reflecting
diminishing support from exports partly offset byrobust domestic demand (Figure 2.1.1; Table
2.1.1). External conditions have deteriorated,reflecting moderating global demand, heightened
trade tensions, and a substantial tightening offinancing conditions. This less favorable
international environment has been accompanied
by financial stress in some large emerging market
and developing economies (EMDEs).
Several large economies in the region have facedsome combination of capital outflows, currency
depreciations, equity market corrections, andforeign reserve losses (e.g., China, Indonesia,
Malaysia, the Philippines, Thailand). In addition
to country-specific factors, the countries mostaffected were those with sizable or widening
current account deficits and dependence onvolatile portfolio flows, those with relatively highasset valuations, or those with exposure to tradedisputes involving major economies (e.g., China,
Indonesia, Myanmar, the Philippines). Bond
spreads in these countries have generally widened,
but less than during the last two financial stress
Note: Ekaterine Vashakmadze. Research assistance was provided
by Liu Cui.

episodes and less than the EMDE average,
reflecting continued strong investor confidence
toward these economies.
China has been easing monetary and fiscal
policies in anticipation of slowing export growthamid trade tensions, while at the same timemaking progress at reducing growth in non-bankfinancing. In contrast, several countries, such as
Indonesia and the Philippines, have stemmed
capital outflows by tightening monetary policy.
Most EAP countries have taken advantage of
flexible exchange rates, which has allowed theircurrencies to act as shock absorbers during timesof stress. Indonesia has also implemented measures
to curb imports while taking steps to increase coalexports, as part of an effort to reduce the current
account deficit and relieve pressure on the rupiah(World Bank 2018a; World Bank 2018b).
Growth in China is estimated to have slowed to astill robust 6.5 percent in 2018, with resilient
domestic demand helping to offset a decelerationof exports (Figure 2.1.2). Consumption has
remained strong and private investment isrecovering, partly in response to more supportive
policies, as authorities attempt to offset the effectsof various United States trade restrictions. The
services sector has continued to outperform. Price
growth of newly constructed residential buildings,
most notably in Tier 1 cities, has rebounded
following several years of correction.

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FIGURE 2.1.1 EAP: Recent developments
Growth in EAP slowed to 6.3 percent in 2018, with robust domestic
demand but softening export growth. Inflation has been trending up but isgenerally below targets. EAP economies faced some combination ofcapital outflows, currency depreciations, equity market corrections, andforeign reserve losses during the 2018 financial market stress episode.
Regional bond spreads have generally widened but less than during thelast two financial stress episodes and less than the EMDE average.
A. Growth

B. Export growth

C. Inflation

D. Balance of payments, EAP ex. China

E. Exchange rates and equity prices

F. Bond spreads

Source: Bloomberg, Haver Analytics, J.P. Morgan, World Bank.
E.F. “Taper T. 2013” refers to mid-2013 global financial market stress episode; “China SM 2015”refers to mid-2015 China stock market (SM) turbulence; “Episode 2018” refers to mid-2018 financial
market volatility episode. The start dates for the stress episodes are: Taper Tantrum: May 23, 2013;
China SM 2015: June 12, 2015; Episode 2018: April 15, 2018.
A. Aggregate growth rates are based on constant 2010 U.S. dollar GDP-weights and include
Indonesia, Malaysia, Mongolia, Philippines, Thailand, and Vietnam. Last observation is 2018Q3.
B. Data include only goods. 12-month moving average. Aggregate growth rate excludes Cambodia,
Fiji, Lao PDR, Mongolia, Myanmar, Solomon Islands, Papua New Guinea, Timor-Leste, Vanuatu, andVietnam due to data limitations. Last observation is October 2018.
C. Average year-on-year growth. The midpoints of targeted ranges in 2018 in Indonesia (2.5-4.5
percent), Philippines (2-4 percent), Vietnam (4 percent), China (3 percent), and Thailand (1-4
percent). For Malaysia, the midpoint of Bank Negara’s 2018 forecast of 2-3 percent is used. Lastobservation is November 2018.
D. Current account balance is based on non-seasonally adjusted data. The aggregate includes
Indonesia, Malaysia, Philippines, and Thailand. Net capital flows and change in reserves are
estimates. Last observation is 2018Q3.
E. Percent change of exchange rates (U.S. dollar vs. local currency) and equity prices in local
currency over 247 days since the start dates of respective stress episodes. EAP aggregate exchange
rate includes China, Indonesia, Malaysia, Mongolia, Philippines, Thailand and Vietnam. EAP
aggregate equity price includes China, Indonesia, Malaysia, Philippines, Thailand and Vietnam.
Orange lines denote minimum-maximum ranges. Green diamonds denote EMDE averages.
F. The change of bond spread over 247 days since the start dates of respective stress episodes. The
average spread of a country’s sovereign debt (as measured by J.P. Morgan’s Emerging Markets
Bond Index) over their equivalent maturity U.S. Treasury bond. EAP aggregate bond spread includes
China, Indonesia, Malaysia, Philippines and Vietnam. Orange lines denote minimum-maximum
ranges. Red diamonds denote EMDE averages.
Click here to download data and charts.

On the external front, China’s current accountsurplus has dissipated as import growth outpaced
export growth. Export growth is estimated to have
dropped from about 9 percent in 2017 to around
4-5 percent in 2018, reflecting the dampening
effects of weaker global demand and new U.S.
tariffs. Since the beginning of 2018, substantial
new tariffs have been introduced on bilateral trade
between China and the United States, accounting
for about 11.4 percent of China’s goods exports
and about 6.5 percent of imports. Capitaloutflows have resumed, and equities and therenminbi have been under pressure, reflecting
heightened trade tensions and diverging monetary
policy: a loosening by the People’s Bank of China(PBoC) and a tightening by the U.S. Federal
Reserve.
Growth in commodity-importing economies
excluding China is moderating. In the Philippines,
activity has slowed as surging inflation, capacity
constraints, and currency pressures have prompted
authorities to hike policy rates. Growth inVietnam remains robust, helped by booming
exports, but authorities have tightened fiscal policy
as part of their commitment to reduce theeconomic role of the state. In Thailand, a cyclicalrecovery continues, but its pace is moderating in
response to tighter fiscal policies and the effects ofsoftening global demand on export growth.
In commodity-exporting EAP economies, theinvestment-led cyclical recovery is maturing, andthe pace and composition of growth continues toreflect country-specific factors. In Indonesia,
growth last year was led by rising investment on
the back of accelerated infrastructure spending andinvestment in the mining sector (World Bank
2018c). In Malaysia, lower public investment isweighing on growth, reflecting the completion ofseveral infrastructure projects and a more prudent
approach toward new ones (World Bank 2018d).
In contrast to the regional trend, import growth in
Malaysia has been weak, reflecting weak demandfor capital goods imports combined with lower
imports of intermediate goods.
Despite the slowdown, EAP remains one of theworld’s fastest-growing regions and has been
relatively resilient to recent bouts of financial
market volatility (Figure 2.1.3). One reason is that

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

most EAP countries (with the exception of the
Pacific Islands) continue to experience growth
above the EMDE average and maintain mostly
diminishing, but still positive current accountbalances net of foreign direct investment (e.g.,
China, Malaysia, Thailand, the Philippines, andVietnam). Another reason is that policy frameworks across EAP region have improved over timewith the shift to floating exchange rates, economic
diversification, and solid buffers.
That said, many countries have pockets ofvulnerabilities, including elevated levels of public
and private debt (e.g., China, Lao PDR, Malaysia,
Mongolia, Papua New Guinea, Vietnam), external
debt (e.g., Malaysia, Mongolia, Lao PDR), foreign
participation in local-currency sovereign bondmarkets (e.g., Indonesia, Malaysia), fiscal deficits(e.g., Cambodia, Lao PDR, Mongolia, Vietnam),and current account deficits and high reliance onvolatile capital flows (e.g., Cambodia, Indonesia,
Mongolia). In addition, deep regional and global
integration makes the region vulnerable to trade
shocks, as described in the risk section below.

EAST ASIA AND PACIFIC

59

FIGURE 2.1.2 China: Recent developments
In China, growth is slowing but remains robust amid resilient consumption.
China’s current account surplus has dissipated as import growth outpaced
export growth. Equities and the renminbi have been under pressure
throughout 2018. Bond spreads, which widened between late-2017 andearly-2018 on trade concerns, have been stable since mid-2018, reflecting
continued strong investor confidence. The PBoC has been easing
monetary policy, but keeping credit growth in check, by reducing shadow
financing. Prices of newly constructed residential buildings most notably inTier 1 cities, have rebounded following several years of correction.
A. Contributions to growth

B. Balance of payments

C. Exchange rates and equity prices

D. Bond spreads

E. Total loan growth

F. Housing prices

Outlook
Regional growth is expected to moderate from 6.3
percent in 2018 to 6 percent in 2019 and stay atthat pace in 2020—broadly unchanged from June
forecasts (Tables 2.1.1 and 2.1.2; Figure 2.1.3).
The outlook is predicated on broadly stablecommodity prices in the next two years, amoderation in global demand and trade, and a
gradual tightening of global financing conditions.
The structural slowdown in China is expected tocontinue. Growth is projected to slow from 6.5
percent in 2018 to 6.2 percent on average in
2019-20, and domestic and external rebalancing
are expected to endure. Authorities in China haveshifted to looser monetary and fiscal policies in
response to a more challenging external
environment, including heightened trade tensions.
They have cut reserve requirements, reduced taxes
and fees, increased export tax rebates, andaccelerated issuance of special purpose local
government bonds to bolster infrastructure
spending. Authorities have also reiterated theircommitment to growth stability and structural

Source: Bloomberg, Haver Analytics, J.P. Morgan, the National Bureau of Statistics of China, WorldBank.
C.D. “Taper T. 2013” refers to mid-2013 global financial market stress episode; “China SM 2015”refers to mid-2015 China stock market (SM) turbulence; “Episode 2018” refers to mid-2018 financial
market volatility episode. The start dates for the stress episodes are: Taper Tantrum: May 23, 2013;
China stock market turbulence: June 12, 2015; Episode 2018: April 15, 2018.
A. Investment refers to gross capital formation, which includes change in inventories. Data for 2018
are estimates.
B. Net capital flows and change in reserves are estimates. Data for 2018 are estimates.
C. Percent changes of exchange rates (U.S. dollar vs. Chinese renminbi) and equity prices in
Chinese renminbi over 247 days since the start dates of respective stress episodes. Orange lines
denote EAP minimum-maximum ranges. Green diamonds denote EMDE averages.
D. Change of bond spread over 247 days since the start dates of respective stress episodes. Bond
spread measures the average spread of a country’s sovereign debt (as measured by J.P. Morgan’s
Emerging Markets Bond Index) over their equivalent maturity U.S. Treasury bond. Orange lines
denote the EAP minimum-maximum ranges. Red diamonds denote EMDE averages.
E. Domestic and overseas loans. Data for 2018 is through November. Last observation is 2018Q3 fornominal GDP and November 2018 for total loan growth.
F. The National Bureau of Statistics of China surveys house prices in 70 cities and divides them into
three tiers. The first tier includes Shanghai, Beijing, Guangzhou, and Shenzhen. The second tierincludes 31 provincial capital and sub-provincial capital cities. The third tier includes 35 other cities.
Dotted lines indicate February 2011-November 2018 averages. Last observation is November 2018.
Click here to download data and charts.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 2.1.3 Outlook and risks

reform objectives in late-2018 (CEWC 2018).

EAP growth is projected to gradually decelerate, reflecting a structural
slowdown in China. Excluding China, growth is expected to remain stablein 2019-20. The region is characterized by deep regional and global
integration, which makes it vulnerable to external shocks. Domestic andexternal vulnerabilities would amplify the impact of external shocks,
especially where policy buffers are limited.

These policy steps are expected to largely offset thedirect negative impact of higher tariffs on China’s
exports, which would otherwise lower growth byabout 0.1-0.3 percentage point in 2019. In
addition, the authorities have stepped up theirstructural reform efforts to improve the businessenvironment, including for foreign firms, havelowered tariffs on imports, with the exception ofsome tariffs on U.S. imports in retaliation to U.S.
tariffs on China’s goods, and have strengthened
intellectual property protection.

A. Growth

C. Impact of 1 percentage point
decline in China’s growth on EAP

E. External debt

B. Global exposure by type of foreign
inflows, 2013-17

D. Change in growth and current
account balance net of FDI, 2010-18

F. Fiscal balance and public debt

Source: Haver Analytics, International Monetary Fund, World Bank.
A. Commodity importers ex. China include Cambodia, Fiji, Philippines, Solomon Islands, Thailand,
Vietnam, and Vanuatu. Commodity exporters include Indonesia, Lao PDR, Malaysia, Mongolia,
Myanmar, Papua New Guinea, and Timor-Leste. Yellow diamonds denote forecasts in the June 2018
edition of the Global Economic Prospects report. Aggregates growth rates are calculated using 2010
U.S. dollar GDP-weights. Data in shaded areas are forecasts.
B. EA = East Asia. PI = Pacific Islands. EA1 includes Brunei Darussalam, Cambodia, Malaysia,
Mongolia, Thailand and Vietnam; EA2 includes Indonesia, Lao PDR, Myanmar and Philippines. PI1
includes Kiribati, Marshall Islands, Micronesia, Timor-Leste, Tonga and Tuvalu; PI2 includes Palau
and Vanuatu; PI3 includes Fiji, Papua New Guinea, Samoa and Solomon Islands.
The linkages presented in this chart only present direct channels. Spillovers may propagate via
indirect channels such as global and regional value chains. Diamond denotes direct exposure to
China. Hyphen denotes share of commodity exports in GDP. Direct exposure to China and the shareof commodity exports in GDP is not shown for PI1 and PI2 country groups due to data limitations.
C. Median of posterior distribution. Estimates based on a Bayesian SVAR, using quarterly data for
1998Q1-2018Q1. The spillovers include the effects through indirect channels, including confidence
and global and regional value chains. Cumulative impact on growth after two years. GDP weighted.
D. CAB ex. FDI = Current Account Balance excluding Foreign Direct Investment. Orange hyphen
denotes GDP growth in 2010; green hyphen—CAB ex. FDI in 2010. Data for 2018 are estimates.
E. PNG = Papua New Guinea. External debt stock is defined as debt owed to nonresidents repayablein foreign currency, goods, or services. It is the sum of public, publicly guaranteed, and private
nonguaranteed debt. Data are in current U.S. dollars. Short-term debt includes all debt having an
original maturity of one year or less and interest in arrears on long-term debt. Diamond denotes shortterm external debt as share of GDP in 2018; hyphen—total external debt as share of GDP in 2010.
F. Diamond denotes estimated fiscal balance as share of GDP in 2018; hyphen denotes public debt
as share of GDP in 2010. The general government debt data for Mongolia is based on World Bank
staff estimates.
Click here to download data and charts.

Growth in the rest of the region is projected toremain broadly unchanged at around 5.2 percent
on average in 2019-20. Resilient domestic demandis expected to offset the negative impact of slowing
exports. Growth among commodity importers is
projected to moderate in 2019 as it converges with
its potential rate. Excluding China, the 2018
growth outlook for EAP commodity importers has
been downgraded because of a moderation in
private consumption amid rising inflation in thePhilippines.
Growth in commodity exporters is expected toremain broadly unchanged at about 5.1 percent in
2019, in line with potential, with significant
cross-country differences. This forecast is slightly
below that of June, reflecting a number ofdownward revisions (e.g., Lao PDR, Malaysia,
Myanmar). Output gaps in most commodity
exporting economies are expected to close over theforecast horizon, as investment growth stabilizes
and trade flows decelerate. For both commodity
exporters and importers, inflation pressures are
expected to intensify over the forecast horizon, inpart reflecting exchange rate pass-through as wellas domestic demand pressures.
Despite the projected robust activity in the region
in the near term, underlying potential growth—which has fallen considerably over the past
decade—is likely to decline further over thelong term. This reflects increasingly adverse
demographic patterns and a projected slowdown
in capital accumulation as credit growth isreined in.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Risks
Risks to the forecasts remain tilted to thedownside and have intensified. Heightened tradetensions involving large economies continue tocreate uncertainty about the future of establishedtrading relationships. A potential disruption of
trade would disproportionately affect the more
open economies in the region. The region ischaracterized by deep regional and global
integration, which makes it vulnerable to external
shocks. The region relies significantly on foreign
income, mostly from exports but also from returns
on foreign assets and direct investment. Total
exports and gross capital inflows exceed 50 percent
of GDP in more than two-thirds of the region’s
economies, and 100 percent in about one-third(Figure 2.1.3). In many regional economies, thecost of rising import tariffs may be magnified byparticipation in complex global value chains (e.g.,
Malaysia, Thailand, the Philippines; Chapter 1,
World Bank 2018a). Furthermore, the impact oftrade measures could be amplified regionally if it
also weighs on investor confidence and reduces
foreign direct investment (IMF 2018a; WorldBank 2018a).
China’s baseline projection assumes that the fiscal
and monetary policy stimulus that has been
introduced in response to rising U.S. tariffs willoffset the immediate economic impact of traderelated headwinds. However, if authorities opt for
additional and stronger fiscal and monetarystimulus initiatives—particularly in the form of
unfunded mandates for local governments toincrease public investment—the measures may
also run counter to the needed deleveraging andde-risking process. These measures, could also
undermine the efforts to contain credit growthand limit risks to corporate and bank balance
sheets (Chapter 1; World Bank 2018a).
A continued intensification of trade tensions,along with the previously introduced measures,
would affect close to all goods trade between
China and the United States. In the extreme casescenario, further escalation of trade tensions,coupled with a downturn in confidence andinvestment, could reduce global exports by up to 3
percent and global income by 1.7 percent over the

EAST ASIA AND PACIFIC

medium term. The largest decline (up to 3.5
percent of income) would be felt in China(Freund et al. 2018).1
A significant disruption to activity in China would
have large regional effects, propagating throughbilateral trade, regional supply chains, andfinancial linkages (Figure 2.1.3). A one-off,
unexpected 1 percentage-point drop in China’s
GDP growth would lower growth in the rest of
the region by 0.5 percentage points after two years(World Bank 2016a, 2018a). Growth spillovers
from China would be particularly large forcommodity exporters, particularly Mongolia, due
to their reliance on commodity exports to China,
and for Cambodia and the Pacific Islands, which
depend on China for tourism and FDI.
Risks of disorderly financial market developments
have also intensified (Chapter 1). A further
tightening of global financial conditions could betriggered by rising inflation, swelling fiscal deficits,
or contagion from financial stress in other
EMDEs, and could place further pressure on
regional exchange rates and asset prices. High debt
levels and external vulnerabilities among some
EAP countries could amplify the impact ofexternal shocks such as a sudden stop in capital
flows or a rise in borrowing costs. If a
combination of downside risks were to materialize,
it could trigger an even sharper slowdown inregional growth.

1 This scenario assumes a 25 percent tariff surcharge on all
products traded between China and the United States, combinedwith a decline in investor confidence, resulting in a 0.5 percentage
point drop in global investment to GDP (Freund et al. 2018).

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TABLE 2.1.1 East Asia and Pacific forecast summary
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE EAP, GDP1

2016

2017

2018e

2019f

2020f

2021f

6.3

6.6

6.3

6.0

6.0

5.8

20

2018e

2019f

2020f

0.0

-0.1

0.0

(Average including countries with full national accounts and balance of payments data only)2
6.3

6.6

6.3

6.0

6.0

5.8

0.0

-0.1

0.0

GDP per capita (U.S. dollars)

5.6

5.9

5.7

5.4

5.4

5.3

0.0

-0.1

-0.1

PPP GDP-0.1

EMDE EAP, GDP2

6.3

6.5

6.3

5.9

5.9

5.8

0.1

-0.2

Private consumption

6.8

6.5

7.7

7.4

7.1

7.2

0.7

0.6

0.1

Public consumption

9.3

8.5

7.6

7.3

7.1

7.1

0.0

-0.2

-0.3

Fixed investment

6.6

4.5

5.6

5.3

5.2

5.1

0.1

0.0

-0.3

Exports, GNFS3

3.3

9.4

4.8

4.7

4.4

4.3

-0.9

-1.3

-1.4

Imports, GNFS3

5.4

8.1

6.8

6.5

5.9

5.8

1.0

0.4

-0.5

Net exports, contribution to growth

-0.6

0.4

-0.6

-0.5

-0.5

-0.5

-0.6

-0.5

-0.3
-0.1

Memo items: GDP
East Asia excluding China

4.9

5.4

5.2

5.2

5.2

5.2

-0.2

-0.1

China

6.7

6.9

6.5

6.2

6.2

6.0

0.0

-0.1

0.0

Indonesia

5.0

5.1

5.2

5.2

5.3

5.3

0.0

-0.1

-0.1

Thailand

3.3

3.9

4.1

3.8

3.9

3.9

0.0

0.0

0.1

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars. 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 2018 projections)(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

Cambodia

6.9

7.0

7.1

6.8

6.8

6.7

0.2

0.1

0.2

China

6.7

6.9

6.5

6.2

6.2

6.0

0.0

-0.1

0.0

Fiji

0.4

3.8

3.5

3.4

3.3

3.3

0.0

0.0

0.0

Indonesia

5.0

5.1

5.2

5.2

5.3

5.3

0.0

-0.1

-0.1

Lao PDR

7.0

6.9

6.5

6.6

6.7

6.6

-0.1

-0.3

-0.2

Malaysia

4.2

5.9

4.7

4.7

4.6

4.6

-0.7

-0.4

-0.2

Mongolia

1.4

5.4

5.9

6.6

6.3

6.2

0.6

0.2

-0.2

Myanmar

5.9

6.8

6.2

6.5

6.6

6.8

-0.5

-0.4

-0.5

Papua New Guinea

2.6

2.8

0.3

5.1

3.1

3.4

2.0

1.1

0.1

Philippines

6.9

6.7

6.4

6.5

6.6

6.6

-0.3

-0.2

0.0

Solomon Islands

3.5

3.5

3.4

2.9

2.8

2.7

0.4

0.0

0.0

Thailand

3.3

3.9

4.1

3.8

3.9

3.9

0.0

0.0

0.1

Timor-Leste2

5.3

-4.7

0.8

3.3

4.9

5.0

-1.4

-0.9

0.9

Vietnam

6.2

6.8

6.8

6.6

6.5

6.5

0.0

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 at market prices and expenditure components are measured in constant 2010 U.S. dollars.
2. Non-oil GDP. Timor-Leste’s total GDP, including the oil economy, is roughly a double of its non-oil economy and is highly volatile as a result of sensitivity to changes in global oil prices
and local production levels.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

EAST ASIA AND PACIFIC

BOX 2.1.1 Informality in East Asia and Pacific
The share of informal output in East Asia and Pacific (EAP) region is below the EMDE average while the share of informal
employment is above average. Within the region, informality is particularly high in lower-income countries, which are alsocharacterized by a lack of diversification, large rural sectors, and weak institutions. Nonetheless, even higher-income economies
within the region have urban informality. This diversity within the region argues for tailored policy approaches to address
challenges associated with informality. Higher-income countries can prioritize urban planning and providing essential social
protection to informal workers. Lower-income countries can focus on policies that increase productivity, lower costs, and increase
the potential benefits of regulatory compliance.

Introduction
The share of informal output in East Asia and Pacific(EAP) is below the EMDE average.1 Nevertheless, despite
a downward trend over the past 30 years, informality
remains high in the lower-middle-income economies,
including Lao PDR, Myanmar, Cambodia. Higherincome countries in the region have made considerable
progress in integrating rural migrants into urban labormarkets, but face challenges related to urban informality,
particularly in providing access to public services andessential social protection.
Against this backdrop, this box examines the following
questions:•

How has informality evolved in East Asia and Pacific?

•

What have been the macroeconomic and social
implications of informality?

•

What policy options are available to address
challenges associated with informality?

Evolution of informality
In the EAP region, informal output accounted for about
30 percent of GDP on average in 2010-2016, slightly
below the EMDE median (Figure 2.1.1.1). However, at
47 percent of total employment, informal employment in
EAP was above the EMDE average during the same
period.2 About 73 percent of the labor force in EAP lacked
basic pension coverage during 2001-10.

Note: This box was prepared by Ekaterine Vashakmadze and Jinxin
Wu.
1 Informality is often defined as market-based legal production of
goods and services that are hidden from public authorities for monetary,
regulatory, and institutional reasons (Schneider, Buehn, and Montenegro2010). Informal output is measured as a percent of total output in official
GDP. In this box, informality is estimated based on the Dynamic
General Equilibrium (DGE) model used in Elgin and Oztunali (2012)(for more detailed discussion see Chapter 3 and Annex 3.1).
2 The most frequently used informal employment measure is the shareof self-employment in total employment, which represents a lower boundof informal employment (La Porta and Shleifer 2014). Self-employed

Informality in the EAP region has declined over the past
two decades (Chapter 3; Schneider, Buehn, andMontenegro 2010). The share of informal output declined
from 35 percent of official GDP to 27 percent between
1990-2000 and 2010-16—the fastest decline among
EMDE regions. Survey-based measures of informality alsosuggest a moderate decline in acceptance and perception of
informality.
The decline in informality has been accompanied bysustained growth, rapid industrialization, urbanization,and improvements in institutional quality (Loayza 2016;
World Bank 2015). A large number of informal, mainly
agricultural, workers in China have been successfully
integrated into the formal labor force mainly by absorbing
migrants into the urban labor market (World Bank
2014a). Total employment in China rose by about 250
million during 1990–2014, amid large-scale rural-to-urban
migrant flows (Lam, Liu, and Schipke 2015). Between
1990-2000 and 2010-16, the share of informal output
declined particularly rapidly in the fastest-growing
countries, in part reflecting the effect of comprehensive
reforms (Cambodia, Myanmar, Lao PDR). For example,
the informal share of output has fallen by 33 percentage
points in Myanmar (to below 30 percent in 2010-16)
following broad-based liberalization measures.
The region is characterized by significant cross-country
heterogeneity in terms of institutional and socio-economic
indicators (Figure 2.1.1.1). Per capita GDP levels vary
widely across EAP, and those economies with higher per
capita GDP generally have lower levels of informality(ILO 2018a; Loayza and Rigolini 2006). The share of
informal output in higher income countries is about 30
percentage points less than in lower-middle-income
countries (Lao PDR and Myanmar). The share of informal
employment is also about one-quarter that of lower-

workers are those workers who, working on their own account, with one
or a few partners, or in a cooperative, hold the type of jobs defined as"self-employment jobs" (for more detailed discussion see Chapter 3 andAnnex 3.1).

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BOX 2.1.1 Informality in East Asia and Pacific (continued)
FIGURE 2.1.1.1 Informality in East Asia and PacificCompared with other EMDE regions, East Asia and Pacific (EAP)’s share of informal output is moderate whereas its share of
informal employment is above average. Informality is particularly high in lower income countries, which are also characterized
by stringent labor regulations and lack of enforcement.
A. Informal economy as share of total
economic output

B. Share of labor force without pension;
share of self-employed

C. Perceived informal activities andattitudes towards informality

D. Informality by different measures

E. Cross country difference in informality

F. Institutional factors

Source: Elgin et al. (forthcoming), World Bank (Doing Business, World Development Indicators, World Governance Indicators), World Economic Forum, World Value
Survey.
Note: Blue bars show simple averages of the informal economy of the region. Red makers show the median of all EMDEs and the vertical lines denote interquartile rangeof all EMDEs.
A. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. The DGE model estimates the size of the informal sector as a percent
of official GDP (see Elgin and Oztunali 2012). The MIMIC model is a structural equations model that considers multiple causes of informal activity and captures multiple
outcome indicators of informal activity (see Schneider, Buehn, and Montenegro 2010). It also estimates the informal output as a percent of official GDP. DGE sample
includes 12 EAP economies and 122 EMDEs; MIMIC sample includes 14 EAP economies and 124 EMDEs.
B. Labor force without pension is presented as the share of the labor force that does not contribute to a retirement pension scheme, derived from data on pension
coverage obtained from WDI. Self-employed is the presented as the share of self-employment in total employment. Labor force without pension sample includes 8 EAP
economies and 103 EMDEs; self-employed sample includes 19 EAP economies and 134 EMDEs.
C. WEF = World Economic Forum. WVS = World Values Survey. WEF index is the average response at the country-year level to the question: “In your country, howmuch economic activity do you estimate to be undeclared or unregistered? (1 = Most economic activity is undeclared or unregistered; 7 = Most economic activity is
declared or registered).” WEF index is inverted; a higher average at the country level indicates a larger informal economy. The index does not use data for 2004–05 due
to inconsistency in survey methods. The WVS asks whether respondents can justify cheating on taxes (1 = never justifiable; 10 = always justifiable). The average
responses at the country-year level are used as a measure of attitude toward informality (or tax morality; Oviedo, Thomas, and Karakurum-Ozdemir 2009). WEF sample
includes 12 EAP economies and 114 EMDEs; WVS sample includes 6 EAP economies and 66 EMDEs.
D. Diamonds represent the average level of EAP region; bars denote the range of EAP countries in each measure.
E. The upper bound of bar indicates the share of informal employment in total employment. The lower bound indicates the share of informal output in official GDP basedon the Dynamic General Equilibrium (DGE) model. For Malaysia, the level of informal output is higher than the level of informal employment.
F. All measures are taken from the latest year available. The first three institutional measures are taken from World Bank’s World Governance Indicators (World Bank
2018e), with a higher value indicating better institutional quality in year 2016. The “ease of doing business” and “ease of paying taxes” are taken from World Bank’s Doing
Business database (World Bank 2018f) and measured as distance to frontier, with a higher value indicating a more favorable business environment. Sample includes 22
EAP economies and 149 EMDEs. An economy’s distance to frontier is reflected on a scale from 0 to 100, where 0 represents the lowest performance and 100 represents
the frontier.
Click here to download data and charts.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

EAST ASIA AND PACIFIC

BOX 2.1.1 Informality in East Asia and Pacific (continued)
middle income economies.3 Informality is most pervasivein Lao PDR and Myanmar, at around 60-80 percent of
total employment. Indonesia, Mongolia, and Vietnam
have below average informal output shares, but theirinformal employment shares are above the EAP average(ADB 2010; Handayani 2016).

Drivers and implications of informality
Informality has been attributed to several drivers. These
included large agricultural sectors, rapid urbanization, lowhuman capital, and overly burdensome regulations.
Size of agricultural sectors. People living in rural areas arealmost twice as likely to be in informal employment as
those in urban areas, and agriculture is the sector with thehighest share of informal employment (ILO 2018a). The
agricultural sector still accounts for about 30 percent of
employment in EAP on average, and these shares are
particularly high in Lao PDR, Myanmar, and Vietnam(ADB 2010; Figure 2.1.2). Informal workers constitutethe vast majority of employment in the agriculture sector
in Cambodia and Thailand, in part because highcompliance costs discourage formal-sector activity ofagricultural small enterprises (ILO 2018a).
Urbanization. Rapid urbanization in EAP has supported
large-scale rural-to-urban migration, stimulated growth,
productivity, and formal and informal job creation (Ghani
and Kanbur 2013). The urbanization process has
coincided with the rapid structural transformation ofChina and other fast-growing East Asian economies andthe shift of activity from agriculture to manufacturing andservices (McMillan, Rodrik, and Sepulveda 2017; Rodrik2015). In general, a larger non-agricultural sector isassociated with a smaller informal sector, and informality
in manufacturing is significantly lower than in services(Atesagaoglu, Bayram, and Elgin 2017). Although thegrowth of urban areas provides opportunities for many,urban expansion, if not well planned, can also contributeto rising urban informality and policy challenges. In
China, for example, unequal access to public services
between citizens with urban household registrations(hukou) and those without, although diminishing, has led
to unregistered urban households that lack essential social
protection (Park, Wu, and Du 2012; World Bank 2014a).

Underinvestment in human capital. In EAP, investment
in human capital and higher levels of educational
attainment have increased labor productivity and havebeen closely associated with a smaller share of the informal
economy (Figure 2.1.1.2; ILO 2018a; Moscoso-Boedo
and D’Erasmo 2012). Workers with higher educationlevels are also more likely to be formally employed. This isalso evident in cross-country comparisons. For example, inIndonesia, the results of the 2009 Informal Sector Survey(ISS) in Yogyakarta and Banten suggest that persons whoare informally employed tended to have lower levels of
education than those with formal jobs (ADB 2010).
Malaysia is among the countries with the highest
educational attainment and the lowest share of informal
employment (25 percent). In contrast, Lao PDR,
Myanmar and Cambodia are characterized by low
educational outcomes and high informality.
Enterprise sector characteristics. In China and Vietnam,
informal economies arose amid economic reforms thatbegan in the 1970s and allowed the emergence of a private
economy in the form of unregulated micro-enterprises,family enterprises, and individual entrepreneurs (Park,Wu, and Du 2012). The informal economy comprises
more than 90 percent of micro and small enterprises
worldwide (ILO 2018b). In EAP, informal workers tend
to be employed in small, low-productivity firms. For
example, in Indonesia, most informal firms are very small(micro) firms with less than five employees. These firms
tend to be less productive than larger firms and pay lower
wages. Their operations tend to be local, predominantly
supplying local markets, with little desire for expansion(Rothenberg et al. 2015).
Taxes and labor regulations. Informality is also a
consequence of higher tax burdens, stringent labor
regulations, limited enforcement capacity, and poor
governance (World Bank 2014a). In EAP, informality ishigher in lower-income countries with markedly weakerinstitutional quality, cumbersome rules and procedures,and pervasive lack of awareness or adequate enforcement(Lao PDR, Myanmar; Figure 2.1.1.2). Within Malaysia,
the Philippines, Thailand, and Vietnam, informality has
been associated with more rigid business regulations andineffective law enforcement (Loayza and Rigolini 2006).
Informality has been associated with a number of adverse
economic outcomes. These include urban poverty,
household vulnerability to shocks and lower productivity.

3 Although the commonly observed link between income growth andinformality generally holds in the EAP region, informality is neverthelessrelatively high in Thailand despite its higher income status (Hassan andSchneider 2016).

Urban poverty and income inequality. EAP is the world’s
most rapidly urbanizing region, with an average annual
urbanization rate of 3 percent (World Bank 2017a). The

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BOX 2.1.1 Informality in East Asia and Pacific (continued)
FIGURE 2.1.1.2 Drivers and implications of informality in East Asia and PacificBetter institutions and business environments, industrialization, and rapid urbanization are associated with low informality inhigher-income economies. Countries with a high share of informality have higher income inequality and lower levels of
educational attainment.

A. Informality and institutions

B. Institutional factors in countries withhigh and low informality

C. Employment in agriculture

D. Urban population as percent of total
population

E. Year of total schooling

F. Human capital index

Source: Barro-Lee (2013), Elgin et al. (forthcoming), World Bank (Doing Business, World Development Indicators, World Governance Indicators).
A. Higher MIC = China, Malaysia, and Thailand; Middle MICs = Indonesia, Mongolia, and the Philippines. Lower MICs = Cambodia, Lao PDR, Myanmar, and Vietnam..
The grouping of countries is based on GDP per capita.
B. All measures are taken from the latest year available. The first three institutional measures are taken from World Bank’s World Governance Indicators (2017), with ahigher value indicating better institutional quality in year 2016. Error bars reflect all EAP countries.
C.-F. CHN = China, IDN = Indonesia, KHM = Cambodia, LAO = Lao PDR, MMR = Myanmar, MNG = Mongolia, MYS = Malaysia, PHL = Philippines, THA = Thailand,
VNM = Vietnam.
C. The vertical and horizonal lines denote EMDE averages.
D. Latest data available is 2014.
E. Data are from Barro-Lee (2013). Average years of total schooling is the average years of education completed among people over age 15.
F. 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 child born today relative to the benchmark of full health and complete education. The vertical and horizonal lines denote EMDE averages.
Click here to download data and charts.

rapid growth of cities has created challenges that include
the lack of affordable housing, resulting in increasingslums, poor provision of basic services, and widening
inequality for urban dwellers. EAP hosts the world’s largest
slum population, many of them informally employed:around 35 percent of urban population (250 million
people) live in slums. In Indonesia, 27 percent of theurban population do not have access to improved
sanitation facilities (WHO and UNICEF 2015), followedby 21 percent in the Philippines (USAID 2017). The
cities with the highest numbers of urban poor are in

China, Indonesia, and the Philippines, while the highest
urban poverty rates are in the Pacific Island countries ofPapua New Guinea, Timor-Leste, Vanuatu, and inIndonesia and Lao PDR (World Bank 2016b, World Bank
2017a).4
In China, the exceptional scale of rural to urban migration
amplifies the challenges from informality. Many of these
4 Approximately 75 million people in EAP region live below the
US$3.10/day poverty line.

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EAST ASIA AND PACIFIC

BOX 2.1.1 Informality in East Asia and Pacific (continued)
workers—approximately 120-150 million— are migrant
workers who are not registered to work in cities, andtherefore lack a number of formal protections (Jutting and
Xenogiani 2007, Huang 2009). These urban migrants gaina large wage premium by migrating; yet both rural andurban migrants tend to work in informal jobs and lackadequate social protection (Gagnon, Xenogiani, and Xing2011). In Thailand, informally employed workers
systematically present lower earnings at all earnings levels,and the difference increases with level of earnings (ILO
2015).
Household vulnerability to shocks. Informality may
impose significant economic risk and result inunderinvestment in human capital of current and future
generations (Oviedo, Thomas, and Karakurum-Ozdemir2009). It is characterized by a lack of adequate social
protection coverage, which increases household
vulnerability to shocks. For middle and lower income
countries in EAP region, pension coverage is extremely low(Figure 2.1.1.2). In China, formal casual workers reportlower monetary and subjective well-being than employees
and business owners (Liang, Appleton, and Song 2016).
Low productivity. Countries characterized by larger
informal sectors are associated with lower shares of skilledworkers and weaker total factor productivity. At the firmlevel, entering and operating in the formal sector is costly,
but provides firms with better access to technologies,
skilled workers, and access to capital (Figure 2.1.1.2;
D’Erasmo, Moscoso Boedo, and Senkal 2014). Thereexists a sharp productivity difference between firms of thesame size in the formal and informal sector whenmeasured in terms of value added per employee, withformal firms being, on average, 30 percent moreproductive (Fajnzylber, Maloney, and Montes-Rojas 2011;
La Porta and Shleifer 2014; Monteiro and Assuncao 2012;
Perry et al. 2007). Despite a well-documented gap
between the performance of formal and informal firms,
less is known about how the allocation of low-productivity
firms in the informal sector affects productivity over time.
If by operating informally firms are able to cut costs andstay more productive, then a shift from the informal to theformal sector will not necessarily lead to an increase inproductivity. Indeed, some recent studies find evidence
that a shift into the formal sector does not necessarily leadto an increase in productivity for firms (De Mel,McKenzie,
andWoodruff
2013;
Demenet,Razafindrakoto, and Roubaud 2016; McKenzie and Sakho2010). Overall, while individual motivations to become orstay informal may differ, the aggregate outcome can becharacterized as low scale output and low productivity.

Policy challenges
A tailored approach can help address the challenges
associated with informality (OECD 2015; World Bank
2014a). Higher-income countries can prioritize providing
essential social protection to informal workers; lowerincome countries can focus on reforms to increase firm
and worker productivity.
Essential social protection. In higher-income countries,
essential social protection coverage can be expanded toshield informal workers from adverse shocks (Olivier,Masabo, and Kalula 2012). This would imply higher
public expenditure on social protection to extend at leastbasic social protection coverage to all (ILO 2017).
Reforms to improve urban planning. Urban planning canhelp improve access to jobs, affordable housing,commercial services, public transportation, and health andeducation services to ensure equal opportunity fordisadvantaged communities (World Bank 2015; Judy andGadgil 2017). Examples of effective metropolitan
governance include Beijing, Jakarta, Kuala Lumpur, Metro
Manila Developments Authority (MMDA) and Shanghai(World Bank and DRCSC 2014; World Bank 2015).
Reforms to increase firm productivity. Agglomeration
benefits can lower the unit costs of public service
provision, enabling governments to extend access to basic
services to more people (Ghani and Kanbur 2013; WorldBank 2014a, 2018g). Policies to support small agricultural
enterprises, which engage a large share of EAP’s workforce,and other micro, small- or medium-sized enterprises
include improving access to services, decreasing red tapeand corruption, facilitating access to financial services, andoffering better education and training (OECD 2009;
World Bank 2018h).
Remove disincentives to formal employment. Removing
disincentives to formal employment could encourage ashift of informal workers into formal employment. Reform
options include lower registration costs; shorter
registration procedures; streamlined registration services,for example, through information and communication
technologies; lower compliance costs by introducing
simplified tax assessment and payment regimes; improved
access to financial services; and improved access totraining, skills development, and business development
services (ILO 2016). As small firms have different
motivations to stay small and informal, measures to lower
cost and increase the potential benefits of regulatorycompliance can be combined with a more effective
enforcement regime.

67Regional growth is estimated to have decelerated to an estimated 3.1 percent in 2018 and is projected to further
slow to 2.3 percent this year, mainly because of weakness in Turkey. Regional growth is expected to pick up
modestly in 2020-21, as a gradual recovery in Turkey offsets moderating activity in Central Europe. The mainrisks to the region are weaker-than-expected investment due to heightened policy uncertainty, and a renewal of
financial pressure in Turkey combined with possible contagion to the rest of the region.

Recent developments
Activity in Europe and Central Asia (ECA) isestimated to have slowed to 3.1 percent in 2018from 4 percent in 2017, reflecting the marked
weakness in activity in Turkey in the second halfof the year. Excluding Turkey, regional growth
remained unchanged at an estimated 2.9 percent
in 2018, as slowing activity in countries in thewestern part of the region, such as Bulgaria andRomania, offset an acceleration in the eastern partof the region that benefitted from higher oil prices(Figure 2.2.1). Regional trade growth declined
during 2018.
In Turkey, the lira declined around 30 percent
over the course of 2018, reflecting capital outflows
in response to accelerating inflation, a perceived
delay in monetary tightening, and rising private
sector debt. The country accumulated a sizable
current account deficit and a large foreign
currency-denominated debt load, leaving it
vulnerable to shifting investor sentiment andcurrency depreciation. Output shrank by 1.1
percent from the second quarter to the third
quarter amid plummeting consumer confidence
and credit scarcity. Despite this contraction,strong growth in the first half of the year willbring Turkish growth to an estimated 3.5 percent
for 2018.
Note: This section was prepared by Yoki Okawa. Research
assistance was provided by Zhuo Chen and Mengyi Li.

Growth among the Central European economies
slowed in 2018. Softening exports and labor
shortages restrained growth in Bulgaria, Croatia,
and Romania. In contrast, despite labor shortages,
growth in Poland accelerated slightly because ofstrong consumption and investment. Robust
domestic demand supported activity in theWestern Balkans, except for Montenegro. In theFormer Yugoslav Republic of Macedonia, growth
rebounded in 2018 as the formation of a new
government ended a prolonged political crisis andimproved investor sentiment (World Bank 2018i).
The Russian Federation and other oil exporters
in Central Asia maintained steady growth in 2018,
supported by a rise in oil prices. Althougheconomic sanctions tightened, Russia experienced
relatively low and stable inflation and increased
oil production. As a result of robust domestic
activity, the Russian economy expanded at a 1.6
percent pace in the year just ended (World Bank
2018j). Higher-than-expected production in the
Kashagan oil field and strong domestic demand
supported growth in Kazakhstan. A stabilization in
the financial sector and higher oil prices
contributed to a slow recovery of growth in
Azerbaijan in 2018.
The stance of fiscal policy in the region varies.
Turkey has committed to tight fiscal policy to help
curb high inflation and currency depreciation.
Romania’s fiscal stance is mixed, with income tax
reductions and increased public sector benefits
offset by an increase in social contribution

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 2.2.1 ECA: Recent developments
Regional growth is estimated to have slowed in 2018 reflecting financial
stress in Turkey and weak regional trade. Financial stress in Turkey, which
experienced a sharp depreciation and an increase in bond spreads, does
not appear to have spilled over to other countries in the region. Slowing
inflation in the eastern ECA region led to loosening in monetary policy,
while a pickup in inflation from 2016 level in the western ECA has not yet
led to monetary tightening. The fiscal stance in the region is mixed.

A. Contribution to regional growth

B. Trade

C. Currency movements in Turkey

D. EMBI spreads

E. Inflation

F. Monetary and fiscal policy

Source: Haver Analytics, World Bank.
A. Aggregates growth rates calculated using constant 2010 U.S. dollar GDP weights.
B. Three-month moving averages of GDP-weighted trade volume indexes for Russia, Turkey,
Poland, Ukraine, Kazakhstan, Hungary, and Armenia.
C. Cumulative change of exchange rate for 400 days from the starting date. Starting dates are
February 1, 2001, May 2, 2013 and May 2, 2018 for 2001 crisis, Taper Tantrum, and 2018 crisis,
respectively. Last observation for the 2018 crisis is December 19, 2018.
E. Last observation is November for each year.
F. Monetary policy tightening/loosening is defined as increase/decrease of the central bank’s policy
rate between January and November 2018. Fiscal policy tightening/loosening is defined as increase/decrease of primary balance in estimated 2018 values compared to 2017.
Click here to download data and charts.

revenue. Fiscal policy has become more procyclicalin some Central European countries. In theeastern part of the region, the Russian government
has implemented a new fiscal rule and is estimatedto have recorded its first surplus since 2012 in2018. As fiscal stimulus measures are phased out,
Kazakhstan has started to tighten its fiscal stance,
resulting in improvements in its non-oil fiscal
balance. Azerbaijan continues to rely on fiscal
measures to support its economy.
For the majority of ECA countries, monetary
policy is either stable or loosening. At the end of
2018, nine countries have policy rates lower thana year ago, while three countries have higher
policy rates (Romania, Ukraine, Turkey). Inflation
peaked at 25 percent in Turkey in October,significantly above the 5 percent target amid an
overheating economy in the first half of 2018 andcurrency depreciation in the second. To ward off
inflationary and currency pressures, Turkey’s
central bank increased the average cost of fundingby more than 10 percentage points over the courseof 2018. In Central Europe, tightening labormarkets and increasing energy prices have pushed
inflation up toward target, with monetary policy
remaining stable in most countries. One exception
is Romania, where robust domestic demand
pushed inflation above the upper bound of thetarget band, prompting monetary policy
tightening. Gradually accelerating inflation has
also led to policy tightening in Ukraine. In theWestern Balkans, Albania, FYR Macedonia, andSerbia have lowered policy rates amid stable andbelow-target inflation. For oil exporters, such as
Azerbaijan and Kazakhstan, the stabilization ofcurrency following the 2014-16 oil price plunge
has resulted in lower inflation and looser monetary
policy. In Russia, monetary policy was tightenedin late 2018 amid pressures on the currency.

Outlook
The lingering effects of financial stress in Turkey
are expected to further slow of regional growth in
2019. Growth is expected to slide to 2.3 percent,before recovering to 2.7 percent in 2020 (Figure
2.2.2). Excluding Turkey, regional growth isexpected to average 2.6 percent during the forecasthorizon, compared to 2.9 percent in 2018, with a
gradual deceleration in Central Europe. Thisoutlook is predicated on an orderly tightening of
global financial conditions, oil prices averaging$67 in 2019-2021, a gradual slowdown in theG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Euro Area, and the absence of heightened
geopolitical tensions.
While the outlook for Turkey is subject toconsiderable uncertainty, the country is expected
to be weighted down by high inflation, highinterest rates, and low confidence, which will
dampen consumption and investment. Turkish
growth is expected to slow to 1.6 percent in 2019and begin to recover by 2020 through a gradualimprovement in domestic demand and continued
strength in net exports. However, this outlook
assumes that fiscal and monetary policy
successfully avert further sharp falls in the liraand, that corporate debt restructurings help avert
serious damage to the financial system. A
comprehensive stabilization package withconsistent policy framework, clear milestones, andeffective communication would help reduce risks
and support recovery.
Spillovers from Turkey to the rest of the region
are expected to remain modest, as trade andfinancial linkages are relatively limited. On the
trade side, Azerbaijan has the largest exposure, as
9 percent of its exports are directed to Turkey.
Financial linkages are also small—only Georgia
receives meaningful amounts of FDI fromTurkey, and foreign bank ownership of Turkishassets is limited in scale.
Growth in western ECA, excluding Turkey, isprojected to gradually slow toward potential,
driven by a slowdown in Central European
economies. Domestic demand in this sub-region
will be constrained by tight labor markets, while acontinued slowdown in the Euro Area will limit
export growth. Poland is expected to slow from
5.0 percent in 2018 to 4.0 percent in 2019, asEuro Area growth slows.
Growth in eastern ECA is forecast to slow
in 2019, as the large economies including Russia.
Kazakhstan and Ukraine decelerate. The VAT
in Russia is expected to rise from 18 to 20 percent
in 2019, weighing on near term growth.
Kazakhstan’s economy is also expected todecelerate as oil production growth levels off
and fiscal consolidation efforts continue (WorldBank 2018k).

E URO PE AND CE NTRAL AS IA

71

FIGURE 2.2.2 ECA: Outlook and risks
Regional growth is expected to slow notably in 2019 and gradually
accelerate in 2020-21, partly reflecting a sharp decline and subsequent
recovery in Turkish growth. A number of countries in the region appear
vulnerable to shifts in investor sentiment, as reflected by their high current
account deficits and corporate debt.
A. Actual and potential growth

B. Growth forecast

C. Trade openness

D. Current accountE. Corporate debt

F. Exchange rate risk in debt

Source: Haver Analytics, Institute of International Finance, International Monetary Fund, World Bank.
A. Blue bars refer to GDP-weighted average actual growth and vertical orange line show
minimum-maximum range of potential growth estimates based of five different methodologies(production function approach, multivariate filter, IMF World Economic Outlook five-year-ahead
forecast, Consensus Forecasts, and potential growth estimates in OECD Economic Outlook andOECD Long-Term Baseline Projections).
A. B. Data in shaded area are forecasts.
C. Share of exports as a percentage of GDP in 2016. 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.
D. Current account balance as a percentage of GDP.
E-F. The data used are IIF end-of-period estimates of non-financial corporate debt as a percentage
of GDP.
Click here to download data and charts.

Risks
While there are some upside risks to the forecasts—for example, that stronger-than-expected energy
prices may support activity in Russia and other

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CHAPTER 2.2

energy exporters—the balance of risks isincreasingly tilted down. The most important
downside risk is the possibility that the recent
financial stress in Turkey worsens and triggers
widespread bank failures. Turkish corporations
carry significant debt, much of which isdenominated in or linked to foreign currencies.
Although many corporations are hedged against
exchange rate risks, and corporate debt
restructuring is on its way, falling domestic
demand and forex exposure of the non-tradable
sector pose risks. Currency depreciation and highinterest rates could push corporate borrowers into
bankruptcy and depleting banks’ capital buffers.
Renewed pressure in currency markets andincreased uncertainty about the policy framework
would increase the probability of a deepening
crisis, implying a longer and more severe
slowdown than currently forecast for Turkey(World Bank forthcoming). While direct linkages
between Turkey and the rest of the region aresmall, an intensification of financial stress inTurkey or other EMDEs could also lead investors
to re-evaluate their exposure in the region, which
in turn could lead to capital outflows, currency
depreciations, and rising borrowing costs.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

The potential for financial stress is more elevatedin countries with domestic vulnerabilities like
Romania and Belarus, which have large current
account deficits or large foreign-currency
denominated debt. Public debt, which remains
high despite recent declines, and private
borrowing in foreign currencies makes Central
European countries vulnerable to financialpressure. Public debt has also been trending up inCentral Asia and the Western Balkans.
Increases in policy uncertainty could undermine
confidence in the region and impact growth. A
slowdown or reversal of ongoing structuralreforms remains a risk in many countries in theregion, especially in Armenia, Azerbaijan, Belarus,
Ukraine, and Turkey. Tension concerning Syria or
Ukraine could trigger new sanctions. Policy
disagreements between the European Union andsome Central European countries could deterinternational investors and reduce fiscal transfers.
An escalation of trade restrictions between the
United States and the Euro Area could have anegative impact on western ECA countries, as theEuro Area is the largest trading partner for all
countries in the sub-region.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 201973

E URO PE AND CE NTRAL AS IA

TABLE 2.2.1 Europe and Central Asia forecast summary
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

EMDE ECA, GDP

1.7

4.0

3.1

2.3

2.7

2.9

-0.1

-0.8

-0.3

EMDE ECA, GDP excl. Turkey

1.2

2.9

2.9

2.6

2.6

2.5

0.1

-0.2

-0.1

1

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

2

1.6

4.0

3.0

2.3

2.7

2.9

-0.2

-0.8

-0.3

GDP per capita (U.S. dollars)

1.2

3.6

2.7

2.0

2.4

2.7

-0.1

-0.8

-0.3

PPP GDP

1.6

3.9

3.0

2.3

2.7

2.9

-0.2

-0.8

-0.3

EMDE ECA, GDP2

Private consumption

1.2

4.8

3.0

2.4

3.2

2.9

-0.1

-0.8

0.1

Public consumption

2.9

2.1

1.9

2.5

2.2

2.1

0.4

1.1

0.9

Fixed investment

0.0

6.3

0.3

2.3

4.6

4.8

-4.9

-2.5

-0.1

Exports, GNFS3

3.4

6.9

5.5

5.3

4.3

4.5

0.7

0.6

-0.4

Imports, GNFS

3.2

10.4

2.8

5.1

5.8

5.8

-2.7

-0.4

0.6

0.2

-0.7

1.1

0.3

-0.2

-0.2

1.1

0.3

-0.2

Commodity exporters4

0.3

2.0

2.1

2.0

2.2

2.3

0.1

-0.3

-0.1

Commodity importers5

3.1

6.0

4.0

2.6

3.2

3.6

-0.3

-1.2

-0.5

Central Europe

3.4

4.9

4.5

3.6

3.3

3.0

0.3

-0.1

-0.2

Western Balkans7

3.0

2.5

3.5

3.5

3.8

3.8

0.3

0.1

0.0

3

Net exports, contribution to growthMemo items: GDP

6

8

Eastern Europe

0.8

2.6

3.5

2.9

3.1

3.4

0.2

-0.7

-0.4

South Caucasus9

-1.6

2.0

2.5

4.0

3.8

3.4

-0.1

0.0

0.1

Central Asia

3.3

4.8

4.4

4.2

4.0

4.1

0.0

0.0

0.0

Russia

-0.2

1.5

1.6

1.5

1.8

1.8

0.1

-0.3

0.0

10

Turkey

3.2

7.4

3.5

1.6

3.0

4.2

-1.0

-2.4

-1.0

Poland

3.1

4.8

5.0

4.0

3.6

3.3

0.8

0.3

0.1

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars.
2. Sub-region aggregate excludes Bosnia and Herzegovina, 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, FYR Macedonia, Moldova, Montenegro, Poland, Romania, Serbia, and Turkey.
6. Includes Bulgaria, Croatia, Hungary, Poland, and Romania.
7. Includes Albania, Bosnia and Herzegovina, Kosovo, FYR Macedonia, Montenegro, 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.

74

CHAPTER 2.2

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

TABLE 2.2.2 Europe and Central Asia country forecasts1
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

Albania

3.4

3.8

4.0

3.6

3.5

3.5

0.4

0.1

0.0

Armenia

0.2

7.5

5.3

4.3

4.6

4.6

1.2

0.3

0.6

Azerbaijan

-3.1

0.1

1.1

3.6

3.3

2.7

-0.7

-0.2

0.1

Belarus

-2.5

2.4

3.4

2.7

2.5

2.5

0.5

0.0

0.0

Bosnia and Herzegovina2

3.1

3.0

3.2

3.4

3.9

4.0

0.0

0.0

-0.1

Bulgaria

3.9

3.8

3.3

3.1

3.0

2.8

-0.5

-0.5

-0.6

Croatia

3.5

2.9

2.7

2.8

2.8

2.6

0.1

0.1

0.0

Georgia

2.8

4.8

5.3

5.0

5.0

5.0

0.8

0.2

0.0

Hungary

2.3

4.1

4.6

3.2

2.8

2.4

0.5

0.0

-0.2

Kazakhstan

1.1

4.1

3.8

3.5

3.2

3.2

0.1

0.2

0.4

Kosovo

4.1

4.2

4.2

4.5

4.5

4.5

-0.6

-0.3

-0.3

Kyrgyz Republic

4.3

4.6

3.1

3.4

3.9

4.0

-1.1

-1.4

-1.1

Macedonia, FYR

2.8

0.2

2.5

2.9

3.2

3.3

0.2

0.2

0.2

Moldova

4.5

4.5

4.8

3.8

3.5

3.2

1.0

0.1

0.0

Montenegro

2.9

4.7

3.8

2.8

2.5

2.5

1.0

0.3

0.4

Poland

3.1

4.8

5.0

4.0

3.6

3.3

0.8

0.3

0.1

Romania

4.8

6.9

4.1

3.5

3.1

2.8

-1.0

-1.0

-1.0

Russia

-0.2

1.5

1.6

1.5

1.8

1.8

0.1

-0.3

0.0
0.0

Serbia

2.8

1.9

3.5

3.5

4.0

4.0

0.5

0.0

Tajikistan

6.9

7.1

6.0

6.0

6.0

6.0

-0.1

0.0

0.0

Turkey

3.2

7.4

3.5

1.6

3.0

4.2

-1.0

-2.4

-1.0

Turkmenistan

6.2

6.5

6.2

5.6

5.1

4.9

-0.1

-0.7

-1.2

Ukraine

2.3

2.5

3.5

2.9

3.4

3.8

0.0

-1.1

-0.6

Uzbekistan

7.8

5.3

5.0

5.1

5.5

6.0

0.0

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 at market prices and expenditure components are measured in constant 2010 U.S. dollars, unless indicated otherwise.
2. GDP growth rate at constant prices is based on production approach.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

E URO PE AND CE NTRAL AS IA

BOX 2.2.1 Informality in Europe and Central Asia
The share of informal output in Europe and Central Asia (ECA) is larger than the EMDE average, even after a decline fromelevated 1995 levels, but informality in the labor market is below average and there is wide heterogeneity within the region.
Informality in ECA has been associated with weak institutions, sizeable agricultural sectors, and large-scale migration as well as
low productivity, fiscal revenue losses, and poor job prospects for youth. In some ECA countries, declines in informality haveaccompanied the simplification of tax systems and labor market reforms, as well as reforms to reduce corruption.

Introduction
Informal output accounts for a larger share of official GDP(36 percent) in Europe and Central Asia (ECA) than in
the average EMDE (Figure 2.2.1.1).1 However, despite awidely shared history of transition from centrally planned
to market economies, there is significant variation in
informality within the region, ranging from 22 percent to56 percent.
Against this backdrop, this box examines the following
questions.
•

How has informality evolved in Europe and Central
Asia?

•

What have been the macroeconomic and social
correlates of informality?

•

What policy options are available to address
challenges associated with informality?

in Central Europe still received “envelope wages” asrecently as 2006, and the informal economy accounted for
10 percentage points of GDP more than in the moreadvanced EU19 economies in 1999-2007 (Fialová andSchneider 2011).3 In the eastern part of the region, thedecline in informality has been considerably lesspronounced, in part reflecting slower implementation ofmarket liberalizing and other reforms, as well as
persistently higher levels of corruption (Kaufmann and
Kaliberda 1996).
Drivers of informality. Informality in ECA economies has
typically been attributed to three factors:•

Agriculture. Higher labor market informality has been
associated with a larger share of workers in the
agricultural sector as they tend to be self-employed(Figure 2.2.1.2; Rutkowski 2006; World Bank 2011).
A larger agricultural sector has also been correlatedwith greater informality in non-agricultural sectors(Atesagaoglu, Bayram, and Elgin 2017).

•

Remittances. In countries with large diasporas,
informal activity has been higher among workers inhouseholds that receive sizeable remittances(Chatterjee and Turnovsky 2018; Shapiro andMandelman 2016). In Kazakhstan, FYR Macedonia,
Moldova, Serbia, Tajikistan, and Ukraine, remittances
provided the capital to establish small businesses,
which tend to be informal, and the income support
needed to accept less secure but often more lucrative
informal work (Ivlevs 2016).

•

Institutions. Institutional quality varies widely within
the region. The east has considerably weakerinstitutional quality indicators than the west, which
implemented substantial reforms in the context of the
EU accession process (Figure 2.2.1.2; Kaufmann and
Kaliberda 1996).4 In general, a favorable business

Evolution and drivers of informality
Evolution of informality. With the collapse of centrally
planned economies in the late 1980s, many firms chose tooperate in the informal sector to avoid burdensome
regulations, taxation, or corruption. Estimates based onelectricity consumption suggest that the average size of theinformal economy more than doubled during 1989-95(Johnson, Kaufmann, and Shleifer 1997). While
informality declined in most countries once they began torecover, there was considerable heterogeneity acrosscountries. In the western part of the region, where
institutions are stronger, informality has declined steeply.2
Notwithstanding this decline, one in ten formal employees

Note: This section was prepared by Yoki Okawa. Research assistance
was provided by Zhuo Chen and Mengyi Li.
1 The methodology of informality estimates is discussed in Chapter 3.
2 The western part of the region includes Central Europe (Bulgaria,
Croatia, Hungary, Poland and Romania) and the Western Balkans(Albania, Bosnia and Herzegovina, Kosovo, the Former Yugoslav
Republic of Macedonia, Montenegro, and Serbia), and Turkey. The
eastern part of the region comprises Eastern Europe (Belarus, Moldova,
and Ukraine), South Caucasus (Armenia, Azerbaijan and Georgia),
Central Asia (Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan,and Uzbekistan) and Russia.

3 “Envelope wages” refers to the practice of paying a portion of wages
in undeclared cash to avoid tax and social contributions (see, for example,
Horodnic 2016, and Williams and Padmore 2013).
4 Institutional indicators include the World Bank’s Doing BusinessIndicators and World Governance Indicators of government effectiveness,
control of corruption, or rule of law.

75

76

CHAPTER 2.2

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 2.2.1 Informality in Europe and Central Asia (continued)
FIGURE 2.2.1.1 Informality in Europe and Central Asia
The share of informal output in the ECA region is higher than the EMDE median throughout the sample period, and it declined
at the roughly same pace as in the other EMDE regions. However, employment informality is low, in part reflecting a low shareof agriculture in some countries in the region. Institutional quality is on par with other regions, albeit with considerable heterogeneity within the region.
A. Share of informal economy in output

B. Share of labor force without pension;
share of self-employed

C. Institutional quality

Source: Elgin et al. (forthcoming), World Bank.
Note: Blue bars show simple averages of the informal economy of the region. Red markers show the median average of all EMDEs and the vertical lines denote
interquartile range of all EMDEs.
A. Both DGE and MIMIC estimates measure the informal output in percent of official GDP.
B. Labor force without pension is the fraction of the labor force that doesn’t contribute to a retirement pension scheme, which is derived from the original data on pension
coverage obtained from WDI. Self-employed is the share of self-employment in total employment.
C. All measures are taken from the latest year available. The first three institutional measures are taken from World Bank’s World Governance Indicators (2017), with ahigher value indicating better institutional quality in year 2016. The “Ease of doing business” (DB 2018) and “Ease of paying taxes” (DB 2017) are taken from World Bank’s
Doing Business database and measured as “Distance to Frontier”, with a higher value indicating an easier environment for businesses.
Click here to download data and charts.

environment encourages firms to do business in theformal sector (Chapter 3). However, the transition
from economies dominated by large state-owned
enterprises to more private-business friendly
economies sometimes created more informal
employment and larger informal sectors (Earle andSakova 2000).

Correlates of informality
Firm productivity. Some country-specific studies suggestthat informal firms tend to be less productive than formal
firms. In Turkey, for example, after controlling for firm
characteristics, informal firms in the manufacturing andservices sectors had 16 percent and 38 percent lower totalfactor productivity than formal firms, respectively, withthe productivity gap attributed to restricted access topublic services and formal markets (Taymaz 2009). By
these estimates, shifting all informal firms in the Turkish
manufacturing and services sectors into the formal sector
could raise total output by 5 percent and 25 percent,respectively (Taymaz 2009). In Kyrgyz Republic,
productivity in the informal sector has declined
significantly since 2009, despite robust productivity

growth in the formal sector (Sattar, Keller, and Baibagsy
Uulu 2015).
Fiscal revenues. Large informal sectors erode tax revenues
and hamper governments’ ability to provide public goods.
However, the magnitude of foregone revenues due toinformality remains a matter of debate. One estimate
suggests that tax revenue losses from informality could
have been as high as 7 percent of GDP in Central Asia andthe Caucasus in 2004 (Grigorian and Davoodi 2007).
However, estimates based on micro survey data suggest
only modest potential revenues gains (0.03-0.07
percentage points of GDP) from turning informal workers
into formal workers in a country such as Ukraine in 2009,as newly formalized are mainly low-skilled and subject to
low tax rates (World Bank 2011).
Labor market prospects. Informal employment is morecommon among young, low-skilled, and female workers.
Some studies suggest that informal employment candamage long-term carrier prospects and entrench income
differentials (Taymaz 2009; World Bank 2007, 2011).
However, informal employment can also be an income
source when formal employment opportunities are scarce,

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

E URO PE AND CE NTRAL AS IA

BOX 2.2.1 Informality in Europe and Central Asia (continued)
as well as help develop human capital that can lead toformal employment or self-employment, as has been foundfor Turkey and Russia(Guariglia and Kim 2006; Taymaz2009).5 Better-paid informal activity may also encourageskilled professionals to forgo migration opportunities inhighly regulated economies with large emigration, such as
Tajikistan (Abdulloev, Gang, and Landon-Lane 2011).
Inequality. In some countries, the low wages paid to
informal workers (the “wage penalty”) compared withformal workers have contributed to inequality. In Serbia,
the wage penalty contributed to rising inequality between
2002 and 2007 (Krstic and Sanfey 2010). A similar wage
penalty in Turkey was found for less educated workers(Taymaz 2009). However, in some cases informal workers
have been found to earn a wage premium, e.g., in Russia,
Romania, Tajikistan, and Ukraine (Lehmann andNorberto 2018; Shehu and Nilsson 2014; Staneva andArabsheibani 2014; Zahariev 2003). In those countries,
the informal wage premium may compensate for the lackof social security and lower job security (Lehmann andNorberto 2018; Marcouiller, de Castrilla and Woodruff
1997).6(Fialová and Schneider 2011; Lehmann and Muravyev
2009).
Fiscal policy. Several countries have changed tax rates ortax enforcement, but the impact on informality has varied.
That said, reducing the tax compliance burden andsubsidizing the transition to formal sectors have typically
been accompanied by declines in informality.7•

Flat tax. A flat labor income tax rate has been
introduced in several ECA countries (e.g., Bulgaria,
Poland, Russia, and Romania). The flat tax reform in
Russia was followed by a decline in informal
employment and informal activity, especially in the
top income bracket (Slonimczyk 2012). A simulationsuggests that the Polish flat tax reform in 2004 could
have led to a 48 percent increase in reported businessincome and 25 percent higher tax revenue, despite alower average marginal tax rate (Kopczuk 2012).
However, flat tax structures can be regressive and need
to be balanced with poverty fighting initiatives.

•

Preferential tax schemes. Certain preferential tax
schemes for the self-employed and small firms canencourage movement away from the informal sector.
One such scheme, indirect assessments of tax
liabilities, has been shown to encourage
entrepreneurship, help revenue collection from hardto-tax sectors, and ease the transition from informal toformal work. However, such preferential schemes canalso encourage formal workers to pursue thepreferential status and may encourage firms to remain
small (Packard et al. 2014).

•

Shift from labor to other taxation. Shifting from labor
income taxes, which constitute a wedge between
informal and formal employment, to less distortingand more easily enforced taxes, such as value-added
taxes and progressive real estate taxes, can shrink theinformal economy (Packard , Koettl, and Montenegro2012).

•

Subsidies. A formal employment subsidy, such as theone introduced in Turkey, can increase the number ofregistered jobs by encouraging informal workers to

Policy challenges
The impact of policies on informality can depend on
country characteristics such as labor market flexibility,
efficiency of tax collection or control of corruption. This
underscores the importance of ensuring that reform efforts
are carefully tailored to country circumstances to avoid
unintended increases in informality.
Labor market policies. The impact of labor market
reforms on informality has been mixed in ECA, andappears to have depended on the types of the reform. In across-sectional study of ECA countries, more restrictive
employment protection legislation has been associatedwith a higher share of the informal economy (both interms of GDP and labor force; Fialová 2011; Lehmann
and Muravyev 2009). In contrast, there was no robust
association of informality with more generous
unemployment benefits or higher minimum wages

5 This is consistent with the finding that informally employed youth
have lower job satisfaction relative to their peers with formal jobs (Shehu
and Nilsson 2014).
6 Controlling for worker characteristics and selection bias, the absenceof male-female wage differentials in the informal economy—in thepresence of large differentials in the formal economy—has beeninterpreted as sign of lesser gender discrimination in the informal
economy than in the formal economy in Turkey (Tansel 2000).

7 On the one hand, higher labor tax rates encourage a move of labor
into untaxed informal employment, especially for low-wage earners(Koettl and Weber 2012). On the other hand, higher labor tax rates havein some cases been associated with a lower share of informal employment,
because higher revenue allow governments to provide better public goods
that can only be accessed in formal employment (Fialová and Schneider
2011, Friedman et al. 2000 ).

77

78

CHAPTER 2.2

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 2.2.1 Informality in Europe and Central Asia (continued)
FIGURE 2.2.1.2 Correlates of informality in Europe and Central Asia
Informality as a percentage of GDP in the eastern part of the region is higher than the western part of the region, in partreflecting differences in institutional quality. Employment informality tends to be higher in countries with larger agricultural
sectors.
A. Informality in output

B. Institutional quality

C. Labor market informality andagricultural employment

Source: Elgin et al. (forthcoming), European Bank of Reconstruction and Development, World Bank.
A-B. Data are from the latest year available, usually 2016. The western part of the region includes Central Europe (Bulgaria, Croatia, Hungary, Poland and Romania) andthe Western Balkans (Albania, Bosnia and Herzegovina, Kosovo, the Former Yugoslav Republic of Macedonia, Montenegro, and Serbia), and Turkey. The eastern part of
the region comprises Eastern Europe (Belarus, Moldova, and Ukraine), South Caucasus (Armenia, Azerbaijan and Georgia), Central Asia (Kazakhstan, Kyrgyz Republic,
Tajikistan, Turkmenistan, and Uzbekistan) and Russia.
A. Orange diamonds indicate subsample average and blue bars indicate one standard deviation range.
C. Agricultural employment and self-employment are shares of employment in agriculture or share of self-employed in total employment.
Click here to download data and charts.

transition to formal employment as well as provide
better social protection (Betcherman, Daysal, Pagés
2010).
Control of corruption. Better governance and more
effective tax authorities can reduce the size of the informal
economy and increase tax revenue. Bureaucratic

corruption has been associated with greater informal
activity in Poland, Romania, and Slovakia (Johnson et al.
2000). Conversely, better control of corruption has
reduced the extent of informal activities in the countries
that joined the European Union in the mid-2000s (Fialová
and Schneider 2011).

Growth in Latin America and the Caribbean was disappointingly weak in 2018, at an estimated 0.6 percent,and notably lower than previously expected. This reflected the impact of Argentina’s currency crisis, a truckers’
strike and policy uncertainty in Brazil, and worsening conditions in Venezuela. Growth is expected to pick up
to 1.7 percent in 2019, as growth accelerates in Brazil and the recession in Argentina begins to fade. Per capita
growth in LAC is projected to pick up moderately, and to outpace that in advanced economies starting in 2020,
after six years of stalled convergence. Downside risks continue to dominate. Key external risks include further
tightening of external financial conditions and additional escalation of international trade policy uncertainty.
The region also faces intraregional and domestic risks, such as spillovers from larger-than-expected growth
contractions in Argentina and Venezuela and the persistent threat of natural disasters and extreme weather.

Recent developments
Growth in Latin America and the Caribbean(LAC) stalled at a subdued 0.6 percent in 2018,
substantially weaker than previously projected.
The disappointing growth outcome reflectedsoftening global trade growth and tighter externalfinancing conditions. Developments in Argentina,
Brazil, and Venezuela hindered regional growth,
despite better performance in several mid-size
economies (e.g., Chile, Colombia, Peru). Growthmoderated in Central America, reflecting a variety
of factors, while it strengthened in almost all
Caribbean economies as the subregion began torecover from a severe 2017 hurricane season.
In Brazil, growth bounced back in the second halfof 2018, following a strike-induced dip aroundmid-year, but remains subdued. In Argentina, thecurrency crisis and associated sharp tightening ofmonetary and fiscal policies, together with theeffect of a severe drought on the agriculture sector,
resulted in a contraction in activity. Venezuela’s
economic collapse has deepened, and there is no
indication that the latest redenomination of thecurrency has had a major impact on ongoing
hyperinflationary dynamics.
Note: This section was prepared by Dana Vorisek. Research
assistance was provided by Brent Harrison.

Commodity price developments are also affecting
LAC economies. The decline in copper prices inthe second half of 2018 contributed to slowing
growth momentum in Chile and Peru, after a anacceleration in the first half. Rising oil prices
underpinned accelerating growth in oil-producing
Colombia, while they were one factor that
inhibited growth in oil-importing Central America
in 2018, despite the decline in prices at the end of
the year. The Central American sub-region was
also affected by weak confidence in Costa Rica
and Panama, political uncertainty in Guatemala,and social unrest in Nicaragua.
A long-awaited rebound in regional fixed
investment that began in 2017 was significantly
weaker in 2018 than previously expected, afterlosing momentum in the first half of the year
(Figure 2.3.1). Export growth in the region was
also lower than expected, owing to the drought inArgentina and slowing global trade growth.
Nearly all LAC economies with floating exchange
rates have experienced nominal depreciation
against the U.S. dollar, particularly Argentina,
Brazil, Chile, and Uruguay. The adjustment in
effective terms has been more modest. In most ofthese
economies,
especially
Argentina,
depreciation is contributing to a rise in inflation.
Recent interest-rate hikes (e.g., in Chile) were80

CHAPTER 2.3

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 2.3.1 LAC: Recent developments
Investment and export momentum in LAC have slowed. Rising U.S. interest
rates and weakening investor sentiment toward EMDEs has translated into
diminished capital inflows and rising bond spreads and credit default swap
spreads in LAC, while a strengthening U.S. dollar is putting upward
pressure on inflation in some countries. Fiscal deficits narrowed in most
LAC countries in 2018, mainly reflecting higher revenues, but debt
continues to build.
A. Investment and export growth

B. Exchange rates against the U.S.
dollar

C. Inflation

D. Bond spreads

E. Gross capital inflows

F. Fiscal balances and government
debt

Sources: Bloomberg, CPB Netherlands Bureau for Economic Policy Analysis, Dealogic, Haver
Analytics, International Monetary Fund, World Bank.
A. Investment growth is the GDP-weighted average of 15 economies, excluding Venezuela, that
represent 93 percent of regional GDP. Investment for 2018Q3 is estimated using actual data for
economies representing 87 percent of regional GDP. Last observation is 2018Q3.
B. Last observation is December 19, 2018.
C. Lines show group averages. Above average and below average groups are delineated according
to currency depreciation against the U.S. dollar between January 2, 2018 and November 1, 2018.
Sample includes 17 economies, excluding Argentina and Venezuela, and excluding those withconventional currency pegs and currency boards and those using the U.S. dollar as their official
currency. Last inflation observation is November 2018.
D. LAC line shows median of 15 economies. Last observation is December 19, 2018.
E. Last observation is November 2018.
F. Sample includes 32 economies.
Click here to download data and charts.

made partly in reaction to exchange rate passthrough to domestic inflation, despite falling passthrough ratios observed over the long term (Ha,Stocker, and Yilmazkuday 2019). Central banks inseveral countries have intervened in foreignexchange markets using derivative instruments to
reduce currency volatility (e.g., Brazil, Uruguay)or to build reserves (e.g., Colombia).
External financing conditions have tightened.
Against the backdrop of rising U.S. interest rates,
U.S. dollar appreciation, and weaker investor
sentiment toward EMDEs, the region has
experienced a generalized rise in bond and credit
default swap spreads and a fall in equity prices.
Capital inflows, particularly bond flows, steadily
diminished through the third quarter of 2018.
Current account deficits have widened in mostcommodity-exporting and commodity-importing
economies. Several Caribbean economies thatwere not significantly damaged by hurricanes in
2017, however, registered narrowing deficits or
widening surpluses as a share of GDP in 2018 onstrong tourism inflows and rising oil prices (e.g.,
The Bahamas, Belize, St. Vincent and the
Grenadines, and Trinidad and Tobago).
Fiscal conditions across the region remain fragile,and government debt continues to build. Fiscal
deficits narrowed slightly in most countries in
2018, however. The improvement mainlyreflected higher revenues, in part stemming fromrising prices of key commodities. The fiscal
austerity program in Argentina will be challenging
to implement but should improve long-term fiscal
sustainability, while a recently legislated fiscal
reform in Costa Rica will boost revenues andshould improve investor sentiment. In Colombia,
a proposed tax reform would boost revenues inorder to comply with fiscal targets. A proposed tax
reform in Chile would integrate and streamline
the tax system.

Outlook
Regional growth is projected to advance to a still
modest 1.7 percent in 2019, lower than previously
projected, and build to 2.5 percent in 2021
(Figure 2.3.2). The acceleration will be supported
mainly by a pickup in private consumption.
Investment growth will accelerate, though at a
slower pace this year than previously expected, inview of tight financing conditions and plannedG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

public spending reductions in a number ofcountries. Decelerating global trade will limit
export growth during the forecast period.
Although the prices of key non-oil commodities
such as soybeans and copper are projected tocontinue rising through the forecast period,
copper prices will increase at a much slower pacethrough 2021 than in 2017 and 2018. Oil prices
are projected to be flat, on average, during 201921, at $67 per barrel, potentially limiting fiscal
and export revenue increases in oil-producing
economies.
In Brazil, growth is expected to steadily build
momentum in 2019, from a weak base. The
forecast of 2.2 percent for this year assumes that
fiscal reforms are implemented expeditiously
under the incoming administration, and that arecovery of consumption and investment,
resulting from improving confidence and investor
sentiment, will outweigh the negative growtheffect of reduced government spending. In
Mexico, policy uncertainty and the prospect ofstill subdued investment is expected to keep
growth at a moderate 2.0 percent in 2019, despite
the decrease in trade-related uncertainty following
the announcement of the United States-MexicoCanada Agreement. Argentina’s economy isexpected to continue contracting in 2019 as deepfiscal consolidation results in a loss of employment
and reduction in consumption and investment,
and as high interest rates place corporate balance
sheets under stress and dampen private
investment.
By 2020, a strengthening recovery in Brazil,
modestly accelerating growth in Mexico, and solid
performance in Chile, Colombia, and Peru, areexpected to help push regional growth to 2.4
percent, consistent with potential. Per capita GDP
growth in the region is also expected to acceleratemoderately, and to outpace per capita growth inadvanced economies starting in 2020, after six
years of stalled convergence.
Achieving sustained improvements in potential
growth in the region over the medium term will
require implementing reforms in several areas.
There is need to improve infrastructure and

LATIN AME RIC A AN D THE C ARIBBE AN

81

FIGURE 2.3.2 LAC: Outlook and risks
Growth in LAC is projected to accelerate only moderately through 2021,
and at a slower pace than previously expected. Risks to the regional
outlook are predominantly to the downside. Further tightening of global
financing conditions and escalation of trade tensions among major
economies are key external risks. The region also faces intraregional anddomestic risks, such as spillovers from a larger-than-expected growth
contraction in Argentina or a worsening collapse in Venezuela, andunexpected disruptions from natural disasters and extreme weather.
A. Growth

B. Commodity prices

C. Debt

D. Current account deficit less FDI

E. Exposure to Argentina, 2017

F. Elections

Sources: Bank for International Settlements, Comtrade, Haver Analytics, International Monetary
Fund, World Bank.
B. Lines show change in nominal prices.
C. Bars show data for 2007Q4, 2012Q4, and 2018Q2.
F. Chart shows GDP of LAC countries holding presidential or parliamentary elections in a given year
as a share of regional GDP. An economy is counted only once when both types of elections occur ina single year.
Click here to download data and charts.

education attainment, reduce labor market
inflexibility, deepen trade integration, and address
the negative economic and social outcomes of
informality, among other challenges (World Bank
2018l; Chapter 3; Box 2.3).

82

CHAPTER 2.3

Risks
Risks to the regional outlook remain tilted to thedownside. The experience of Argentina in 2018 isa stark reminder of the risk of sudden andwidespread shifts in investor sentiment.
Tightening global financing conditions are aparticular concern for countries with large current
account deficits or reliance on volatile capital
inflows (e.g., Argentina, Bolivia, and several
Caribbean countries), with high external debt
loads (e.g., Jamaica, Nicaragua, Venezuela), orwith sizable foreign-currency-denominated debt asa share of GDP (e.g., Costa Rica, Honduras,
Nicaragua).
Trade tensions are another key external risk.
Although trade diversion in response to risingtrade restrictions in the United States and Canada
may benefit some LAC economies in the shortterm, continued trade tensions may dampen
regional growth in the medium term throughexport, confidence, and commodity market
channels.
LAC economies also face intraregional anddomestic sources of risk. Thus far, the recession in
Argentina has had limited spillovers on the rest of
the region. But a larger-than-expected contraction
in Argentina could spill over to the rest of theregion through trade and financial flows. Bolivia
and Paraguay are most reliant on Argentina as adestination for goods exports and a source ofremittance inflows. Although Uruguay has
diversified its trading partners in recent years, itremains reliant on Argentina for services export
revenues through tourism. Cross-border banklending data for Latin American economies ispatchy but suggests that Panama is most exposed,
although with bank claims on Argentina still
limited at approximately 0.6 percent of domestic
GDP.
Continued outward migration from Venezuela is
producing spillovers elsewhere in the region. In
Colombia, the cost of providing basic public
services to migrants and Colombian returnees atlevels similar to those delivered to the local
population is an estimated 0.2–0.4 percent of
GDP per year in the short term (World Bank

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 20192018m).1 However, in the medium and long term,
inward migration to Colombia could result in a
growth boost as a result of a larger labor supply
and higher consumption and investment.
Poor fiscal conditions and slow progress inaddressing of fiscal imbalances are downside risks,and may have negative repercussions for debt
sustainability and market confidence. In
Argentina, for instance, adherence to the fiscal
consolidation plan is key to a quick emergencefrom the recent currency crisis. Plans to
implement fiscal reform in other countries (e.g.,
Costa Rica) need to be carried out to retain
investor confidence. In Brazil, the newadministration needs to urgently make plans to
reduce fiscal vulnerabilities arising from anunsustainable pension system.
Election-relatedrisks,
which
generatedconsiderable uncertainty in countries such as
Brazil and Mexico in 2018, are expected to recede,
given that the elections scheduled in the next two
years are in economies representing a much lower
share of regional GDP. However, it will beincumbent on some new governments toimplement challenging policy reforms.
Unexpected disruptions related to natural disasters
and extreme weather represent a significant
ongoing risk. Hurricanes, floods, droughts, andearthquakes have long had detrimental impacts on
growth in several economies in the region inrecent years. The region remains highly vulnerable
to such events, underscoring the need to use risk
instruments such as catastrophe bonds anddomestic and multi-country catastrophe risk
insurance funds (Végh et al. 2018).

1 Calculations of the cost of public services are made using thenumber of migrants and returnees in Colombia as of September
2018.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

83

LATIN AME RIC A AN D THE C ARIBBE AN

TABLE 2.3.1 Latin America and the Caribbean forecast summary
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE LAC, GDP1

2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

-1.5

0.8

0.6

1.7

2.4

2.5

-1.1

-0.6

-0.1

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

2

-1.4

0.8

0.6

1.7

2.4

2.5

-1.1

-0.6

-0.1

GDP per capita (U.S. dollars)

-2.5

-0.2

-0.4

0.7

1.4

1.5

-1.1

-0.6

-0.1

PPP GDP

-0.8

1.2

0.9

1.8

2.5

2.6

-1.0

-0.6

-0.1

EMDE LAC, GDP2

Private consumption

-1.6

1.6

0.5

1.8

2.7

2.8

-1.6

-0.7

0.0

Public consumption

0.1

-0.7

0.1

-0.1

0.1

0.4

0.3

-0.3

-0.6

Fixed investment

-7.0

-0.6

1.4

2.1

4.8

4.6

-2.3

-1.9

0.2

Exports, GNFS

3

1.2

2.4

3.3

4.0

3.6

3.6

0.1

0.3

-0.3

Imports, GNFS3

-3.1

5.2

2.7

3.7

4.8

4.9

-1.6

-0.4

0.2

Net exports, contribution to growth

0.9

-0.6

0.2

0.1

-0.2

-0.3

0.4

0.2

0.0

Memo items: GDP
South America4

-3.1

0.3

-0.1

1.4

2.3

2.4

-1.4

-0.7

0.0

Central America5

3.9

3.8

2.7

3.4

3.5

3.6

-1.0

-0.5

-0.4
0.2

Caribbean6

4.4

3.4

4.4

4.0

4.0

3.8

0.9

0.5

Brazil

-3.3

1.1

1.2

2.2

2.4

2.4

-1.2

-0.3

0.0

Mexico

2.9

2.1

2.1

2.0

2.4

2.4

-0.2

-0.5

-0.3

Argentina

-1.8

2.9

-2.8

-1.7

2.7

3.1

-4.5

-3.5

-0.1

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars.
2. Aggregate includes all countries in notes 4, 5, and 6, and Mexico, except those for which data limitations prevent the forecasting of demand-side GDP components: Dominica,
Grenada, Guyana, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago.
3. Exports and imports of goods and non-factor services (GNFS).
4. Includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela.
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, Suriname, St. Kitts and Nevis, St. Lucia, St. Vincent
and the Grenadines, and Trinidad and Tobago.
Click here to download data.

84

CHAPTER 2.3

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

TABLE 2.3.2 Latin America and the Caribbean country forecasts1
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

Argentina

-1.8

2.9

-2.8

-1.7

2.7

3.1

-4.5

-3.5

-0.1

Belize

-0.5

1.2

1.5

1.9

1.7

1.7

-0.5

0.0

0.0

Bolivia

4.3

4.2

4.5

4.3

3.8

3.4

0.6

0.7

0.4

Brazil

-3.3

1.1

1.2

2.2

2.4

2.4

-1.2

-0.3

0.0

Chile

1.3

1.5

3.9

3.5

3.3

3.2

0.6

0.1

-0.2

Colombia

2.0

1.8

2.7

3.3

3.7

3.6

0.0

0.0

0.1

Costa Rica

4.2

3.3

2.7

2.7

2.8

3.0

-0.7

-0.9

-0.8

Dominican Republic

6.6

4.6

5.8

5.1

5.0

4.8

0.8

0.4

0.4

Ecuador

-1.2

2.4

1.0

0.7

0.7

1.2

-1.2

-0.8

-0.2

El Salvador

2.6

2.3

2.8

2.5

2.4

2.4

0.5

0.3

0.2

Grenada

3.7

5.1

5.2

4.2

2.8

2.8

1.9

1.4

0.0

Guatemala

3.1

2.8

2.7

2.9

3.0

3.1

-0.4

-0.4

-0.3

Guyana

2.6

2.1

3.4

4.6

30.0

24.8

-0.4

0.8

1.0

Haiti

2

1.5

1.2

1.6

2.3

2.4

2.5

-0.2

-0.1

0.0

Honduras

3.8

4.8

3.6

3.8

3.8

3.7

0.1

0.2

0.0

Jamaica

1.4

1.0

1.7

1.8

2.0

2.0

0.0

-0.1

0.0

Mexico

2.9

2.1

2.1

2.0

2.4

2.4

-0.2

-0.5

-0.3

Nicaragua

4.7

4.9

-3.8

-0.5

2.6

3.6

-8.5

-5.0

-1.8

Panama

5.0

5.3

4.0

6.0

5.4

5.2

-1.6

0.4

-0.2

Paraguay

4.3

5.0

4.0

3.9

4.0

4.0

-0.3

-0.3

-0.2

Peru

4.0

2.5

3.9

3.8

3.8

3.7

0.4

0.0

0.0

St. Lucia

3.4

3.8

1.5

2.7

2.8

2.3

-1.3

0.4

0.5

St. Vincent and the Grenadines

1.3

0.5

1.2

1.6

1.6

2.0

-0.9

-0.9

-1.1

Suriname

-5.6

1.7

1.4

1.6

1.8

1.9

0.3

-0.1

-0.3

Trinidad and Tobago

-6.1

-2.6

1.0

0.9

1.2

1.2

-0.6

-1.0

0.0

Uruguay

1.7

2.7

2.1

2.1

2.3

2.5

-1.2

-1.0

-0.6

Venezuela

-16.5

-14.5

-18.0

-8.0

-5.0

-4.0

-3.7

-1.0

-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 at market prices and expenditure components are measured in constant 2010 U.S. dollars.
2. GDP is based on fiscal year, which runs from October to September of next year.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

LATIN AME RIC A AN D THE C ARIBBE AN

BOX 2.3.1 Informality in Latin America and the Caribbean
Informal sector output in Latin America and the Caribbean, equivalent to about one-third of GDP, is slightly higher than in the
median emerging market and developing economy, despite a steady decline during recent decades. Roughly six out of ten of those
employed in the region are employed informally. Informality has been associated with lower growth, weaker productivity, andhigher levels of inequality. Policies to reduce payroll taxes and increase labor inspections have been found to reduce informality.

Introduction
Informality in Latin America and the Caribbean (LAC)
during the past decade was slightly higher than in the
median emerging market and developing economy(EMDE), whether measured in terms of informal output
or the share of self-employment (Figure 2.3.1.1; Box 3.2).
Yet there is substantial heterogeneity in the incidence of
informality within the region. Informality tends to behigher in countries with poorer institutional environments.
Against this backdrop, this box addresses the following
questions:•

How has informality evolved in Latin America andthe Caribbean?

•

What have been the macroeconomic and social
correlates of informality?

•

What policy options are available to address
challenges associated with informality?

Evolution and drivers of informality
Moderate informality. On average, the informal economy
in LAC was equivalent to 34 percent of official GDP in
2016, slightly above the median EMDE.1 Informal
employment averaged 62 percent of total employment in2016 (slightly below the EMDE median), while 38
percent of those employed were self-employed. Within theregion, informality varies considerably.
Regional heterogeneity. The amount of output generated
by the informal sector (output informality) ranged from
16 percent of GDP in Chile, in line with rates observed inadvanced economies, to 56 percent in Bolivia. Haiti also
has very high informality, at 61 percent of GDP.2 Surveybased measures of labor informality show a similarly wide
range. For Caribbean countries with available data, self-

Note: This box was prepared by Dana Vorisek. Research assistance wasprovided by Brent Harrison and Jinxin Wu.
1 Output informality based on DGE estimates of Elgin and Oztunali(2014), unless otherwise specified.
2 For lack of data on DGE estimates, this figure refers to MIMIC
estimates (Chapter 3). DGE and MIMIC estimates are similar at thecountry level.

employment as a share of formal employment tends to bevery low: 12 percent in Suriname (2014), 14 percent in
The Bahamas (2011), and 17 percent in Barbados (2013).
Again, Bolivia appears at the top end of the spectrum, with
self-employment equivalent to 64 percent of formal
employment in 2015. In most countries, labor informality
is higher than output informality, although Brazil,
Guatemala, and several Caribbean countries are
exceptions.
Trend decline in output informality. Output informality
in the region has steadily declined since the early 2000s(Figure 2.3.1.2). Several of the countries with the highest
incidence of output informality (e.g., Bolivia, Panama,Peru) have also experienced some of the largest declines
during the past two decades, in part due to rapid formal
job creation in the context of strong output growth. Yet
even where labor informality has fallen, the decline did notnecessarily affect all workers equally. In Argentina andBrazil, two of the largest economies in LAC, middle-aged
men, the highly skilled, and those working full time werethe most likely to shift from informal to formal
employment during the 2000s (Maurizio 2015).
Moreover, the decline in output informality has not always
been accompanied by a similar decline in labor
informality, which has been persistently high in countries
such as Bolivia, Colombia, Honduras, Jamaica, Nicaragua,
and Peru.

Correlates of informality
Informality has been associated with weak institutions andbusiness climates as well as poor macroeconomic,
microeconomic, and social outcomes in LAC. These
include lower output and productivity growth, weaker
financial resilience of households, and greater poverty.
Weak governance and business climates. Most of theinstitutional factors associated with informality are at orslightly above the EMDE average in LAC. However, LAC
economies with below-average institutional quality havealso tended to be those with high informality. For
instance, Peru’s higher labor informality compared to
3 Dougherty and Escobar (2013); Estevão and de Carvalho Filho(2012); Loayza (1997); Loayza, Servén, and Sugawara (2010); Vuletin(2008).

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BOX 2.3.1 Informality in Latin America and the Caribbean (continued)

FIGURE 2.3.1.1 Informality in Latin America and the Caribbean
Output-based informality in LAC has fallen since the 1990s, on average, yet remains above the median in EMDEs. Employment-based informality in the region has risen slightly, to about the EMDE median. The key institutional factors that are often
associated with informality, other than the difficulty of paying taxes, are slightly better in LAC than in all EMDEs.

A. Informal activity as share of total
economic output

B. Share of self-employed; perceived
informal activity

C. Institutional quality

Sources: Elgin et al. (forthcoming), Eurostat; Haver Analytics, Inter-American Development Bank, national statistical bureaus and offices, Organisation for Economic Cooperation and Development, World Bank (Doing Business, World Development Indicators, and World Governance Indicators).
A.-C. Blue bars show simple averages of economies in the region. Red markers show the median of all EMDEs. Vertical lines denote interquartile range of all EMDEs.
A. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. The DGE model estimates the size of the informal sector as a percent ofofficial GDP (see Elgin and Oztunali 2012). The MIMIC model is a structural equations model that considers multiple causes of informal activity and captures multiple
outcome indicators of informal activity (see Schneider, Buehn, and Montenegro 2010). It also estimates the informal output as a percent of official GDP. DGE sample
includes 26 LAC economies and 122 EMDEs; MIMIC sample includes 25 LAC economies and 124 EMDEs.
B. Self-employed is the presented as the share of self-employment in total employment. WEF = World Economic Forum. WEF index is the average response at thecountry-year level to the question: “In your country, how much economic activity do you estimate to be undeclared or unregistered? (1 = Most economic activity is
undeclared or unregistered; 7 = Most economic activity is declared or registered).” WEF index is inverted; a higher average at the country level indicates a larger informal
economy. The index does not use data for 2004–05 due to inconsistency in survey methods. The WVS asks whether respondents can justify cheating on taxes (1 = neverjustifiable; 10 = always justifiable). The average responses at the country-year level are used as a measure of attitude toward informality (or tax morality; Oviedo, Thomas,
and Karakurum-Ozdemir 2009). Self-employed sample includes 32 LAC economies and 134 EMDEs; WEF sample includes 25 LAC economies and 114 EMDEs.
C. All measures are taken from the latest year available. The first three institutional measures are taken from World Bank’s World Governance Indicators (2017), with ahigher value indicating better institutional quality in 2016. The “ease of doing business” and “ease of paying taxes” are taken from World Bank’s Doing Business databaseand measured as distance to frontier, with a higher value indicating a more favorable business environment. Sample includes 32 LAC economies and 149 EMDEs.
Click here to download data and charts.

Chile has been mostly attributed to poor governance(Loayza and Wada 2010a). One of the most common
explanations for informality in LAC countries has been
restrictive business and labor regulations, which discouragefirms from entering the formal sector.3
High tax burdens. High tax rates or burdensome tax
regulations have also encouraged informality in the region(Loayza 1997; Ordóñez 2014; Vuletin 2008). Bothcorporate and personal income tax rates tend to be higher
in LAC than in the average EMDE—indeed, LAC is the
only EMDE region where the average personal income tax
rate has risen since the early 2000s.
Trade liberalization amid inflexible labor markets. Someinstances of trade liberalization have also been associatedwith rising informality in LAC. The reduction of tradebarriers in the 1980s and 1990s led to fears that domestic
firms in the formal sector would be rendered
uncompetitive and shift to the informal sector. In Brazil,

the association between trade liberalization and informality
was ambiguous in the early literature (Bosch, GoñiPacchioni, and Maloney 2012; Goldberg and Pavcnik
2003; Menezes-Filho and Muendler 2011). However,recent research has established that trade liberalization was
followed by increased informality in Brazil, though only inthe long run (Dix-Carneiro and Kovak 2017; DixCarneiro et al. 2018). In Colombia, trade liberalization
was associated with slightly higher informality, yet only
prior to a subsequent reform that increased labor market
flexibility (Goldberg and Pavcnik 2003).
Sectoral and worker characteristics. Informality has beenshown to be higher in the presence of large agricultural
sectors. Other structural factors, such as poor educationand skills, have also been identified as underlying reasonsfor labor informality (Fernandez and Villar 2016). In some
LAC countries, a considerable share of people working
informally entered the informal sector voluntarily.
Switching between the formal and informal sectors has

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

LATIN AME RIC A AN D THE C ARIBBE AN

BOX 2.3.1 Informality in Latin America and the Caribbean (continued)
FIGURE 2.3.1.2 Evolution and correlates of informality in Latin America and the Caribbean
Although output-based informality in LAC has fallen, the incidence of informality still varies considerably within the region. In
LAC economies where corruption and the burden of paying taxes is high, output-based informality tends to be high. Selfemployment tends to be high where labor market efficiency is low. Both corporate and personal income tax rates are higher in
LAC than in all EMDEs.
A. DGE-based informal activity

B. DGE-based informal activity, bycountry

C. DGE-based informal activity

D. DGE-based informal activity

E. Self-employment

F. Average tax rates

Source: Elgin et al. (forthcoming), Haver Analytics, Inter-American Development Bank, national statistical bureaus and offices, Organisation for Economic Co-operation
and Development, Végh and Vuletin (2015), World Bank (Doing Business, World Development Indicators, and World Governance Indicators), World Economic Forum(Global Competitiveness Index).
A. Sample includes 23 economies. The median of the MIMIC-based estimate of informality shows a similar downward trend.
B. CHL = Chile, ARG= Argentina, CRI= Costa Rica, BRB = Barbados, BHS = The Bahamas, ECU = Ecuador, MEX = Mexico, DOM = Dominican Republic, COL =Colombia, JAM = Jamaica, SUR = Suriname, BRA = Brazil, PRY = Paraguay, NIC = Nicaragua, SLV = El Salvador, BLZ = Belize, URY = Uruguay, HND = Honduras, PER= Peru, GTM = Guatemala, PAN = Panama, BOL = Bolivia.
C. Bars show medians. Sample includes 21 LAC economies.
D. Bars show medians. Sample includes 20 LAC economies. Tax burden is measured as the ease of paying taxes in the World Bank’s Doing Business indicators.
E. Bars show medians. Sample includes 16 LAC economies.
F. Corporate tax rate sample includes 17 LAC economies and 49 EMDEs; personal tax rate sample includes 17 LAC economies and 47 EMDEs.
Click here to download data and charts.

been common in the largest economies in the region(Bosch and Maloney 2010; Fiess, Fugazza, and Maloney
2008; Perry et al. 2007). This may reflect a higher regard
for self-employment in LAC relative to other regions, or a
response to adverse employment and income shocks in theformal sector.
Lower output growth. In studies of a large number of
LAC economies, informality has been negatively associatedwith growth, even after controlling for country
characteristics (Loayza 1997; Loayza, Servén, andSugawara 2010). However, studies at the country level areless conclusive. In Mexico, for instance, informality has
been accompanied by slowing growth, yet in Brazil, fallinginformality may not be associated with higher GDP (Levy
2008; Ulyssea 2018).
Lower productivity growth. The informality literature on
LAC has established a link between informality andaggregate productivity (Loayza, Servén, and Sugawara2010). Linkages between informality and productivity
have also been identified at the firm level. Informal firmsin Brazil, for instance, have been less productive thanformal firms (de Paula and Scheinkman 2011). In

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BOX 2.3.1 Informality in Latin America and the Caribbean (continued)
Paraguay, not only are informal firms less productive, but
their low productivity has had negative spillovers to formal
firms (Vargas 2015).
Lower savings and access to finance for households andfirms. For workers and firms, there are negative financial
implications of informality. Informal workers in Chile, forinstance, have not been able to save as much as formal
workers, and have had less access to finance than formal
firms (Schlcarek and Caggia 2015). In Brazil, poor accessto finance was the key reason for informal firms being
small and unproductive: their cost of capital was at least
1.3 times that of formal firms (de Paula and Scheinkman2011). Similarly, in Ecuador, lower productivity andprofitability in informal firms was due in part to worse
access to credit (Medvedev and Oviedo 2013). Across theregion, rising informality has been associated with lower
pension contributions (Vuletin 2008).
Higher poverty and inequality. Informality in LAC has
also been associated with inequality and poverty, in partreflecting the wage gap between the informal and formal
sectors. In Argentina, past poverty has been associated withcurrent informal employment, and past informality has
been associated with current poverty (Devicienti,
Groisman, and Poggi 2015). The process of increasing
formal-sector employment contributed significantly to thedecline in inequality in Argentina and Uruguay during the
2000s (Aramante, Arim, and Yapor 2016; Beccaria,
Maurizio, and Vazquez 2015). In Colombia, informal
workers received lower wages than formal workers due not
only to lower returns to their education, but also to
educational mismatches (Herrera-Idárraga, López-Bazo,and Motellón 2015).

Policy options
Designing policies to address informality requires anunderstanding of its causes and characteristics. These varyconsiderably, even within individual countries in LAC(Fernandez and Villar 2016; Perry et al. 2007).
Tax system. Making tax policy less restrictive, by lowering
tax rates or simplifying tax systems, could incentivize firms
to become formal and increase demand for formal workers.

Indeed, a large reduction in payroll tax rates in Colombia
in 2012 reduced labor informality in the main
metropolitan areas by about 7 percentages points(Fernandez and Villar 2016). The results of Brazil’s
reduction and simplification of business taxes in 1996 havebeen more ambiguous. Early studies found that the reform
was associated with a significant increase in the incidenceof formal firms, and that newly formalized firms achieved
higher revenue and profits than those operating informally,
although the impact of the reform on informality varied
across economic sectors (Fajnzylber, Maloney, and Montes-Rojas 2011; Monteiro and Assuncão 2012). Recent
studies have found no evidence of increased formalization
as a result of the reform (e.g., Piza 2016).
Labor market regulation. Tighter labor inspections havebeen effective in reducing informality in the region,
through a variety of mechanisms. In Brazil, tighter
enforcement of labor market regulations raised wages andoutput by improving the allocation of workers between theformal and informal sectors (Meghir, Narita, and Robin
2015). More frequent labor inspections in Brazil also
induced some informal workers to become formal, albeit
due to wage rigidity in the formal sector (Almeida andCarneiro 2012). Inspections were also more effective thanincentives in convincing firms in Brazil to operate in theformal sector (de Andrade, Bruhn, and McKenzie 2013).
Other regulations. Policy reforms intended to ease barriers
to entering the formal sector have had diverse outcomes.
A reform that simplified the process of opening a businessin Mexico was successful in increasing the numberof registered businesses (Bruhn 2011; Kaplan, Piedra, andSeira 2011). However, the reform had no impact on
informality: the owners of the new businesses were former
employees of formal firms, rather than informal workers.
Financial deepening contributed to a reduction in
informality in Uruguay, particularly for women and older
workers (Gandelman and Rasteletti 2016). Finally,
the emerging “gig” economy presents unique policy
challenges that may require regulatory changes to smootheconomic risks for “gig” workers (World Bank 2014b,
2016c, and 2018n).

Growth in the Middle East and North Africa is expected to follow a recovery in 2018 and rise slightly to 1.9
percent in 2019, supported by improvements in both oil exporters and oil importers. Rising investment andeasing fiscal consolidation are supporting the recovery of some oil exporters, while oil importers continue tobenefit from policy reforms. Regional growth is projected to reach 2.7 percent in 2020, as domestic demandremains generally resilient. Risks are tilted to the downside, including the possibility that activity will be
constrained by intensified geopolitical tensions, stronger external trade headwinds, abrupt tightening of global
financing conditions, and slower-than-expected reform pace.

Recent developments
Growth in the Middle East and North Africa(MENA) region is estimated to have improved to
1.7 percent in 2018, rebounding from a sharp
deceleration a year earlier driven by oil production
cuts in oil exporters and fiscal tightening (Figure
2.4.1).1 Growth among oil importers has picked
up in the past two years and continues to garnerpositive momentum. Although positive spillovers
to the region via external demand are softening
amid weaker global economic prospects, domestic
factors continue to support growth. These include
generally resilient domestic demand and policy
reforms that are helping the region’s transition
away from dependence on commodity exports andthe public sector.
Growth in oil exporters is estimated to haverecovered further in 2018. In the Gulf
Cooperation Council (GCC), increased oil
Note: This section was prepared by Lei Sandy Ye. Research
assistance was provided by Mengyi Li.
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.

production and prices have eased the pressure forfiscal consolidation, enabled higher public
spending, and supported higher current accountbalances. Non-oil sector activity in the GCC haslargely been stable. Among non-GCC oil
exporters, activity in Iran has been severely
affected by U.S. sanctions and has been asignificant drag on oil exporters’ and regional
growth. Growth in other non-GCC oil exporters
has been supported by public spending andinvestment.
Among oil importers, growth has been steadily
improving as reforms proceed. In Egypt, thelargest country in this group, tourism and natural
gas activity have continued to show strength. Its
unemployment rate has generally fallen, andpolicy reforms have contributed to an upgrade ofits sovereign rating in August 2018. Fiscal
adjustments in Egypt have also been steadily
progressing. More generally, robust agricultural
production and tourism have helped support
growth of the oil importers in the region,
especially Morocco and Tunisia. However, whileinternational reserves have strengthened in Egypt,
they have declined in other oil importers amid
higher external vulnerabilities. Policy reforms in
oil importers have helped promote innovation
capacity among firms, but the scope for
improvement remains large, given fundamental
challenges like the quality of electricity supply thathinder the potential for private sector dynamism

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FIGURE 2.4.1 MENA: Recent developments
Growth in the MENA region is estimated to have improved to 1.7 percent in
2018, supported by increased oil production and eased fiscal stance in the
GCC. Growth among oil importers has been supported by policy reforms,
contributing to greater capacity to innovate. The region continues to tackle
long-term challenges, such as high youth unemployment and electricity
access, through structural adjustment programs. Inflation has been volatilein Egypt and Iran, but remains generally stable in the region. High public
debt is a significant headwind to growth for oil importers.

A. Growth

B. Oil production: GCC

C. Innovation capacity and electricity
access: Oil importers

D. Youth unemployment: non-GCC

E. Inflation

F. Public debt positions(Arezki et al. 2018). These reforms also address
challenges in the labor market, including highyouth unemployment (Purfield et al. 2018;
Schiffbauer et al. 2015).
Headline inflation in Egypt remains near its end2018 target level of 13 percent, despite edging up
recently. Core inflation has been contained andthe central bank has conducted two policy ratecuts in 2018, despite tighter external financing
conditions. In Iran, inflation rose sharply in thesecond half of 2018, partly reflecting thedepreciation of the rial in the parallel market
relative to early 2018. Inflation is generally
contained across the rest of the MENA region,
averaging less than 3 percent in the GCC, andrising moderately in smaller oil importers.
Bond issuance across the region, particularly in the
GCC, was robust at the start of 2018, but slowed
around mid-year amid tighter external financing
conditions and rising investor risk aversion.
Although international financing conditions havebecome less favorable, investor confidence in theregion were supported by efforts by GCC
countries to diversify their economies as well as
their recent inclusion in the MSCI Emerging
Markets Index (Saudi Arabia) or JP Morgan
EMBI bond indexes (5 GCC economies). Thesedevelopments kept the region somewhat insulated
from the turmoil affecting many emerging markets
and developing economies (EMDEs) in thesecond half of 2018.

Outlook
Source: Bank for International Settlements, Haver Analytics, International Energy Agency,
International Monetary Fund, World Bank, World Economic Forum.
A. Weighted average growth rates of real GDP. Gray denotes forecasts.
B. Sum of production in Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE. Oil price denotes
average of Brent, Dubai, and WTI.
C. Based on World Economic Forum surveys. “Capacity for Innovation” denotes response to thequestion: “In your country, to what extent do companies have the capacity to innovate? [1 = not at all;
7 = to a great extent].” “Quality of Electricity Supply” denotes response to the question “In yourcountry, how reliable is the electricity supply (lack of interruptions and lack of voltage fluctuations)? [1= extremely unreliable; 7 = extremely reliable].” Unweighted averages. Years denoted refer to edition
year of data. Includes 5 oil importers.
D. Youth unemployment as a percent of youth labor force (age 15-24). Includes 10 MENA
economies. Unweighted averages. Based on 2017 data.
E. Monthly year-on-year growth rates of CPI inflation. Last observation is October 2018.
F. General government debt as a ratio to GDP. 2018 data are estimates. Unweighted averages.
Includes 6 GCC economies, 3 non-GCC oil exporters, and 6 oil importers.
Click here to download data and charts.

GDP growth is projected to rise slightly to 1.9
percent in 2019 and pick up to 2.7 percent later inthe forecast horizon. Both oil exporters and oil
importers will show steady growth improvement
over the forecast period. Despite the headwinds
from a less favorable international economic
environment, which is expected to be marked byslower global trade growth and tighter externalfinancing conditions, domestic factors—inparticular, policy reforms—continue to bolster
growth in the region.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Among oil exporters, growth in 2019 is expected
to improve slightly, supported by continued
strengthening in the GCC that is partly offset by
weakness among the large non-GCC oil exporters.
Higher investment and improved regulatoryenvironments are expected to support higher
growth in GCC economies. Over the medium
term, growth among the GCC economies willremain steady, underpinned by planned
diversification programs, infrastructure projects,
and medium-term reform plans (World Bank
2018o, 2018p). Outside of the GCC, activity in
Iran is expected to contract as U.S. sanctions bite.
Algeria’s growth is projected to moderate after its
budgeted strong increase in government spendingin 2018 tapers.
Among oil importers, growth is forecast to risefurther, led by improvements among the larger
economies. Investment will be further supported
by reforms that strengthen the business climate
and a pickup in domestic demand (World Bank
2018q). Tourism is envisioned to continue
supporting activity in Egypt, Morocco, andTunisia. Positive spillovers via external demand in
the Euro Area are likely to taper somewhat amid
the area’s weaker growth prospects. While smaller
oil importers’ growth is envisioned to pick up
slightly, these economies continue to grapple withelevated public debt, and in some cases, thechallenges associated with the ongoing refugee
crisis.
Medium-term growth forecasts for the MENA
region are predicated on the assumption that there
will not be a significant escalation of geopoliticalconflicts and that there will be limited regional
spillovers from conflict-ridden economies.
Continued IMF and World Bank programs inmany economies (e.g., Egypt, Morocco) are
expected to provide a basis for needed structuraladjustments (e.g., stronger fiscal management
frameworks, higher public infrastructure quality),
as well as steps to address the vulnerabilities
associated with the informal sector (Chapter 3;
Box 2.4.1). Financial reforms—such as newly
approved bankruptcy laws in Egypt, Saudi Arabia,
and the United Arab Emirates—should help
relieve financial constraints in the corporate sector
and support investor confidence (World Bank
2014b). Multilateral efforts to promote rural

MIDDLE EAST AND NORTH AFRICA

91

FIGURE 2.4.2 MENA: Outlook and risks
Heightened geopolitical tensions have been associated with volatile
sovereign default spreads and may amplify fragile economies’ significant
income losses. Trade disputes involving major economies may weigh on
external demand of both oil exporters and importers, while a more abruptthan-expected tightening of global financing costs may raise external debt
vulnerabilities, especially if accompanied by sharp dollar appreciation.

A. Sovereign default spreads

B. Per capita income: fragile
economies

C. Export exposure

D. External debt: oil importers

Source: Haver Analytics, International Monetary Fund, World Bank.
A. Denotes 5 Year USD Credit Default Swap Par Mid Rate. Oil importers include 4 economies. Oil
exporters include 6 economies. Three month-rolling unweighted averages. UAE denotes average of
Abu Dhabi and Dubai.
B. Estimated per capita income in thousands of US dollars. Data not available for Syria.
C. Share of goods exports to respective economies denoted as a ratio to each country group’s totalexports. Denotes latest available data in 2017. Includes 6 GCC economies, 3 non-GCC oil exporters,
and 7 oil importers.
D. Unweighted averages. 2018 data are estimates. Includes 6 oil importers.
Click here to download data and charts.

transportation, electricity access, and private sector
financing (e.g., Gaza Solar Fund, Compact with
Africa) are likely to enhance the business climate.
Collectively, policy reforms across the region are
expected to improve growth potential in themedium term.

Risks
Risks to the regional outlook are tilted to thedownside. A diverse range of geopolitical risks
have been associated with volatile sovereigndefault spreads in both oil exporters and importers(Figure 2.4.2). New conflicts in fragile economies
illustrate the potential for an escalation of military

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CHAPTER 2.4

conflicts to inflict even greater damage to incomesand economic activity (Devarajan and Mottaghi2017). These conflicts may also diminish access tohealth and water services in fragile economies, aswell as compound the impact of the refugee crisis
on host and origin economies. Regional conflicts
could also deter tourism, foreign direct
investment, and remittances. A substantial further
escalation of U.S.-Iran tensions could have adverse
spillovers to the rest of the region.2 Geopoliticalfactors, as well as uncertainty in oil production in
response to these factors, could trigger volatility in
oil prices. Together, these could complicate or stallfiscal and current account adjustments in both oil
exporters and importers.
Escalating global trade tensions may negatively
impact the MENA region. Although direct trade
exposure with the United States is low, the regionis tightly interconnected to the European Union
and, to a lesser degree, China. A further rise of
trade tensions could weigh heavily on the demandfor exports from the MENA region (World Bank
2016a). This risk may be slightly mitigated bydeeper trade integration across regional neighbors(e.g., Djibouti-Ethiopia).
Abrupt tightening of global financing conditions
may affect both oil exporters and importers.
Interest rates in GCC economies have moved
broadly in tandem with advanced economies’
policy rates, especially that of the U.S., and their
net external assets positions are strong. Combined

2 The current sanctions feature waivers from eight economies thatimport oil from Iran, as well as proposed Special Purpose Vehicles
designed by the EU to facilitate transactions with Iran.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019with the gradual nature of advanced economy
monetary policy normalization, the dampening
effect on borrowing costs and non-oil activity
associated with higher interest rates have so far
been modest. However, a more abrupt tighteningof advanced economy monetary policy could
weigh on capital flows to the region and dampen
foreign investor confidence in large GCC
economies, which had recently relaxed foreign
investment restrictions (World Bank 2018p).
High external debt denominated in foreign
currency in some oil importers implies that they
are also vulnerable to unexpected sharp
appreciation of the U.S. dollar.
Post-election political uncertainty in some
economies delayed the formation of new
governments. This may slow the pace of reforms.
Several oil importers also depend on IMF/WB
multi-year fiscal adjustments programs, which
hinge on progress in the pace of reforms. Potential
delays in reforms may also be reflected in some oil
exporters through inefficiencies in management ofcontingent liabilities and large investment
projects.
On the upside, rising reconstruction spending inconflict affected economies (e.g., Iraq) may havepositive spillovers to neighboring economies,
supporting higher investment in physicalinfrastructure as well as soft infrastructure (e.g.,
broadband internet, mobile telephony) (Arezki etal. 2018).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 201993

MIDDLE EAST AND NORTH AFRICATABLE 2.4.1 Middle East and North Africa forecast summary
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE MENA, GDP

1

2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

5.1

1.2

1.7

1.9

2.7

2.7

-1.3

-1.4

-0.5

(Average including countries with full national accounts and balance of payments data only)2
4.8

1.4

1.7

1.6

2.7

2.7

-1.3

-1.7

-0.6

GDP per capita (U.S. dollars)

3.0

-0.3

0.1

0.1

1.3

1.4

-1.3

-1.7

-0.6

PPP GDP

5.1

1.7

1.8

1.6

2.8

2.8

-1.4

-1.8

-0.7

2.8

2.2

1.5

1.6

2.3

2.3

-1.9

-1.9

-1.2

Public consumption

-6.3

2.3

3.4

1.1

1.9

1.9

2.1

-0.3

0.3

Fixed investment

-2.0

1.0

2.8

3.6

4.7

4.8

-2.3

0.0

-0.1

Exports, GNFS3

8.5

2.9

1.9

1.8

3.4

3.4

-1.8

-2.3

-0.6

Imports, GNFS3

-1.2

5.1

1.3

1.9

3.1

3.1

-2.4

-1.2

-0.3

Net exports, contribution to growth

4.2

-0.4

0.5

0.2

0.5

0.5

0.0

-0.7

-0.3

5.6

0.6

1.2

1.4

2.3

2.3

-1.5

-1.7

-0.6

2.4

-0.3

2.0

2.6

2.7

2.7

-0.1

-0.1

0.0

Saudi Arabia

1.7

-0.9

2.0

2.1

2.2

2.2

0.2

0.0

-0.1

Iran

13.4

3.8

-1.5

-3.6

1.1

1.1

-5.6

-7.7

-3.1

2.9

3.9

4.1

4.2

4.6

4.7

0.1

-0.2

0.0

4.3

4.7

5.5

5.7

5.9

6.0

0.2

0.0

0.1

4.3

4.2

5.3

5.6

5.8

6.0

0.3

0.1

0.0

EMDE MENA, GDP2

Private consumption

Memo items: GDP
Oil exporters4
GCC countries5

Oil importers

6

Egypt
Fiscal year basis

7

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars. 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 2017 reflects the fiscal year ended June 30, 2017.
Click here to download data.

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TABLE 2.4.2 Middle East and North Africa economy forecasts1
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

Algeria

3.2

1.4

2.5

2.3

1.8

1.8

-1.0

0.3

0.5

Bahrain

3.2

3.9

3.2

2.6

2.8

2.8

1.5

0.5

0.7

Djibouti

8.6

5.7

6.7

7.3

7.5

7.5

0.2

0.9

1.2

Egypt

4.3

4.7

5.5

5.7

5.9

6.0

0.2

0.0

0.1

Fiscal year basis2

4.3

4.2

5.3

5.6

5.8

6.0

0.3

0.1

0.0

Iran

13.4

3.8

-1.5

-3.6

1.1

1.1

-5.6

-7.7

-3.1

Iraq

13.0

-2.1

1.9

6.2

2.9

2.8

-0.6

2.1

1.0

Jordan

2.0

2.0

2.1

2.3

2.4

2.7

-0.1

-0.1

0.0

Kuwait

2.9

-3.5

1.7

3.6

3.6

3.6

-0.2

0.1

0.6

Lebanon

1.7

1.5

1.0

1.3

1.5

1.5

-1.0

-0.7

-0.5

Morocco

1.1

4.1

3.2

2.9

3.5

3.5

0.2

-0.6

-0.2

Oman

5.0

-0.9

1.9

3.4

2.8

2.8

-0.4

0.9

-0.1

Qatar

2.1

1.6

2.3

2.7

3.0

3.0

-0.5

-0.5

0.2

Saudi Arabia

1.7

-0.9

2.0

2.1

2.2

2.2

0.2

0.0

-0.1

Tunisia

1.1

2.0

2.6

2.9

3.4

3.6

0.2

0.0

0.0

United Arab Emirates

3.0

0.8

2.0

3.0

3.2

3.2

-0.5

-0.2

-0.1

West Bank and Gaza

4.7

3.1

1.7

1.9

1.9

1.9

-0.8

-0.4

-0.4

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 economies’ prospects do not significantly differ at any given moment in time.
1. GDP at market prices and expenditure components are measured in constant 2010 U.S. dollars. 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 2017 reflects the fiscal year ended June 30, 2017.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

MIDDLE EAST AND NORTH AFRICA

BOX 2.4.1 Informality in the Middle East and North Africa
Middle East and North Africa’s (MENA’s) informal sector output, on average, amounts to nearly one quarter of official GDP.
However, there is wide heterogeneity across the region. Informality is high among non-GCC economies, the young population, as
well as the agricultural workforce. Levels of informality in the region are closely linked to its economic structure and governanceclimate, including low private sector vibrancy and limited economic diversification. Policy options that reduce regulatory barriers,
streamline public sector efficiency, and enhance workforce skills can help improve access to the formal sector and unlock thepotential of a relatively young informal workforce.

Introduction
The extent of informal output in the Middle East andAfrica region amounts to nearly one quarter of official
GDP during 2008-16, lower than in other EMDE regions.
However, there is considerable heterogeneity within theregion, with higher informality among non-Gulf
Cooperation Council (GCC) economies. Moreover,although the share of informal activity in MENA has beensteady over the past two decades, perceptions of
informality in the MENA region have edged upward.
Employment informality is high among lower-skilledworkers and the youth, which poses important challenges
for MENA’s ongoing transition to a more diversified
economic structure and jobs-oriented growth.
Against this backdrop, this box examines the following
questions:•

How has informality evolved in the Middle East andNorth Africa?

•

What are the macroeconomic and social correlates of
informality?

•

What policy options are available to address
challenges associated with informality?

Evolution of informality
On average during 2008-2016, informal sector output in
MENA amounted to about one quarter of official GDP,lower than other EMDE regions (Figure 2.4.1.1; Chapter
3). During the same period, about 24 percent of the labor
employment are reported to be self-employed.
Broadly stable over time. The extent of informal sector
output in MENA appears to have remained steady overthe past two decades, although survey-based measures of
informality suggest that perceived informality may haveincreased. The persistence of informality is linked to thelong-standing economic structure of MENA economies,
including dependence on commodities production in oil
Note: This box was prepared by Lei Sandy Ye. Research assistance wasprovided by Mengyi Li and Jinxin Wu.

exporters, a limited private sector, low labor mobility, andlack of economic diversification.
Regional heterogeneity. The moderate average level of
informality masks disparate trends within the region. The
share of informal output in GCC economies is about 8
percentage points less than in non-GCC economies (18percent and 26 percent, respectively), and the share of selfemployment to total employment in non-GCC economies
is about 10 times that of the GCC.

Correlates of informality
Informality in MENA has reflected a number of economic
and development challenges. These ranged from limited
private sector activity to conflict situations. Large informal
sectors have been associated with lower productivity, low
wages, and less inclusive growth. Although informality canprovide helpful employment opportunities where theformal sector features distortions and governance is poor,
the structural, policy, and institutional features that foster
informality in MENA poses challenges for the region’s
efforts to diversify and reduce its reliance on commodity
production and the public sector.
Economic structure. Low informality in the GCC reflects
high reliance on expatriate workers and high public
employment for nationals (World Bank 2018o). In the
non-GCC economies, informal workers constitute the vast
majority of the employed in the agriculture and mining
sectors. Across countries, a higher share of agricultural
employment had been associated with higher informality(Elshamy 2015; Gatti et al. 2014; UNDP 2013; WorldBank 2014c). Urban workers were also 5-12 percent lesslikely to be informally employed than rural workers (Angel-Urdinola and Tanabe 2012), altogether consistent with
the negative correlation between stage of development andinformality.
Governance and business climates. Informality in MENA
is closely linked to governance quality, which has been
negatively correlated with informality (Elbadawi and
Loayza 2008). In non-GCC economies, where informality
is higher, institutional quality indicators also tend to bemarkedly lower than in the GCC. This issue is further

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BOX 2.4.1 Informality in the Middle East and North Africa (continued)
FIGURE 2.4.1.1 Informality in the Middle East and North Africa
MENA’s informal sector output comprises nearly one quarter of official GDP, lower than other EMDE regions. However, perceptions of informality in MENA has risen somewhat while they have declined in the median EMDE.
A. Informal economy as share of
economic output

B. Share of self-employed worker

C. Perceived informal activity

Source: Elgin et al. (forthcoming), International Labor Organization, World Bank, World Economic Forum.
A.-C. Blue bars show simple averages of economies in the region. Red markers show the median of all EMDEs. Vertical lines denote interquartile range of all EMDEs.
A. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. The DGE model estimates the size of the informal sector as a percent ofofficial GDP (see Elgin and Oztunali 2012). The MIMIC model is a structural equations model that considers multiple causes of informal activity and captures multiple
outcome indicators of informal activity (see Schneider, Buehn, and Montenegro 2010). It also estimates the informal output as a percent of official GDP. DGE measureincludes 6 GCC economies and 9 non-GCC economies. MIMIC measure includes 6 GCC economies and 10 non-GCC economies. Excludes Djibouti, Iraq, Libya (DGE
only), and West Bank and Gaza.
B. Self-employed is the share of self-employment in total employment. Includes 6 GCC economies and 10 non-GCC economies (excludes Djibouti, Iraq, and Libya).
C. WEF index is the average responses at the country-year level to the following question (surveyed by World Economic Forum): “In your country, how much economic
activity do you estimate to be undeclared or unregistered? (1=Most economic activity is undeclared or unregistered; 7 = Most economic activity is declared or registered).”WEF indices are re-ordered (i.e. 1= Most economic activity is declared or registered; 7= Most economic activity is undeclared or unregistered) so that a higher average at
the country level indicates a larger informal economy. The index does not use data for year 2004-2005 due to inconsistency in survey methods. Includes 6 GCC
economies and 10 non-GCC economies (excludes Djibouti, Iraq, and West Bank and Gaza).
Click here to download data and charts.

compounded by poor public services and burdensome
regulatory environment, which raise the costs of operatingin the formal sector (World Bank 2016d).
Conflict. In a number of countries (e.g., Syrian Arab
Republic), wars and violent conflicts have severely limited
the number of public sector jobs, which also led workers to
shift into the informal sector for lack of alternatives(Devarajan and Mottaghi 2017; Ianchovichina and Ivanic2014). In neighboring countries of fragile and war-torn
economies (e.g., Jordan, Lebanon), the massive influx ofrefugees—many of whom are unregistered—has boosted
the informal sector, where jobs tend to be labor intensive
and low skilled.
Lower productivity. High informality has been associatedwith lower labor productivity and more limited export
potential, partly reflected in its relatively low informal
share of output compared to that of employment (Box 3.2;
Elbadawi and Loayza 2008; Gatti et al. 2014). Hindrancesin the formal sector, including regulatory barriers to entry
and burdensome taxation, divert otherwise productive
firms and workers to enter and remain in the informal

sector where productivity is lower.1 Moreover, based onenterprise survey data, a sizable portion of firms in oil
importers (e.g., Morocco, Tunisia) consider competitors’
practices in the informal sector as hindering their own
business operations (Figure 2.4.1.2; World Bank 2004).2
Restricted market access. Informal workers in the region
tend to be concentrated in small and medium-sized firms,
which constitute more than 90 percent of MENA’s private
enterprises (Purfield et al. 2018). Although these firms can
include young start-ups with high entrepreneurial
potential, they have tended to be oriented toward
local markets, with limited regional or global market access(World Bank 2004, 2016d). Among these enterprises, a 1
percentage point increase in the share of informal
workers was associated with a 6-percentage-points lower
1 Within small and medium-sized enterprises in MENA, a 1
percentage point increase in the share of informal workers was associatedwith 3 percentage point lower relative wages (Elbadawi and Loayza2008).
2 Informal business operations may also imply lower contributions togovernment revenues, while possibly raising resource utilization on public
services, such as infrastructure use (Galal 2005; Gatti et al. 2014).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

MIDDLE EAST AND NORTH AFRICA

BOX 2.4.1 Informality in the Middle East and North Africa (continued)
FIGURE 2.4.1.2 Correlates of informality and policy challenges
Informal activity is higher among non-GCC economies in the MENA region, and competition from the informal sector presents
a major obstacle to businesses in several large economies. Low wages for informal sector women workers have been
associated with particularly low female labor force participation rates in the region. Informality is also high among the youth in
MENA, a group that often has insufficient access to education and training programs. In non-GCC economies, where
informality is more pervasive, policies that improve access to finance and public-sector effectiveness can help increasemobility from the informal to the formal sector.
A. Informality in regional subgroups

B. Firms citing informal sector competitor
business practices as biggest obstacle

C. Female labor force participation

D. Youth unemployed or not in education

E. Firms citing access to finance as amajor constraint

F. Public sector effectiveness: non-GCC

Source: Elgin et al. (forthcoming), International Labor Organization, World Bank, World Economic Forum.
A. Based on DGE estimates of informal output in percent of official GDP (chapter 3). 2008-16 averages. “EMDEs” denote the median of all EMDEs during the same
period. Includes 6 GCC economies, 2 non-GCC oil exporters, and 5 oil importers. Excludes Djibouti, Iraq, Libya, Syria, Yemen, and West Bank and Gaza.
B. Columns denote the percent of firms citing “informal business practices” as the biggest obstacle to their business. Based on the latest available World Bank Enterprise
Survey year since 2013 for each economy denoted.
C. Workforce as a percent of female population ages 15-64. Based on 2017 data. Unweighted averages. Includes 6 GCC economies, 3 Non-GCC oil exporters, and 7 oil
importers.
D. Denotes share of youth (aged 15-24 years) not in education, employment, or training in percent of youth population. Based on latest available data since 2010 for eachcountry.
E. Percent of firm citing access to finance as a major constraint to business, based on World Bank enterprise surveys (surveys in the MENA region only include non-GCC
economies). EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA=Middle East and North Africa, SAR = SouthAsia, and SSA = Sub-Saharan Africa. Based on latest survey year since 2010 for each country. Includes 9 MNA economies.
F. Based on the Worldwide Governance Indicators of regulatory quality and government effectiveness, in which a lower index denotes weaker regulatory quality andweaker effectiveness. Index ranges from -2.5 to 2.5. Includes 13 non-GCC economies. Unweighted averages. “EMDEs” denotes the year 2017.
Click here to download data and charts.

revenue share destined for non-local markets (Elbadawi
and Loayza 2008).
Wage differentials. Informality presents a source of
employment but also income vulnerability among womenand the youth. The wage gap between informal and formal
workers (i.e., formality premium) has been higher for
women than men. For example, in Egypt, the formal wage

premium was about 20 percent for males but more than
50 percent for females (Gatti et al. 2014). Informality rates
are higher among the young workers, who often do notenter public sector jobs until later in age (Angel-Urdinola
and Tanabe 2012; Elbadawi and Loayza 2008). In
Morocco, the formal wage premium among the youth wasmore than 50 percent (Gatti et al. 2014). Further, returns
to education have been lower in the informal sector than

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BOX 2.4.1 Informality in the Middle East and North Africa (continued)
the formal sector, which has discouraged skills acquisition(Angel-Urdinola and Tanabe 2012).3
Less inclusive growth. High informality in the region isassociated with lower levels of educational attainment andenrollment, as in many economies a majority of informal
workers are school dropouts or have not received asecondary education (Gatti et al. 2014). High informality
is associated with limited access to health care and legal
services, especially in fragile areas (Cho 2011). Workers in
the informal sector have also reported harsher job
conditions and poorer work safety, and among young
informal workers, lower levels of job satisfaction (Gatti etal. 2014). These social disparities have the potential toslow reform momentum in the region by constraining
consensus-building.

Policy challenges
Informality in non-GCC MENA countries, where
informality is widespread, reflects deep-rooted economic
structures. These economies have some of the highest
youth unemployment rates and lowest female labor force
participation rates among all EMDEs. Public sector
employment constitutes more than 15 percent of total
employment, about twice the EMDE average (IMF
2018b). Multi-pronged policies can aim to create a morevibrant private sector and strengthen human capital of
workers, part of building a new social contract in theregion (Devarajan and Mottaghi 2015). Policies targeting
specific vulnerable groups can lessen the negative
externalities associated with informality.
Fiscal reforms. Burdensome taxation has been a key
constraint to formal sector firms in MENA (Gatti et al.
2014). In non-GCC MENA economies where informality
is more pervasive, reforms to align tax systems withinternational best practices and strengthen enforcement
could further attract informal firms to productive formal
activity while also raising revenue collection. Such reforms
may include reducing excessive corporate tax rate burdens
and enhancing revenue collection through harmonizedelectronic filing systems (e.g., Morocco).

3 Evidence from Egypt, for example, suggests that a worker in theformal sector who has completed 5 years of education earns comparable
wages to those of an informal sector worker with 12-14 years ofeducation (Angel-Urdinola and Tanabe 2012).

Access to finance. Access to finance is a larger obstacle todoing business in non-GCC MENA than in most other
EMDE regions. Boosting access to finance, including
through a stronger legal framework and improved credit
protection regimes, can help promote private sector
activity by increasing transparency of firms to investors
and facilitating investment (Farazi 2014; Straub 2005). A
number of economies in MENA have recently adopted
policies in this area, such as new insolvency resolution lawsin Egypt, Saudi Arabia, and the United Arab Emirates.
The adoption of financial technologies (Fintech), such as
innovations that automate financial transactions, can also
facilitate financial services to informal unbanked
individuals or small and medium-sized enterprises (Arezki
et al. 2018; Lukonga 2018).
Regulatory effectiveness. Beyond its large size, public
sector effectiveness and regulatory quality in non-GCC
MENA countries have deteriorated in the last decade.
Corruption is cited among the biggest hindrances to
MENA firm operations and increases incentives for firms
and workers to operate informally (World Bank 2016d).
Together with low regulatory efficiency, corruption
reduces the effectiveness of labor market regulations andenforcement (Gatti et al. 2014). Policies that reduce
regulatory costs help increase mobility of MENA firms
between the informal and formal sector, while those thatstrengthen property rights may assist the rural or
agricultural-sector populations to access financing (e.g.,enabling collateralized loans). Policies to promote
entrepreneurial activities, such as easing of businesslicensing requirements, can also facilitate entry of informal
workers into more productive jobs in the formal sector.
Education. Policies that encourage higher education andexpand job training can be especially relevant for younger
workers by facilitating their entry into more productive
formal jobs. Training programs may be particularly
effective if coupled with mechanisms to increase women’s
mobility, which is constrained in the region, and offer a
combination of soft and hard skills. Training is also more
effective if extended to areas (e.g., rural) where educational
levels are lower, as MENA region’s training programs
tended to serve higher income and more educatedindividuals (Angel-Urdinola, Semlali, and Brodmann2010). A holistic approach that combines job training withjob creation efforts, such as through public-private sector
programs, can also be effective; given higher
unemployment rates for university graduates than lowskilled workers in the region (World Bank 2018r).

South Asia remains the world’s fastest growing region. India’s domestic demand is strengthening as the country
reaps the benefits of structural reforms and of a revival of credit growth. Growth in the region is projected toaccelerate to 7.1 percent in 2019 from 6.9 in the previous year. Over the medium term, robust domestic
demand will continue to underpin growth, which is expected to average 7.1 percent. However, risks to theoutlook are tilted down. On the domestic side, vulnerabilities are being exacerbated by fiscal slippages andrising inflation, and the possibility of delays in needed structural reforms to address weaknesses in the balance
sheets of banks and non-financial corporates. Key external risks include a further deterioration in current
accounts and a faster-than-expected tightening of global financing conditions.

Recent developments
Growth in South Asia accelerated to an estimated
6.9 percent in 2018 from 6.2 percent the previous
year, with domestic demand strengthening inIndia as temporary disruptions fade and thebenefits from ongoing structural reforms start tomaterialize. The recovery was in line withexpectations, and recent high frequency data–
including purchasing managers’ indices andindustrial production–have broadly remained solid(Figure 2.5.1).
Throughout the region, private consumption
picked up in 2018 while investment remainedsolid. The solid investment was supported by thefading of a number of temporary disruptions, arevival of credit growth, and ongoing
infrastructure projects. Strong domestic demand
boosted imports, while exports remained subdued
amid weak global trade sentiment, causing current
account deficits to widen (World Bank 2018s).
India’s growth accelerated to an estimated 7.3
percent in FY2018/19 (April to March) as
economic activity continued to recover withstrong domestic demand. While investment

Note: This section was prepared by Temel Taskin. Research
assistance was provided by Ishita Dugar and Brent Harrison.

continued to strengthen amid GST harmonization
and a rebound of credit growth, consumption
remained the major contributor to growth(Ahmad et al. 2018).
Excluding India, regional growth moderated
slightly in 2018. Pakistan’s GDP (factor cost) is
estimated to have grown 5.8 percent in
FY2017/18 (July 16 to July 15), with solid
contributions from consumption and investment.
Activity was supported by strengthening in the
agricultural and industrial sectors, and a sustained
acceleration in services.
In Bangladesh, growth was broad-based,remaining strong at an estimated 7.9 percent in
FY2017/18 (July 1 to June 30). Private
consumption was the main driver of growth,
supported by strong remittance inflows. Net
exports turned negative because of rising food andcapital machinery imports and weak exports(World Bank 2018t).
In Sri Lanka, activity accelerated to an estimated
3.9 percent in 2018 on the back of a recovery in
the agriculture and services sectors. In Nepal,
economic activity remained solid with a 6.3
percent growth in FY2017/18 (July 16 to July 15).
Less favorable monsoons led to weakness inagricultural activity, but this was offset by arecovery in remittances and robust industrial

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FIGURE 2.5.1 SAR: Recent developments
South Asia remains the fastest growing EMDE region. High frequency
indicators of economic activity are mixed across the region but are broadly
consistent with the recovery underway. Remittance inflows are an
important source of income in the region. Inflation is below official targets ingeneral despite a recent acceleration in some countries. Sovereign credit
default spreads have been rising as the Federal Reserve continues totighten monetary policy and U.S. dollar appreciates.
A. Growth

B. Purchasing Managers’ Indexes

the Maldives. In Bhutan, hydropower and otherinfrastructure projects supported investment, andGDP expanded by an estimated 4.6 percent in
FY2017/18 (July 1 to June 30). Maldives’ GDP
accelerated to an estimated 8.0 percent in 2018,reflecting strength in tourism and construction.
Growth in Afghanistan is estimated to have edged
down to 2.4 percent. Although activity was
supported by agriculture and services, subdued
business confidence and security challenges
continued to weigh on growth.
There were some signs of rising inflation pressure
across the region, and both India and Pakistan
raised rates in 2018 to counter the effects ofcurrency depreciation, rising energy prices, anddomestic capacity constraints.

C. Industrial production indexes

D. Remittance inflows

E. Inflation

F. Sovereign credit default spreads

Source: Haver Analytics, International Monetary Fund, World Bank.
A. SAR = South Asia Region. Aggregate growth rates are calculated using constant 2010 U.S. dollar
GDP-weights. Data for 2018 are estimates. Shaded areas indicate forecasts.
B. PMI readings above 50 indicate expansion in economic activity; readings below 50 show
contraction. Last observation is November 2018.
C. Last observation is October 2018.
D. Data present the workers' remittances and compensation received by countries. Last observationis 2017.
E. Last observation is October 2018.
F. Data present five-year U.S. dollar credit default swap (mid-rate). Last observation is
December 2018.
Click here to download data and charts.

sector growth, particularly for manufacturing
activities (World Bank 2018u).
Investment and services remained the major
contributor to economic activity in Bhutan andSovereign bond yields surged in the region lastyear. Fiscal consolidation stalled owing to elections
in several countries, contributing further to theregion’s high levels of government debt. In India,
the government deficit was higher than planned,
reflecting lower-than-expected revenues fromtelecom spectrum auctions and low dividends
from public sector enterprises (World Bank
2018v). The central government is budgeting a
reduction in the fiscal deficit for next fiscal year.
Pakistan’s fiscal deficit rose to 6.6 percent of GDP
last year, well above the government’s target of 4.1
percent, as tax collection fell short of expectations.
External vulnerabilities are also rising in theregion. In Sri Lanka and to some extent inPakistan, external debt is sizable and current
account deficits have deteriorated considerably.
Recent currency pressures have eroded Pakistan’s
foreign exchange reserves significantly—theycurrently amount to only around two months ofimports.

Outlook
The outlook for South Asia is robust, despite thefinancial stress that has affected a number of
EMDEs and continued trade disputes. Regional
growth is expected to accelerate in 2019, to 7.1
percent (Figure 2.5.2). Economic activity will be
underpinned by solid investment and robust
consumption. While exports and imports will beG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019held back owing to slowing global trade, theregion’s relatively low exposure to international
trade will mitigate the impact of this slowdown on
the regional outlook.
India’s GDP is forecast to grow by 7.3 percent in
FY2018/19 and 7.5 percent thereafter, in line with
June forecasts. Private consumption is projected toremain robust and investment growth is expected
to continue as the benefits of recent policy reforms
begin to materialize and credit rebounds. Strong
domestic demand is envisioned to widen thecurrent account deficit to 2.6 percent of GDP
next year. Inflation is projected to rise somewhat
above the midpoint of the Reserve Bank of India’s
target range of 2 to 6 percent, mainly owing toenergy and food prices.
In the rest of the region, economic activity will
average 5.6 percent over the forecast horizon. In
Pakistan, macroeconomic imbalances weigh on
growth outlook. Pakistan is expected to facefinancing needs due to large current account andfiscal deficits combined with low international
reserves. GDP growth is projected to decelerate to
3.7 percent in FY2018/19, with financial
conditions tightening to help counter rising
inflation and external vulnerabilities. Activity is
projected to rebound and average 4.6 percent overthe medium term with support from stabilizing
macroeconomic conditions (World Bank 2018u).
In Bangladesh, robust economic activity is
expected to be sustained. GDP growth is forecast
at 7.0 percent in FY2018/19 and is expected todecelerate only slightly over the forecast horizon.
Activity will be supported by strong private
consumption and investment on the back ofinfrastructure projects. Net exports are projected
to contribute negatively to GDP growth asimports outpace exports in response to strong
domestic demand.
In Sri Lanka, last year’s recovery from adverse
weather conditions is expected to continue in
2019, with 4.0 percent GDP growth. Activity will
be supported by robust domestic demand asconsumption rebounds following natural disasters,
and investment is boosted by infrastructure
projects.
Nepal’sstrongpost-earthquake
momentum is expected to moderate—GDP

SOUTH ASIA

101

FIGURE 2.5.2 SAR: Outlook and risks
Economic activity is projected to remain strong. Possible fiscal slippages
could further worsen already-high public debt positions. Non-performing
assets remain high despite recent efforts to improve the quality of financial
sector balance sheets. External imbalances pose a risk to the outlook.
Major economies in the region have tightened their monetary stance tostabilize inflation and mitigate external risks.
A. Growth

B. Fiscal balance

C. Non-performing assets

D. Net portfolio inflows

E. Current account balance

F. External debt

Source: Haver Analytics, World Bank.
A. SAR = South Asia Region. Aggregate growth rates are calculated using constant 2010 U.S. dollar
GDP-weights. 2018 data are estimates. Shaded areas indicate forecasts.
B.E. 2018 data are estimates. Shaded areas indicate forecasts. The data represent fiscal years ofcountries except for Sri Lanka, as described in Table 2.5.1.
C. Data present the ratio of bank non-performing loans to total gross loans. Last observation is 2017.
D. Last observation is 2018 Q3 for Bangladesh and Pakistan, and 2018 Q2 for India and Sri Lanka.
F. Gross external debt position including both public and private sectors, as of 2018 Q2.
Click here to download data and charts.

growth is forecast to decelerate to 5.9 percent
in FY2018/19. Activity will be underpinned
by strong infrastructure investment andconsumption.
In Bhutan and the Maldives, activity will remain
reliant on construction and tourism. Bhutan’s

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CHAPTER 2.5

growth is projected to accelerate to 7.6 percent
2018/19, supported by ongoing infrastructure
projects and rising tourism. In the Maldives,
growth is expected to moderate to 6.3 percent in2019 as construction activity returns to long-term
averages, and capital investment projects graduallyslow down. Afghanistan’s economy is expected toremain subdued, expanding 2.7 percent in 2019,as a result of security challenges ahead of elections,declining business confidence, and worsening
drought conditions.
In South Asia, a large proportion of activity is
informal, which may constrain productivity,
wages, and access to social protection systems(Kanbur 2017). Investing in education and skills,
improving the business environment by enhancing
regulatory frameworks and boosting the quality of
government services provided to formal firms areamong the policy measures which can encourageformal activity (Box 2).

Risks
The risks to the outlook are tilted downside.
Domestic vulnerabilities are being exacerbated byfiscal slippages and rising inflation, escalation inpolitical uncertainty, and the possibility of delaysin the needed structural reforms to address
weaknesses in balance sheets of banks and nonfinancial corporates. Key external risks include afurther deterioration in current accounts and afaster-than-expected tightening of global financing
conditions.
South Asian economies have high levels of public
debt in general. Fiscal slippages could further
worsen already-precarious public debt positions
and result in a costly rise in already- elevatedinterest payments.
The upcoming election cycle next year elevates
political uncertainty in the region. The challenging political environment could adversely

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

affect the ongoing reform agenda and economic
activity in some countries (e.g. Afghanistan, Sri
Lanka).
In South Asia, non-performing assets (NPAs) arestill high despite recent measures taken to improve
the recognition of these assets (Figure 2.5.2).
Especially, public sector banks in India, which
represent roughly 70 percent of the banking sector
assets, still report low profitability and high NPAs.
Credit expansion could be limited in some major
South Asia economies unless further steps aretaken to deal with financial and corporate balance
sheets.
On the external front, the region has relatively low
exposure to international trade, which limits thebenefits from trade over the long term. However,the low exposure also suggests that that it could be
more insulated from the effects rising tradeprotectionism than other regions. Moreover, theregion may even benefit from trade diversion amid
the recent dispute between some major economies(World Bank 2017b).
Persistent current account deficits and high levelsof external debt make the region more vulnerable
to a faster-than-expected tightening of global
financial conditions.
South Asia is one of the most vulnerable regions tonatural disasters (World Bank 2017c). In recent
years, the number of affected people andgeographical areas from natural disasters such as
drought, floods, and earthquakes have risen in theregion. Increasingly common natural disasters
could disrupt infrastructure, agricultural output,
and economic activity in general. The realizationof these domestic or external risks could weaken
investor confidence and result in capital outflows,
currency depreciation leading to rising external
debt, a tightening of domestic financing
conditions, and a slowdown in regional growth(Eichengreen and Gupta 2015; Kose et al. 2017).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019103

SOUTH ASIA

TABLE 2.5.1 South Asia forecast summary
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE South Asia, GDP1, 2

2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

7.5

6.2

6.9

7.1

7.1

7.1

0.0

0.0

-0.1

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

7.6

6.2

6.9

7.1

7.2

7.1

0.0

-0.1

0.0

GDP per capita (U.S. dollars)

6.2

4.9

5.7

5.9

6.0

6.0

0.1

0.0

0.0

PPP GDP

7.6

6.2

6.9

7.1

7.2

7.1

0.0

0.0

0.0

Private consumption

7.6

6.0

6.9

7.0

7.0

7.0

0.3

0.1

0.0

Public consumption

8.5

11.1

10.7

9.1

8.5

8.5

1.0

0.3

0.0

Fixed investment

9.4

8.0

8.2

8.0

7.8

7.5

0.6

0.3

0.1

Exports, GNFS4

1.9

6.2

5.6

5.6

5.9

6.0

-0.1

-0.5

-0.2

Imports, GNFS4

2.6

14.6

1.0

-0.2

0.6

Net exports, contribution to growth

-0.3

-2.3

-1.1

-0.6

-0.6

-0.6

-0.4

-0.2

-0.3

8.5

6.3

6.7

6.8

16/17

17/18

18/19e19/20f

20/21f

21/22f

18/19e19/20f

20/21fSouth Asia excluding India

5.8

5.9

5.7

5.5

5.6

5.6

0.1

-0.1

-0.1

India

7.1

6.7

7.3

7.5

7.5

7.5

0.0

0.0

0.0

Pakistan (factor cost)

5.4

5.8

3.7

4.2

4.8

4.8

-1.3

-1.2

-0.6

Bangladesh

7.3

7.9

7.0

6.8

6.8

6.8

0.3

-0.2

-0.2

Memo items: GDP2

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars.
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. Sub-region 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.

TABLE 2.5.2 South Asia country forecasts
Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

Calendar year basis

2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f
-0.1

1

Afghanistan

2.4

2.7

2.4

2.7

3.2

3.2

0.2

0.2

Maldives

6.2

7.1

8.0

6.3

5.6

5.6

2.5

1.8

0.7

Sri Lanka

4.5

3.3

3.9

4.0

4.1

4.1

-0.9

-0.5

-0.4

Fiscal year basis1

16/17

17/18

18/19e19/20f

20/21f

21/22f

18/19e19/20f

20/21fBangladesh

7.3

7.9

7.0

6.8

6.8

6.8

0.3

-0.2

-0.2

Bhutan

5.8

4.6

7.6

6.4

6.4

6.4

2.2

0.4

-2.3

India

7.1

6.7

7.3

7.5

7.5

7.5

0.0

0.0

0.0

Nepal

7.9

6.3

5.9

6.0

6.0

6.0

1.4

1.8

1.8

Pakistan (factor cost)

5.4

5.8

3.7

4.2

4.8

4.8

-1.3

-1.2

-0.6

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 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 (CY). 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.
Click here to download data.

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CHAPTER 2.5

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 2.5.1 Informality in South Asia
South Asia’s share of informal employment is the largest among EMDE regions, despite a below-average share of informal output.
Heavy tax burdens, above-average corruption, and low government effectiveness have contributed to high employment
informality. Informal employment is concentrated among low-skilled, young, female and rural workers. The sizable informal
sector is associated with lower productivity, lower government revenues, and higher poverty in the region. Policy options to addressthese challenges include investing in human capital in the form of training programs and improving access to finance.

Introduction
South Asia (SAR) is the EMDE region with the highest
average share of informal employment among EMDE
regions, despite a below-median and declining share of
informal output. Nonetheless, there is significant
heterogeneity in the share of employment as well as output
informality among South Asian countries.
Against this backdrop, this Box examines the following
questions:•

How has informality evolved in South Asia?

•

What have been the macroeconomic and social
correlates of informality?

•

What policy options are available to address
challenges associated with informality?

Evolution of informality
Informality in SAR. In aggregate, output informality in
the SAR region is below the average of other EMDE
regions—the size of informal sector relative to official
GDP was on average 30 percent in South Asia comparedwith 35 percent in average EMDE during 2008-2016(Figure 2.5.1.1). During the same period, 96 percent ofworkers lacked pension coverage and 63 percent were selfemployed.
Evolution of informality in SAR. Output informality
declined from 37 percent in 1990s to 32 percent in the2010s, broadly in line with the decline in informality inother EMDEs. However, labor informality over the same
period persisted or rose depending on the measure of
informality. For example, the share of the labor force
without pension coverage rose from 88 percent to 96 andself-employment remained around 63 percent.
Regional heterogeneity. The extent of informality varies
substantially across countries in South Asia. Sri Lanka had

Note: This box was prepared by Temel Taskin. Research assistance
was provided by Brent Harrison and Jinxin Wu.

the highest degree of informality (output in the informal
sector is about 40 percent of total output) in 2016 andIndia had the lowest share (below 20 percent). However,this ranking is reversed using labor market indicators of
informality: Sri Lanka has the lowest share of selfemployment (42 percent) and India the highest (76percent) as of 2016. These differences are reflected inlower labor productivity in the informal sector (relative tothe formal sector) in India than in Sri Lanka.

Correlates of informality
Business climates. Costs to doing business—such as tax
burdens, labor regulation, and cost of starting business—
are among the main drivers of informality identified in theempirical literature (FICCI 2017; Goldar and Aggarwal2012). Over the past decade, SAR has suffered fromgreater corruption and weaker government effectivenessthan other EMDE regions (Figure 2.5.1.2). Tax burdens
and indicators of ease of doing business have also been lessfavorable than in the average EMDE (World Bank 2017c).
Among costs to doing business, heavy tax burdens wereparticularly strongly associated in India and Pakistan witha larger fraction of firms operating unregistered (Ghani,
Kerr, O’Connell 2013; Waseem 2013).
Worker characteristics. South Asia’s informal labor force
consists predominantly of low-skilled, female, rural, andyoung workers (Bahadur and Parajuli 2014; Goldar andAgarwal 2012; Gunatikala 2008; Williams, Shahid, andMartinez 2015). The intensity of informal employment inSouth Asia reflects a lack of formal jobs and skills, as well
as a preference towards self-employment (Arby, Malik, andHanif 2010; Williams, Shahid, and Martinez 2015). Thismeans that informal firms are usually small, agricultural,and consist mostly of self-employed workers (FICCI2017).
Lower productivity and incomes. In South Asia, informal
workers have had lower earnings, fewer skills, and lessaccess to social protection systems; this has been reflected
in lower productivity and higher poverty (Kanbur 2017;
Likhi2013).
Informal
employment
among
underrepresented groups in labor markets, such as womenand the young, has grown over the past decade andG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

SOUTH ASIA

BOX 2.5.1 Informality in South Asia (continued)

FIGURE 2.5.1.1 Informality in South Asia
South Asia’s share of informal employment is the largest among EMDE regions, despite a below-average share of informal
output.

A. Output informality

B. Labor informality

C. Survey-based perceived informality

Source: Elgin et al. (forthcoming); World Bank.
A. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. Both DGE and MIMIC estimates measure the informal output inpercent of official GDP.
B. Labor force without pension is the fraction of the labor force that doesn’t contribute to a retirement pension scheme, which is derived from the original data on
pension coverage obtained from WDI. Self-employed is the share of self-employment in total employment.
C. WEF = World Economic Forum. WVS = World Values Survey. WEF index is the average responses at the country-year level to the following question (surveyed byWorld Economic Forum): “In your country, how much economic activity do you estimate to be undeclared or unregistered? (1=Most economic activity is undeclared or
unregistered; 7 = Most economic activity is declared or registered).” WEF indices are re-ordered (i.e. 1= Most economic activity is declared or registered; 7= Mosteconomic activity is undeclared or unregistered) so that a higher average at the country level indicates a larger informal economy. The index does not use data for year
2004-2005 due to inconsistency in survey methods. The World Value Survey asks whether respondents can justify cheating on taxes, with responses ranging from 1(never justifiable) to 10 (always justifiable). The average responses at the country-year level are used as a measure for attitudes towards informality (or tax morality,
Oveido et al. 2009), labeled as WVS. A higher average at the country level implies that people find cheating on taxes more justifiable.
Click here to download data and charts.

constrained these groups’ income security. Low earnings
and limited options available to informal workers constrain
their benefit from economic growth, which means thatgrowth has been less inclusive than otherwise (ADB 2016;
Heintz 2012). Conversely, in India, an easing of labormarket restrictions and measures to foster genderequality—such as increasing female education andstrengthening
law
enforcement
againstgenderdiscrimination—have been associated with stronger
growth as well as larger formal employment (Goldar andAggarwal 2012; Khera 2016).
Lower government revenues. Large informal sectors—inaddition to other factors such as inefficient tax
administration and narrow tax base—weigh on tax
revenues in South Asian economies (Cevik 2016; Ilzetzki
and Lagakos 2017). On average, tax revenues as a percent
in GDP have historically been below the EMDE average.
The lack of tax revenues ultimately affects the ability of
governments to fund its infrastructure investment, social
programs, etc., and therefore limiting their ability to tackle
poverty and inequality (Chapter 3).

Policy challenges
In South Asia, informal employment is concentrated
among young, low-skilled, female, and rural workers.
Policies targeting training and education of these groups,
especially in rural areas, could help their transition toformal employment (Khera 2016).
There is significant room to improve the ease of doing
business in South Asia. This could reduce informality by
reducing the cost of entry and cost of operating in formal
sector. Measures to reduce the time, cost, and complexityof registration would also improve the business climate
and foster growth (FICCI 2017).
High quality public services can also provide an incentive
for informal firms to become formal in order to accessthese services. Enhanced monitoring and enforcement,
including of tax regulations, could help discourage
informality (Ilzetzki and Lagakos 2017). Also, in India therecent introduction of a Goods and Services Tax and steps
toward demonetization are expected to encourage a shift
from the informal to the formal sector.

105

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CHAPTER 2.5

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 2.5.1 Informality in South Asia (continued)
FIGURE 2.5.1.2 Drivers of informality in South Asia
Heavy tax burdens, above-average corruption, and low government effectiveness likely have contributed to high employment
informality. The sizable informal sector is associated with weaker government revenues and higher poverty in the region.
Youth unemployment is much higher among women who represent a higher share of informal workers than men.

A. Doing business tax burden indicator:
DTF

B. Doing business overall indicator

C. Government effectiveness and control
of corruption

D. Poverty

E. Government revenues

F. Youth employment and NEET

Source: World Bank, World Development Indicators.
A.B. Index denotes Distance to Frontier Score (100-regional score), where 100 equals international best practices. Greater distance indicates further below best practicescore.
D.E. Episodes are determined based on data availability.
F. NEET stands for the share of youth Not in Employment, Education, or Training. Youth is defined as the population between ages of 15 and 24.
Click here to download data and charts.

South Asia’s self-employed, which account for about 80
percent of informally employed, have limited access tofinancial resources that could finance growth- or
productivity-enhancing investment (Ghani, Kerr, andO’Connell 2013). Greater access to credit for the self-

employed and household enterprises could help them growinto formality (Beck and Hoseini 2014). Microfinance canbe an effective instrument for providing financial access to
informal firms, as many of them are self-employedenterprises (ILO 2013; Likhi 2013).

The recovery in Sub-Saharan Africa continues, albeit at a softer pace. Growth in the region is estimated at 2.7
percent in 2018, significantly slower than expected, partly due to weaknesses in Angola, Nigeria, and South
Africa. Growth is foreseen to rise to 3.4 percent in 2019 and 3.7 percent in 2020-21, as reduced policy
uncertainty helps support a cyclical rebound in these large economies. However, per capita income growth willremain modest, and progress in poverty reduction limited. Risks to the outlook are tilted to the downside. Key
external risks include an unexpectedly sharp decline in commodity prices, an abrupt tightening of global
financial conditions, and escalating trade tensions involving major economies. Domestic risks pertain to fiscal
slippage, political uncertainty, domestic conflicts, and adverse weather conditions.

Recent developments
The recovery in Sub-Saharan Africa (SSA)
continued in 2018, but activity lost momentum inseveral countries. Growth in the region isestimated to have risen marginally from 2.6
percent in 2017 to 2.7 percent in 2018, slower
than expected and still below potential. This
reflected a sluggish expansion in the region’s threelargest economies—Angola, Nigeria, and South
Africa (Figure 2.6.1). The region faced a moredifficult external environment last year as global
trade growth moderated, financing conditions
tightened, and the U.S. dollar strengthened.
Commodity prices diverged, with metals andagriculture prices dampened by weakening global
demand, while oil prices were higher in most of
2018, mainly due to supply factors.
In Nigeria, oil production fell, partly owing topipeline closures in mid-2018, while non-oil
activity was dampened by lackluster consumer
demand, as well as conflicts over land between
farmers and herders that disrupted crop
production. In Angola—the region’s second
largest oil exporter—stagnant non-oil activity was
aggravated by a contraction in oil production,
Note: This section was prepared by Gerard Kambou and Rudi
Steinbach. Research assistance was provided by Mengyi Li.

which fell sharply due to underinvestment and to
key oil fields reaching maturity. South Africa’s
economy emerged from a technical recession in
the second half of 2018, in part due to improved
activity in the agricultural and manufacturingsectors. However, growth remains subdued, as
challenges in the mining sector and weak
construction activity are compounded by policy
uncertainty and low business confidence. Against
this backdrop, the South African government
announced measures to support the economy
through reprioritized spending and structuralreforms to improve the business environment andinfrastructure delivery.
Growth in the rest of the region was broadly
steady, although performance varied between
country groups. While growth among metals
exporters was subdued in 2018, activity in several
oil exporters rebounded. In the Central African
Economic and Monetary Community (CEMAC),
growth benefitted from an increase in oil
production and higher oil prices. Economic
activity in non-resource-intensive countries
remained robust, supported by agricultural
production and services on the production side,and household consumption and public
investment on the demand side. Several countries
in the West African Economic and Monetary
Union (WAEMU) grew at 6 percent or more,
including Benin, Burkina Faso, Côte d’Ivoire, and108

CHAPTER 2.6

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

FIGURE 2.6.1 SSA: Recent developments
The recovery in Sub-Saharan Africa has continued. However, growth
remains well below its long-term average due to a sluggish expansion
in Angola, Nigeria, and South Africa—the region’s largest economies.
The current account deficit has narrowed in oil exporters but deteriorated
among metals exporters due to weaker export growth. Fiscal deficits
have narrowed, mainly reflecting consolidation measures in some oil
exporters. Public debt remains elevated, especially among non-resourceintensive countries due to their continued reliance on public investment toboost growth.

across countries. For large oil exporters (e.g.,
Angola, Nigeria), external balances improved,
driven by higher oil prices and soft importdemand. The current account deficit also narrowed
significantly in CEMAC, underpinned by strong
fiscal adjustments. By contrast, external balances inmetals exporters deteriorated amid weaker exports
in some countries and higher imports in others. In
non-resource-intensive countries, current accountdeficits remained elevated due to high fuel imports
and capital goods imports related to public
infrastructure projects. Across the region, balanceof payments financing became more difficult
against the backdrop of rising external borrowing
costs and weakening capital inflows. Eurobond
issuance slowed markedly in the second half of theyear, while FDI inflows remained subdued(UNCTAD 2018).

A. GDP growth

B. Nigeria

C. South Africa

D. Current account balance

Currencies in the region depreciated in effective
terms amid a broad-based strengthening of the
U.S. dollar and weaker investor sentiment toward
emerging markets. Investors’ renewed focus oncountry-specific vulnerabilities contributed to a
rapid sell-off of the South African rand and theZambian kwacha since mid-2018. Elsewhere in theregion, the pace of currency depreciation has been
more modest.

E. Fiscal balance

F. Public debt

Inflationary pressures persist in the region. Despite
steep declines, inflation in Angola and Nigeria
remained in double digits, partly due to continued
exchange rate depreciation (Angola) and elevatedfood price inflation (Nigeria). In South Africa,
inflation stayed within the 3 to 6 percent target
range. Among non-resource-intensive countries,
inflation rose sharply in Ethiopia and Sudan, due
to a rapid expansion in credit and currency
depreciation (Ethiopia) and the monetization of alarge fiscal deficit (Sudan).

Source: Haver Analytics; International Monetary Fund, World Economic Outlook; National Bureau of
Statistics (Nigeria); Statistics South Africa; World Bank.
A. Aggregate growth rates calculated using constant 2010 U.S. dollar weights.
D.-F. Median of groups. Non-resource-intensive countries include agricultural exporters andcommodity importers.
Click here to download data and charts.

Senegal. A strong rebound in agriculture in Kenya,
Rwanda, and Uganda, following prior droughts,
underpinned the pickup in activity in East Africa.
The median current account deficit is estimated tohave widened from 5.8 percent of GDP in 2017 to
6.1 percent in 2018, but sizable differences persist

The median fiscal deficit for the region isestimated to have narrowed from 4 percent of
GDP in 2017 to 3.7 percent in 2018. The fiscal
balance improved sharply among many oil
exporters. The narrower deficit in Angola partly
stemmed from higher oil prices. CEMAC
countries substantially reduced their fiscal deficits
through revenue mobilization efforts and cuts incapital expenditures. By contrast, the fiscal deficit

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

remained elevated in Nigeria, due to low tax
revenue collection.
In metals exporters, the median fiscal deficit isestimated to have deteriorated sharply, as spending
levels remain elevated in some countries, while
revenues are suppressed. In non-resource-intensive
countries, the median fiscal deficit is estimated tohave widened modestly, reflecting continued
public investment supported by enhanced revenue
mobilization efforts.
In all, vulnerabilities remain: government debt-toGDP ratios are estimated to have risen in morethan half of the countries in 2018 and were above
60 percent in one-third (World Bank 2018w).
Exchange rate depreciations (e.g., Zambia), negative growth (e.g., Equatorial Guinea, Republic ofCongo), and the reporting of previously
undisclosed debt (e.g., Mozambique, Republic ofCongo) contributed to the deterioration.
In addition to the rise in debt ratios, changes in
the composition of debt have made many
countries more vulnerable to sharp movements infinancing conditions (Chapter 4). As countries
have gained access to international capital markets,and non-resident participation in domestic debt
markets expanded, non-concessional debt has
increased. Non-concessional financing accounted
for more than half of total public debt in many
countries (e.g., Cote d’Ivoire, Ghana, Republic ofCongo, Sudan, Zambia, Zimbabwe).

SUB-SAHARAN AFRICA

FIGURE 2.6.2 SSA: Outlook and risks
A gradual recovery is expected, as an increase in oil production supports amodest growth pickup in Angola and Nigeria, and easing drought
conditions boosts agricultural production. A rise in investment, as policy
uncertainty gradually recedes, should further boost growth in the large
economies. Activity in the rest of the region is expected to expand at asolid pace. Nevertheless, sluggish per capita growth implies continued
slow progress in poverty reduction. A significant amount of internationalbonds are maturing, posing refinancing risks. Rising non-concessional
debt is making countries more vulnerable to changes in international
financial conditions.
A. Growth

B. Growth per capita

C. International bond redemption in
SSA

D. Non-concessional debt

Source: Dealogic, World Bank.
A.B. Aggregate growth rates calculated using constant 2010 U.S. dollar weights. Shaded areas
represent forecasts.
C. Data reflects the principal amount at date of maturity, and excludes any interest payments.
D. Excludes Equatorial Guinea, Eswatini, Namibia, Seychelles, Somalia, and South Sudan due todata limitations.
Click here to download data and charts.

Outlook
Growth in Sub-Saharan Africa is expected to pickup to 3.4 percent in 2019, rising to an average of
3.7 percent in 2020-21 (Figure 2.6.2). This ispredicated on diminished policy uncertainty andimproved investment in large economies, together
with continued robust growth in non-resourceintensive countries. However, external headwinds
have intensified, as growth among main trading
partners moderates, global financial conditions
tighten, and trade policy uncertainty persists(Chapter 1). Per capita income growth ispredicted to remain well below its long-term
average in many countries, yielding little progressin poverty reduction, and highlighting the need109

for policy measures to raise potential output whileraising the productive capacity of the poor (WorldBank 2018x).
Growth in Nigeria is projected to rebound to 2.2
percent in 2019 and 2.4 percent in 2020-21.
These forecasts are unchanged from June andassume that oil production will recover, but peak
below government targets, while a slow
improvement in private demand will constrain
growth in the non-oil industrial sector. In Angola,
the growth forecast has been upgraded to 2.9
percent in 2019, moderating to an average of 2.7
percent in 2020-21. A recovery in the oil sector, as
new oil fields come on stream, is expected to boost

110

CHAPTER 2.6

growth, along with a pickup in activity in the nonoil sector as reforms bolster the businessenvironment.
Growth in South Africa is projected to recover
more slowly than previously expected, to 1.3
percent in 2019, before rising to 1.7 percent in
2020-21. High unemployment and slow growthin household credit extension are expected toconstrain domestic demand in 2019, while fiscal
consolidation limits government spending. Higher
growth in 2020 reflects the expectation that the
government’s structural reform agenda will
gradually gather speed, helping to boost
investment growth, as policy uncertainty recedes
and investor sentiment improves.
Excluding Angola, Nigeria, and South Africa,
growth in the rest of Sub-Saharan Africa is
expected to remain relatively solid, but withsignificant variation between country groups.
Economic activity in CEMAC should benefit
from higher oil production and an increase indomestic demand as fiscal tightening eases.
Growth is expected to rise moderately amongmetals exporters, supported in part by stronger
mining activity. However, non-mining activity
remains subdued owing to weak business
confidence, accelerating inflation in somecountries, and sluggish credit growth.
Among non-resource-intensive countries, economic activity is expected to remain robust in fastgrowing countries, such as Cote d’Ivoire, Kenya,
and Tanzania, boosted by public investment andstrong agricultural production, and in smaller
economies, such as Madagascar, on the back ofsolid export performance. While growth inEthiopia is expected to remain strong, it will beweighed down by fiscal consolidation efforts tostabilize public debt.
Inflation is expected to pick up across the regionin 2019, reflecting the pass-through of currency
depreciations during 2018 and domestic pricepressures among metals exporters and nonresource-intensive countries. Notably, inflation isenvisioned to continue to recede in Angola andNigeria. However, it may rise temporarily in
Angola if the anticipated increases in utility tariffs
and fuel prices are implemented. In addition,

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019price pressures are likely to intensify in Kenya,
Tanzania, and Uganda.
Fiscal balances are expected to improve further,reflecting fiscal consolidation efforts among thelarge oil exporters and continued adjustment in
CEMAC. Policy tightening is likely to yieldsmaller fiscal deficits in metals exporters, while
fiscal deficits in non-resource-intensive countries
should also continue to narrow as public
investment spending slows to stabilize public debt.

Risks
Risks to the regional outlook are tilted to thedownside. On the external front, slower-thanprojected growth in China and the Euro Area,
which have strong trade and investment links with
Sub-Saharan Africa, would adversely affect theregion through lower export demand andinvestment. Moreover, Sub-Saharan African
metals producers would likely be among thehardest hit by escalating trade tensions between
China and the United States, as metals prices
would fall faster than other commodity prices as aresult of weakening demand from China (WorldBank 2018y). Furthermore, a faster-than-expected
normalization of advanced-economy monetary
policy could result in sharp reductions in capital
inflows, higher financing costs, and disorderly
exchange rate depreciations, especially in countries
with weaker fundamentals or higher political risks(Arteta et al. 2015; IMF 2018c). Sharp currency
declines would make the servicing of foreigncurrency-denominated debt, already a risingconcern in the region, more challenging.
The increased reliance on foreign currency
borrowing has heightened refinancing and interest
rate risk in debtor countries (Chapter 4).
Furthermore, the rise in non-resident participationin domestic debt markets has exposed somecountries to the risk of sudden capital outflows. In
some countries, sizable loans to state-owned
enterprises, backed by commodity exports, haveincreased the risk that a negative commodity priceshock could trigger financial crises.
Domestic risks, in particular, remain elevated.
Political uncertainty and a concurrent weakening

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019of economic reforms could continue to weigh on
the economic outlook in many countries. In
countries holding elections in 2019 (e.g., Malawi,
Mozambique, Nigeria, South Africa), domestic
political considerations could undermine thecommitment needed to rein in fiscal deficits or

implement structural reforms, especially where
public debt levels are high and rising. Insurgencies
and armed conflicts, with their adverse effects on
economic activity, remain an important risk inseveral countries. Adverse weather shocks andrising financial sector stress are additional risks.

TABLE 2.6.1 Sub-Saharan Africa forecast summary

Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE SSA, GDP1

111

SUB-SAHARAN AFRICA

2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

1.3

2.6

2.7

3.4

3.6

3.7

-0.4

-0.1

-0.1

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

2

EMDE SSA, GDP2,3

1.3

2.6

2.7

3.4

3.6

3.7

-0.4

-0.1

-0.1

GDP per capita (U.S. dollars)

-1.4

-0.1

0.0

0.8

0.9

1.0

-0.4

0.0

-0.1

PPP GDP

1.6

2.8

3.0

3.7

3.8

3.9

-0.4

0.0

-0.1

Private consumption

0.5

1.8

2.5

2.8

2.9

2.5

-0.1

0.0

0.0

Public consumption

1.3

2.8

2.6

2.9

3.0

2.8

0.1

0.0

0.0

Fixed investment

1.5

4.5

5.2

6.9

7.0

7.5

-1.6

-0.5

-0.6

Exports, GNFS4

2.4

3.6

2.4

3.1

3.4

3.1

-0.8

-0.4

-0.4

Imports, GNFS4

-0.4

3.1

3.2

3.5

3.6

3.8

0.2

0.2

0.2

Net exports, contribution
to growth

0.8

0.2

-0.2

-0.1

-0.1

-0.3

-0.3

-0.2

-0.3

SSA excluding Nigeria,
South Africa, and Angola

4.3

4.7

4.7

5.4

5.4

5.4

-0.2

0.1

-0.1

Oil exporters5

-0.7

1.4

1.7

2.9

2.8

2.8

-0.6

0.3

0.0

2.9

3.3

3.8

4.9

4.7

4.6

-0.3

0.4

-0.2

-0.8

-0.2

1.0

3.0

2.6

2.3

-0.4

0.7

-0.4

6.5

6.6

6.3

6.4

6.4

6.3

-0.1

0.1

0.0

-0.8

0.9

1.1

1.9

2.1

2.1

-0.6

-0.1

-0.1

Memo items: GDP

CFA countries

6

CEMAC
WAEMU
SSA3
Nigeria

-1.6

0.8

1.9

2.2

2.4

2.4

-0.2

0.0

0.0

South Africa

0.6

1.3

0.9

1.3

1.7

1.8

-0.5

-0.5

-0.2

Angola

-2.6

-0.1

-1.8

2.9

2.6

2.8

-3.5

0.7

0.2

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economy. 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 at market prices and expenditure components are measured in constant 2010 U.S. dollars. Excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan.
2. Sub-region aggregate excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan, for which data limitations prevent the forecasting of GDP components.
3. Sub-region historical 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 Príncipe in the sub-region 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.

112

CHAPTER 2.6

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

TABLE 2.6.2 Sub-Saharan Africa country forecasts1

Percentage point differencesfrom June 2018 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2016

2017

2018e

2019f

2020f

2021f

2018e

2019f

2020f

Angola

-2.6

-0.1

-1.8

2.9

2.6

2.8

-3.5

0.7

0.2

Benin

4.0

5.8

6.0

6.2

6.5

6.6

0.0

0.1

0.2

Botswana2

4.3

2.4

4.4

3.9

4.1

4.1

1.4

0.6

0.3

Burkina Faso

5.9

6.3

6.0

6.0

6.0

6.0

0.0

0.0

0.0

Burundi

-0.6

0.5

1.9

2.3

2.5

2.8

0.0

0.0

0.0

Cabo Verde

4.7

4.0

4.5

4.7

4.9

4.9

0.3

0.7

0.9

Cameroon

4.6

3.5

3.8

4.2

4.5

4.5

-0.1

0.1

0.2

Chad

-6.3

-3.0

3.1

4.6

6.1

4.9

0.5

2.1

0.3

Comoros

2.2

2.7

2.7

3.1

3.1

3.1

-0.2

0.1

0.1

Congo, Dem. Rep.

2.4

3.4

4.1

4.6

5.5

5.9

0.3

0.5

1.1

Congo, Rep.

-2.8

-3.1

1.0

3.2

-0.1

-1.5

0.3

-1.4

1.1

Côte d'Ivoire

8.0

7.7

7.5

7.3

7.4

6.8

0.1

0.1

0.2

Equatorial Guinea

-8.5

-4.9

-8.8

-2.1

-5.8

-5.8

-2.4

4.9

-5.3

Eswatini3

3.2

1.9

-0.6

1.7

1.8

1.8

-1.7

0.0

0.0

Ethiopia2

8.0

10.1

7.7

8.8

8.9

8.9

-1.9

-0.9

-1.0

Gabon

2.1

0.5

2.0

3.0

3.7

3.7

-0.6

-0.7

-0.2

Gambia, The

0.4

4.6

5.3

5.4

5.2

5.2

-0.1

0.2

0.3

Ghana4

3.7

8.5

6.5

7.3

6.0

6.0

-0.4

0.6

0.6

Guinea

10.5

8.2

5.8

5.9

6.0

6.0

-0.2

0.0

0.0

Guinea-Bissau

5.8

5.9

3.9

4.2

4.4

4.5

-1.2

-1.0

-1.0

Kenya

5.9

4.9

5.7

5.8

6.0

6.0

0.2

-0.1

-0.1

Lesotho

3.1

-1.7

1.2

1.2

0.2

1.8

-0.6

-1.4

-2.6

Liberia

-1.6

2.5

3.0

4.5

4.8

4.8

-0.2

-0.2

0.0

Madagascar

4.2

4.2

5.2

5.4

5.3

5.3

0.1

-0.2

0.0

Malawi

2.5

4.0

3.5

4.3

5.3

5.5

-0.2

0.2

0.4

Mali

5.8

5.4

4.9

5.0

4.9

4.8

-0.1

0.3

0.2

Mauritania

2.0

3.5

3.0

4.9

6.9

6.9

-0.6

0.3

1.7

Mauritius

3.8

3.9

3.9

4.0

3.6

3.6

-0.1

-0.1

-0.2

Mozambique

3.8

3.7

3.3

3.5

4.1

4.1

0.0

0.1

0.5

Namibia

0.6

-0.9

0.7

1.8

2.1

2.1

-0.8

-0.5

-0.9

Niger

4.9

4.9

5.2

6.5

6.0

5.6

-0.1

1.1

0.2

Nigeria

-1.6

0.8

1.9

2.2

2.4

2.4

-0.2

0.0

0.0

Rwanda

6.0

6.1

7.2

7.8

8.0

8.0

0.4

0.7

0.5

Senegal

6.2

7.2

6.6

6.6

6.8

6.9

-0.2

-0.2

-0.2

Seychelles

4.5

5.3

3.6

3.4

3.3

2.9

-0.4

-0.4

-0.2

Sierra Leone

6.3

3.7

3.7

5.1

6.3

6.3

-1.4

-0.6

-0.2

South Africa

0.6

1.3

0.9

1.3

1.7

1.8

-0.5

-0.5

-0.2

Sudan

4.7

4.3

3.1

3.6

3.8

3.8

0.5

0.5

0.3

Tanzania

7.0

7.1

6.6

6.8

7.0

7.0

0.0

0.0

0.0

Togo

5.1

4.4

4.5

4.8

5.1

5.1

-0.3

-0.2

0.1

Uganda

4.8

3.9

6.1

6.0

6.4

6.5

0.6

0.0

-0.1

Zambia

3.8

3.5

3.3

3.6

3.8

3.8

-0.8

-0.9

-1.0

Zimbabwe

0.6

3.2

3.0

3.7

4.0

4.0

0.3

-0.1

0.0

2

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 at market prices and expenditure components are measured in constant 2010 U.S. dollars. Excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan.
2. Fiscal-year-based numbers.
3. Formerly known as Swaziland.
4. Growth rates reflect GDP data prior to recent rebasing.
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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

SUB-SAHARAN AFRICA

BOX 2.6.1 Informality in Sub-Saharan Africa
Sub-Saharan Africa has high levels of informality, especially in West and East Africa, low-income countries, fragile states, andcommodity exporters. Policies to increase human capital and foster productivity, improve access to resources, reduce regulatoryburdens, and strengthen governance have been associated with a decline in informality, which in turn has been associated with
better macroeconomic and social outcomes. However, for these policies to be effective, they need to be tailored to the specific natureof informality and types of informal firms.

Introduction
Despite a decline over the past three decades, employment
informality in Sub-Saharan Africa (SSA) remains among
the highest in emerging market and developing economies(EMDEs), with nine out of ten workers informallyemployed (of which six are self-employed). Output
informality (around 40 percent of official GDP) andperceptions of informality are also high compared to otherregions. Yet, there is considerable heterogeneity within theregion—informality is higher in West and East Africa, low-income countries, fragile states, and commodity exporters.
Pervasive informality contributes to lower government tax
revenues, which limits the fiscal resources available formuch-needed public investment and social programs.
Against this backdrop, this box examines the following
questions:•

How has informality evolved?

•

What are the macroeconomic and social correlates of
informality?

•

What are the policy options to address challenges
associated with informality?

Evolution of informality
High average informality. On average in 2010-16, theinformal economy in SSA countries amounted to 36-40
percent of official GDP, informal employment made up
90 percent of employment and, more narrowly, selfemployment accounted for 58 percent of total
employment (ILO 2018a; Figure 2.6.1.1).1 Alternative
measures of informality, such as the share of the labor
force without pension coverage and perceptions of
informal activity, were also high compared with other
EMDE regions.
Heterogeneity. There is wide cross-country heterogeneity.
West and East Africa had much higher average shares ofNote: This box was prepared by Wee Chian Koh with research
assistance from Jinxin Wu.
1 A recent enterprise census in Senegal finds that 97 percent of firms
are informal (ANSD 2017).

self-employed workers in total employment during 201016, at 80 percent and 68 percent, respectively. In contrast,
the shares of self-employed workers in Central andSouthern Africa were 48 and 43 percent respectively, only
slightly above the EMDE average. Self-employment madeup more than 85 percent of employment in Benin,
Burundi, Madagascar, and Uganda whereas it was less than
20 percent in South Africa and Mauritius.
Evolution of informality in SSA. Informality in SSA has
declined gradually over the past three decades, broadly inline with the EMDE trend. Some countries, however, havemade significant progress in lowering the shares of
informal output and employment, such as Botswana,
Ethiopia, Ghana, Malawi, Rwanda, and Tanzania.

Correlates of informality
High informality in SSA reflects wide-ranging economic
and development challenges in the region. It also reflects
economic structures and a dearth of skilled labor.
Weak growth and conflict. SSA hosts all but seven of theworld’s 34 low-income countries and nearly half of theworld’s 36 fragile states (World Bank 2018z, 2018aa). In
general, informality is higher in low-income SSA countries
and, especially, fragile states. Economic disruptions duringconflict and violence have forced people to earn theirlivelihoods in the informal economy (Heintz and Valodia2008). Employment losses during recessions or shocks tocrop production have also been associated with increases in
informal labor supply (Calvés and Schoumaker 2004;
Daniels 2003; Otsuka and Yamano 2006).
Economic structure. In commodity-exporting countries,
the capital-intensive mining sector creates few formal
employment opportunities, and economies in mostcountries in SSA have large agricultural sectors that havehigh rates of informal self-employment. In the nonagricultural sector, there is also considerable selfemployment in labor-intensive services such as street
vendors, craftsmen, and home-based activities (Fox andSohnesen 2012). Rural-urban migration and increasedlabor force participation, especially among women, wasmostly absorbed by the informal sector (Kessides 2005). In
some societies, informal businesses are hereditary in

113

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

BOX 2.6.1 Informality in Sub-Saharan Africa (continued)
FIGURE 2.6.1.1 Informality in Sub-Saharan Africa
Informality has declined in Sub-Saharan Africa, but remains among the highest in the world. Informality is higher in West andEast Africa, low-income countries, fragile states, and commodity exporters.

A. Measures of informality

B. Self-employment in Sub-Saharan Africa

C. Reduction in informality acrosscountries

Source: Elgin et al. (forthcoming), International Labor Organization, World Development Indicators.
Note: A. Orange lines are the inter-quartile ranges for EMDEs.
A. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. The DGE model estimates the size of the informal sector as a percent ofofficial GDP (see Elgin and Oztunali 2012). The MIMIC model is a structural equations model that considers multiple causes of informal activity and captures multiple
outcome indicators of informal activity (see Schneider, Buehn, and Montenegro 2010). It also estimates the informal output as a percent of official GDP. Self-employed isthe share of self-employment in total employment.
B. World Bank classifications. Data for the period 1990-2016.
C. BWA = Botswana, ETH = Ethiopia, GHA = Ghana, MWI = Malawi, RWA = Rwanda, TZA = Tanzania. Percent change between 1990-2009 and 2010-16.
Click here to download data and charts.

nature, where businesses are passed down to the next
generation (Chen 2012). In others, social norms restrict
the mobility of women, compelling them to be informallyemployed (ILO 2009).
Low human capital. The average years of schooling in SSA
are well below those in any other EMDE regions(Figure 2.6.1.2). Informal workers in SSA tend to be lower
skilled and less educated than formal workers (Adams, deSilva, and Razmara 2013). This limits opportunities forwage employment in the formal economy. Self-employedworkers with low human capital, and hence low
productivity, have an incentive to operate in the informal
economy to avoid paying taxes and incurringother administrative costs (Oviedo, Thomas, andKarakurum-Özdemir 2009). Informal firms often havelower managerial ability and tend to produce low-quality
inexpensive products with little demand from theformal sector (La Porta and Shleifer 2016). The HIV/AIDS pandemic has also taken a large toll on humancapital and forced workers into less secure informal
employment where discrimination is sometimes lesspronounced (ILO 2009).
Limited access to resources and markets. Informality is
associated with restricted access to electricity, finance, andland (Ingram, Ramachandran, and Desai 2007). Limited
availability of resources curtails informal firms’ growth andproductivity improvements (Steel and Snodgrass 2008).
There are also obstacles to market access, such as lack oftelecommunications or transport infrastructure, which isparticularly important for firms that need to frequently
interact with suppliers and customers. Access to public
space and urban amenities are also important (Heintz andValodia 2008).
High regulatory burden. Compared with other EMDEs,
SSA has considerably higher regulatory burdens.
Burdensome regulations such as lengthy processes inregistering a business, complicated procedures in filingtaxes, high costs of export and import documentarycompliance, strict labor regulations, and high tax burdens
can make it prohibitively expensive to operate in theformal economy (Mbaye and Benjamin 2015).
Weak governance. Compared with other EMDEs, SSA
has considerably weaker governance and institutions. Poor
governance and institutions may result in failures inenforcing regulations and containing corruption. This
creates an environment for informal enterprises to easily
conceal their activities and evade taxes (Mbaye andBenjamin 2015).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

SUB-SAHARAN AFRICA

BOX 2.6.1 Informality in Sub-Saharan Africa (continued)
FIGURE 2.6.1.2 Economic and institutional indicators in Sub-Saharan Africa
Low human capital, limited access to resources, heavy regulatory burden, and weak governance are potentially importantdrivers of informality.

A. Economic and social characteristics

B. Doing business indicators

C. Governance indicators

Source: Barro and Lee (2013), World Bank (Doing Business, World Development Indicators, Worldwide Governance Indicators).
Note: A.-C. Blue bars are +/- one standard deviation of SSA mean. Other EMDE refers to all EMDEs except SSA countries.
A. GDP per capita is based on 2011 PPP in thousand dollars, expressed in logarithm. Life expectancy at birth is in years. Poverty is the headcount at $1.90 a day (2011
PPP) in percent of population.
B. The index represents the distance to the frontier (100) in the World Bank’s Doing Business database. A higher index represents better performance. Data for the period
2004-16.
C. The score is based on Worldwide Governance Indicators. It ranges from -2.5 to 2.5. A higher score represents better performance. Data for the period 1996-2016.
Click here to download data and charts.

Low productivity. Productivity differentials between theformal and informal sectors are large: value added per
worker of informal firms is only 14 percent that of formal
firms in the median SSA country, lower than the medianin other EMDEs (La Porta and Shleifer 2014).
Competition from informal firms, which do not shoulder
the cost of compliances with taxes and regulations, also
weigh on the profitability and investment of formal firms(Oosthuizen et al. 2016; Box 3.3). Although practices ofcompetitors in the informal sector is only the third biggest
reported obstacle in SSA, after electricity and access tofinance, it is more problematic in SSA compared to other
EMDEs (Dinh, Mavridis, and Nguyen 2010; La Porta andShleifer 2016). In addition, since informal firms do not
pay taxes, governments’ ability to provide quality public
services is constrained.
Poverty and social outcomes. While the informal
economy can provide important opportunities for
employment, the majority of those engaged in informal
activities lack income security, employment benefits, andsocial protection. Moreover, higher informality in SSA is
associated with lower life expectancy and worse poverty
outcomes (Figure 2.6.1.3). Gender inequality is alsoprevalent in the informal economy in SSA: women areoften placed in the most hazardous jobs with no access to
occupational health and safety measures (ILO 2009).

Policy challenges
Unlocking the potential of the informal economy. While
informality is more pervasive in SSA than in other EMDE
regions, the move from informality to formality is more
dynamic: more SSA formal firms started out as informal
and the duration of informality is shorter than in other
EMDEs (Figure 2.6.1.4). SSA also possesses a morepositive attitude toward business opportunities than other
EMDE regions, despite a higher proportion of people who
became entrepreneurs out of necessity. Two-thirds (65percent) of survey respondents believe they have therequired skills and knowledge to start a business, 59
percent indicate they see good opportunities to start afirm, and 42 percent intend to start a business within three
years. This intrinsic entrepreneurial spirit, despite highregulatory burdens and a weak entrepreneurshipecosystem, could render the informal sector a reservoir of
untapped economic potential (De Soto 1989; Grimm,
Knorringa, and Lay 2012).
To unlock this potential, both broad-based policy tools—
such as increasing human capital—and policy tools
targeted at specific parts of the informal sector areavailable. In Kenya, for example, improved managerial
skills and new marketing channels induced by competition
helped metalwork enterprises in the Kariobangi Light

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BOX 2.6.1 Informality in Sub-Saharan Africa (continued)
FIGURE 2.6.1.3 Correlates of informality in Sub-Saharan Africa
Improvements in economic and institutional factors are associated with a reduction in informality. High informality is
associated with worse macroeconomic and social outcomes. Years of schooling and primary school learning assessmentscores in Sub-Saharan Africa are among the lowest in the world. Investing in human capital is critical to improve labor skills.
A. Correlates of informality: Economic
factors

B. Correlates of informality: Regulationand governance

C. Correlates of informality:
Macroeconomic and social outcomes

Source: Barro and Lee (2013), Elgin et al. (forthcoming), World Bank (Doing Business, World Development Indicators, Worldwide Governance Indicators).
Note: The orange diamonds are the coefficient estimates and the blue bars denote the 90 percent confidence intervals. OLS estimators are applied, with country meansover the sample period used for both the dependent and independent variables. The share of self-employed in total employment is the measure of informality. Informality
is the dependent variable in A.-B., and it is the independent variable in C. 37 SSA countries are included in the regressions. The coefficient estimate measures the effecton the dependent variable of a unit change in the independent variable. For example, in A., a 1 percent increase in the tax rate is associated with a 0.2 percent increase in
informality. In C., a 1 percent increase in informality is associated with a 1.6 percent decline in government tax revenue.
A. GDP per capita is based on 2011 PPP in thousand dollars, expressed in logarithm. Trade openness is total trade (exports + imports) as a share of GDP. Financial
development is proxied by private credit as a percentage of GDP. Tax burden is the total tax rate using data from Doing Business. Data for the period 1990-2016.
B. The correlates are the distance to the frontier in Doing Business (data for the period 2004-16) and the scores based on Worldwide Governance Indicators (data for theperiod 1996-2016).
C. Investment is gross fixed capital formation as a percentage of GDP. Tax revenue is expressed as a share of GDP. Life expectancy at birth is in years. Poverty is theheadcount at $1.90 a day (2011 PPP) in percent of population.
Click here to download data and charts.

Industries grow and transition to the formal economy(Sonobe, Akoten, and Otsuka 2011). The local
government provided little support other than designating
an area for these artisans to operate, but that proved to be
sufficient.2
Investing in human capital. Policies should be prioritized
toward increasing human capital. Less than 20 percent ofprimary school students in Sub-Saharan Africa—typicallyfrom poor households—pass a minimum proficiency
threshold in learning assessment, which is the lowest
among EMDEs (World Bank 2018n). Teachers are also
often absent from classrooms. These learning deficiencies
amplify over time and eventually show up as weak laborskills. Although technically and politically difficult, serious
efforts must be made to improve learning outcomes.
2 Also in Kenya, the M-Pesa mobile money transfer system, combinedwith affordable ICT services, increased microenterprises’ profitability(Mbogo 2010). Improving the survival chances of these microenterprises
is one pathway toward growing the formal economy. David et al. (2012)provide other examples of successes at the local government level.

Increasing firm productivity. Small informal firms,
lacking in human capital, would not sharply increase theirproductivity by merely registering (La Porta and Shleifer2016). In contrast, large informal firms resemble formal
firms much more than their small informal counterparts:
productivity differentials of large informal firms relative toformal firms are minor (Benjamin and Mbaye 2012). In
West Africa, the largest and fastest growing sectors are, infact, dominated by large informal firms. This argues forpolicies to encourage small firms to grow into moreproductive formal firms, through skills upgrading andbetter access to inputs and resources such as businessdevelopment services, transport and communications
connectivity, financial services, health services, land andproperty rights, infrastructure, technology, and productmarkets (Oosthuizen et al. 2016). As these firms become
more productive and produce higher quality products,
they may be able to participate in supply chains in theformal sector (La Porta and Shleifer 2016). For large firms
or those that voluntarily remain informal to evade taxes or
avoid labor codes, incentives to encourage formal

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SUB-SAHARAN AFRICA

BOX 2.6.1 Informality in Sub-Saharan Africa (continued)
FIGURE 2.6.1.4 Entrepreneurial conditions, entrepreneurship attitude, and informality
indicators in Sub-Saharan Africa
Despite a higher proportion of necessity-driven informal entrepreneurs, Sub-Saharan Africa benefits from more dynamic
entrepreneurial attitudes. More formal firms in Sub-Saharan Africa than in other EMDE regions started out as informal firms.
However, small informal firms often lack managerial skills and resources. Skills upgrading and improving access to resources
can help informal firms become more productive and therefore compete in the formal sector.

A. Entrepreneurial framework conditions

B. Entrepreneurship attitude

C. Informality indicators

Source: Global Entrepreneurship Monitor, World Bank Enterprise Surveys.
Note: Blue bars are +/- one standard deviation of SSA mean. Other EMDE refers to all EMDEs except SSA countries.
A. The score is based on National Expert Survey of the Global Entrepreneurship Monitor. It ranges from 1 to 9. A higher score represents better perceived condition.
B. Data from the Adult Population Survey of the Global Entrepreneurship Monitor for the period 2001-16. Motivation index is the percentage of those who have recently
started a business that are improvement-driven opportunity motivated divided by the percentage that is necessity-motivated. A lower ratio indicates a higher proportion
that is necessity-driven.
C. Data from the World Bank Enterprise Surveys for the period 2006-16.
Click here to download data and charts.

registration can be combined with tighter enforcement(Mbaye and Benjamin 2015).
Building institutions. Regulatory and institutional reforms
to build public trust can strengthen incentives for firms tooperate formally (Mbaye and Benjamin 2015). This
includes improving the business environment by removing
unnecessary regulatory barriers, strengthening monitoringand enforcement capabilities, and upholding legal andjudicial systems. These policies apply equally to formal
firms as an enabling environment is critical for investment
and employment generation. Improving macroeconomic
stability with sound fiscal and monetary policy frameworks
is also essential.

Stakeholder engagement. Governments can actively
engage with the informal community to encourage a shift
towards greater formality (ILO 2009). This can involve
educating informal firms on the benefits of formal
registration, providing information on the procedures,
participating in social dialogues to understand pressing
issues for informal firms, customizing household surveys tobetter capture important aspects of informality, andcollaborating with informal actors to design andimplement effective development policies.

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CHAPTER 3

GROWING IN THE SHADOW
Challenges of Informality

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CHAPTER 3

The informal sector accounts for about a third of GDP and 70 percent of employment (of which selfemployment is more than a half) in emerging market and developing economies. Informality is more widespreadin lower-income countries with a large agricultural sector and a high share of unskilled workers. While offering
the advantage of flexibility and employment in some economies, a larger informal sector is associated with lower
productivity, reduced tax revenues, and greater poverty and inequality. Overcoming the challenges of
informality requires a balanced mix of policies that carefully take into account country-specific drivers. A welldesigned policy framework could complement lowering regulatory and tax burdens with increasing the efficiencyof public revenue collection and regulatory frameworks. It could also include expanded access to finance,
markets, and inputs to foster firm productivity and growth; better education to facilitate formal sector
employment; improved public services to bolster tax morale; and enhanced safety nets to cushion household risks.

Introduction
The livelihoods of the poor in emerging market
and developing economies (EMDEs) often
depend on informal activity. In these economies,
informal sector output on average accounts forabout one-third of GDP and informal
employment constitutes about 70 percent of
employment (of which self-employment accounts
for more than a half; Figure 3.1). In somecountries in Sub-Saharan Africa, informal
employment accounts for more than 90 percent of
employment and informal output is as much as 62
percent of official GDP (ILO 2018a).
Informality is a multidimensional concept,
differing in nature across workers and countries(Perry et al. 2007). Some workers and firms are“excluded” from the modern economy or criticalstate benefits by tax and regulatory burdens (deSoto 1989; Loayza, Oviedo, and Servén 2006).
This type of informality is frequently associatedwith low productivity and poorly paid low-skilledemployment (La Porta and Shleifer 2014; Loayza2018). Other informal workers voluntarily “exit”
the formal sector and choose informal activity for
its flexibility and independence (Maloney 2004;
Günther and Launov 2012).1 In lower-income

Note: This chapter was prepared by a team led by M. Ayhan
Kose, Franziska Ohnsorge and Shu Yu, and including Mohammad
Amin, Sinem Kilic Celik, Gene Joseph Kindberg Hanlon, Ergys
Islamaj, Sergiy Kasyanenko, Cedric Okou, Naotaka Sugawara, Temel
Taskin, and Collette Wheeler.
1 Several studies find that some informal workers in EMDEs
operate voluntarily in the informal sector. For instance, Falco et al.
(2015) use survey data from Ghana and find little evidence for theoverall inferiority of working in the informal sector compared withthe formal sector. Falco and Haywood (2016) report that returns to
productive characteristics in self-employment have increased
significantly in Ghana between 2004 and 2011 whilst self-countries, the informal sector is a major source ofincome to many low‐skilled individuals whose
income would otherwise fall below subsistence(Docquier, Müller, and Naval 2017). These
reasons for participating in the informal economy
mean informal workers range from agricultural
day laborers to self-employed lawyers with a few
employees.2
Regardless of the reason why individual workers or
firms choose between formal and informal
activity, a large informal sector has been associatedwithunfavorable
macroeconomic
anddevelopment outcomes. On average, economies
with larger informal sectors tend to have lower
productivity, slower physical and human capital
accumulation, higher poverty and inequality, andsmaller fiscal resources.3 The informal sector itself
has, on average, lower productivity than theemployment has attracted increasingly skilled workers. Blanchflower,
Oswald, and Stutzer (2001) note that many workers in advanced
economies, such as the United States and Portugal, report preferring
to be self-employed.
2 Research suggests the coexistence of both “excluded” and“exiting” types of informality (e.g., Perry et al. 2007; Hazans 2011;
Bosh and Maloney 2008, 2010; Lehmann and Pignatti 2007; Fiess,
Fugazza, and Maloney 2010; Nordman, Rakotomanana, andRoubaud 2016).
3 La Porta and Shleifer (2014) provide evidence that informality is
associated with lower productivity, less access to financing, and lesseducated managers. Some studies show that informality is associatedwith higher income inequality and poverty (Rosser, Rosser, andAhmed 2000; Perry et al. 2007; Chong and Gradstein 2007;
Loayza, Servén, and Sugawara 2010). Lower physical investment in
the informal sector could reflect an unwillingness of informal firms to
adopt technologies or scale up that would make them visible to tax
and other authorities (Dabla-Norris and Inchauste 2008; Gandelman
and Rasteletti 2017). Docquier, Müller, and Naval (2017) develop amodel that predicts that the informal sector would lead to slower
human capital formation. Less educated managers partially explain
lower labor productivity observed in informal firms (Cirera andMaloney 2017). Benjamin et al. (2014) show that informality is
associated with weaker international competitiveness.

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formal sector because it tends to employ lessskilled workers, have restricted access to funding,services and markets, and lack economies of scale.4
Employment in the informal sector can provide asafety net by keeping or creating employment
during periods when the formal sector is shedding
jobs (Loayza and Rigolini 2011). However,workers in the informal economy are largely
excluded from the social security system and lessprotected against negative shocks than workers in
the formal sector (Box 3.1).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019•

Informality and ﬁscal indicators. The chapter
is the first to document the empirical link
between higher informality and unfavorable
aggregate fiscal outcomes, including revenues
and expenditures. This goes beyond previous
studies that have focused on the implications
for specific tax categories, such as value-added
taxes (Keen 2008).

•

Informal competition and formal ﬁrm
productivity. The chapter presents the firstcross-country study that quantifies the lower
productivity of formal firms that facecompetition from informal firms. This adds to
the rich literature that documents the sizableproductivity differential between formal andinformal firms.

•

Policy implications. The chapter analyzes theimplications of country-specific policy changes
for the informal sector and synthesizes thelessons from these changes to offer a menu ofpolicy options that takes into account theimportance of complementarities.

Against this backdrop, this chapter examines themain features of informal economies and possible
policies to address the challenges associated withinformality. Specifically, it addresses the following
questions:•

What are the main features of the informal
economy?

•What are the empirical linkages between
informality and development outcomes?

•

Which policies can mitigate the adverse effects
of informality?

The chapter makes the following contributions to
the literature on informality:•

Broad database on informality. The chapter
compiles a new database from a wide range of
informality measures. It employs thesemeasures to provide a rich set of stylized facts
on informality that are robust to the choice ofmeasure.

•Informality, poverty, and income (wage)
inequality. The chapter documents thathigher informality is associated with greater
poverty. This may, in part, reflect lower wages
for informal workers than formal workers.
While many survey-based studies havedocumented the existence of such wage
differentials (especially at the country-level),
this chapter distills broader lessons from alarge number of studies.

4 For details, see Jovanovic (1982), Amaral and Quintin (2006),
Galiani and Weinschelbaum (2012), Loayza (2018).

The chapter reports the following main findings:•

Main features of the informal economy. In
EMDEs, the informal economy in 2016 on
average accounts for 32 percent of GDP and,70 percent of employment, with selfemployment accounting for 43 percent of
employment (Figure 3.1). Informality tends
to be higher in lower-income economies thatare less open to international trade, have larger
agricultural sectors, and have larger pools ofyoung and unskilled workers. Both informal
output and employment have declined since1990. Informality declined faster in countries
with higher output growth, rapid physicalcapitalaccumulation,andstronger
improvements in governance and businessclimates.

•Prevalence of informality. One-half of theworld’s informal output and 95 percent of its
informal employment is in EMDEs. Three
EMDE regions (East Asia and Pacific (EAP),
Latin America and the Caribbean (LAC), andEurope and Central Asia (ECA)) accounted
for more than one-third of the world’s
informal output, but only one-quarter of its

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

formal output. Meanwhile, South Asia (SAR)
hosts almost half of the world’s informal
workers, although the region accounts for lessthan one-tenth of the world’s formal
employment and less than one-twentieth of
the world’s formal output. Another 14 percentof the world’s informally employed are in
Sub-Saharan Africa (SSA), two to five times
the region’s share of the world’s formal output
or formal employment.
•

•

•

•

Informality and development outcomes.
Higher informality is associated with lower
output growth, lower productivity, and higher
poverty and income inequality. Potential
reasons for greater poverty in economies withhigher informality may include a lack of fiscal
resources to fund public services or wage
differentials between informal and formal
workers. Workers in the formal economy
earn, on average, about 19 percent more thanworkers in the informal economy. These wage
differentials largely reflect the characteristics ofinformal workers, who tend to be lowerskilled than formal workers.
Informality and ﬁrm productivity. The
average informal firm in EMDEs is only onequarter as productive as the average firm
operating in the formal sector. Moreover,firms in the formal sector that face informalcompetition are, on average, only threequarters as productive as those that do not.
Better business climates can mitigate some ofthese productivity differentials.
Informality and ﬁscal indicators. In EMDEs
with the most pervasive informality,
government revenues are lower by 5-10
percentage points of GDP (and expenditures
lower by 4-10 percentage points of GDP)
than in those with the lowest levels
of informality. In developing economies,
pervasive informality further limits governments’ limited ability to implement redistributive measures, invest in public
infrastructure, or carry out other growthenhancing policies.
Policy implications. A review of studies ofpolicy measures that have had repercussions

CHAPTER 3

131

FIGURE 3.1 Informality: Magnitude and correlates
The informal sector accounts for about a third of GDP and 70 percent of
employment (of which self-employment is more than a half) in emerging
market and developing economies (EMDEs). A large informal sector isassociated with slower GDP growth and weaker governance as well as
greater poverty and income inequality. Widespread informality is also seenin economies with lower government expenditures and revenues, and a
skew towards trade-based taxation.
A. Share of informal output andemployment

B. Informality: Output, employment,
and perception

C. Informal output and institutional
quality, 2016

D. Brazil: Share of informal workers
preferring informal over formal
employment

E. Informality, poverty, and income
inequality

F. Differences in fiscal indicators
between the lowest and highest
output informality in EMDEs

Source: Elgin et al. (forthcoming a); International Monetary Fund, World Economic Outlook; Maloney(2004); International Labour Organization; World Bank (Doing Business, World Development
Indicators, World Governance Indicators); World Economic Forum; World Value Survey.
A. Unweighted averages. Informal employment (in red) uses self-employment shares (with additional
informal employment shares in shaded red) in the closest (latest) available year around 1990 and2016. World averages between 1990-2016 are in orange. EMDEs= Emerging market and developing
economies.
B. Unweighted average for each informality measure for latest available year (with the corresponding+/-1 standard deviation shown in vertical bars). See Annex 3.1 for details.
C. Group averages of DGE-based informal output in percent of official GDP in year 2016 are shownin diamonds, with bars showing 95 percent confidence intervals. The dashed line shows the world
average. “High” (“Low”) indicates countries with above- (below-) median values in the following two
measures: Doing Business distance-to-frontier and governance effectiveness (WGI).
D. The shares of informal workers preferring informal over formal employment (Maloney 2004).
E. Data are for 1990-2016. Group means (diamonds) and 95 percent confidence intervals (bars) are
shown for poverty headcount ratio at $1.90 a day (2011 PPP, percent of population) and Gini
coefficients. “High informality” (“Low informality”) indicates countries with above- (below-) median
informal output (DGE-based estimates).
F. Differences in the 2000-16 average fiscal indicators among the third of EMDEs with the highest
and lowest informality (measured by the share of DGE-based informal output averaged during 200016). Vertical bars indicate 90 percent confidence intervals of the difference. Sample includes 70 nonenergy exporting EMDEs with populations above 3 million people.
Click here to download data and charts.

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BOX 3.1 Linkages between formal and informal sectors
Empirical evidence on the degree of cyclicality of the informal economy with the formal economy is mixed. The cyclicality andsensitivity of informal employment to formal business cycles depends on the sources of shocks driving business cycles, the presence ofrigidities in the formal sector, the initial extent of informality, and the availability of informal jobs.
While there is broad consensus that the informal economy
is sizable in emerging market and developing economies(EMDEs), evidence for its behavior over the businesscycles remains inconclusive. An informal economy that
expands while the formal economy contracts may support
household incomes during economic downturns and could
serve as a safety net (Loayza and Rigolini 2011). An
informal economy that behaves procyclically could
function as a “growth engine” by providing more services
and intermediate inputs to the formal economy during
economic expansions but, conversely, could also amplify
the adverse effect of recessions (Dell’Anno 2008; Chen
2005; Meagher 2013).1 Earlier work suggests that thedegree of cyclicality of the informal economy depends onthe measure of informality, the types of shocks causingbusiness cycles, and country characteristics.
Against this backdrop, this box reviews the literature andpresents results from a set of empirical exercises to address
the following questions:•

What conclusions does the literature offer about thecyclical behavior of the informal economy?

•

How synchronized have been movements in informal
and formal economies?

•

What are the policy implications of cyclicality?

Literature review
The literature on the cyclical behavior of the informal
economy offers mixed conclusions. Studies focusing on theshare of the informal economy in total output or
employment tend to find countercyclical behavior whereas
studies focusing on output or employment levels tend tofind procyclical behavior. The theoretical literaturesuggests that the degree of procyclicality depends on thesource of shocks causing business cycle fluctuations and on
the presence of labor market rigidities. This section
summarizes this literature.2
Note: This box was prepared by Sergiy Kasyanenko and Shu Yu.
1 See Meagher (2013) for a literature review on studies concerning thelinkage between formal and informal economy.
2 Several recent studies also argue that pervasive informality may
influence the measured cyclicality of the formal economy. For example,
Restrepo-Echavarria (2014) and Horvath (2018) show that models with alarge and poorly measured informal sector can generate excess volatility offormal consumption relative to formal output – a common feature ofbusiness cycles in many EMDEs.

Informal economy as a countercyclical safety net
The informal sector can serve as buffers and safety nets forthe poor if it absorbs labor during recessions.3 This canfacilitate an economic recovery provided that re-entry into
the formal sector is possible when the formal economy
returns to expansion (Colombo, Onnis, and Tirelli 2016;
IMF 2017; Loayza and Rigolini 2011).
Macroeconomic evidence. Macroeconomic studies suggestthat the informal economy can behave “countercyclically”
in the sense that the share of informal employment indeed
rises during business cycle downturns.4 Using data from 54
countries for 1984-2008, Loayza and Rigolini (2011) show
that, on average, a one standard deviation slowdown in
GDP per capita growth (i.e., 3 percentage points) is linked
with a short-run increase in the share of self-employment
in the total labor force by 1.2 percentage points. However,they also find considerable heterogeneity acrosscountries—the counter-cyclicality of informal employment
is much weaker in economies with more pervasive
informality.5
Using quarterly data for Mexico, Fernández and Meza(2015) find that the correlation between informal
employment and official GDP is modest (about -0.3),
whereas the correlation between formal employment andformal output is strongly positive. The authors argue that
this lowers cyclicality of total employment. Colombo,
Onnis, and Tirelli (2016) use electricity consumption as aproxy for total economic activity to study cyclical
properties of informality in 48 countries over the period

3 Due to its flexibility, the informal sector is able to adjust in wages
rather than employment during recessions, which explains the informal
employment’s lack of responses to economic downturns (Maloney 2004;
Guriev, Speciale and Tuccio 2016).
4 Remittances, as a buffer against shocks to formal economy, may also
influence the cyclicality of the informal sector. Remittances appear to belargely absorbed by the informal sector as Ivlevs (2016) finds that highlevel of remittances tends to be associated with more informality. Shapiro
and Mandelman (2016) and Chatterjee and Turnovsky (2018) show thatcountercyclical remittances are associated with higher informal
employment during recessions as formal employment declines.
5 The extent of countercyclicality drops as the share of informal
employment in total employment increases and disappears when informal
employment accounts for more than 42-43 percent of total employment.
Theoretically, Shapiro (2014) suggest that while the share of selfemployment tends to decline during economic upturns, the ease of entry
into self-employment explains the differences in cyclical behavior acrosscountries.

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BOX 3.1 Linkages between formal and informal sectors (continued)
1984-2005 and illustrate that the informal economy
expands following a banking crisis. Finally, Kaufmann and
Kaliberda (1996), Busato and Chiarini (2004) and Elgin(2012) present empirical evidence that the informal
economy acts as a buffer, increasing its share of official
GDP during economic downturns.6
More procyclical fiscal policy in less developed economies
with weaker institutions may contribute to countercyclicality of informal activity. Fiscal policy tends to be
more procyclical in countries with higher informality(Çiçek and Elgin 2011). In particular, procyclical fiscal
consolidation during recessions, including through higher
taxes, may encourage more informal employment andstrengthen the counter-cyclicality of informal activity.
Microeconomic evidence. In work flow data for Brazilian
metropolitan labor markets between 1983 and 2002,
Bosch, Goni, and Maloney (2007) find that the informal
sector is able to absorb more labor during economic
downturns as jobs became scarce in the formal sectors.
Bosch and Esteban-Pretel (2012) use the same data andfind that the share of formal employment falls as formaleconomy output contracts, in part because the rate at
which workers find formal jobs plummets while that at
which they find informal jobs remains broadly stable.7
Informal economy as an engine of growth
Since informal firms provide services, as well as final andintermediate goods to the formal sector, one would expect
a positive correlation between formal and informal sector
activity (Lubell 1991; Arvin-Rad, Basu, and Willumsen
2010; Moreno-Monroy, Pieters, and Erumban 2014). In
addition, informal-economy income can support formaleconomy demand.8

6 This empirical relationship between informal and formal activities
appears to be present in both advanced economies and EMDEs. For
example, Kaufmann and Kaliberda (1996) cover a panel of 16 Central
and Eastern European countries in 1989-1994; Busato and Chiarini(2004) used data on the share of informal output in total GDP from theUnited States, Italy, the United Kingdom, and New Zealand over theperiod 1960-1997; Elgin (2012) utilizes a panel of 152 countries from1999 to 2007.
7 Job separation rates are countercyclical (i.e rise during recessions) for
both sectors, with a much higher probability of losing an informal job
during recessions.
8 See Schneider (1998), Gibson (2005), Docquier, Müller, and Naval(2017), Kanbur (2017), Eliat and Zinnes (2002), and World Bank
(2014). Although the relationship between formal and informal sectors
may be symbiotic in the short run especially when policymakers areconcerned about the welfare of low-skilled working poor, argue that in
the long-run pervasive informality may create poverty traps and stymie
economic development.

Macroeconomic evidence. Using informal output levels(as opposed to the share of the informal economy), Bajada(2003), Giles (1997), Tedds and Giles (2000), andDell’Anno (2008) find that informal-economy output
movements correlate positively (i.e., move pro-cyclically)
with formal-economy output movements in Australia,
New Zealand, Canada, and a group of 19 Latin Americancountries. In a group of developing countries, Fiess,
Fugazza, and Maloney (2010) identify episodes whererelative demand or productivity shocks to the nontradables sector (as opposed to the tradables sector) areassociated with higher informal employment (hence, procyclicality). In Brazil and Mexico, higher separation rates
from informal jobs and a large drop of the formal job
finding rate may induce labor outflows from the informal
sector during recessions (Bosch and Maloney 2008). Arvin-Rad, Basu, and Willumsen (2010) develop a theoreticalmodel that establishes procyclical informal-formal sector
linkages, particularly when formal firms sub-contract labor-intensive stages of production to the informal sector.
Microeconomic evidence. Schneider (1998) reports that inGermany and Austria at least two-thirds of the income
earned in the informal economy is immediately spent in
the official economy resulting in considerable (positive)
stimulating effects on the official economy. In firm-level
data for India, Moreno-Monroy, Pieters, and Erumban(2014) find that formal and informal sector employment
are positively correlated, in part because subcontracting byformal-sector firms to informal firms contributes to job
creation in the informal sector. Data from Indian
manufacturing firms show that the gross value added forseveral predominantly informal industries is positively
correlated with that in the formal sector and FDI. Thismay be indicative of technological spillovers contributing
to both formal and informal sectors (Beladi, Dutta, andKar 2016).
Factors determining the degree of procyclicality of theinformal economy
Cross-country heterogeneity. There is considerable
cross-country heterogeneity in the degree of pro-cyclicalityof informal employment. It tends to be higher when
informality is greater (Loayza and Rigolini 2011), when
informal employment is more common (Shapiro 2014),
when there are stronger informal-formal sector linkages
such as through subcontracting (e.g., Moreno-Monroy,
Pieters, and Erumban 2014; Mbaye, Benjamin, andGueye 2017).
Source of shocks causing business cycles. The informal
economy can move procyclically or countercyclically,

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BOX 3.1 Linkages between formal and informal sectors (continued)
depending on the sectoral origin of the shocks thatgenerate business cycles in the presence of wage rigidities,
especially in the formal sector (Fiess, Fugazza, andMaloney 2010). Positive relative demand or productivity
shocks to the non-tradable (largely informal) sector could
increase informal employment, i.e. generate procyclicality
in informal employment, especially when combined withwage rigidities in the formal sector. For instance, inColombia, capital account liberalization in the context ofbroader reforms during 1991-1996 raised permanent
income and constituted a positive demand shock to the
non-tradeable sector. This upturn resulted in an expanding
non-tradable informal sector. Conversely, in the presenceof wage rigidities, a negative shock to the tradables sector
would expand informal (nontradables) employment andthus appear as countercyclicality.

peaks (Elgin et al. forthcoming b). In contrast,
turning points in self-employment, as a proxy for
informal employment, rarely coincide with turning
points in formal employment or formal output.
•

Correlations. Lead, lag, and contemporaneous
correlations of formal-economy output with informaleconomy output are highly and statistically significant
whereas those between formal output and informal
employment are statistically insignificant (Figure
3.1.1; Elgin et al. forthcoming b). This is consistent
with studies that find countercyclicality in the share of
the informal economy and those show that informal
firms are flexible enough to adjust in wages rather
than employment during economic downturns(Maloney 2004; Loayza and Rigolini 2011; Guriev,
Speciale and Tuccio 2016).10

Synchronization in formal and informal-economy
movements•

As in other studies that examine levels of employment andoutput, the data set used in this chapter suggests that, at
the macroeconomic level, formal employment levels andinformal output levels comove with formal output levels
but informal employment levels do not. Several
methodologies point to this finding, including analyses ofvolatility, business cycle turning points, correlations andfactor models.

Common factors. A dynamic factor model applied toformal and informal output and employment findsthat a single common factor accounts for 38 and 40
percent of the output variance of the informal andformal economies, respectively (Kose, Prasad, andTerrones 2003; Elgin et al. forthcoming b). This
common factor explains only a negligible share of thevariance in informal employment.

Policy implications•

Macroeconomic volatility. Since formal and informal
employment move marginally (but statisticallysignificantly) in opposite directions, the volatility of
total (formal and informal) employment is somewhat
lower than the volatility of each type of employment
in isolation (Figure 3.1.1, Elgin et al. forthcoming a;
Loayza and Rigolini 2011; Fernández and Meza
2015).9 Self-employment (as a proxy for informal
employment) is somewhat less volatile than formal
employment. In contrast, informal output issomewhat less volatile than formal output, possiblyreflecting flexible adjustments in hours worked in theinformal economy (Meghir, Narita, and Robin 2015;
Guriev, Speciale, and Tuccio 2016).

•

Business cycle turning points. About three fourths ofbusiness cycle troughs in formal output coincide witha trough in the informal output; seven out of ten
formal output peaks coincide with informal output

9 The correlation between formal and informal employment growth
rates is above 0.2 and significant at 1 percent level.

A large degree of comovement of informal employment
and formal output in and of itself may not warrant policy
action for two reasons. First, the direction of comovement
can change over time if business cycle fluctuations arecaused by changing sources of sectoral shocks. Second, theappropriate policy response would depend on the source ofthe shock that generates comovement. If a procyclicalexpansion in informal employment is largely the reflectionof shocks in the nontradable sector, such as inconstruction, no policy response specifically related to
informality may be needed. In contrast, if a countercyclicalexpansion in informal employment reflects a downturn inthe tradable sector, such as in manufacturing, in thepresence of labor market rigidities, measures to ease labormarket rigidities may be the appropriate response (Fiess,
Fugazza, and Maloney 2010).

10 This lack of comovement between formal output and informal
employment is particularly pronounced in EMDEs, possibly reflecting
data challenges in EMDEs, genuinely lesser synchronicity between formal
economic output and formal employment in advanced economies, or
higher labor market rigidities in EMDEs (Neumeyer and Perri 2005;
Botero et al. 2004; Campos and Nugent 2012).

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CHAPTER 3

BOX 3.1 Linkages between formal and informal sectors (continued)
FIGURE 3.1.1 Interaction between formal and informal economies
Formal employment and informal output levels tend to comove with formal output levels, whereas informal employment levels
do not. Since formal and informal employment are marginally negatively correlated, total (formal and informal employment) is
less volatile than each in isolation.
A. Synchronization of turning points

B. Volatility of output

C. Volatility of employment

Source: Elgin et al. (forthcoming a, forthcoming b).
Note: Data are from 1990 to 2016. DGE-based estimates are used for informal output in A-B, while data on self-employment are used for informal employment.
A. Troughs and peaks are identified as in Harding and Pagan (2002). The bars show the share of formal peaks (or troughs) that coincide with informal peaks (or troughs).
B-C. Standard deviations in annual growth rates are shown in bars. “***” indicates statistically significant differences at least at 10 percent level between informal andinformal output (employment). “+++” indicates statistically significant differences at least at 10 percent level between advanced economies and EMDEs.
Click here to download data and charts.

In addition to measures taken explicitly to address
informality, many measures undertaken for other reasons,such as tax measures, may have implications for
informality. The discussion in this box highlights thatthese implications warrant a carefully calibrated policymix.
The resilience of informal employment to business cycle
swings, juxtaposed with the weaker development outcomes
associated with informality (discussed in the main text),
suggests a trade-off. In the short run, informal activity canprovide a safety net during business cycle swings and labor
dislocations caused by major structural changes such as
trade liberalizations; in the long term, however, theinformal sector can be a source of poverty and stymie
development (Docquier, Müller, and Naval 2017; Dix-

for informality highlights the need for acomprehensive policy package that takes into
account country-specific features that lead to
informality and determine its consequences.
First, strategies to reduce informality outrightmay hurt vulnerable groups and disrupt
formal activity that relies on informaleconomy inputs. These effects can be
mitigated by stronger safety nets, greater labor

Carniero, Goldberg and Meghir 2018). Policy measuresthat—deliberately or inadvertently—reduce informality
can therefore protect vulnerable population groups better
if they are accompanied by strengthened social safety nets
that can fulfill some of the roles of the informal sector.
Similarly, if comovement between formal and informal
output reflects synergies, such as through subcontracting,policy measures aimed at curtailing informal activity candisrupt formal activity.
These effects could be mitigated if measures that reduce
informality were accompanied by greater labor andproduct market flexibility in the formal sector thatfacilitates a reallocation of informal workers and firms.

and product market flexibility, and better
access to resources for informal firms. Second,
policies to spur development, as a collateralbenefit, can help reduce informality. Specificmeasures discussed in this chapter include
simplification of tax codes and enhanced
enforcement of revenue collection, which can
reduce the incentive to operate informallydepending on country-specific circumstances;

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easing of labor market regulations to lower the
relative cost of employing formal workers andcreate a level playing field for formal andinformal firms and workers; as well as greater
access to finance and public services to help
increase productivity in the informal sector
and encourage a shift of activity to the formal
sector.

Informality: Conceptual
considerations andmeasurement
Definition of informality
Informality is often defined as market-based legal
production of goods and services that are hiddenfrom public authorities for monetary, regulatory,
and institutional reasons (Schneider, Buehn, andMontenegro 2010).5 This general definitionencompasses many types of informal activity
among workers and firms.6 Some studies
distinguish different types of informality by the
motives of participating in the informal economy.
For example, some classify informal workers andfirms into those that are “excluded” and those that“voluntarily exit” from the formal sector (Perry et
al. 2007). Others focus on “subsistence
informality,” which is pervasive in lower-income
countries and characterized by low‐skill
technology. In the absence of such an informal
economy, the income of low-skilled workers
would fall below subsistence levels (Docquier,
Müller, and Naval 2017).
Some others classify informal workers and firms
into evaders, avoiders, and outsiders depending on
their compliance with regulations and regulations’applicability (Kanbur and Keen 2015; Kanbur
5 Monetary reasons include avoiding taxes and social security
contributions; regulatory reasons include avoiding government
bureaucracy or regulatory burdens, while institutional reasons include
corruption, the quality of political institutions and weak rule of law.
For the purposes of this chapter, the informal economy reflects
activities that, if recorded, would contribute to GDP, and does notcover illegal activities or household production (Schneider, Buehn,
and Montenegro 2010; Medina and Schneider 2018). The difference
between informal production and household production is that thelatter does not encounter monetary transactions.
6 The definition and classification of informality is deeply context
specific. Similarly, the choice of informality measures largely depends
on the research question (see Elgin et al. forthcoming a, for details).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 20192009). Evaders are firms that are covered by theregulation but do not comply; avoiders are firmsthat adjust to be outside the remit of theregulation; outsiders are firms that are simply notcovered by the regulation. More recent studies
distinguish different types of informality by theentities engaged in informal activity, separate fromtheir motivation: within firms, formal andinformal workers or activities (“interfirm margin”)
and, within sectors, informal and formal firms orworkers (“intersectoral margin,” Maloney 2006;
Ulyssea 2018).7 Individual country practices vary
widely but typically adhere to these broad
principles.
•

Informal workers. Informal employment
covers all workers of the informal sector andinformal workers outside the informal sector(ILO 2018a).8 The former comprises all
persons who were employed in at least one
informal firm. The latter group includes bothself-employed and workers that are not
employed in formal contractual arrangements
or not subject to social protection or
employment benefits. Some have defined
informal employment more specifically as thatof workers without pension coverage, which isa part of social protection (Loayza, Servén,
and Sugawara 2010).

•

Informal ﬁrms. An informal firm satisfies thefollowing criteria (ILO 2018a).9 First, it is not

7 See Perry et al. (2007, p.27) for a more detailed description of
informal employment and different types of informal employment.
8 The most frequently used informal employment measure is theshare of self-employment in total employment, which is a lower
bound of informal employment (e.g., La Porta and Shleifer 2014). As
defined by the 1993 International Classification of Status in
Employment (ICSE-93), self-employed workers are those workers
who, working on their own account or with one or a few partners orin a cooperative, hold the type of jobs defined as "self-employment
jobs." The other measure, informal employment, comprises all
workers of the informal sector and informal workers outside theinformal sector. The former covers all persons who, during a givenreference period, were employed in at least one informal sector
enterprise, irrespective of their status in employment and whether it
was their main or a secondary job. The latter covers self-employment
and employees holding informal jobs. See Annex 3.1 for details. For
the remainder of the chapter, informal employment will be proxiedby self-employment since data on informal employment is notavailable for advanced economies. The numbers here refer to thelatest available years.
9 Benjamin and Mbaye (2012) and Mbaye, Benjamin, and Gueye(2017) provide an alternative definition of informal firms as acontinuum depending on size, registration, honest accounting, tax
payments, mobility of work-place and access to bank credit.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019an incorporated enterprise that is a legal entity
separate from its owners, has its own complete
set of accounts, but is not owned nor
controlled by one or a few household
members. Second, it is a market enterprisethat sells its goods or services. Third, it falls
into one of the following categories: it keeps
the number of workers employed on acontinuous basis and below a thresholddetermined by the country; it is not registered;or its workers are not registered.
Measurement of informality
Reflecting the difficulty of measuring informality,
the literature has developed a wide range of
estimation methods to capture its extent. In thischapter, a database of all commonly used measuresof informality is compiled (summarized in Annex
Table 3.1.1), ranging from model-based estimatesof the share of informality in official GDP(MIMIC and DGE estimates), to survey-based
measures of the share of informality in total
employment (share of self-employed and share ofworkers covered by pension schemes), and public
perceptions of the extent of informality (World
Economic Forum index, World Value Survey
index, and Enterprise Surveys).10 The databaseincludes up to 196 economies (36 advanced and
160 emerging market and developing economies)
over the 1990-2016 period. Both cross-country
rankings and time trends are consistent for mostcountries. That said, the chapter relies mainly on
the two model-based (DGE and MIMIC)
estimates of the share of informal output and theshare of self-employed (from International Labour

10 For the compiled data set, see Elgin et al. (forthcoming-a). In
the data set, the Multiple Indicators Multiple Causes (MIMIC)
model is a model of structural equations that use observable causes
and indicators to capture the latent level of informal output. Elgin etal. (forthcoming a) follow Schneider, Buehn, and Montenegro (2010)closely when estimating the MIMIC model for 160 countries overthe period 1993-2015. The dynamic general equilibrium (DGE)
model of Elgin and Oztunali (2014) provides an alternative estimateof the size of the informal sector for 158 countries (36 AEs and 122
EMDEs) over the period 1950-2016. To make the measurescomparable with those in the literature, both measures are reported inpercent of official GDP. In the following sections, “in percent of
GDP or output” is used as the equivalent of “in percent of official
GDP” in the context of the share of informal output (both DGEbased and MIMIC-based estimates), while “in percent of
employment” is used as the equivalent of “in percent of total
employment.”

CHAPTER 3

Organization, World Development Indicators,
and national statistical offices’ databases), which
stand out in their time and country coverage.11

Main features of theinformal economy
Informality: Lower in advanced economies thanin EMDEs. On average, the size of the informal
economy is about 32 percent of official GDP and,in EMDEs, 71 percent of total employment in
2016, of which self-employment accounts for 43
percent of total employment (based on latest
available data for each country). A higher level ofdevelopment, e.g., as measured by per capita
income, is associated with lower informality,
regardless of the measure of informality or the year
chosen (La Porta and Shleifer 2014; Figure 3.2).
As a result, informality tends to be considerably
more pervasive in EMDEs than in advanced
economies: in advanced economies, informal
output accounts for about 17 percent of output
and self-employment accounts for 14 percent of
employment. Perceptions from business owners
and managers about the pervasiveness of
informality also suggest greater informal activity in
EMDEs than in advanced economies.
Cross-country heterogeneity: Pronounced in
EMDEs. The size of the informal economy varies
widely across countries (Figure 3.2). In EMDEs,
the informal economy ranged from around 10 (inChina) to 69 (in Equatorial Guinea) percent of
GDP—depending on the measure used—and selfemployment ranged from near-zero (Qatar) toaround 90 (Burundi) percent of employment.
Among advanced economies, the share of informal
output in GDP has varied from less than 12
percent, in Switzerland and Singapore, to about
32 percent in Estonia. During 2006-16, Greece
registered the highest share of informal
employment (37 percent) among advanced
economies.

11 For presentational simplicity, throughout this chapter, theoutput share of informality refers to the share of informal output
based on DGE model estimates, unless otherwise noted. The main
results for features of informality, correlates of informality, anddevelopmental implications are robust to the use of MIMIC-based
estimates.

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FIGURE 3.2 Informality in advanced economies and
EMDEs
There is wide cross-country heterogeneity in informality, especially among
EMDEs. Since 1990, the share of informal employment and output has
declined in both advanced economies and EMDEs, despite largely
unchanged perceptions of the size of the informal sector.

A. Share of informal activity

C. Share of informal employment

E. Perceived informal activity

B. Informal activity over time

D. Informal employment over time

F. Perceived informal activity overtime

Source: Elgin et al. (forthcoming a); International Labour Organization; World Bank, World
Development Indicators; World Economic Forum.
Note: See Annex 3.1 for data definitions.
A.C.E. Group means for the period 2006-16 are shown in blues with +/-1 standard deviation shown inorange vertical bars. World means are shown in yellow lines (dashed line for MIMIC estimates in A).
The group statistics are calculated for the world, advanced economies (AEs) and emerging market
and developing economies (EMDEs).
B.D.F. Group means are calculated for advanced economies (AEs, in blue) and emerging anddeveloping economies (EMDEs, in red). Dashed lines are for MIMIC estimates in B. In D, missing
data for informal employment are extrapolated in EMDEs for earlier years and filled using the latest
available observation in recent years.
E.F. WEF (World Economic Forum) index of informality.
Click here to download data and charts.

Regional informality: Common in all EMDE
regions. Informality is common in all EMDE
regions but takes different forms (World Bank
2012). On average, the informal economy’s shareof output is highest in Sub-Saharan Africa (SSA),
Europe and Central Asia (ECA), and Latin
America and the Caribbean (LAC). The share ofself-employment, however, is highest in SubSaharan Africa (SSA), South Asia (SAR), and East
Asia and the Pacific (EAP; Figure 3.3).
Two country examples illustrate differences acrossregions. In Brazil, the informal sector employs
one-third of total employment and produces onethird of GDP. In Pakistan, the informal sector
provides two-thirds of total employment but
produces only about one-third of GDP. Thisdifference points to considerably lower informal
labor productivity relative to total laborproductivity in Pakistan than in Brazil, in partreflecting lower educational attainment of
informal workers (La Porta and Shleifer 2014;
Loayza 2018).
Three EMDE regions (EAP, LAC and ECA) alone
accounted for more than one-third of the world’s
informal output in 2016, but only one-quarter ofthe world’s formal output. Almost half (42percent) of the world’s informal workers can befound in South Asia (SAR), although the region
only accounts for 9 percent of the world’s formal
employment and 3 percent of the world’s formal
output (Box 3.2). Another 14 percent of theworld’s informally employed are in Sub-SaharanAfrica (SSA), well above SSA’s share of the world’s
formal output (2 percent) or formal employment(5 percent).
Informality over time: Downward trend. The
shares of both informal output and selfemployment have declined since 1990, especiallyin EMDEs (Figure 3.2 and 3.4). Between 199016, on average, the share of informal output fell by
about 7 percentage points of GDP in EMDEs (to32 percent of GDP) and by about 4 percentage
points (to 17 percent of GDP) in advanced
economies. Over the same period, the average
share of self-employment declined by about 4
percentage points (to 14 percent of total
employment) in advanced economies and by

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019about 4.5 percentage points (to 43 percent of total
employment) in EMDEs. These declines werebroad-based: the share of informal output declinedby 5 percentage points of output or more between
1990 and 2016 in all advanced economies and 86
percent of EMDEs.12
In EMDEs, the largest declines in the shares of
informal output and employment occurred fromthe mid-2000s onwards in a reversal of a decade ofrising informal employment and barely shrinking
informal output.13 In advanced economies, thelargest declines in the share of informal
employment occurred between the late 1990s andthe global financial crisis; they have since partly
reversed, amid anemic post-crisis growth (Figure3.2).14 Among EMDE regions, the informal
economy’s share of output dropped most in EAP,
LAC, and SAR, while the share of selfemployment dropped most in EAP, ECA, and the
Middle East and North Africa (MENA; Box 3.2).
Characteristics of informal sector business cycles.
The main features of recessions and recoveries in
the formal economy, defined as in Harding andPagan (2002) and Claessens, Kose, and Terrones(2012), do not differ statistically significantly fromthose in the formal economy (Figure 3.5; Elgin etal. forthcoming a). On average, both formal andinformal recessions last about 1.5 years, which areabout 0.5 years shorter than formal and informal
recoveries. The speeds of adjustment in recessions(about 4 percentage points decline per year) and ofrecoveries do not differ statistically significantly
between formal and informal sector.
The DGE-based measure of informal output shows thatbetween 1990 and 2016, the share of informal output over official
GDP fell in 140 (36 AEs and 104 EMDEs) out of 158 countries
where data are available. Similar results are found in MIMIC-based
measure on informal output. During the same period, 84 (18 AEs
and 66 EMDEs) out of 127 countries experienced a drop in the shareof self-employment.
13 The small-scale decline in the beginning of the 1990s is also
driven by the expanding informal sector in countries in Eastern andCentral Europe during their economic transition (Kaufmann and
Kaliberda 1996).
14 A country-specific regression of the share of the informal
economy in GDP and employment on a time trend over the period
1990-2016 captures this secular decline. In 50 (WEF)-100 (DGE)
percent of advanced economies (depending on the measure) and 48(WEF)-81 (MIMIC) percent of EMDEs, there has been a significant
downward trend in the share of the informal economy in GDP and
employment.
12

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FIGURE 3.3 Informality by EMDE region
Informality is common in all EMDE regions but takes different forms. On
average, the share of informal output is highest in Sub-Saharan Africa,
Europe and Central Asia, and Latin America and the Caribbean. The shareof self-employment is highest in Sub-Saharan Africa, South Asia, and East
Asia and the Pacific.
A. DGE-based informal activity

B. MIMIC-based informal activity

C. Informal employment

D. Labor force without pension

E. Perceived informal activity

F. Share of EMDE regions of world
output and employment

Source: Elgin et al. (forthcoming a); International Labour Organization; World Bank, World Development
Indicators; World Economic Forum.
Note: See Annex 3.1 for data definitions. Blue and red bars indicate group means for 2006-16, withorange vertical bars indicating +/-1 standard deviation. EAP = East Asia and Pacific, ECA = Europe andCentral Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR =South Asia, and SSA = Sub-Saharan Africa. EMDEs = Emerging market and development economies.
C. Self-employment shares are used here.
E. Informality index provided by World Economic Forum (a higher value indicates more informality).
F. DGE-based estimates of informal output in each region as a proportion of total estimated informal
GDP. Shares of self-employment are used as proxies for shares of informal employment. Formal output
is equivalent to official GDP. Estimates are based on their respective average shares of output andemployment during 2010-16.
Click here to download data and charts.

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BOX 3.2 Regional dimensions of informality: An overview
Informality, especially employment informality, is most prevalent in less developed EMDE regions. Together, South Asia and SubSaharan Africa account for nearly 60 percent of all informal workers in EMDEs. However, even in some wealthier EMDE
regions, such as Latin America and the Caribbean and Europe and Central Asia, informality remains significant in part due to
weak institutions and high levels of taxation and regulation. Both the drivers and implications of informality vary across andwithin regions, suggesting the need for tailored policy responses.
The informal economy accounts for a significant
proportion of both employment and output across
EMDEs. Around three-quarters of EMDE employment isestimated to be in the informal sector. Self-employment,
which is relatively easy to measure and provides a lower
bound estimate of informality, is 43 percent of total
employment in the average EMDE, although this
proportion ranges from 22 percent in the Middle East andNorth Africa (MENA), to 62 percent in Sub-SaharanAfrica (SSA).1
Informality has both costs and benefits. It can provide animportant source of income in EMDEs, often to thosewith few available alternatives. That said, informal
employment is often associated with lower and more
uncertain incomes for workers and lower revenues
available for governments to fund their development
objectives.
The regional disparities in the scale of informality depend
on a wide range of factors. To summarize these regional
distinctions, this box addresses the following questions:•

Where is global informality concentrated?

•

What have been the correlates of informality across
EMDE regions?

•

What policy options are available?

Where is global informality concentrated?
Regional composition of EMDE informal sectors. Onehalf of the world’s informal output and 95 percent of
informal employment is located in EMDEs. Three EMDE
regions alone accounted for one-third of the world’s
informal output in 2016: East Asia and Pacific (EAP),
Latin America and the Caribbean (LAC), and Europe andCentral Asia (ECA). They are also the largest EMDE

Note: This box was prepared by Gene Kindberg-Hanlon with research
assistance from Jinxin Wu and Zhuo Chen. It summarizes six boxes on
the regional dimensions of informality featured in Chapter 2.
1 For the purposes of this box, informal employment is proxied by selfemployment because of good data coverage, and the regional disparities
identified in this box are robust to other measures.

regions by formal GDP, accounting for one-quarter of theworld’s formal output.
In terms of employment, almost half (42 percent) of theworld’s informal workers can be found in South Asia
(SAR), although the region accounts for just 9 percent of
the world’s formal employment and 3 percent of theworld’s formal output (Figure 3.2.1). Sub-Saharan Africa(SSA) is also over-represented in its share of informal
employment, accounting for 14 percent of the world’s
total, well above its share of the world’s formal output (2percent) or formal employment (5 percent).
Informal-sector productivity in EMDE regions. In all
EMDE regions, the proportion of informal employment
exceeded the share of informal output, reflecting atendency for the informal sector to be less productive than
the formal sector (La Porta and Shleifer 2014; Fajnzylber,Maloney, and Montes-Rojas 2011). This difference isparticularly pronounced for SAR and SSA, where theinformal employment share is approximately double theinformal output share.
Trend decline in informality. In all EMDE regions, theinformal sector has steadily declined in relative importancesince the 1990s. On average, informality has fallen by 5
percentage points of GDP since the 1990s, partially driven
by economic development and improvement ingovernance. The decline in relative importance was largest
in EAP and SAR with informality falling by 8 percentage
points in both regions. Faster-than-average formalizationof the economy in these two regions is likely to in partreflect faster-than-average per capita GDP growth sincethe 1990s. Conversely, informality in the Middle East andNorth Africa (MENA) decreased only modestly amid
persistently weak growth and entrenched weak governance.
Within-region heterogeneity. The regions with the widest
per capita income heterogeneity were also those with thewidest range of informality as a share of output or
employment. Informality is significantly more prevalent inlower-income economies within EAP despite the relatively
low share of informal output for the region as a whole. In
MENA, the non-Gulf Cooperation Council (GCC)
economies have elevated levels of informality while theshare for MENA as a whole is the lowest of all EMDE
regions (Box 2.4). In contrast, in SSA, where the variation

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BOX 3.2 Regional dimensions of informality: An overview (continued)
FIGURE 3.2.1 Informality in EMDE regions
Informality is pervasive across all EMDEs—although the share of informal output in GDP has been falling over time, its
incidence is higher in the poorest regions.
A. Share of EMDE regions of world
output and employment

B. Share of aggregate EMDE informal
employment and output

C. Employment and output share by
regionD. Share of informal GDP over time

E. GDP per capita and share of informal
outputF. GDP per capita and share of informal
employment

Source: Elgin et al. (forthcoming a), World Bank.
Note: Data are between 2010 and 2016. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East andNorth Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.
A.B. DGE-based estimates of informal output in each region as a proportion of total estimated informal GDP. Estimates of self-employment shares are used to proxyinformal employment. Formal output equals official GDP. Estimates are based on their respective average shares of output and employment.
C. Mean of informal output (DGE-based estimates, percent of official GDP) and employment estimate (self-employment, percent of total employment) in each region.
D. Average DGE estimate of informal output relative to total output in each time period.
E.F. Grey markers show average log GDP (2011 PPP $ - averaged 2010-16) relative to DGE/SEMP estimate of informal output/employment for 154 and 147 economies
respectively, with the fitted lines shown in blue and the corresponding +1 and -1 standard errors shown in shaded gray areas. Regional markers show median GDP per
capita and median informal output/employment in EMDE regions.
Click here to download data and charts.

of per capita incomes is one-fifth that of MENA (theEMDE region with the largest per capita income
heterogeneity), informality amounted to over 30 percent
of output and 39 percent of employment in three-quartersof countries.
What have been the correlates of informality
across regions?
Informality is concentrated in countries which are lessdeveloped and suffer from a range of institutional
weaknesses. Poverty and low human capital are strongly
associated with those regions with the highest incidence of
informality. In contrast, in wealthier regions such as LAC
and ECA, institutional weaknesses and tax policy havecontributed to elevated levels of informality.

Economic development. Informality is most prevalent in
EMDE regions with low income per capita, reflecting therole of informality as both a driver and consequence ofpoverty (La Porta and Shleifer 2014). None of the regional
shares of informal output or employment deviates
statistically significantly from what might have been
expected based on average per capita incomes alone(Figure 3.2.1).
Low human capital. Informality is also more prevalent
where educational attainment is weak.2 In SSA, where
2 Docquier, Müller, and Naval (2017) demonstrates that a sizeable
informal sector that competes with the formal sector for low-skilledworkers reduces the incentives to invest in human capital in the long run.
In addition, weak educational attainment is a feature of lack ofdevelopment, which contributes to informality (Loayza 2016).

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BOX 3.2 Regional dimensions of informality: An overview (continued)
FIGURE 3.2.2 Regional correlates of informality
Informality is most prevalent in regions with poor educational attainment and large agricultural sectors. In some regions
where tax avoidance is relatively easy, there is a strong relationship between the region’s above-average tax rates and thelevel of informality.
A. Years of schooling, by EMDE region

B. Share of agricultural sector in total
output, by EMDE region

C. Corporate tax rates

Sources: Barro and Lee (2013), Végh and Vuletin (2015), World Development Indicators, World Bank.
Note: 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. Average years of schooling for those aged 15 and older, taken from Barro and Lee (2013).
B. Average agricultural output as a percentage of total GDP.
C. Corporate tax rates are constructed as regional averages using Végh and Vuletin (2015).
Click here to download data and charts.

educational attainment is the lowest on average among
EMDEs, informal sector workers are much less likely tohave completed primary education than those in theformal sector (Figure 3.2.2; Adams, de Silva, and Razmara2013). Education levels have also been found to be an
important correlate of informality in SAR, whereattainment is also below the EMDE average.3
High regulatory and tax burdens. In LAC, several studies
have found a strong relationship between the region’s
above-average tax rates and ease of tax avoidance, and thelevel of informality (Figure 3.2.2; Loayza 1996; Vuletin
2008; Ordóñez 2014). For ECA, labor market regulations
that are more restrictive than elsewhere have been
identified as drivers of informal employment (Fialová andSchneider 2011).
Weak institutions. Some economies in ECA have belowaverage institution quality, which may explain the region’sslightly above-average degree of output informality despite
ECA’s relatively high per capita income. In non-GCC
MENA economies, corruption has been cited among thebiggest hindrances to firms which may increase incentives
to operate informally (World Bank 2016).
Region-specific factors. A number of region-specificfactors have contributed to informality.
3 Williams, Shahid, and Martinez (2016), Bahadur and Parajuli(2014), and Gunatikala (2008).

•

In ECA, a high share of informal output is partly alegacy of the collapse of the Soviet Union in the late1980s and early 1990s as well large remittance inflowsthat have financed informal sector activity (Box 2.2).

•

In some LAC economies, the trade liberalizations of
the 1990s have been identified as contributors to
growing informality, as formal firms that were unable
to compete in a liberalized formal economy retreatedinto informality (Box 2.3).

•

In MENA, although informality is particularly
pronounced in non-GCC economies, in the GCC
informality is low partly because of its heavy relianceon documented foreign workers and government
employment (Box 2.4; World Bank 2018c).

•

In SSA, large agricultural sectors help explain
widespread informal employment as does the conflictand violence that have afflicted the region and forced
people to earn their livelihoods in the informal
economy (Box 2.6).

What policy options are available?
To mitigate the damaging effects associated withinformality, policy responses can be tailored to thecircumstances and drivers of informality in that economy.
Policy options can be broadly split into several categories:

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BOX 3.2 Regional dimensions of informality: An overview (continued)
Improving human capital. By investing in education andsocial services to improve human capital, policy makers
can improve the productive capacity of workers that are
currently uncompetitive in the formal sector. Training has
been found to boost worker income and firm revenue instudies in the informal sectors of SSA and SAR (Vernerand Verner 2005; Burki and Abbas 1991).
Improving access to public services and ﬁnance. Efforts tofacilitate informal sector business can benefit informal
sector workers and make them more competitive (Box 2.6;
Sonobe, Akoten, and Otsuka 2011). For example, in SSA,
providing informal traders public goods, such as a market
to trade in or access to water and sanitation, has helped
increase informal firm profitability and product quality. In
SAR, a lack of access to financial resources is common forthe self-employed (Ghani, Kerr, and O’Connell 2013; Box2.5). Enabling access to microfinance has been found toincrease investment and productivity in the informal sector(Likhi 2013; Donou-Adonsou and Sylwester 2017; Imaiand Azam 2012).
Easing tax and regulatory burdens. Several studies in LAC
have found that policies to reduce tax rates and simplify
tax systems have incentivized firms to transition to theformal sector. Payroll or business tax cuts in Colombia,
Brazil and Uruguay have been associated with higher
formal employment and firm registration.4 However, inregions where tax rates or tax compliance costs are notelevated, cutting taxes can be counterproductive in

4 See Fernández and Villar (2016), Fajnzylber, Maloney, and MontesRojas (2011), and Monteiro and Assunção (2012).

Causes and implicationsof informality
Causes of informality. Theoretical models present
two major reasons for the emergence of informal
activity: lack of development (Harris and Todaro
1970; Loayza 2016), and poor governanceincluding burdensome regulations, corruption, or
poor public services (de Soto 1989).
•

Lack of development. Informality has often
been attributed to under-development. This
reflects an inability of an urban modern

reducing informality. In ECA, where corporate tax rates
are lower than the EMDE average, higher taxation was
associated with increased formalization in some studies
because of the lack of public goods provided in regionswith insufficient tax revenue (Fialová and Schneider 2011;Friedman et al. 2000). Separately, less restrictive
employment protection has been associated with a smaller
informal economy (both in employment and output) in
ECA countries (Fialová and Schneider 2011: Lehmann
and Muravyev 2009).
Tightening enforcement. Enforcement that is
economically and socially sensible can help reduce thepresence of the informal sector (Loayza 2018). In LAC,
policies such as labor inspections have been found to
induce informal workers and firms to formalize (DeAndrade, Bruhn, and McKenzie 2013; and Almeida andCarneiro 2012). Studies in ECA, SAR and EAP have also
found that lower levels of enforcement are associated withhigher rates of informality (Box 3.4). Regulatory and tax
compliance rates increase more if increased labor or tax
inspections are accompanied by other measures such asawareness campaigns (Rani et al. 2013).
Reducing corruption. In ECA, where informality rose
considerably following the disruptions associated with thecollapse of the Soviet Union, higher corruption has been
linked with higher informality (Friedman et al. 2000).
Economies in ECA that were slower to implement
structural reforms and control corruption in the 1990s saw
a smaller-than-average decline in informality (Kaufmann
and Kaliberda 1996). Corruption is also a key disincentive
to enter the formal sector in MENA according to firm
surveys.

formal sector to absorb rural migrants during
the urbanization process (Harris and Todaro
1970; Fields
1975; Loayza2016).
Development can further shrink the informal
sector because households tend to shift awayfrom agricultural and informal sector goods as
their incomes grow (Saracoglu 2008). Finally,
limited access to credit, often associated withless development, constrains informal firms’
ability to overcome barriers to entry into theformal sector.15
15 See Ferreira-Tiyaki (2008), D’Erasmo and Moscoso Boedo(2012), and Capasso and Jappelli (2013).

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FIGURE 3.4 Changes in informality
Informality declined in EMDEs during the period 1990-2016, with the shareof output dropping especially in East Asia and the Pacific, Latin America
and Caribbean, and South Asia.
A. Changes in informality: Income
groupsB. Changes in informality: EMDE
regions

Source: Barro and Lee (2013), Elgin et al. (forthcoming a), World Bank (World Development
Indicators).
Note: See Annex 3.1 for data definitions. Data are 1990-2016 group averages. EAP = East Asia andPacific, 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. Unweighted group averages for advanced economies and EMDEs, with self-employment (inpercent of total employment) shown in red and DGE-based informal output (in percent of official
GDP) shown in blue. Unweighted world averages are shown in dashed lines.
B. Unweighted group averages for EMDE regions, with self-employment (in percent of total
employment) shown in red and DGE-based informal output (in percent of official GDP) shown in blue.
Unweighted EMDE averages are shown in dashed lines.
Click here to download data and charts.

••

Heavy-handed regulation. Higher taxationand heavy-handed regulation increases firms’
incentives to reduce taxation or the cost of
regulatory compliance by remaining informal(Ihrig and Moe 2004; Amaral and Quintin
2006; D’Erasmo and Moscoso Boedo, 2012;
Auriol and Warlters, 2005; Prado 2011;
Kanbur 2017; Dabla-Norris et al. 2018;
Ulyssea 2018).16 Excessive labor regulations
encourage informal employment by increasing
the cost of formal employment (Rauch 1991;
Loayza 2016).
Poor governance. Corruption and rentseeking
bureaucracies
increase
firms’
incentives to avoid interaction with the state
by remaining informal (Sarte 2000; Choi andThum 2005; Freidman et al. 2000).
Conversely, access to productivity-enhancing
public goods, such as to electricity or the
court system, can lead to an increase in theshare of formal production (Mendicino andPrado 2014).

16 Also see Busato and Chiarini (2004), Charlot, Malherbet, andTerra (2015), Saracoğlu (2008), and Ordóñez (2014).

Implications of informality. A sizeable informal
sector could impede growth, encourage poor
governance, and limit a government’s ability to
reduce income inequality.
•

Slower growth. A sizeable informal sector thatcompetes with the formal sector for lowskilled workers reduces the incentives to invest
in human and physical capital and newtechnologies and slows growth in the long run(Docquier, Müller, and Naval 2017; Loayza
1996; Sarte 2000).

•

Poor governance. Several theoretical models
attribute corruption and excessive regulations
to the presence of an informal economy.
Government officials are incentivized to
impose excessive regulations and permits tohave the power to collect bribes in return forproviding permits (Shleifer and Vishny 1993).
Others have argued that the government
strategically designs a system of poor
governance to promote informality for thepoor, which acts as an alternative
redistributive strategy (Marjit, Mukherjee, andKolmar 2006).

Correlates of informality
The causes and implications of informality
predicted by theoretical models are also confirmed
by empirical studies as many correlates of
informality are symptoms of under-development.
A large informal economy is associated withweaker economic outcomes, such as a underdevelopment, less access to credit, limited trade
openness, less skilled labor force, as well as weaker
output, investment and productivity growth (Box3.3). Informality is also associated with lesseffective institutions, such as weak governance andexcessive tax and regulatory burdens (Loayza,
Oviedo, and Servén 2006; Enste and Schneider1998).
Under-development.
A
lower
level
of
development, as measured by per capita income, isassociated with higher informality (Figure 3.6).17

17 See also Loayza, Servén, and Sugawara (2006) and La Porta andShleifer (2014).

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In the case of both output and employment
informality, GDP per capita in countries withbelow-median (“low”) informality is about 2-3
times of those in countries with above-median(“high”) informality (Figure 3.6).18 The lower
productivity and resource misallocation associatedwith higher informality may also be reflected inslower output growth.19
Slower accumulation of physical and humancapital. A larger informal economy is associatedwith a lower rate of output growth. This may
reflect slower accumulation of physical or humancapital (Ovedio, Thomas, and KarakurumOzdemir 2009). At the firm level, informality canlimit access to conventional bank credit, becauseof a lack of documentation for assets andinadequate financial statements.20 Investment
activity in the informal sector may also be subdued
because informal firms may be unwilling to adopt
technologies that would make them more visible
to tax and other authorities (Dabla-Norris andInchauste 2008; Gandelman and Rasteletti 2017).
For example, about 11,600 firms that participatedin Enterprise Surveys in 18 countries during 20072014, the fraction of firms that invested in any
given year in the formal sector was significantly
higher than that in the informal sector. In the longrun, the tendency to hire less skilled workers in
the informal sector may slow human capital
accumulation. Indeed, countries with belowmedian informality tend to have significantly
higher levels of human capital (Maloney, 2004;
Docquier, Müller, and Naval 2017; Figure 3.6).
Slowerproductivity
growth.
At
themacroeconomic level, the evidence for acorrelation between productivity growth andinformality has been mixed (Perry et al. 2007;
D’Erasmo and Moscoso Boedo 2012). At the firmlevel, in contrast, many studies have shown that
informal firms tend to be less productive than
18 Median informality amounts to about 32 percent of GDP for
DGE-based informal output and 34 percent of total employment for
self-employment.
19 See Hsieh and Klenow (2009), Loayza and Rigolini (2011),
Docquier, Müller, and Naval (2017), Cirera and Maloney (2017),
Levy (2018), and Bachas, Jaef, and Jensen. (2018).
20 See Koeda and Dabla-Norris (2008) for details. Empirically,
greater access to credit has been associated with lower informality(Maloney 2004; Straub 2005; La Porta and Shleifer 2014).

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FIGURE 3.5 Characteristics of informal- and formaleconomy business cycles
Informal-economy business cycles are not significantly different fromformal-economy business cycles.
A. Recessions in the formal andinformal sectors

B. Recoveries in the formal andinformal sectors

Source: Elgin et al. (forthcoming a); Penn World Table; World Bank, World Development Indicators.
Note: Data are for the period 1990-2016. Diamonds indicate sample means; bars indicate 95 percent
confidence intervals. Business cycle turning points are determined based on formal and informal
GDP levels (i.e., official GDP statistics, DGE estimates) using the algorithm of Harding and Pagan(2002). Recession is defined as the phase from peak to trough, and recovery as the phase fromtrough to a return to pre-recession output levels. “Duration”, and “Speed of adjustment” (often termed
as “Slope”) are defined as in Annex 3.2.
Click here to download data and charts.

their formal counterparts; although thisproductivity differential in part reflects thecharacteristics of informal firms.21 On average,
informal labor productivity is lower than totallabor productivity in EMDEs, although not inadvanced economies (Figure 3.6; Loayza 2018). In
addition, competition from informal firms has
been associated with lower productivity of formal
firms. The presence of informal competitors,
which do not shoulder regulatory and tax burdens,
can reduce formal firms’ profitability, thus eroding
their ability to invest in productivity-enhancing
technologies or human capital (Perry et al. 2007;
Box 3.3).
Less trade openness. A smaller informal sector isassociated with greater economic openness,
especially to trade.22 On average, the trade-toGDP ratio is lower by 17 percentage points in

21 La Porta and Shleifer (2014), Fajnzylber, Maloney, andMontes-Rojas (2011), de Mel, McKenzie, and Woodruff (2012),
Demenet, Razafindrakoto, and Roubaud (2016), and McKenzie andSakho (2010).
22 Empirical studies, such as Goldberg and Pavcnik (2004, 2007),
Sharma (2009), Boly (2018), and McCaig and Pavcnik (2018), showthat informality declined following some trade liberalization episodes.
Conversely, a short-term increase in informality has been attributed
to trade liberalization amid inflexible labor markets in studies such as
Goldberg and Pavcnik (2003), Attanasio, Goldberg and Pavcnik(2004), and Bosch, Goñi-Pacchioni, and Maloney (2012).

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BOX 3.3 Casting a shadow: Productivity in formal and informal firms
The average informal firm in emerging market and developing economies (EMDEs) is only one-quarter as productive as theaverage firm operating in the formal sector. Moreover, firms in the formal sector that face informal competition are, on average,
only three-quarters as productive as those that do not. This suggests that competition from the informal sector can erode formal
firms’ market share and resources available to boost productivity where formal firms shoulder the additional cost of regulatorycompliance. More effective governance and stronger control of corruption can help mitigate these effects.
The productivity differential between formal and informal
firms is well established in the literature (Loayza and
Rigolini 2006; Oviedo 2009). However, there is mixed
evidence on the impact of a large informal sector on
formal firms’ productivity. Some studies suggest that the
informal and formal sectors operate independently so that
there are no productivity spillovers (La Porta and Shleifer2014). Others report that competition from the informal
sector may erode the profitability of firms that operate in
the formal sector, which leads to limited resources toenhance firm productivity.1 The aggregate effect depends
on country characteristics.
Against this backdrop, this box documents the
productivity gap between formal and informal firms andtheir interactions. Specifically, it addresses the following
questions:•

How large is the productivity differential between
formal and informal firms?

•

To what extent are formal firms exposed to informalcompetition?

•

How does informal competition
productivity of formal firms?

affectthe

Productivity differential between formal andinformal firms
Literature review. The literature documents that informal
firms in EMDEs are less productive than formal
firms, with a productivity gap ranging between 30 to 216
percent (Perry et al. 2007; La Porta and Shleifer 2008).
This productivity gap between informal and formal firms
is attributed to modest technological improvements,
reliance on unskilled labor, limited economies of scale,and restricted access to services, markets, and funding.2
Moreover, labor productivity varies within the informal
sector along different dimensions such as firm size
Note: This box was prepared by Mohammad Amin and Cedric Okou.
1 Gonzalez and Lamanna (2007), Heredia et al. (2017), Mendi andCostamagna (2017).
2 Jovanovic (1982), Amaral and Quintin (2006), Galiani and
Weinschelbaum (2012).

and type of activity (Amin and Huang 2014; Amin andIslam 2015).
Methodology. In this box, the productivity gap between
formal and informal firms is estimated using World Bank’s
Enterprise Survey data collected over a period spanning2007 to 2014 for a cross-section of 4,036 informal firms
and 7,558 formal firms in 18 EMDEs (Annex Table 3.1).
Formal firms are those that comply with tax, customs,labor, and licensing regulations and register with therelevant authorities; unregistered firms belong to theinformal sector. To estimate the productivity gap, ameasure of labor productivity—log annual sales in 2009
U.S. dollars per worker—is regressed on a dummy variable
that takes the value 1 for informal firms and 0 otherwise
and a set of control variables capturing additional firm
characteristics (employment size, time in business,
location, sector, country).3
Lower productivity in informal than formal ﬁrms.
Virtually across the board, firm-level labor productivity ismuch lower in the informal sector than in the formal
sector (Annex Table 3.1).4 The productivity differentials
vary widely in this sample, from 48 (Côte-d’Ivoire) to 93
percent (Argentina). On average across the whole sample,
the productivity of informal firms is only one-quarter ofthe productivity of formal firms (Figure 3.3.1).
Drivers of productivity gap between informal and formal
ﬁrms. Firm size, age, location in the capital city andmanager experience are associated with significantly larger
productivity gaps between informal and formal sectors(Figure 3.3.1, Annex Table 3.2).5 Formal firms appear tobe better equipped to reap the productivity benefits fromsize, age, and location than informal firms.

3 Commonly used revenue-based measures of productivity may
conflate efficiency and price effects. Disentangling efficiency and priceeffects, by relying on physical productivity measures, may shed new lighton productivity patterns, especially at the firm level (Jones and Nordhaus
2008; Cusolito and Maloney 2018).
4 Exceptions are Democratic Republic of Congo and Cabo Verdepossibly due to a low productivity of formal firms.
5 The results are robust to comparing the coefficient estimates for the
informal-firm dummy between a baseline regression including all controls
and an alternative regression dropping each dummy one at a time (Annex
Table 3.2).

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CHAPTER 3

BOX 3.3 Casting a shadow: Productivity in formal and informal firms (continued)
FIGURE 3.3.1 Characteristics of informal firms
Among informal firms, those with managers with higher education and those without any employees other than the owner are
significantly more productive. The average informal firm in emerging market and developing economies has only one-quarterof the productivity of the average firm operating in the formal sector. This productivity differential between formal and informal
firms is particularly pronounced among larger and older firms that operate in the capital city and are led by experienced
managers.
A. Average productivity in formal andinformal firms

B. Productivity differential between
different types of informal firms

C. Productivity differential between
formal and informal firms, by type of
informal firms

Source: World Bank.
Notes: World Bank’s Enterprise Survey data for 135 countries (2008-18). Labor productivity is proxied by the annual sales in 2009 U.S. dollars per worker. Bars show theestimates with the corresponding +/- 2 standard errors shown in whiskers.
A. Labor productivity in the average formal and average informal firm, controlling for firm characteristics (firm size and age, manufacturing sector activity, location in thecapital city and country fixed effects) as shown in column (1) in Annex Table 3.2.
B. Cross-country average of percent difference between labor productivity in the median informal firm with a manager with higher education or without any employees
other than the owner, and the median informal firm with a manager without higher education or with more employees than the owner. Estimates from Annex Table 3.1.
C. Difference in log of labor productivity between the average formal and average informal firm in each group, as estimated in coefficient estimates of Annex Table 3.2.
“Other” stands for “not located in capital city”; “Cap.” stands for “located in capital city.”
Click here to download data and charts.

•

Firm age. As firms grow older, they are either
sufficiently productive to survive or they disappear(“selection effect”; Brandt, Van Biesenbroeck, andZhang 2012). In addition, learning from experience
may have taught older firms productivity gains(“learning effect”; Luttmer 2007). These effects appear
to be much more pronounced among formal firms
than among informal firms. As a result, theproductivity differential between formal and informal
firms widens as the age of firms increases. Among oneyear-old firms, informal firms have about half the
productivity of formal firms. Among ten-year-old
firms, informal firms have less than one-quarter the
productivity of formal firms.

•

Firm location. Capital cities are typically among
countries’ largest economic centers and so can offer
agglomeration benefits: larger markets, better
infrastructure to access markets and operate, a larger
pool of workers, greater technology spillovers(Rosenthal and Strange 2004; Duranton and Puga
2004). Again, formal firms appear to be better able tobenefit from these locational advantages, but the effectis economically modest (although statisticallysignificant). Among firms operating inside the capitalcity, informal firms’ productivity is 31 percent that ofsimilar formal firms; outside the capital city, informal
firms’ productivity is 30 percent that of similar formal
firms.

•

Firm size. Larger firms can reap economies of scalethat raise their productivity compared to smallerfirms. Again, in this sample, this effect appears to bestronger among formal firms than among informal
firms. Among firms with one employee, informal
firms have just under one-third the productivity offormal firms; among firms with ten employees,
informal firms have less than one-quarter the
productivity of formal firms.

•

Manager experience. Managerial ability has been
associated with higher productivity, through a varietyof channels including hiring decisions and input
choices (Fernandes 2008). Again, managerial
experience appears to benefit formal firms’
productivity more than informal firms’ productivity.
Among firms managed by managers with one year ofexperience, informal firms’ productivity is just over
one-third that of formal firms; among firms with

147

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BOX 3.3 Casting a shadow: Productivity in formal and informal firms (continued)
managers with ten years of experience, informal firms’
productivity is less than one-quarter that of formal
firms.
Productivity differentials across informal firms. Labor
productivity also differs across different types of informal
firms, although the characteristics that are associated withhigher labor productivity of informal firms differ acrosscountries.6 In two-fifths of countries, informal firms
managed by a manager with higher education or without
any employees other than the owner are significantly moreproductive than other informal firms (column (1) inAnnex Table 3.2). Other informal firm characteristics,
such as operating in the services sector or being a startup,
are accompanied by higher productivity in some countries
but lower productivity in other countries.

Productivity of formal firms amid highinformality
Impact of informal competition on formal ﬁrms: Theory.
The extent of competition between formal and informal
firms depends on the underlying reasons for the existenceof informal firms.7
Informality as a survival strategy of unproductive firms. Lowproductivity firms may be forced into informal operations
or, even if they operate formally, employing informal
workers because this may reduce their costs (Ulyssea 2018;
Boly 2018). Operating in the informal sector andemploying informal labor may, therefore, be a survival
strategy for less-productive firms that belong tofundamentally different markets (La Porta and Shleifer2014). “Surviving” informal firms are likely to operate invery different markets and sell different products thanformal firms (La Porta and Shleifer 2014). In suchcircumstances, competition between informal and formal
firms and its impact on formal firms may be limited.
Informality as an evasion strategy of productive firms. Some
informal firms may be sufficiently productive to survive in

6 Haltiwanger,
Lane, and Spletzer (1999), Maloney (2004),|Deininger, Jin, and Sur (2007), de Mel, McKenzie, and Woodruff(2011), Grimm et al. (2012), Amin and Huang (2014), Amin and Islam(2015), Islam (2018).
7 This discussion assumes that firms are either formal or informal. In
practice, the degree of informality can vary (Perry et al 2007; Ulyssea
2018). At the extensive margin are firms that operate fully informally, in
product markets and labor markets. They sell their output informally andemploy informal labor. At the intensive margin are firms that operatesemi-formally: they sell their output into formal product markets but
employ, in part, informal labor, as observed in EMDEs and LICs.

the formal sector yet choose to remain informal to benefit
from the cost advantage of noncompliance with (possibly
excessive) taxes and regulations (Maloney 2004; de Mel,McKenzie, and Woodruff 2011).8 Such informal firms
could constitute an untapped potential for a productivity
boost (de Soto 1989). On the other hand, they can create
aggressive competition with formal firms that do shoulder
the additional cost of tax and regulatory compliance. Suchinformal competition can reduce the profitability necessary
for formal firms to invest in productivity-enhancing newtechnologies or to innovate, especially in a context of weakproperty rights enforcement.9 Alternatively, this very
competition could force formal firms to increase
productivity or, for the lowest-productivity ones, to exit.10
Extent of informal-firm competition for formal firms. In
the World Bank’s nationally representative survey data for
75,137 formal (registered) firms in 135 countries between
2008 and 2018, about 55 percent of formal firms reported
facing competition form informal firms.11 The share of
informal firms competing against formal firms was about
60 percent in EMDEs, 13 percentage points higher thanin advanced economies. The level of competition varied
widely across countries, ranging from about 7 percent in
Bhutan to 95 percent in Uganda. Smaller firms weresignificantly more likely to be exposed to informalcompetition than larger firms but there is little evidence of
any other systematic difference between firms that were
exposed and those that were not (Figure 3.3.2).

Impact of informal competition on the
productivity of formal firms
Methodology. OLS regressions are used to estimate thedifference in labor productivity between formal firms thatcompete against informal firms and those that do not. In
the baseline specification, the dependent variable is again

8 Such circumstances are likely to be associated with an environmentof weak regulatory and tax enforcement (Quintin 2008; Dabla­Norris,
Gradstein, and Inchauste 2008; Ulyssea 2010; Benjamin and Mbaye2012).
9 This has been documented for some Latin America countries, India,
Poland, Portugal, Russia, and Turkey. For evidence, see Heredia et al.
(2017), Perry et al. (2007), Farrell (2004), Capp, Elstrodt, and Jones(2005), Cunha (2006), Gonzalez and Lamanna (2007), Friesen andWacker (2013), Allen and Schipper (2016), Iriyama, Kishore, and Talukda (2016), and Distinguin, Rugemintwari, and Tacneng (2016).
10 This has been documented for Egypt, see Ali and Najman (2017);
Melitz (2003); Schipper (2016).
11 In the World Bank’s Enterprise Surveys, formal firms are asked thefollowing question: “Does this establishment compete against unregistered or informal firms?”

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149

BOX 3.3 Casting a shadow: Productivity in formal and informal firms (continued)
FIGURE 3.3.2 Formal firms facing informal competition
On average, more than half (55 percent) of formal firms reported facing informal competition. Nearly 60 percent of formal firmsin EMDEs were exposed to informal competition whereas 47 percent of formal firms in advanced economies reported facing
informal competition. The degree of informal competition reported by formal firms was higher for smaller than larger firms, but
comparable across sectors or formal firms’ productivity.
A. Formal firms reporting competition
from informal firms, by country

B. Formal firms reporting competition
from informal firms, by firm size

C. Formal firms reporting competition
from informal firms, by firm sector

Source: World Bank.
Note: World Bank’s Enterprise Survey data for 135 countries (2008-18). Figures show the shares of formal firms.
Click here to download data and charts.

labor productivity measured by the (log of) annual sales in2009 U.S. dollars per worker. The main explanatory
variable is the informal competition indicator proxied by
the proportion of formal firms in a cell that report facing
competition from informal firms. A cell is defined as agroup of firms of similar size and in the same region andsector.12
Productivity gap between formal ﬁrms with and without
informal competition. Formal firms that face informalcompetition are, on average, 24 percent less productive
than those that do not (Figure 3.3.3; Annex Table 3.3).
After controlling for the informal competition, formal
firms in the manufacturing and retail industries havehigher productivity than those in other services. Older,
exporting, and foreign-owned formal firms also havehigher productivity even if they face competition frominformal firms.

shape the productivity gap between formal firms that face
informal competition and those that do not. This iscaptured in interaction terms between the share of similar
formal firms reporting informal competition andindicators of development (the logarithm of per capitaGDP), the quality of business climate as proxied by the
distance to the frontier in the Doing Business Index, thecontrol of corruption of the World Governance Indicators,
and the Business Freedom index of the Economic
Freedom indicators (Annex Table 3.3). Higher GDP per
capita, better control of corruption, and a businessenvironment that is freer and closer to best-practices
dampen the detrimental impact of informal competition
on formal firm productivity.
•

Development. The sample is split into those countries
with per capita income in the highest quartile in thesample and those in the lowest quartile in the sample.
Formal firms that face informal competition in the
average country with the highest per capita incomesare only 14 percentage point less productive thanformal firms that do not face such competition. In
contrast, on average in countries in the lowest quartileof per capita incomes, formal firms facing informalcompetition are 30 percent less productive than thosefirms that do not face such competition.

•

Control of corruption. Again, the sample is split into
those countries in the quartile of countries with theRole of the business climate and development. Economic
development and the business climate may substantially

12 As a caveat, the informal competition faced by a specific firm may
also be driven by its productivity, thus generating endogeneity concerns.
To address possible endogeneity issue, we use the proportion of formal
firms facing informal competition in a group of firms of similar size in thesame region and sector (a “cell”) rather than a firm dummy. A cell
proportion should be much less correlated with the productivity of a
specific firm, and therefore, should be more robust to endogeneity
concerns.

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BOX 3.3 Casting a shadow: Productivity in formal and informal firms (continued)
FIGURE 3.3.3 Productivity of formal firms facing informal competition
On average, formal firms that face informal competition have only three-quarters of the productivity of firms that do not face
informal competition, after controlling for firm characteristics. Better business climates and governance and more economic
development can narrow this productivity differential.
A. Productivity differential of formal
firms with and without informalcompetition, by intensity

B. Productivity differential of formal
firms with average informal competition
and without, conditional on level ofdevelopment

C. Productivity differential of formal
firms with average informal competition
and without, by business climate
indicator

Source: World Bank.
Note: Based on coefficient estimates from Annex Table 3.3, which shows results from an OLS regression with labor productivity as the dependent variable, as proxiedby annual sales (in 2009 U.S. dollars, in thousands, logs) per worker, in a sample of World Bank’s Enterprise Survey data collected during 2007-14 for 4,036 informal
firms and 7,558 formal firms in 18 countries. Bars show the estimates with the corresponding +/- 2 standard errors shown in whiskers.
A. Log productivity differential between formal firms facing informal competition and formal firms not facing informal competition. Maximum informal competition
assumes that all firms in a cell face informal competition. Average informal competition assumes that 55 percent of firms in a cell face informal competition.
B-C. Log productivity differential between formal firms facing informal competition and formal firms not facing informal competition, conditional on development andinstitutional quality. It is assumed that 55 percent of firms in a cell face informal competition. Each bar conditions on the GDP per capita (B), control of corruption (C),ease of Doing Business (C), or Business Freedom index (C) of the median country in the top (“highest quartile”) or bottom (“lowest quartile”) quarter of countries interms of GDP per capita, control of corruption, ease of Doing Business, or Business Freedom index.
Click here to download data and charts.

strongest control of corruption and those in thequartile with the weakest control of corruption. In
countries with the strongest control of corruption, on
average, formal firms that face informal competition
are only 22 percentage point less productive thanformal firms that do not face such competition,
whereas in the countries with the weakest control ofcorruption, this differential grows to 35 percent.
•Ease of Doing Business. Similarly, the productivity
differential between formal firms that face informalcompetition and those that do not might halve (to 21
percent) if a country like Angola (in the quartile ofcountries with the most difficult business climates)
were to improve its business climate to the level of acountry like the Former Yugoslav Republic ofMacedonia (among the countries with the mostconducive business climates).

Conclusion
The productivity gap between informal and formal firms is
substantial in EMDEs, averaging 75 percent in a sample of
18 EMDEs between 2007-14. Competition from informal
firms also appears to weigh on the productivity of exposed
formal firms: the productivity of formal firms that competewith informal firms is only three-quarters that of formal
firms that do not compete with informal firms, after
controlling for other firm characteristics. Improvements inthe business climate, and economic development more
broadly, can mitigate some of these negative productivity
spillovers from informal to formal firms.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

countries with a greater share of self-employment
than countries with a smaller share of selfemployment (Figure 3.6).23 Similarly, higher
capital account openness is associated with less
output and employment informality. That said,
the impact of major trade liberalization episodes
on informality varies across countries and differs
between the short and the long term (Box 3.4;
Goldberg and Pavcnik 2003; Fugazza and Fiess2010; Dix-Carneiro and Kovak 2017).
Heavier regulatory burden. Both empirical andtheoretical studies suggest that heavier regulatory(or administrative) burdens may encourage
informality as workers and firms join the informal
sector to avoid regulatory and administrativecompliance costs.24 The Doing Business distanceto-frontier scores for countries with below-median
informality (by DGE estimates) is 60 points—
which is significantly higher (by about 6 points or
three-fifths of a standard deviation) than incountries with high (above-median) output
informality (Figure 3.6). Similarly, the Business
Freedom index is 7.5 points (about half of astandard deviation) higher in countries with low(below-median) output informality than incountries with high (above-median) informality.
Weaker governance. Research points to thecontribution of poor governance to the pervasive
informality in some EMDEs, especially in Latin
America and the Caribbean and Europe andCentral Asia regions (Box 3.2).25 On average,
countries with above-median informality over theperiod 1990-2016 have had weaker government

However, the trade-to-GDP ratio is not different between
countries with a greater share of informal output than countries witha smaller share of informal output.
24 Perry et al. (2007), Ulyssea (2010), Bruhn (2011), de Mel,McKenzie, and Woodruff (2013), Rocha, Ulyssea, and Rachter(2018).
25 Loayza, Oviedo, and Servén (2006) find that poorer
bureaucratic quality is associated with more informality. Choi andThum (2005) and Dreher and Schneider (2010) report an association
between higher informality and weaker law and order and control ofcorruption. Iriyama, Kishore, and Talukda (2016) show that firms
are more likely to engage in corruption when facing competition
from informal firms. Dabla-Norris, Gradstein, and Inchauste (2008)
show that the quality of the legal framework is important indetermining the size of the informal sector. Loayza and Wada (2010)
estimate, for example, that 75 percent of the difference in labor
informality between Peru and Chile is due to causes related to poor
governance.
23

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151

FIGURE 3.6 Correlates of informality: Economic andinstitutional factors
Higher informality is associated with lower levels of development, poorer
access to credit, heavier regulatory burdens, and weaker governance.

A. Economic correlates in EMDEs withhigh and low output informality

B. Economic correlates in EMDEs withhigh and low employment informality

C. Informality and GDP growth

D. Ratio of informal labor productivity
to total labor productivity

E. Regulatory burdens in EMDEs withhigh and low informality

F. Governance in EMDEs with highand low informality

Source: Barro and Lee (2013), Elgin et al. (forthcoming a), Heritage Foundation,
World Bank (Doing Business, World Development Indicators, World Governance Indicators).
Note: Data are between 1990 and 2016.The corresponding 90 percent confidence intervals areshown as bars (except in D).
A-B. The group means for the following correlates are calculated for EMDEs with “highinformality” (i.e., above median DGE-based informal output measure, A; above median selfemployment share, B) and those with “low informality” (i.e., below median DGE-based informal output
measure, A; below median self-employment share, B) over the period 1990-2016: GDP per capita (inlogs, PPP, constant 2011 international $, WDI), access to credit (i.e., private sector credit in percent
of GDP); human capital (i.e., average years of schooling), trade openness (i.e. the sum of exports
and imports in percent of GDP).
C. Annual GDP growth rates are regressed against the six measures of informality while controlling
for real GDP per capita (in logs, WDI),
D. The average relative ratio of informal labor productivity over total labor productivity in 2016 areshown in bars for advanced economies, EMDEs, and world, with corresponding 95 percent
confidence interval shown in orange vertical bars. The relative ratio is calculated using DGE-based
estimates and the share of self-employment following the method in Loayza (2018).
E-F. Unweighted group averages over the period 1990-2016 (shown as the orange diamonds) for
EMDEs with high informality (above median DGE-based informal output measure) and those with low
informality are shown for the following correlates: Doing Business (measured as the overall distanceto frontier with 100 being the frontier and 0 being the farthest from the frontier, Doing Business);
Business Freedom and Economic Freedom (Heritage Foundation); government effectiveness, control
of corruption, and rules of law (as defined in World Governance Indicators).
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FIGURE 3.7 EMDEs: Correlates of changes in informality
Among EMDEs, countries with larger declines in informality also havefaster physical accumulation, improved access to credit, and better
governance.
A. Correlates of changes in
informality: Economic factors

B. Correlates of changes in
informality: GovernanceSource: Barro and Lee (2013), Elgin et al. (forthcoming a), World Bank (World Development
Indicators, World Governance Indicators).
Note: See Annex 3.1 for data definitions. Data between 1990 and 2016 in EMDEs are used here.
A-B. Diamonds show the coefficient estimates obtained from regressing the annual change in
informal output (DGE-based estimates in percent of official GDP) or informal employment(self-employment in total employment) upon annual changes in various economic and governanceindicators, with the corresponding 90 percent confidence intervals shown in bars. The indicators
include real GDP growth, investment (in percent of GDP), access to credit (private sector credit inpercent of GDP), human capital (average years of schooling), trade openness (the sum of exports
and imports in percent of GDP) and three measures from WGI (2018). Fixed-effects estimators areused here.
Click here to download data and charts.

effectiveness (by about 0.6 points, or threequarters of a standard deviations) than countries
with below-median informality (Figure 3.6).
Similar differences are found in the case of control
of corruption and rule of law. For example, inGeorgia, during the period 1996-2016, thetransition to a market economy broughtsignificant improvements in government effectiveness, control of corruption, and rule of law.
With output growth averaging about 6 percent
per year, the share of informal output fell by 9
percentage points of GDP, and the share of
informal employment in total employment fell bya similar magnitude.
Correlates of the decline in informality since1990. The decline in informality was larger incountries with the bigger improvements ingovernance and, for output informality, faster
growth in GDP and investment, and better accessto credit (Figure 3.7).26 Perceptions of informality
appear to change much more slowly than actual

26 A panel regression suggests that faster declines in the share of
agricultural employment and faster increases in the share of industrial
employment are associated with larger long-term reductions in
informality, controlling for per capita GDP.

informal output and employment shares. Since
2000, perceptions have shifted significantly (into a
different quartile of informality) in only 14
percent of all EMDEs (Elgin et al. forthcoming a).

Informality, poverty, andincome inequality
Many studies document that informal-sector
wages are below those in the formal sector, for avariety of reasons.27 This raises concerns that, overthe long term, informality may entrench earnings
differentials and income inequality and may
contribute to greater poverty in countries withhigh informality.28
Worker earnings differentials
Causes of wage differentials. Lower wage in theinformal sector could result from different worker
characteristics in the formal and informal sectors,
possibly in response to the comparative advantagethat some workers might have in informal sector
activities, or to non-wage benefits that might
accrue to work in the informal sector (Maloney
2004; Heckman and Li 2003). Alternatively, wage
differentials could stem from rigidities and otherfactors that create a wedge in wages between
similar workers in informal and formal
employment. These factors can include labor
regulations or tax provisions that force workers
into the informal sector (Harris and Todaro1970). An alternative to measuring wage
differentials could be an assessment of thesubjective well-being or job satisfaction of workers
in the formal and informal sectors where workers
benefit from flexibility and independence (e.g.,
Blanchflower, Oswald, and Stutzer 2001; Sanfey
and Teksoz 2007; Falco et al. 2015).

27 Perry et al. (2007), Marcouiller, de Castilla, and Woodruff(1997), Tansel and Kan (2012), Bargain and Kwenda (2014),
Goldberg and Pavcnik (2003), Pavcnik et al. (2004), Goldberg andPavcnik (2003, 2007), and Paz (2014) all document the existence ofwage premia. Pratap and Quintin (2006), El Badaoui, Strobl, andWalsh (2008), El Badaoui, Strobl, and Walsh (2010) caution that
this premium disappears depending on model specifications,estimation methods or country samples.
28The linkage between informality and poverty could also be dueto the absence of better formal jobs in underdeveloped countries(Perry et al. 2007).

95% confidence interval
Mean

Percentage points
60

95% confidence interval
Mean across studies

Percentage points
55
40

30

25

0

10

Percentage points

ECA

LAC

CRI (1)
ZAF (19)
SLV (4)

ECU (6)

TUR (12)

MDG (6)
PER (2)

BRA (4)

MEX (2)Formal vs. Self-employedself-employed vs. informal

VNM (2)
RUS (6)Overall-35

UKR (2)

SSA

LAC

ECA

SSA

Overall

LAC

ECA

SSA

Formal vs.
informal

Overall

LAC

-20
ECA

-5

-60

Overall-30

Percentage points
3

SSA

65

0

50

-3

35

-6

20

-9

5

-12

-10

Poverty headcount ratio
Self-employment
Informal output

-15

-25
20

30
40
50
60
Income inequality (Gini coefficient)

70

-18
SSA

EAP

SAR

LAC

ECA

MNA

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FIGURE 3.9 Informality, poverty, and income inequality
A larger informal economy is associated with higher poverty rates andincome inequality. It is also linked with smaller declines in poverty rates.
A. Informality and poverty

C. Informality and change in poverty

B. Informality and income inequality

D. Informality and change in income
inequality

Source: Elgin et al. (forthcoming a); World Bank, World Development Indicators.
Note: See Annex 3.1 for data definitions. “SEMP” is the share of self-employment in percent of total
employment.
A.B. The average measure of poverty (i.e. the poverty headcount ratio at $1.90 a day at 2011 PPP
exchange rate in percent of the population) and Gini coefficient (World Bank estimates) over 19902016 for countries with higher informality (above median) and those with lower informality (below
median) are shown in diamonds with the 95 percent confidence intervals shown in bars. Output
informality is measured as DGE-based informal output in percent of official GDP, while employment
informality is measured as self-employment in percent of total employment.
C.D. Estimates from Annex Table 3.4.1. Informality indicators are averages over 1990-2005. Control
variable, initial level of poverty rate (earliest year over 1990-2005), is included (the same applies toGini index). The dependent variable is an annual change in poverty headcount ratio at $1.90 a day(2011 PPP, percent of population) over the earliest year and the latest year (as in C; or the annualchange in Gini index over the earliest and the latest year in D). For the comparison and scaling
issues, coefficients of DGE, MIMIC, and self-employment are multiplied by 10. Estimated coefficientsare shown in diamonds while the corresponding 90 percent confidence intervals (calculated fromrobust standard error) are shown in bars. The results are robust when controlling for initial GDP
per capita.
Click here to download data and charts.

capital accumulation in the long run (Docquier,
Müller, and Naval 2017). Thus, differences incharacteristics of workers (e.g., education) largely
account for the formal sector wage premium.
Aggregate income inequality and poverty

Conversely, the decline in poverty rates across all
EMDEs regions (and especially in SAR and SSA)
during 2005-15 was accompanied by a contractionof informal activities (Figure 3.8). At the countrylevel, a larger informal economy is associated witha higher poverty headcount (Figure 3.9).
However, the direction of causality between
informality and poverty remains an open question.
Regression analysis. The relationship between
pre-existing informality and changes in the shareof the population living in extreme poverty (i.e.,
the poverty headcount ratio at $1.90 a day at 2011
PPP exchange rate in percent of the population) or
the Gini coefficient (World Bank estimates) is
estimated in an ordinary least squares regression.
Specifically, the annual average change in thepoverty headcount ratio (or Gini coefficient)
between the latest (in the period 2011-16) andearliest available data (in the period 1990-2005)
for up to 74 countries is regressed on 1990-2005
average informality. To mitigate concerns about
endogeneity, time horizons considered for
informality measures precede those for the change
in the poverty rate or Gini coefficient (Loayza,
Servén, and Sugawara 2010; Annex 3.4). The
regression controls for the initial level of poverty(or Gini coefficient) and income per capita, using
the earliest available income data.
Pre-existing informality and changes in poverty.
The estimated impact of pre-existing informality
on changes in the poverty rate (but in this sample
not on inequality) is statistically significant (Figure3.9).32 The association with changes in poverty issimilar for employment and output informality. In
the average EMDE, the share of extreme poor in
the population (the headcount ratio) declined byabout 0.8 percentage point between 2011 and2016. These estimates imply that a country with a
10 percentage points higher share of informal
output than its peers witnessed 0.1 percentage
point slower poverty reduction per year.

The wage gap between the formal economy andthe informal economy has been associated withpersistent income inequality and poverty.31
Chong and Gradstein (2007), Amaral and Quintin (2006);
Pratap and Quiten (2006), and Loayza, Servén, and Sugawara(2010).
31

32 This is in line with other studies that find an insignificant
relationship between inequality and informality after controlling for
institutional outcomes (Perry et al. 2007) or focus on causality
running from inequality to informality (Chong and Gradstein 2007).

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BOX 3.4 Under the magnifying glass: How do policies affect informality?
Country-specific studies have found that reductions in tax and regulatory burdens have often been associated with lower
informality. In contrast, trade liberalization that raised the competition level in the tradable sector has been associated withgreater informality in the short run, unless it was accompanied by measures that increase labor market flexibility. The reduction
in informality was greater for reforms accompanied by business development and training programs, public awareness campaigns
and stronger enforcement.
Cross-country studies have identified a range of policies
associated with lowering informality. These policies havetypically fallen into three categories: tax reform, regulatory
reform, and trade liberalization.1•••

Tax reform. Lower tax rates, simplified tax systems,
harmonized tax regulations, technology-based
monitoring and consolidated electronic tax payment
systems can encourage firms and workers in theinformal sector to move to the formal sector.
Regulatory reform. Lower minimum wages and lower
barriers to worker recruitment and dismissal havebeen associated with lower informal activity. In
addition, a wide range of institutional factors havebeen associated with reduced informality: more
efficient legal systems, better property rights
protection,
lower
regulatoryburdens,
lesscumbersome registration processes, easier access tocredit, and lower corruption.
Trade liberalization. In Latin America, tradeliberalization has often been followed by an increase
in informal activity in the short run, unlessaccompanied by complementary measures to increase
labor market flexibility. Trade liberalization raises real
wages, by depressing prices, and thus encourages
worker entry into the informal economy where entry
cost is lower than in the formal economy (Arias et al.
2018).

Many EMDEs have implemented these types of reforms
either with the explicit purpose of reducing informal

Note: This box was prepared by Cedric Okou.
1 Lower tax rates have been associated with smaller informal sectors(Loayza and Rigolini 2006; Loayza 1996). Greater labor market flexibility
has been associated with lower informality (Maloney 1999; Heckman andPagés 2004; Oviedo 2009). Institutional reforms that improve thebusiness climate have been accompanied by lower informal activity (Beck,
Demirgüç-Kunt, and Maksimovic 2006; Bosch, Goni, and Maloney
2007; Friedman et al. 2000; Loayza 1996; Loayza, Oviedo, and Servén
2005; Loayza and Rigolini 2006; Monteiro and Assunção 2012; Perry et
al. 2007; Rocha, Ulyssea, and Rachter 2018; Schneider and Enste 2000;
Ulyssea 2018; Wellalage and Locke 2016). Trade liberalization in acontext of labor market rigidity has been associated with higher
informality in the short run (Goldberg and Pavcnik 2004, 2007).

activity, or for other purposes with collateral effects on
informal activity. Many of these reforms were
implemented as part of broad-based, multi-pronged
reform packages. Against this backdrop, this box compiles
a comprehensive review of single-country studies on theimpact of policy changes on informal activity. Specifically,
the box addresses the following questions:•

Which policy changes have been studied?

•

What are the common lessons from these policychanges?

•

What is the role of complementary policy measures?

Studies of policy changes
Selection of studies. 19 studies are selected based on two
criteria: (1) they examine specific policy changes in a single
EMDE and (2) they measure an outcome that relates toinformal activity, such as the share of informal workers or
firms.2 These studies cover 15 policy changes in Brazil(mid-1980s, 1990s, 2003), Colombia (1980s, 1990s),
Egypt (1998, 2004), Georgia (2010), India (1988-2000,2017), Indonesia (1996-2004), Mexico (2002-06),
Pakistan (2009), Russia (2001), Turkey (2004-05), andVietnam (1999-2013). Five of these country cases
implemented tax changes, four implemented regulatorychanges in labor markets, two implemented otherregulatory changes, and four implemented tradeliberalization measures (Annex Table 3.4).3
Tax reform. The studies examined both tax rate changes
and tax simplification. In 2017, India streamlined and2 Studies are identified from the English-language repositories ofacademic articles and working papers, including EconLit, JSTOR,EBSCO, Google Scholar, RePEc, Social Science Research Network(SSRN), the National Bureau of Economic Research (NBER), WorldBank Policy Research Working Paper Series, International Monetary
Fund Working Paper Series, and IZA Working Papers.
3 Other studies documented the outcomes of randomized experiments
and counterfactual prototypical policies in Benin, Brazil, Cameroon,
Malawi, Sri Lanka and several other Sub-Saharan Africa countries(Nguimkeu 2015; Bandaogo 2016; Benhassine et al. 2016; Ulyssea 2018;
Campos, Goldstein, and McKenzie 2015; de Mel, McKenzie, andWoodruff 2012).

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BOX 3.4 Under the magnifying glass: How do policies affect informality? (continued)
lowered the average tax rate of goods and services(Government of India 2017). Georgia introduced apreferential tax regime for small businesses in 2010 (Bruhn
and Loeprick 2014). Russia introduced a flat personal
income tax and cut payroll taxes and social security
contributions in 2001 (Slonimczyk 2012). Conversely,
Pakistan raised income taxes on noncorporate partnershipfirms in 2009 (Waseem 2018). In addition to lowering the
average tax rate for small firms, the SIMPLES reform in
Brazil in 1996 simplified the tax and social security
contributions regime for small firms (Fajnzylber, Maloney,
and Montes-Rojas 2011; Maloney and Mendez 2004).4

limits on union power (Busch, Goni and Maloney 2007).5
In 1991, India liberalized trade, removed price controls,
and removed license requirements in most industries(Sharma 2009).

Regulatory changes. A few episodes of labor market andother regulatory reforms have been studied. In 2001, aspart of fiscal decentralization in Indonesia, minimum-wage
setting responsibilities were transferred to provinces andlocal governments. The move was accompanied by a sharp
increase in the average real minimum wage (Comola andMello 2011). In 2004-05, Turkey implemented two
employment subsidy schemes that strengthened incentives
to register for the social security system (Betcherman,
Daysal, and Pagés 2010). Mexico simplified businessregistration by introducing its Rapid Business Opening
System (SARE) in various municipalities during 2002-06(Fajnzylber, Maloney, and Montes-Rojas 2011).

Tax simpliﬁcation and tax cuts were associated with lower
informality in India, Russia, Georgia and Mexico—in theform of greater formal firm registration (India, Brazil,
Georgia), greater income reporting (Brazil, Russia), greateror a greater share of formal employment (Brazil, Russia).
The reforms were followed by an increase in the number ofregistered firms by about 5 percent in Brazil and by 18-30
percent in Georgia (Bruhn and Loeprick 2014; Fajnzylber,Maloney, and Montes-Rojas 2011). In India, theintroduction of the Goods and Services Tax has beenaccompanied by a 50 percent increase in the number ofindirect taxpayers (Government of India 2017).
Conversely, Pakistan’s corporate tax hike was followed byrising informality as firms switched business models andreported lower earning.

Trade liberalization. Several studies have examined
episodes of major trade liberalization. Comprehensive
trade liberalizations with drastic tariff reductions were
implemented in Colombia in the late 1980s and early
1990s. They followed Colombia’s GATT accession in1981 (Goldberg and Pavcnik 2003; Attanasio, Goldberg
and Pavcnik 2004). Egypt introduced gradual tradeliberalization
measures
in
1998
and,
more
comprehensively, again in 2004 in the context ofmacroeconomic stabilization plans (Selwaness and Zaki
2015). In Vietnam, the U.S.-Vietnam bilateral trade
agreement (BAT) came into effect in 2001 (McCaig andPavcnik 2015, 2018) and, in the span of ten years, turned
the United States from Vietnam’s fifth-largest to its largest
export destination between 1998 and 2008. The trade
agreement was followed by reforms in 2006 to increase
labor market flexibility. In 1988, Brazil took initial steps toliberalize trade but at the same time restricted labor market
flexibility in its Constitutional Reform. The 1988 reform
included cuts in maximum work hours, higher vacation
pays, longer maternity leave, higher dismissal cost, and
4 Recent studies (e.g., Piza 2016) found mixed results regarding
robustness of Fajnzylber, Maloney and Montes-Rojas (2011)’s finding.

Common lessons
Most studies have found the expected impact of thesepolicy changes on informality (Figure 3.4.1). Tax
simplification, tax cuts and regulatory easing tended toreduce informality. Trade liberalization tended to increase
informality unless it was accompanied by increased labormarket flexibility.

Regulatory changes to encourage reporting (Turkey) or
simplify business registration (Mexico) were associatedwith greater formal employment and firm registration,
whereas higher minimum wages were associated withgreater informal employment. Employment subsidy
schemes in Turkey were followed by an increase in thenumber of registered jobs in eligible provinces by up to 13
percent (Betcherman, Daysal and Pagés 2010). In India,
following broad-based industrial liberalization measures,
the number of informal establishments fell faster (by 25
percentage points) in states with more pro-employer laborlaws than in states with less flexible labor laws (Sharma2009). A 5 percent increase in the number of registeredfirms was attributed to simplified business registration
procedures in Mexico (Bruhn 2011, 2013). Conversely, inIndonesia a 10 percentage point increase in the minimum

5 Recent Bosch, Goni, and Maloney (2007) focus on this initial reform
phase. From 1990-1997, the pace of trade liberalization picked up
significantly (Ferreira and Rossi 2003) and was accompanied by the 1994
Plano Real of fiscal reform, social security reform, state monopoly reform,and civil service reform.

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BOX 3.4 Under the magnifying glass: How do policies affect informality? (continued)
FIGURE 3.4.1 Overview of policy changes
Most surveyed policy changes, including five tax reforms, six regulatory reforms, and four trade reforms were conducted in
Latin America and the Caribbean, East Asia and Pacific, and Europe and Central Asia. The bulk of these reforms delivered the
expected outcomes and were implemented post-2000.
A. Number of reform episodes acrossregions

B. Share of reform episodes delivering
expected informality outcomes

C. Number of reform episodes over time

Source: World Bank.
Note: Descriptive summary of surveyed reform episodes.
A. Number of surveyed policies across regions. 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. “Other” includes MNA, SAR and SSA.
B. “Yes (No)” means that the outcome of a policy intervention is (not) consistent with the expected impact. “Mixed” means that the outcome of a policy intervention varies
over time. The expected impacts of reforms are: (1) reduced tax burden would reduce informality; (2) increased labor market flexibility would reduce informality; (3) loweredentry and exit barriers in formal sector would reduce informality; (4) trade liberalization would increase informality due to intense foreign competition that disrupts existing
formal firms.
C. Number of surveyed policies implemented before and after 2000. Waves of trade liberalization in Egypt were fielded pre- (1998) and post-2000.
Click here to download data and charts.

wage over the mean wage was associated with a 0.9-1.1
percent increase in informal employment (Comola andMello 2011).

employment, a growing share of formal employment, andshrinking informal employment (McCaig and Pavcnik
2018; Boly 2008).

Trade liberalizations in Brazil, Colombia, and Egypt weretypically associated with greater informality in the shortrun—unless accompanied by measures to improve labormarket flexibility. During Colombia’s trade liberalizationin the 1980s and 1990s, a 10-percentage-point decline intariffs in a given industry was associated with a 1
percentage point increase in the probability of informal
employment—but only for the period preceding a major
labor market reform that increased labor market flexibility(Goldberg and Pavcnik 2003; Attanasio, Goldberg, andPavcnik 2004). In Egypt, the trade liberalization of 1998
was associated with increased informal employment
whereas the trade liberalization measures of 2004—whichwere preceded by 2003 reforms to increase labor market
flexibility—were not (Selwaness and Zaki 2015). Similarly,
trade liberalization accompanied by measures to reducelabor market flexibility, such as in Brazil in the late 1980s
and early 1990s was accompanied by rising informal
employment (Bosch, Goni, and Maloney 2007). In
Vietnam, rapid export growth was associated with a 5
percentage point higher share of formal manufacturing

Role of complementary policy measures
Several of the policies discussed above were not primarily
implemented with informality in mind. Yet, they had theunintended consequence of raising informality: tax
increases in Pakistan, decentralization of minimum wage
regulation in Indonesia, and trade liberalization in Egypt,
Brazil and Colombia. Other reforms did not have as large
an effect on informality as expected, such as the tax reformin Georgia. Three factors accounted for these: interactions
between multiple reforms; scale of reform; andenforcement.
Interactions between multiple reforms. In Egypt, tradeliberalization implemented in a supportive environment,with reforms to increase labor market flexibility, wasassociated with lower informality in 2004 but, in theabsence of labor reforms, informality increased following
the 1998 trade liberalization. Similarly, trade liberalization
combined with increased labor market rigidities raised

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BOX 3.4 Under the magnifying glass: How dopolicies affect informality? (continued)
informality in Brazil in the late 1980s and early 1990s. When
product markets were restructured, such as during tradeliberalization, greater labor market flexibility facilitated thereallocation of workers to more competitive industries. A
large share of unskilled labor may also have increased thelikelihood that trade liberalization raised informal
employment (Loayza and Rigolini 2006; Selwaness and Zaki
2015). In Brazil, Colombia, and Vietnam, the short-term
increase in informal employment was particularly
pronounced among less skilled workers (Goldberg andPavcnik 2003; McCaig and Pavcnik 2015).
Scale of reform and persistence of eﬀects. Some reforms
were simply too narrowly targeted to have a sizeable orlasting impact on informality. For example, the short-lived
impact of tax reform found by some studies—only in thefirst year—has been attributed to the modest scale of
the reform (Bruhn and Loeprick 2014). Moreover, policy
reforms may have different short- and long-run effects
on informality. For instance, trade liberalization may increase
informality in the short run, but not necessarily in thelong run (Goldberg and Pavcnik 2003; Dix-Carneiro and
Kovak 2017).
Weak enforcement. Particularly in environments with weak
enforcement of firm and employment regulation, higher
taxes or minimum wages can encourage informal activity. In
Pakistan, Turkey, and Indonesia, weaker enforcement was
associated with greater informality.6

Conclusion
The studies of microeconomic impacts of policy changes area reminder of the importance of comprehensive reform
packages. Several of the packages discussed above, as anunintended consequence, raised informal employment or
firm activity. Such unintended reform impacts can be
mitigated by bundling mutually reinforcing reforms, such as
trade liberalization with labor market reform, or tax andminimum wage hikes with strengthened enforcement andpublic awareness campaigns.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Informality and fiscal
outcomes
A large informal economy erodes the tax base andconstrains governments’ ability to provide public
services, conduct countercyclical policies, servedebt, and implement redistributive measures(Chapter 4; Ordóñez 2014; Besley and Persson2014). This puts a premium on designing tax andsocial security systems that avoid unintended
incentives to shift activity from the formal to theinformal sector and level the playfield for bothformal and informal sectors (Perry et al. 2007;
Djankov et al. 2010; Loayza 2018; Dabla-Norris
et al. 2018).
Revenue outcomes. Regardless of the measure of
informality, on average, government revenues in
EMDEs with the most pervasive informality havebeen 5-10 percentage points of GDP below thosewith the least pervasive informality (Figure 3.10).
The composition of tax revenues is tilted towards
trade taxes in economies with more pronounced
informality. Revenues from trade taxes have been
0.7-1.0 percentage points of GDP higher in
EMDEs with greater informality compared withthose with the lowest levels of informality. Income
tax revenues, in contrast, tend to be lower in the
EMDEs with the highest output informality.
Greater reliance on indirect taxation makes the tax
system less progressive and, hence, lessredistributive than a system based on moreprogressive direct taxation.
Expenditure outcomes. Revenue weakness is also
reflected in lower government expenditures. In
EMDEs with the most pervasive informality,
government expenditures were 4-10 percentage
points of GDP lower than in those with the lowest
informality (Figure 3.10). Insufficient resources
for redistributive policies may contribute to thecorrelation between informality and poverty.

Policy options
Oviedo (2009), Betcherman, Daysal, and Pagés (2010), Vargas (2015),
Waseem (2018), Comola and Mello (2011), and Loayza (1996).
6

Many EMDE governments implemented policies
at the microeconomic level and found that the
implications for informality were more benign
when these reforms were implemented in a

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019supportive institutional and macroeconomic
environment. For instance, trade liberalization
programs that raised real wages and reduced firms’
profitability in the tradable sector were associatedwith greater informality in the short term—unlessthey were accompanied by higher labor market
flexibility and more skilled labor force (Box 3.4;
Goldberg and Pavcnik 2003; McCaig and Pavcnik
2015).
Country experiences suggest the need for acomprehensive development strategy that is
informed by the drivers of and challenges posed byinformality and carefully tailored to country
circumstances. Policies that seek to improve fiscal
accounts, such as strengthened tax administration
or streamlined tax regulations, can be associatedwith lowering informality in some economies.
Separately, policies that aim at invigorating private
sector activity and productivity and leveling theplayfield for all workers and firms, particularly
measures to make the labor market more flexible,
the regulatory framework more adaptable, andgovernance more effective, can lower informality
and/or improve the working conditions in theinformal sector. Finally, supportive macroeconomic and social policies (such as enhancing
public service and social protection) can ease theimplementation of these reforms and facilitate asmoother transition from the informal sector to
the formal sector.
These policy measures can help lower informality
while also spurring growth more broadly. They
should be accompanied by strengthening the basicsocial safety nets to preserve incomes of vulnerable
groups. Disruptions to formal activity frominterventions to lower informality could be
mitigated by reforms to increase labor and productmarket flexibility.

CHAPTER 3

FIGURE 3.10 EMDEs: Informality and fiscal outcomesWidespread informality is associated with lower government revenues, askew towards trade-based taxation, and lower government expenditures.

A. Differential in fiscal indicators
between third of EMDEs with thelowest and highest output informality•

Tax compliance has been encouraged bysimplifying tax codes, improving tax
enforcement (e.g., via the use of information

B. Differential in fiscal indicators
between third of EMDEs with thelowest and highest employment
informality

Source: Elgin et al. (forthcoming a); International Monetary Fund, World Economic Outlook; World
Bank, World Development Indicators.
Note: Fiscal indicators and informality measures are 2000-16 averages. Sample includes 70 nonenergy exporting EMDEs with populations above 3 million people.
A.B. Difference (in percentage points of GDP) between the average fiscal indicators among the thirdof EMDEs with the highest and lowest informality by the share of informal output (as measured by the
DGE methodology) in percent of official GDP (A) or by the share of self-employment in percent of
total employment (B). Vertical bars indicate 90 percent confidence intervals of the differences.
Click here to download data and charts.

technology and communication tools),
building tax administrations’ capacity,
harmonizing tax regulations or forms (e.g.,
across firms of different sizes), limiting the useof cash transactions, and encouraging the useof bank-based tax payments (Morales andMedina 2016; Ulyssea 2018; Rocha, Ulyssea,
and Rachter 2018; Awasthi and Engelschalk2018).
•Tax burdens have been reduced for formal
firms by offering tax relief for new employees
or simplifying tax bases in industries with ahigh percentage of undeclared workers (e.g.,domestic work). Reducing tax burdens has
been among the most common policy reforms
in EMDEs, especially in East Asia and Pacific(EAP) and Latin America and Caribbean(LAC; Figure 3.11).33•

Value-added taxation (VAT) can help
strengthen tax collection even in the presenceof a sizable informal sector (World Bank 2018

Fiscal policy measures
Some countries have implemented reforms toaddress the fiscal challenges associated withinformality, including, in the collection process, toreduce fiscal barriers or incentives for firms tooperate informally.

159

33 In China, for example, the computerization of VAT invoices
between 1998-2007 explained roughly 15 percent of cumulative
VAT revenues and increased the effective average tax rate byapproximately 5-14 percent paid by firms (Fan, et al. 2018).

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FIGURE 3.11 EMDEs: Policies to address challenges of
informality
Governments have implemented a wide range of reforms that could affectinformality.
A. Reforms across regions, 2008-18

policies) and encourage informality may
warrant reform.
•

An improved provision of public goods andservices, such as better education or
infrastructure, could help improve theproductivity in both formal and informal
sectors (Oviedo, Thomas, and KarakurumOzdemir 2009; Benjamin and Mbaye 2012;
Kim, Loayza, and Meza-Cuadra 2016; WorldBank 2018b).

•

Social security systems can be reformed toreduce the incentives to hire informal
workers.35 Measures include steps to shift theburden of payments of contributions from
employers to employees (e.g., in Latvia,
Poland, Slovenia), to reduce employers’ social
security contributions (e.g., in Bulgaria), andto link social benefits to personal
contributions (e.g., in most EU 27 countries;
Oviedo, Thomas, and Karakurum-Ozdemir2009). Transitions from an employmentbased social security system to a well-designed
model of risk sharing can provide a better
safety net for informal workers and help
protect both formal and informal workers
during economic downturns (World Bank
2013, 2018a; Box 3.1).

B. Reforms over time

Source: World Bank, Doing Business.
Note: See Doing Business 2008-18 for reform details.
A. The number of policy reforms that have been implemented after year 2008 and are regarded as“improvement” in the ease of doing business or “neutral” (which only applies to “labor market
regulation”) by Doing Business 2008-18.
B. The annual average number of policy reforms that have been implemented during 2008-10 incomparison to the annual average number of reforms conducted during 2016-18 (shown in bars).
Click here to download data and charts.

b). Since informal firms would not be allowed
to claim VAT refunds on taxed inputs, theVAT would implicitly serve as an input tax(de Paula and Scheinkman 2010; Loayza2018). Conversely, more effective VAT
administration, including through digitalization of receipts, could raise tax revenues
while also increasing incentives to register for
tax refunds.34

Business climate and governance measures

Better tax morale, reflecting the perceptionthat tax dollars are spent judiciously (for theappropriate objectives and in the correct way),
can encourage greater tax compliance andlessen informality (Sung, Awasthi, and Lee2017). Measures to cultivate better tax morale
include appeals to people to declare theiractivities, campaigns to encourage a culture ofcommitment to declaration, and efforts tochange perceptions of the tax system’s fairness(Williams and Schneider 2016). Tax systems
that create an unlevel playfield for different
types of firms (e.g., size-dependent tax

Many reforms that are designed to invigorate
private sector growth can also help reduce
informality, such as reducing corruption,
improving business climates and governance,
strengthening enforcement, or liberalizing laborand product markets, including through tradeliberalization.36 Policy measures that narrow theearnings gap between informal and formal workers
or those that reduce the productivity gap between
informal and formal firms (for example, throughmeasures to improve education or expand access toconventional sources of credit) can also help lower
the extent of informal activity. Trade

34 See Loayza (2018) for a detailed discussion on how to reformthe social security system to reduce informality. See World Bank
(2018b) for a discussion on how to provide better social security to
informal workers. Levy (2008) and Maloney (2004) suggest that
establishing parallel non-contributory systems in the presence of
informality could further encourage informality.

35 See Johnson, Kaufman and Zoido-Lobaton (1998), Djankov etal. (2002), Prado (2011), USAID (2015), Baksi and Bose (2016),
Kanbur (2017), and Divanbeigi and Ramalho (2015).
36 Kuddo (2018) shows that about 60 percent of the reforms
passed between 2007 and 2017 throughout the world aimed at
improving labor market flexibility.

•

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019liberalization, however, may encourage informality
in the short term unless complementary reforms
are implemented (Box 3.4; Figure 3.11).
Labor regulations. Over the past decade,governments—especially in ECA, SSA and, more
recently, LAC—have implemented reforms toincrease labor market flexibility.37 These include
less restrictive regulations with respect to hiringand firing, to working arrangements, and to wage
rates. Other types of policy changes, such asproviding incentives for worker registration (e.g.,legalization of undocumented workers) and
improved enforcement of existing labor laws, mayalso encourage workers to move to the formal
sector (Anand and Khera 2016; Munkacsi and
Saxegaard 2017). For example, Japan has allowed
undeclared workers to claim certain social benefits,
thereby improving the monitoring of theiremployment. In rapidly urbanizing countries withstill-large rural populations, easing labor market
regulation could play an important role inenabling workers to move into the urban, moreproductive and more modern sectors (Annex
Figure 3.5.1; Annex 3.5; Loayza 2016).38
Firm regulations. A variety of measures canencourage firms to participate in the formal sector.
For example, formal entry of firms can befacilitated and encouraged by creating “one-stopshop” registration to simplify the process (e.g., in
Australia, Belgium, Ukraine), training andbusiness services can be provided to firms that
register (e.g., in Mexico and Malawi; Campos,
Goldstein, and McKenzie 2018), and access tocredit can be made easier for firms in the formal
sector. EMDEs in the ECA and SSA regions have
implemented an above-average number of reforms
to reduce the costs of starting a business during
the past decade (Figure 3.11). Easier firm

37 Loayza (2016) develops a theoretical model that traces
informality, government regulations, economic growth and urban
migration through the process of development. The model highlights
the potential effect of the minimum wage on labor misallocation andon capital accumulation. A higher minimum wage slows capital
accumulation and pushes workers into the informal economy. See
Annex 3.5 for details. Caballero et al. (2013) show that job security
regulation hampers the creative-destruction process, which could
impede growth.
38 See, for instance, Oviedo, Thomas, and Karakurum-Ozdemir(2009), Bruhn and McKenzie (2014), Awasthi and Engelschalk(2018), and De Giorgi, Ploenzke, and Rahman (2018).

CHAPTER 3

registration and lower registration costs can also
encourage the entry of young and productive
firms, which can boost the productivity of theeconomy (Haltiwanger, Jarmin, and Miranda
2013; Nguimkeu 2015; Loayza 2018).
Regulatory enforcement. While other policy
options increase the benefits of joining the formal
economy, stricter enforcement can increase thecost of remaining in the informal economy. Policy
options include increasing the frequency ofinspections (in most EU15 countries andBangladesh), creating a national-level firm or
employee registry (in Poland), and launching
public awareness campaigns regarding tax
compliance (e.g., in China and Korea).39
However, these enforcement measures tend to be
most effective when implemented in conjunction
with steps to improve the governance and businessclimate (e.g., making the labor market flexible)
and when they are applied even-handedly to bothformal and informal firms (Loayza 2018).
Education. Informal workers tend to be lessproductive than formal workers. To the extent
that workers remain in the informal sector for lackof human capital or skills, better and moreaccessible public education may help workers (ortheir dependents) to move into better paid formal
employment (Maloney 2004; Perry et al. 2007;
Andrews, Sánchez, and Johansson 2011). This canalso have the benefit of reducing income
inequality and poverty.
Access to finance. Firms in the informal sector
have more limited access to credit from thebanking sector and capital markets, which restricts
their ability to invest in productivity-enhancing
newtechnologies(Ferreira-Tiyaki
2008;
D’Erasmo 2016; Capasso and Jappelli 2013). Oneof the options to have greater access to finance isto improve personal property registration, whichmakes loans more accessible for firms operating in
the informal economy (e.g., Czech Republic;
Doing Business 2012). Improving access to credit
has been a common policy reform in EAP,
MENA, SAR and, more recently, in SSA.
Separately, digital payment systems can provide an
39 See, for instance, Oviedo, Thomas, and Karakurum-Ozdemir(2009), Bruhn and McKenzie (2014), Awastchi and Engelschalk(2018), and De Giorgi, Ploenzke, and Rahman (2018).

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entry point to the formal financial system andencourage a shift away from informal finance(World Bank 2017).
From a comprehensive strategy toimplementation
A comprehensive strategy: The right policy mix.
Policy interventions in isolation may only have alimited impact on informality but can haveunintended consequences (Box 3.4; Ulyssea 2018;
Oviedo, Thomas, and Karakurum-Ozdemir2009).40 A coherent reform strategy calls forwell-integrated reforms that complement eachother and address the complexity of informality(Loayza 2018).
Tailoring
implementation.
Cross-country
experiences also highlight the importance of acountry-specific implementation plan: eachreform component requires a diagnosis of thecountry’s current situation, followed by specificreforms to address the main weaknesses associatedwith and underlying sources of informality(Loayza 2018). In SSA, SAR, and the non-GCC
economies of MENA, for example, general
education and training programs to raise humancapital could be prioritized (Box 3.2; Boxes
2.1-2.6). In LAC, reducing particularly high tax
and regulatory costs to businesses could
incentivize firms to join the formal sector. In
ECA, improving government effectiveness andreducing corruption could be policy priorities.
The success of implementation also depends on
careful monitoring of potential unintended
consequences and a supportive macroeconomic,
political and institutional environment. The latterensures the political and fiscal viability of theimplementation and reduces the transition costs
for workers moving from the informal sector to
the formal sector.
Emerging policy opportunities andchallenges
Human capital adaptability. The emerging “gig”
economy poses opportunities and policy
40 Ulyssea (2018) shows that formalization policies differ in theirimpact on informality and GDP. For instance, reducing form sector’sentry costs is not as effective in reducing informality as otherformalization policies, but it leads to greater GDP and wages. The
reverse holds for increasing enforcement.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

challenges with its higher accessibility, more fluidlabor arrangements, and greater reliance on digitaltechnology than more traditional forms of
informality. Since “gig” workers do not fully
participate in the social security system, they are,by some definitions, informal workers (Loayza,
Servén, and Sugawara 2010). Regulatory changes,
especially in the context of social security systems,
may be needed to ensure that “gig” workers’
economic risks are manageable and that they do
not permanently lose access to the formal
economy (World Bank 2014, 2016, 2018b). Since
these workers will likely take on many different
assignments over the course of their careers, theability to learn and adapt will be essential. Policies
can support this adaptability with more provisionof education and (re)training programs (WorldBank 2019; Card, Kluve, and Weber 2018).
Emphasis should also be given to the developmentof cognitive skills in primary and secondary
education or via intentional instruction at earlier
ages, and the improvement of the terms of
employment (Almeida, Behrman and Robalino,
2012; World Bank, 2018a, 2018b).
Harnessing new technology. New technologies
offer governments an opportunity to both reducethe incentives for and increase the cost ofoperating informally. For example, newtechnologies can also help strengthen tax
administration and improve access to finance,
including by improving the ability to broaden thetax net and assess credit worthiness (Gupta et al.
2017; Junquera-Varela et al. 2017; Awasthi andEngelschalk 2018; Capasso, Monferra, andSampagnaro 2018). Digitalization can lower
regulatory burdens, thus reducing the cost ofoperating in the formal economy. For example,
Costa Rica reduced the time required to register abusiness by digitizing tax registration records andcompany books in 2009 (Doing Business 2009).
This was followed by a drop in the share of
informal employment by 4 percentage points of
total employment and a fall in the share of
informal output by about 2 percentage points ofofficial GDP during 2009-16. Similar reforms
have been carried out in Guyana (2010) andKenya (2011) (Doing Business 2010, 2011).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

ANNEX 3.1 Measures of
informality
The database includes most informality measures
employed by the literature. These measures cover
up to 196 economies (36 advanced economies and
160 emerging market and developing economies)
for as much as 1950-2016 (Annex Table 3.1.1).
Measures can be divided into indirect (modelbased) estimates and direct (survey-based)
estimates.
Indirect estimates
Previous studies use various indirect approaches toestimate the size of the informal sector, including
the currency-demand approach (e.g., Ardizzi et al.
2014), and the electricity-demand approach (e.g.,
Johnson, Kaufmann, and Shleifer 1997; Lackó
2000), the Multiple Indicators Multiple Causes(MIMIC) model (e.g., Schneider, Buehn, andMontenegro 2010), and the Dynamic General
Equilibrium (DGE) model (e.g., Ihrig and Moe
2004; Elgin and Oztunali 2014; Orsi, Raggi, andTurino 2014). Among all indirect estimation
methods, the MIMIC and DGE models stand out
in their year and country coverage. The other two
indirect approaches, that is, the electricity-demand
approach and the currency-demand approach,suffer from limited data availability and theoreticalcaveats (see Ahumada, Alvaredo, and Canavesa
2007; Schneider and Buehn 2016 for details).
Therefore, the MIMIC and DGE models are usedhere to estimate the size of the informal sector.
The multiple indicators multiple causes model(MIMIC).1 The Multiple Indicators Multiple
Causes model is a model of structural equations
that can be applied to estimate the size of informal
economic activity. There are two features of
MIMIC that make it a preferred estimation
approach for some researchers. First, it explicitly
considers multiple causes of informal activity and
1 The limitations of the standard MIMIC model of Schneider,
Buehn, and Montenegro (2010) and others include (e.g., Medina andSchneider 2018; Feige 2016): (1) the use of GDP (GDP per capita
and growth of GDP per capita) as both cause and indicator variables,(2) its reliance on another independent study’s base-year estimates on
the informal economy to calibrate the size of informal economy inpercent of GDP, and (3) the estimated coefficients are sensitive toalternative model specifications and sample coverage.

CHAPTER 3

captures multiple outcome indicators of informal
activity.2 Second, it estimates informal activity
across country and over time. The data on causes
and indicators of informal activity identified in theliterature are largely based on macroeconomic
series in a panel setting and updated annually.
To estimate the size of the informal sector (i.e., inpercent of official GDP) with the MIMIC model,
this study closely follows Schneider, Buehn, andMontenegro (2010). Six causes and three
indicators are used in the estimation to capture thehypothesized relationships between the informal
sector (the latent variable) and its causes andindicators. Once the relationships are identified
and the parameters are estimated, the estimation
results are used to calculate the MIMIC index,
which gives the absolute values of the size of theinformal sector after a benchmarking procedure.
The MIMIC approach delivers a panel of
estimates (labelled as MIMIC) for 160 economies
over the period 1993-2015.
Six causes and three indicators are used in theestimation (as in Schneider, Buehn, andMontenegro 2010). The six cause variables used
are: (1) size of government (general government
final consumption expenditure, as a percent ofGDP, obtained from UN, spliced with WDI) as
proxy for indirect taxation; (2) share of direct
taxation (direct taxes in percent of overall taxation,
WDI); (3) Fiscal Freedom index obtained from
Heritage Foundation as a tax burden variable in awide sense; (4) Business Freedom index provided
by Heritage Foundation; (5) the unemployment
rate and GDP per capita to capture the state of theeconomy (obtained from WDI, the latter is
spliced with IMF World Economic Outlook(WEO)); and (6) a measure on government
effectiveness provided by Worldwide GovernanceIndicators. The three indicator variables include:(1) growth rate of GDP per capita (WDI, spliced
with IMF WEO); (2) the labor force participation
rate (people over 15 economically active as a

2 Indirect approaches like the currency demand approach or theelectricity approach condense the full range of informal activity acrossproduct and factor markets into just one indicator. However, theinformal sector shows its effects in various markets (Schneider,
Buehn, and Montenegro 2010), which would be captured better in a
MIMIC model.

163

ῆt
ῆt
η^t

ῆt
η^t =

ῆ2000

Ht > 0η*2000tῆ2000η*2000max,N It ,N Ft    t U (C t )t 1

C ,I ,K
t

ῆt

t

t 0

t 0


t

s .t .C t  I t  (1   t )AFt K N Ft1  AIt N It
η*2000

Kt+1 = It + (1 - δ)Kt
η*2000NIt + NFt = Ht
β < 1

Ct

It

AFt
NIt,

Kt
τt

NIt
AIt

δ ∈

τt

Gt
{τt},

{C t , I t , K t 1 , N It , N Ft ,Gt }t0t  tU (C t )


0

YIt
YFt


It

AIt N
AFt K t N Ft1

α
γ
Kt

Ct
δ

NFt
τt

Kt

AIt
Aft 6

δ, α, γτtInformal employment
Employment (excl. formal
sector)

166

CHAPTER 3

covers self-employment and employees holding
informal jobs. ILO presents a detailed definitionof these two measures (http://www.ilo.org/ilostatfiles/Documents/description_ IFL_EN.pdf).
Combining various cross-country databases andadditional data from the national statistical offices
and other sources, the resulting data set on selfemployment is a panel of 180 economies over theperiod 1955-2016. The data set on informal
employment covers 53 countries/regions fromvarious years during 2001-2016 while the data set
on employment outside the formal sector contains
57 countries/regions from various years during
1999-2016.
Data on pension coverage (labeled as Pension
coverage) are also gathered from various issues ofthe World Bank’s World Development Indicators(WDI book version, reported until 2012). The
measure is defined as the fraction of the labor
force that contributes to a retirement pension
scheme Loayza, Oviedo, Servén 2010; WDI). It
yields a panel that covers 135 countries from 1990
to 2010.
Firm surveys. Two data sets of firm surveys havecomprehensive coverage: World Bank Enterprise
Surveys, and Executive Opinion Surveys
conducted by World Economic Forum. WorldBank Enterprise Surveys cover 139 economies
over the period 2006-2016 while Executive
Opinion Surveys cover 151 countries over theperiod 2006-2016.
Both surveys are answered by top managers andbusiness owners, who are business experts andshould be familiar with the business climate in acountry. The surveys could reflect somedimensions of informality (e.g., the extent ofcompetition from the informal sector) that are notcaptured in the other informality measures.
Similar to labor-related measures, measures fromfirm surveys also have the advantages of being freeof strong assumptions and base-year estimates forcalibration.99 There are two drawbacks of informality measures from firm
surveys. First, firm surveys tend to have limited year coverage.
Second, since people’s perception does not move much over time,

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

World Bank Enterprise Surveys compile responses
on various topics (including informality) from face-to-face interviews with top managers and businessowners in over 130,000 companies in 146
countries. The surveys yield the following
measures of informality (e.g., used in Amin andIslam 2015; La Porta and Shleifer 2014): percent
of firms competing against unregistered orinformal firms (WB1), percent of firms formallyregistered when they started operations in the
country (WB2), (average) number of years firms
operating without formal registration (WB3), andpercent of firms identifying practices ofcompetitors in the informal sector as a major
constraint (WB4). A higher value of WB1, WB3
and WB4 indicates a higher level of informality,
while the reverse holds for WB2.
In comparison to Enterprise Surveys, Executive
Opinion Surveys provide a more balanced panel
data set, making them more suitable for businesscycle analysis. World Economic Forum has been
conducting the Executive Opinion Survey every
year since 1979. As reported in the 2014 edition,over 13,000 executives in 144 economies weresurveyed. From year 2006, when conducting thesurvey, the following question is asked, “In yourcountry, how much economic activity do you
estimate to be undeclared or unregistered?
(1=Most economic activity is undeclared or
unregistered; 7 = Most economic activity is
declared or registered).” The average responses atthe country-year level constitute a series of
informality measures, labeled as WEF. A lower
average at the country level indicates a larger
informal economy.
Household surveys (HS). Household surveys
either report the extent of informality in an
economy or report people’s opinions on informal
economic activities. Among all, World Value
Surveys (WVS) stand out in their country andyear coverage with others focusing on European
countries. It asks whether respondents can justify

this type of measures do not have much time variation. Bothdrawbacks limit their application in time-series analysis. However,they shed light on the perceived extent of informality in a country
and provide guidance for constructing and validating indirect model
estimates.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019cheating on taxes in five waves from 1981-1984 to
2010-2014. The responses range from 1 (neverjustifiable) to 10 (always justifiable). In total, 94
economies participated in the survey. The average
responses at the country-year level are used as ameasure for attitudes towards informality (or tax
morality; Oviedo, Thomas, and KarakurumÖzdemir 2009), labeled as WVS. A higher averageat the country level implies that people findcheating on taxes more justifiable. Former studies
show that the lack of tax morality is associatedwith a higher level of informality.

ANNEX 3.2 Characteristics ofinformal-economy
business cycles
Harding and Pagan (2002)’s approach is used
to identify business cycle turning points in formal
and informal sectors in annual data: Peaks(troughs) are identified in years when output ishigher (lower) than the two subsequent and two
preceding years. A recession is defined as theperiod from a business cycle peak to a trough.
An expansion is the converse, the period from abusiness cycle trough to its peak. A recovery is
the early part of an expansion and is defined as
the period from the business cycle trough to theyear in which the output level recovers to that of
the most recent business cycle peak (Claessens,
Kose, and Terrones 2012). The main
characteristics of recessions and recoveries include
duration and speed of adjustment (often termed as“slope”) are defined as in Claessens, Kose, andTerrones (2012).
••

Duration captures, for a recession, the period
from peak to trough, for a recovery, theperiod it takes for output to return to its pretrough peak, and for an expansion the period
from trough to peak.
Speed of adjustment (“slope”) measures the
speed of a cyclical phase and is defined as theratio of amplitude over duration for arecession and the ratio of the change from thetrough to the last peak divided by the
duration for a recovery (Claessens, Kose, andTerrones 2012).

CHAPTER 3

ANNEX 3.3 Informality andearnings inequality:
A meta-analysis approach
Selection of studies. The collection of therepresentative sample of studies on informality
and wage inequality follows selection guidelines
outlined in Stanley et al. (2013) and is broadly
similar to criteria applied by van der Sluis, vanPraag, and Vijverberg (2005). An initial search
was conducted in the major English language
repositories of academic articles and working
papers.10 A study was included in the database if it:(1) provided a quantitative estimate of theinformal-formal wage gap and a corresponding
standard error or a t-statistic; (2) used data frommicro-level household or labor surveys to obtainthese estimates; (3) analyzed a developing country
or a group of developing economies as defined by
the World Bank classification; and (4) was
published after 1990.11 The resulting databaseincluded 18 studies with a total of 83 individual
coefficient estimates covering 20 emerging market
and developing economies (Annex Table 3.3.1).
Definitions matter. Differences in estimates of theincidence of informal employment and the wage
differentials between formal and informal workers
in part reflect differences in data coverage anddefinitions of informal workers.12 Self-employedworkers constitute the core of informal
employment since they typically lack registration
at the national level, do not contribute to social
security and are not entitled to paid annual andsick leave.13 However, not all informal workers are
self-employed, while the informal sector itself may
be divided into several tiers such as informal self-

10 Covered online databases include EconLit, JSTOR, EBSCO,
Google Scholar, RePEc, Social Science Research Network (SSRN),
the National Bureau of Economic Research (NBER), World Bank
Policy Research Working Paper Series, International Monetary FundWorking Paper Series, and IZA Working Papers.
11 Prior to 1990, reliable and comparable individual or household
level survey data, which is used to estimate wage gaps between theformal and informal sectors, is very limited for developing countries.
12 Perry et al. (2007), Hussmanns (2004), ILO (2013).
13 According to ILO 2018, nearly 90 percent of all own-accountworkers—the largest component of self-employed, in the emerging
markets and developing economies—are in the informal sector accounting for over 45 percent of all informal jobs.

167ˆi ~ N (i , 2 ),σ2ˆi ~ N (  , 2 )μτ2

I2=τ2/(τ2+σ2)kˆi     j X ij  i i
j

ˆi
∈i

τ2

ϑi
τ2

i

X(ij)

x i ,19902005

•
FEi

1

Initial0yi•
FEMALEi

1

MALEi is 1

0

0

•
•

Self-employedi

 y i   0  1x i ,19902005  2Initial y i  i
y i

i

y i

170

CHAPTER 3

only labor with a low-productivity technology.
The modern economy, itself, consists of two
sectors: a capital-intensive modern formal sector
that complies with government-mandated laborcosts including the minimum wage; and a modern
informal sector that is less capital intensive, pays
low labor costs and high capital costs andproduces with lower productivity by contravening
labor regulations.
A developing economy passes through three stagesof development as it becomes richer. In the firstphase, modern informal employment expands as
falling relative cost of urban living encourage rural
workers (in the rudimentary informal sector) tomigrate to cities. In the second phase, rural-urban
migration slows, the relative shares of the modern
informal and formal sectors stabilize, but therelative size of the rudimentary informal sector
shrinks.18 In the third phase, modern informal
employment declines as rural-urban migration
stalls and a rising capital-labor ratio reduces therelative (and absolute) size of the modern informal
sector.19
Theoretical impact of changes in minimum
wages on informality
The model provides a framework for tracing out
the implications for growth and informality ofchanges to labor market regulations, here
represented by the minimum wage.20 When theminimum wage is higher than the unregulated
market wage, it creates a distortion in the labormarket, which moves labors to the modern
informal sector where the minimum wage is notbinding. For 127 economies, of which 28 areadvanced and 99 are EMDEs, the evolution of therelative size of informal output and employment
over 2015-2035 is considered for two scenarios.21
18 The relative shares of modern informal sector remain stable dueto the constant urban capital-labor ratio during the second phase.
19 The size of the modern informal sector diminishes when therate of natural increase in urban population is not too large and whenthe minimum legal wage is no longer binding.
20 The relative sizes of different sectors are projected using theparameter values, population projections and total factor productivity
growth from Loayza and Meza-Suadra (2016). 2015 is taken as thestarting year and the relative sizes of the three sectors are projected foryear 2015-2035. The real cost of capital are assumed to match laborproductivity growth.
21 The country classification is listed in Annex 3.5.1. See Loayza(2016) for the list of countries.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019•Baseline scenario. The minimum wage rises at
the rate of labor productivity.

•

Reformist scenario. The minimum wage rises
one percentage point more slowly than laborproductivity growth.

The outcomes of both scenarios depend on theinitial conditions of the country, future
population and TFP growth rates, and the rate ofchange of the minimum wage.22 In the baseline
scenario, the minimum wage is assumed to growin line with labor productivity growth, such that
informal rudimentary employment shrinks while
the formal and informal modern employment
expand at a similar rate. In the reformist scenario,
slower minimum wage growth will speed up
capital accumulation, increase rural-urban
migration, raise capital-labor ratio, reduce thewage distortion created by the minimum wage,
and result in an expanding modern and formal
sector.
Global implications in theory: Employment in
the modern economy. On average, in both thebaseline and reformist scenarios, the employment
share of the modern economy is predicted to
expand, by, respectively, 18 (more than onequarter) and 23 (more than one-third) percentage
points (Annex Figure 3.5.1). In both scenarios,
capital accumulation attracts rural workers fromthe rudimentary informal sector, reduces the wage
distortion created by the minimum wage, andresults in allocating more labor in the modern
formal sector. In the baseline scenario, capital
accumulation encourages rural-urban migrationand modern employment. Employment in bothformal and informal modern sectors grow atsimilar rates. As a result, share of informal modern
employment in modern employment remains
steady but its share in total (modern andrudimentary) employment increases by 9
percentage points.
In the reformist scenario, the slower growth in theminimum wage encourages faster capital accumu-

22 The initial conditions include the capital stock, total factor
productivity, and the labor force, and the share of formal andinformal labor, both rudimentary and modern (See Loayza 2016 fordetails).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019lation and migration to the modern economy,
which speeds up the modernization process.
Minimum wage growth below productivity
growth in the formal sector gradually widens thewage gap between the formal and informal wages
in the modern sector and encourages more formal,
modern employment. As a result, the share offormal modern employment in modern
employment rises by 22 percentage points and theshare of formal modern employment in total(modern and rudimentary) employment increases
by 33 percentage points.
Differences between advanced economies and
EMDEs. Advanced economies have small ruraland modern informal sectors compared with
EMDEs, to begin with. The rural sector is alreadynegligible in advanced economies, accounting for
9 percent of employment, whereas it accounts for
44 percent of employment in EMDEs. The
modern informal sector already accounts for asimilarly modest 8 percent of employment inadvanced economies, but 28 percent of
employment in EMDEs. Under the baseline
scenario, and even more quickly andcomprehensively under the reformist scenario, themodern informal and rudimentary sectors willvirtually disappear in advanced economies,
together accounting for about 10 percent of
employment from 18 percent initially. In EMDEs,
the rural sector will continue to play an important,albeit shrinking, role, accounting for 15-22
percent of employment. Rural-urban migration
will continue to fuel the expansion of the modern
economy, but only in the reformist scenario will
this migration ensure that the modern formal
sector grows more rapidly than the modern
informal sector.
Differences across EMDE regions. EMDE regions
differ widely in their initial conditions, hence also
in the implications of policy changes. In 2015,
Sub-Saharan Africa (SSA) and South Asia (SAR)
had large rural economies, accounting for morethan 60 percent of employment, whereas Europe
and Central Asia (ECA) and the Middle East andNorth Africa (MNA) had predominantly modern
economies, which accounted for about 80 percent
of employment. Over the next two decades,
reformist or baseline policies would reduce theCHAPTER 3

171

ANNEX FIGURE 3.5.1 Implications of relaxing theminimum wage restraint
Over the next two decades, according to the model, the employment shareof the modern economy is expected to expand. In the baseline scenario,
the informal economy would grow faster than the formal economy, whereas
the informal economy would shrink in the minimum wage restraint scenario.
The theoretical model suggests that lowering the minimum wage would
speed up formalization in regions like Europe and Central Asia and Middle
East and North Africa and accelerate economic modernization in South
Asia and Sub-Saharan Africa.
A. Employment composition, 2015

B. Employment composition, 2035

C. Employment composition, 2015

D. Employment composition, 2035:
Baseline scenario

E. Employment composition, 2035:
Reformist scenario

F. Average output growth, 2015-35:
Baseline and reformist scenario

Source: World Bank staff calculation using the model of Loayza (2016).
Note: “MF” stands for employment in the modern formal sector (in orange), “MIF” stands for
employment in the modern informal sector (in red), while “RIF” stands for employment in the
rudimentary informal sector (in blue). The relative sizes of different sectors are projected using theparameter values, population projections and total factor productivity growth from Loayza (2016).
Under the baseline scenario, the minimum wage rises at the rate of labor productivity. Under thereformist scenario, the minimum wage rises one percentage point more slowly than the rate of laborproductivity growth. 2015 is taken as the starting year for the projection exercise. “Advanced
economies” represents the unweighted average of the 28 advanced economies in the sample, while“EMDEs” represents the unweighted average of the 99 emerging markets and developing economies
in the sample. These group averages are calculated for East Asia and Pacific (EAP), Europe andCentral Asia (ECA), Latin America and Caribbean (LAC), Middle East and North Africa (MNA), South
Asia (SAS), and Sub-Saharan Africa (SSA).
A. The stacked bars show the average employment shares in 2015 for each sector in advanced
economies and in EMDEs.
B. The stacked bars show the average employment shares projected for 2035 in advanced
economies and in EMDEs.
C. The stacked bars show the average employment shares across all EMDE regions in the sample foreach sector in 2015.
D.E. The stacked bars show the average employment shares across all EMDE regions in the sample
for each sector in 2035 under baseline and reformist scenario.
F. The diamond shows average annual GDP growth between 2015-35 across all EMDE regions forthe reformist scenario. The blue bars show the growth rate for the baseline scenario.
Click here to download data and charts.

172

CHAPTER 3

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

share of rural employment in SSA below thecurrent EMDE regional median and would
virtually eliminate rural employment in ECA and
MNA (under reformist policies, also in East Asia
and Pacific, EAP, Latin America and the
Caribbean, LAC, and South Asia, SAR). The
reformist scenario would speed up thisformalization process, especially in MNA and
LAC, where the migration to the modern
economy is almost coming to a halt and further

capital accumulation will raise the unregulated
market wage, making minimum wage no longer
binding in the formal modern sector andallocating more labor in the formal modern sector.
The reformist scenario could raise growth by 0.11.2 percentage point per year over the baseline
scenario. In SAR and SSA, the reformist scenario
could generate the largest boosts to output growth
because of their initially large rural sectors increase
the potential for rural-urban migration.

ANNEX TABLE 3.1 Labor productivity differential between types of firms (percent)Informal firms
Manager has
highereducation

Main owneris maleServices
sectorFirm hasbank loan

Single-employee
firmYoung firm(<=5 years)

Informal versus
formal firms

Angola

45.8

70.0

44.9

-60.0

225.0

20.0

-75.5***

Argentina

25.0

200***

0.0

0.0

11.1

-16.7

-92.5***

Burkina Faso

-6.2

-6.2

28.6

6.7

66.7

-10.0

-79.8***

Botswana

89.4*

72.7**

-29.1

100.0

-35.0

-18.2

-89.8***

Côte d'Ivoire

0.0

25.0

66.7**

-40.0

50.0

40.0

-47.5*

Cameroon-41.7*

36.4

77.8**-24.0

140.0***

56.2**-55.8***

Congo, Dem. Rep.

33.3

0.0

36.0**

50.0

50.0***

0.0

10.7

Cabo Verde

133.3

-25.0

185.7

1585**

566.7*

100.0

0.89

Ghana

0.0

12.5

0.0

25.0

66.7***

0.0-51.8***

Guatemala

25.0

46.7***

33.3**

50.0

57.1***-20.0-86.0***

Kenya

50.0***

6.7-40***

44.0**

12.0-20.0**-81.6***

Madagascar

40.0

-33.3

100***

33.3

60.0*

8.3

-88.1***

Mali

13.2

14.3

-19.4

31.4

57.1

-46.2**-71.3***

Myanmar

80.0*

-11.1

63.6***

11.3

31.2

0.0

-89.1***

Mauritius

66.7*

6.7

114.3***

25.0

6.7

25.0

-82.9***

Nepal

11.1

0.0

0.0

33.3

150.0***-16.7-56.5***

Peru

28.6*

12.5

-50***

-11.1

2.9

-7.4

-74.2***

Rwanda50.0***

28.6**

25.0*

-25.9

50.0***-11.1-91.4***All countries

48.1***

10.2

8.2

20.0**

41.2***-6.7-79.4***

Source: World Bank.
Note: Productivity differential between the median informal and the median formal firm (last column) or between median informal firms among different groups of firms (all other columns). For
example, “Manager has higher education” shows the difference in the median productivity among informal firms with managers with higher education and the median productivity among
informal firms with managers without higher education. Other firm characteristics are not controlled for, hence results are similar but not identical to column (1) in Annex Table 3.2.
Productivity is defined as annual sales (in 2009 U.S. dollars) relative to the number of workers. “All countries” is the unweighted average across each column. ***, **, * indicates statisticalsignificance at the 1, 5, and 10 percent level.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

CHAPTER 3173

ANNEX TABLE 3.2 Labor productivity of formal and informal firms
Informal firm Y:1 N:0

Firm age (logs)
Firm size (logs, workers)
Manufacturing Y:1 N:0
Capital city Y:1 N:0
Manager experience (logs, years)

[1]

[2]

[3]

[4]

[5]

-1.400***-0.648***-1.131***-1.200***-1.008***(0.091)(0.184)(0.131)(0.121)(0.160)0.120***

0.285***

0.118***

0.116**

0.137***(0.045)(0.053)(0.045)(0.045)(0.045)-0.102***-0.119***-0.056*-0.104***-0.108***(0.027)(0.027)(0.032)(0.028)(0.028)-0.402***-0.407***-0.401***-0.401***-0.399***(0.056)(0.056)(0.056)(0.056)(0.056)0.201***

0.190***

0.187***

0.394***

0.201***(0.061)(0.061)(0.061)(0.087)(0.061)

0.094**

0.141***

0.107***

0.091**

0.190***(0.040)(0.041)(0.040)(0.040)(0.055)

Informal firm * Firm age (logs)-0.353***(0.069)

Informal firm * Firm size (logs, workers)-0.208***(0.066)

Informal firm * Capital city Y:1 N:0-0.360***(0.114)

Informal firm * Manager experience(logs, years)-0.176***(0.060)Country fixed effects
Constant

Yes
9.013***

Yes
8.552***

Yes
8.859***

Yes
8.909***

Yes
8.748***(0.131)(0.164)(0.149)(0.139)(0.162)

Number of observations

10,527

10,527

10,527

10,527

10,527R-squared

0.291

0.296

0.293

0.293

0.292

Source: World Bank.
Note: Standard errors in brackets. Significance is denoted by *** (1 percent), ** (5 percent), * (10 percent). OLS regression with labor productivity as dependent variable, as proxied by annual
sales (in 2009 U.S. dollars, in thousands, in logs) per worker, based on a sample using World Bank’s Enterprise Survey data collected during 2007-14 for 4,036 informal firms and 7,558
formal firms in 18 countries. “Informal firm” is a dummy variable taking the value of 1 if a firm is unregistered and 0 otherwise. “Manufacturing” is a dummy variable taking the value of 1 if a
firm operates in the manufacturing sector and 0 otherwise. “Capital city” is a dummy variable taking the value of 1 if a firm is located in the capital city and 0 otherwise.

174

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ANNEX TABLE 3.3 Labor productivity of formal firms facing informal competition
[1]

[2]

[3]

[4]

[5]

Informal Competition-0.268***-1.642***-1.919***-0.574***-1.657***(Proportion of firms in the cell that report competing withinformal firms)(0.067)(0.602)(0.618)(0.059)(0.307)

Number of workers (logs)-0.197***-0.150***-0.175***-0.166***-0.179***(0.016)(0.017)(0.019)(0.019)(0.020)Firm's age (logs)

0.208***

0.215***

0.296***

0.286***

0.356***(0.023)(0.026)(0.032)(0.029)(0.032)

Firm belongs to manufacturing sector: Yes 1 No 00.137***

0.077*

0.164***

0.157***

0.139***(0.044)(0.046)(0.052)(0.048)(0.053)Firm belongs to retail sector: Yes 1 No 00.695***

0.747***

0.896***

0.862***

0.879***(0.045)(0.047)(0.053)(0.049)(0.054)Top manager is female: Yes 1 No 0
Exports (proportion of sales)
Firm has foreign owners: Yes 1 No 0-0.051-0.125**-0.128*-0.086-0.063(0.048)(0.058)(0.073)(0.067)(0.070)0.268**

0.403***

0.431***

0.385***

0.397***(0.114)(0.117)(0.145)(0.133)(0.148)0.638***

0.836***

0.821***

0.658***

0.781***(0.062)(0.070)(0.066)(0.074)(0.063)
Log GDP per capita (PPP, 2009 Int'l Dollars)

0.631***

Informal Competition * Log GDP per capita

0.138**(0.043)(0.067)Distance to Frontier (Doing Business)

0.031***(Higher values imply better regulatory practices)(0.006)

Informal Competition * DTF

0.022**(0.010)Corruption (Governance Indicators)

0.574***(Higher values imply less corruption)(0.048)

Informal Competition * Corruption

0.177**(0.085)Business Freedom index (Economic Freedom of the World)

0.015***(Higher values imply less regulation and more freedom for businesses)(0.003)

Informal Competition * Business Freedom index (Economic Freedom of the World)

0.016***(0.005)Constant

8.771***

3.818***

7.469***

9.410***

8.163***(0.224)(0.178)(0.390)(0.381)(0.088)Country fixed effects

YES

NO

NO

NO

NO

Number of observations

45,99645,99644,77045,996

43,760R-squared

0.404

0.259

0.184

0.191

0.154

Source: World Bank.
Note: Standard errors in brackets. Significance is denoted by *** (1 percent), ** (5 percent), * (10 percent). OLS regression with labor productivity as dependent variable, as proxied by annual
sales (in 2009 U.S. dollars, in thousands, in logs) per worker, based on a sample of formal firms only using World Bank’s Enterprise Survey data collected during 2007-14 for 4,036 informal
firms and 7,558 formal firms in 18 countries. “Informal competition” is the share of firms in a cell (a group of firms of similar size in the same region and sector) that report competition frominformal firms. It is worth mentioning that one could use a firm-level dummy rather than the proportion of formal firms in a cell to proxy informal competition. However, endogeneity concerns
may arise because the informal competition faced by a specific firm may also be driven by its productivity. Therefore, the proportion of formal firms facing informal competition in a cell, which
would be uncorrelated with the productivity of a specific firm, should be more robust to endogeneity concerns. “Manufacturing” is a dummy variable taking the value of 1 if a firm operates in
the manufacturing sector and 0 otherwise. “Capital city” is a dummy variable taking the value of 1 if a firm is located in the capital city and 0 otherwise.

Pakistan

2009

2017

Waseem (2018)

India

Keats (2017)

1996

2001

BrazilFajnzylber,Maloney, andMontes-Rojas(2011); Maloneyand Mendez(2004)

2010

Years

Slonimczyk (2012) Russia

Georgia

Country

Bruhn and
Loeprick (2014)

Tax reforms

Study

Policy change

Estimated impact as expected

The GST reform introduced in 2017
simplified the taxation of goods andservices and reduced the incidence of YES. The GST reform reduced the percentage of informal firms by 50 percent.
taxation from 26.5 percent to 15-20
percent.

YES. SIMPLES raised the proportion of firms that have a license to operate by
4.5 percent (20.8 to 25.3 percent), are registered as a legal entity, pay taxes andmake social security contributions. Newly created firms (with employees) that
opted for operating formally achieved higher levels of revenue and profits,
employed more workers and were more capital intensive. This occurred, notthrough greater access to credit or contracts with larger firms, but through lower
cost of contracting labor that allowed the adoption of more productive
technologies.

The tax reform implemented in 2009
YES. In a context of weak enforcement and widespread informality, an increase
Difference-In-Differences raised the income tax rate on earnings in the tax rate (from 5 to 25 percent) on noncorporate partnership income led(DID) estimation
for noncorporate partnership firms
firms to report significantly lower earnings (roughly half), migrate into informality,from 5 to 25 percent.
and switch business form to avoid the additional tax burden.

The fiscal reform implemented in 2001
reduced payroll and social taxes. The
Difference-In-Differences
YES. The tax reform reduced the share of informal labor. The decline wasreform lowered the average personal(DID) estimation
sharpest among individuals with the largest gains from the tax reform.
income tax (PIT) to a flat rate of 13
percent.

descriptive

OLS regression on firmlevel survey data

The SIMPLES program introduced in
November 1996 consolidated multiple
taxes and social security contributions
into a single payment and reduced tax
burdens (on average 8 percent) for
eligible small firms.

YES. The introduction of preferential tax regimes for micro and small businesses
Introduction of preferential tax regimes
in 2010 increased the number of newly registered formal firms by 18-30 percent
Regression Discontinuity
for micro and small businesses in
below the eligibility threshold during the first year of the reform. No significant
Design (RDD) regression
2010.
effect was seen in subsequent years.

Methodology

ANNEX TABLE 3.4 Survey of policy changes

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175

Country

Years

India

MexicoSharma (2009)

Bruhn (2011,2013)
2002 to 2006

YES. The fiscal decentralization led to a sharp increase in the real value ofthe minimum wage. District-level survey data suggests that an increase in theratio of the minimum wage to the mean wage by 10 percentage point was
associated with a rise in informal sector employment by 0.9-1.1 percentage
point and a drop in formal sector employment by 0.5-0.7 percentage point.

YES. From 1999 to 2009, the labor force surged (up by 35 percent) in a fastgrowing Vietnamese economy (with 78 percent increase in GDP per capita).
This economic upturn led to a contraction of the informal employment (from86 to 79 percent). Younger and more educated male workers were more
likely to migrate from informal to formal activities, in sharp contrast to older
and poorly educated females. Firms opting out of informality achieved higher
profit and greater value added.

YES. Informality dropped, and the reduction in informality was greatest in
states with more pro-employer labor laws. In states with pro-employer labor
laws, the number of informal establishments declined by 25 percent morethan in states with less flexible labor laws.

YES. SARE was exclusively implemented for eligible low-risk industries suchas commerce and restaurants, excluding high-risk industries (e.g., chemicalplants, transportation). The reform induced a 5 percent increase in thenumber of registered businesses. This increase was mainly driven by former
wage earners switching from ineligible industries to launch eligible
businesses, rather than the registration of existing informal businesses.

As part of fiscal decentralization, thecentral government transferred
minimum-wage setting responsibilities
to provinces and local governments
after 2001.

Labor market reforms in 2006
established a new flexible system inwhich minimum wages vary according
to location and sector of employment.

The major deregulation in 1991 in India
removed license requirements on the
setup and expansion of factories innearly half of all industries.

The business registration reform
established a Rapid Business Opening
System (SARE) in various
municipalities in Mexico from 2002 to
2006.

Linear probability modelOLS regression on firmlevel survey data

OLS regression on firmlevel survey data

seemingly unrelated
regression (SUR)

YES. Employment subsidies significantly raised the number of registered jobs
in eligible provinces (5-13 percent for the first program and 11-15 percent forthe second).

YES. A large fraction of the 10 percentage point rise in informal employment
in Brazil during 1990-2000 was driven by rising labor costs and reduced
flexibility.

Estimated impact as expected

Two employment subsidy schemes
were introduced in 2004 and 2005.

Difference-In-Differences(DID) estimation

The constitutional reform in 1988 cut
maximum work hours, raised vacation
pay, extended maternity leave, andraised dismissal cost.

Policy change

CHAPTER 3

1988 to 2000

1999 to 2009
and 2005 to2013

Vietnam

McCaig andPavcnik (2015);
and Boly (2018)2004 and 2005

1996 to 2004

TurkeyBetcherman,
Daysal, andPagés (2010)

Methodology

Mid 1980s and Cross-section weighted
1990s
least squares

Comola and Mello
Indonesia(2011)

BrazilBosch, Goni, andMaloney (2007)

Regulatory (labor and business) reforms

Study

ANNEX TABLE 3.4 Survey of policy changes (continued)

176
G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Colombia

Egypt

Vietnam

Goldberg andPavcnik (2003);
Attanasio,
Goldberg, andPavcnik (2004)

Selwaness andZaki (2015)

McCaig andPavcnik (2018)

Policy change

on household survey
data

1998 and2004

2001/2002
and
2003/2004

NO. Evidence from household surveys in 2001/2002 and 2003/2004 shows that US tariffs
reduction (20.9 percent average annual drop) induced a sharp increase in exports to theUS, which grew from 3.6 to 10.4 percent of Vietnam’s GDP. This positive export shock
US-Vietnam bilateral trade
generated 5 percentage point increase in the share of manufacturing workers in theagreement (BAT) went into effect informal sector. In addition, the prevailing labor productivity gap of 3.7 (when heterogeneity
2001.
and measurement errors are accounted for) between the informal and the formal sector
induced a reallocation of labor towards the formal sector, and increased the aggregate
labor productivity within manufacturing by 2.8 percent per year.

MIXED (YES in 1998, NO in 2004). The impact depended on the observation period andthe degree of labor market rigidity. Trade liberalization reforms increased informality
among workers in 1998, but lowered the likelihood of informal employment post-2004.
This difference may be attributed to labor reforms implemented in 2003 that added
flexibility to the market during the second wave liberalization.

Waves of trade liberalization in1998 and 2004 reduced tariffs
nearly by 70 percentage points,from 110 percent at the end of the
1980s to reach 40 percent by theend of 1990’s.

OLS regression on
individual-level survey
data

YES. A small fraction of the 10 percentage point rise in informal employment in Brazil
during 1990-2000 was driven by trade liberalization in the mid1980s and 1990s.

Estimated impact as expected

YES. Employment informality expanded (i.e. a 1-percentage point decline in a tariff in a
Trade liberalization measures were
given industry is associated with a 0.1 percentage point increase in the probability of
implemented in the 1980s andinformal employment), but only for the period preceding a major labor market reform that
1990s.
increased labor market flexibility.

In addition to major changes in
Cross-section weighted labor legislation, the constitutional
least squares
reform in 1988 introduced tradeliberalization policies.

Methodology

Two-step restricted
least squares
estimation

1980s and1990s

Mid 1980s
and 1990s

Years

Source: World Bank.
YES (NO) means that the outcome of a policy intervention is (not) consistent with the expected impact. MIXED means that the outcome of a policy intervention varies over time. The expected impacts of reforms are: (i) reduced tax burden would reduce
informality; (ii) increased labor market flexibility would reduce informality; (iii) lowered entry and exit barriers in formal sector would reduce informality; (iv) trade liberalization would increase informality due to intense foreign competition that disrupts
existing formal firms.

BrazilCountry

Bosch, Goni,
and Maloney(2007)

Trade liberalization

Study

ANNEX TABLE 3.4. Survey of policy changes (continued)

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177

178

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ANNEX TABLE 3.1.1 Data coverage

OutputFirms(b)

Perception(a)

Employment

Labor Force Surveys
Firm surveys
HS

Direct (survey-based)# of AE# of
EMDE

Time period

DGE (percent of GDP)

36

122

1950-2016

MIMIC (percent of GDP)

36

124

1993-2015

Pension coverage (percent of labor force)

31

104

1990-2010

Self-employment (percent of total employment)

36

144

1955-2016

Informal employment (percent of total employment)

053

2001-2016

Employment outside the formal sector (percent of total employment)

057

1999-2016

WEF(1-7=Most informal)

36

115

2006-2016

WB: percent Competing against informal firms

81312006-2016

WB: percent firms formally registered when founded7

129

2006-2016

WB: Number of years operated without registration

7

129

2006-2016

WB: percent firms that found competitors in the informal sector as a constraint7

131

2006-2016

WVS: Justifiable (Cheating on taxes)

26

68

1981-2010

AspectIndirect

Estimation
methodMeasuresNote: DGE is benchmarked to Schneider, Buehn, and Montenegro (2010). World Value Survey (WVS) asks whether cheating on taxes is justifiable (1 is “never justifiable” and 10 is “always
justifiable”) and reports average responses at the country-year level, with a higher level suggesting that the country is more tolerant towards the informal sector. World Economic Forum(WEF) asks “In your country, how much economic activity do you estimate to be undeclared or unregistered? (1= Most economic activity is undeclared or unregistered; 7= Most economic
activity is declared or registered)” and reports average responses at the country-year level. Here the average responses have been reordered to make “7= Most economic activity is
undeclared or unregistered; 1= Most economic activity is declared or registered” where a higher level suggesting a larger informal sector in the country. The WEF data for year 2004 and2005 are dropped since different ordering were used before 2006, which makes the numbers incomparable over time. WB shows the results for World Bank Enterprise Surveys. “HS” stands
for “Household surveys”. “(a)” stands for “Output, and “(b)” stands for “Opinions/Tax Morality”. See Elgin et al (forthcoming a) for detailed information.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

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179

ANNEX TABLE 3.1.2. MIMIC model estimation results (1993-2015)[1]
88 Developing
Countries
Size of government

Share of direct taxation[2]
98 Developing
Countries

[3]

[4]

[5]

120 Countries

151 Countries

161 Countries

0.133***

0.143***

0.157***

0.152***

0.145***(0.023)(0.021)(0.024)(0.019)(0.019)

0.035

0.009

(0.023)(0.022)Business Freedom

0.035(0.021)(0.020)(0.024)

Fiscal Freedom

0.002-0.010-0.038(0.023)(0.020)(0.025)0.078***

0.105***

0.055**

0.067***

0.066***(0.023)(0.021)(0.022)(0.019)(0.019)-0.342***-0.324***-0.393***-0.381***-0.385***(0.035)(0.027)(0.029)(0.022)(0.022)-0.069***-0.043**-0.042**(0.020)(0.018)(0.018)-0.306***

Unemployment rate

GDP per capita

0.040**

Government effectiveness

Growth rate of GDP per capita

Labor force participation rate

0.058**-0.835***-0.618***-0.362***-0.310***(0.119)(0.085)(0.079)(0.064)(0.064)-0.321***-0.219***-0.167***-0.155***(0.091)(0.073)(0.053)(0.052)Growth rate of labor force-0.091(0.064)Currency (M0/M1)

1.000

1.000

1.000

1.000

1.000

(0.000)(0.000)(0.000)(0.000)(0.000)

RMSEA

0.061

0.057

0.070

0.087

0.089

p(RMSEA<=0.05)

0.097

0.190

0.002

0.000

0.000

Statistical tests

Chi-squared (p)63.922 (0.00)

60.646 (0.000)

124.517 (0.000)

153.29 (0.000)

160.63 (0.000)

AIC

27388.448

33527.217

41436.305

43231.405

44080.904

BIC

27464.278

33602.241

41522.616

43306.446

44156.205

CFI

0.820

0.852

0.761

0.771

0.764

TLI

0.685

0.734

0.590

0.571

0.558

SRMR

0.033

0.030

0.041

0.046

0.047

CD

0.846

1

1

1

1

Number of observations

1,159

1,570

1,627

2,374

2,422

Note: Absolute z-statistics in parentheses. ***, **, * denote significance at the 1, 5, and 10percent significance levels. All variables are used as their standardized deviations from the mean.
Data sources for variables used in the model are listed in Section II footnote 6. Following the MIMIC models’ identification rule, the currency (M0/M1) variable is fixed to an a priori value. The
currency variable shows the level of money (cash) in circulation. “AIC” stands for “Akaike’s information criterion” and “BIC” stands for “Bayesian information criterion. “RMSEA” stands for“Root Mean Square Error of Approximation.” “TLI” stands for “Tucker Lewis Index.” “CFI” stands for “Comparative Fit Index.” “SRMR” stands for “Standardized Root Mean Square Residual”
and “CD” shows the coefficient of determination. These are goodness-of-fit statistics.

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ANNEX TABLE 3.3.1 Database of studies for meta regressions analysis
StudyCountries / Estimates

Sample period

Methodology

Mean wage gap*

Aydin, Hisarciklilar, Ilkkaracan (2010)

1/4

1998-2007

OLS, ML logit

57.75

Baskaya and Hulagu (2011)

1/2

2005-2009

OLS, PSM

15.45

Bargain and Kwenda (2014)

3/6

2001, 2005

OLS, FE

19.19

Botelho and Ponczek (2011)

1/2

1995-2001

OLS, FE

11.76

Earle and Sakova (2000)

6/6

1993, 1994

ML Logit-13.33

El Badaoui, Strobl, and Walsh (2008)

1/17

2001-2003

OLS, DID, PSM

28.48

El Badaoui, Strobl, and Walsh (2010)

1/6

1994

OLS, PSM

25.65

Funkhouser (1997)

1/4

1991-1992

OLS

23.82

Gindling (1991)

1/1

1982

OLS

28.50

Huber and Rahimov (2014)

1/2

2007

OLS

-34.98

Lehmann and Pignatti (2007)

1/2

2004

OLS

-6.80

Lehmann and Zaiceva (2013)

1/5

2003-2011

OLS, QR, FE

6.90

Magnac (1991)

1/1

1980

OLS

30.30

Marcouiller, de Castilla, and Woodruff (1997)

3/6

1990

OLS

16.50

Nguyen, Nordman, and Roubaud (2013)

1/4

2002-2006

OLS, FE

4.83

Nordman, Rakotomanana, and Roubaud (2016)

1/6

2000-2004

OLS, FE

15.33

Pratap and Quintin (2006)

1/3

1993-1995

OLS, FE

28.49

Tansel and Kan (2012)

1/6

2006-2009

OLS, FE

11.56

Source: World Bank.
Note: OLS=pooled ordinary least squares, FE=fixed effects regression, ML logit=multinomial logit regression, PSM=propensity score matching, DID=difference-in-difference estimators,
QR=quantile regression. The sample covers these EMDE countries: Argentina, Brazil, Columbia, Costa Rica, Czech Republic, Ecuador, El Salvador, Hungary, Madagascar, Mexico, Peru,
Poland, Russia, Slovakia, South Africa, Tajikistan, Turkey, Ukraine and Vietnam.
*Average formal sector premium across all estimates, percent; a negative number indicates a wage penalty for formal sector workers.

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CHAPTER 3

ANNEX TABLE 3.3.2 Meta regression analysis summary
Moderator variables

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

µ

0.195***

0.11**

0.23***

0.21***

0.14***

0.24***

0.17***

0.18***(0.03)(0.04)(0.03)(0.03)(0.04)(0.04)(0.05)(0.06)

Female

Male

0.16*

0.15*

0.12

0.12

(0.08)(0.08)(0.08)(0.08)

0.14**

0.13**

0.11*

0.10

(0.06)(0.06)(0.06)(0.06)

Fixed Effects-0.15**-0.13**-0.14**-0.13**-0.13**(0.07)(0.06)(0.06)(0.06)(0.07)-0.34*-0.32**-0.25*-0.26*(0.14)(0.13)(0.14)(0.14)

Self-employed

Latin America and the Caribbean

0.00(0.07)

Europe and Central Asia

-0.03
(0.07)

Adjusted R-squared
Number of observations

7.8

5.8

6.4

12.0

11.4

14.8

12.4

83

83

83

83

83

83

83

83

τ

2

0.06

0.05

0.05

0.05

0.05

0.05

0.05

0.05

I

2

99.6

99.5

99.4

99.5

99.4

99.4

99.4

99.1

Source: World Bank.
Note: *** p<0.01, ** p<0.05, * p<0.1; standard errors are in parenthesis. Within study standard errors of the estimates are used as weights to correct for the heterodasticity. The dependent
variable is the informal-formal wage gap estimates by former studies (listed in Annex Table 3.3.1). τ 2 captures the degree of across-study variations, and I 2 reflects the impact of acrossstudy heterogeneity.

182

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ANNEX TABLE 3.4.1 Pre-existing informality and changes in poverty and income inequality: OLS
[1]

[2]

[3]

[4]

[5]

[6]

Moderator variables
Annual change in poverty
Initial poverty rate

DGE

Annual change in income inequality-0.024-0.025-0.029(7.90)***(7.94)***(7.31)***

Initial Gini index

DGE

0.010
(1.84)*

MIMIC-0.020-0.020-0.017[-5.58]***[-5.51]***[-5.58]***0.001[0.24]

MIMIC

0.011

-0.000(1.83)*
Self-employment[-0.04]0.010

Self-employment

0.001(2.23)**
Constant-0.507-0.533-0.466(2.13)**(2.13)**(2.81)***

R-squared

0.48

0.47

0.45

Number of observations

73

74

71

[0.84]
Constant

0.671

0.705

0.534

[4.72]***[4.91]***[3.45]***

R-squared

0.28

0.26

0.26

Number of observations

72

73

69

Source: World Bank.
Note: Estimated by ordinary least squares method. Dependent variable in column [1]-[3]: Annual change in poverty headcount ratio (i.e., Poverty headcount ratio at $1.90 a day (2011 PPP),
percent of population) over the earliest year and the latest year, in percentage points. Dependent variable in column [4]-[6]: Annual change in Gini index over the earliest year and the latest
year, in percentage points. Annual change in poverty headcount ratio (i.e., Poverty headcount ratio at $1.90 a day (2011 PPP), percent of population) over the earliest year and the latest
year, in percentage points. Initial poverty rate (or Gini index for column [4]-[6]) is the earliest available year between 1990-2005. Informality indicators are averages over 1990-2005. *, **,and *** denote that the coefficients are statistically significant at the 10 percent, 5 percent, and 1 percent levels, respectively. Heteroskedasticity-robust standard errors are estimatedwith t-statistics presented below the corresponding coefficients.

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183

ANNEX TABLE 3.5.1 Sample
EMDEs (99)

Advanced Economies (27)AngolaEswatini

Niger

Australia

Albania

Gambia, The

Nigeria

Austria

Algeria

Ghana

Oman

Belgium

Argentina

GuatemalaPakistan

CanadaBahamas, The

GuineaPanamaCyprusBahrainGuinea-Bissau

Paraguay

DenmarkBangladesh

Haiti

Peru

Finland

Barbados

HondurasPhilippines

FranceBelizeHungary

PolandGermany

Benin

India

Qatar

GreeceBhutanIndonesia

Romania

Iceland

Bolivia

Iran

RwandaIreland

Botswana

Iraq

Saudi Arabia

IsraelBrazilJamaica

SenegalItaly

Brunei Darussalam

JordanSierra Leone

Japan

Bulgaria

Kenya

South Africa

Korea, Rep.

Burkina Faso

Kuwait

Sri Lanka

Luxembourg

BurundiLao PDR

St. Lucia

Malta

Cabo Verde

LebanonSt. Vincent and the Grenadines

Netherlands

Cambodia

Liberia

Sudan

NorwayCameroon

Madagascar

Tanzania

Portugal

Central African Republic

MalawiThailandSingapore

Chad

Malaysia

Togo

Spain

Chile

Mali

Trinidad and Tobago

SwedenChina

Mauritania

Tunisia

Switzerland

Colombia

Mauritius

TurkeyUnited Kingdom

Congo, Dem. Rep.

MexicoUgandaUnited States

Congo, Rep.

Mongolia

Uruguay

Côte d'Ivoire

MoroccoVenezuela

Dominican Republic

MozambiqueVietnamEcuadorNamibiaWest Bank and Gaza

Egypt

Nepal

ZambiaEl Salvador

Nicaragua

Zimbabwe

Source: World Bank.
Note: The country sample and classification are taken from Loayza (2016).

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Revolution. Washington, DC: World Bank.

Vegh, C., and G. Vuletin. 2015. "How Is Tax Policy
Conducted over the Business Cycle?" American
Economic Journal: Economic Policy 7 (3): 327-70.

World Bank. 2018a. Risk Sharing for a Diverse andDiversifying World of Work. Social Protection and Jobs
White paper. (Forthcoming). Washington, DC: WorldBank.

Verner, D., and M. Verner. 2000. “Economic Impactsof Professional Training in the Informal Sector of Coted’Ivoire: Evaluation of the PAFPA.” Policy ResearchWorking Paper 3668, World Bank, Washington, DC.

World Bank. 2018b. World Development Report 2018:
Learning to Realize Education’s Promise. Washington,
DC: World Bank.

Vuletin, G. 2008. “Measuring the Informal Economy
in Latin America and the Caribbean.” IMF Working
Paper 08/102, International Monetary Fund,
Washington, DC.
Waseem, M. 2018. “Taxes, Informality and Income
Shifting: Evidence from a Recent Pakistani Tax
Reform.” Journal of Public Economics 157 (January):
41-77.
Wellalage, N. H., and S. Locke. 2016. “Informality andCredit Constraints: Evidence from Sub-Saharan African
MSEs.” Applied Economics 48 (29): 2756-2770.

World Bank. 2018c. Gulf Economic Monitor: Deepening
Reforms. Washington, DC: World Bank.
World Bank. 2019. World Development Report 2019:
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CHAPTER 4

TWO TOPICAL ESSAYS
Debt in Low-Income Countries:
Evolution, Implications, and Remedies
Poverty Impact ofFood Price Shocks and Policies

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CHAPTER 4

Debt in Low-Income Countries: Evolution,
Implications, and Remedies
Debt vulnerabilities in low-income countries (LICs) have increased substantially in recent years. Since 2013,
median government debt has risen by about 20 percentage points of GDP and increasingly comes from nonconcessional and private sources. As a result, in most LICs interest payments are absorbing an increasing
proportion of government revenues. The majority of LICs would be hard hit by a sudden weakening in trade or
global financial conditions given high levels of external debt, lack of fiscal space, low foreign currency reserves,and undiversified exports. A proactive effort to identify and reduce debt-related vulnerabilities is a priority formany LICs. Policymakers should focus on mobilizing domestic resources, improving debt transparency, andstrengthening debt management practices. These efforts should be complemented by measures to strengthen fiscal
frameworks, improve the efficiency of public expenditures and public investment management, and develop
domestic financial systems.

Introduction
In recent years, many low-income countries(LICs) have gained access to additional sources offinance, including private and non-Paris Club
creditors.1 While this has enabled these countries
to fund important development needs, it has also
led to higher levels of public debt. The increasing
share of market-based debt exposes many LICs tointerest rate, and refinancing risks. These trends
take place as the external environment is
becoming more challenging and borrowing costs
are expected to rise around the world, as describedin Chapter 1. This means that, in the event of an
abrupt deterioration in market conditions, some
LICs may struggle to refinance debts from foreignsources and are at risk of capital flow reversals anddislocating currency depreciations. In this context,
it is important for LICs to develop their domestic
financial systems, strengthen capacity for domestic
resource mobilization, improve macro-fiscal
frameworks, and improve their resilience to shocks
through the sound management of public debt
and investment.
Against this backdrop, this essay addresses thefollowing questions:This essay was prepared by Sinem Kilic Celik and Patrick Kirby incollaboration with Andre Proite and Sebastian Essl from the Global
Macro and Debt Analytics Group of the Macro, Trade, andInvestment Global Practice.
1 LICs refers to countries meeting the World Bank Group’s
definition of countries with per capita gross national income
below $995 per year in 2017. This group includes 33 countries(Annex 4.1.1).

•

What are the key characteristics of the recent
rise in LIC debt?

•

How does rising debt relate to other LIC
vulnerabilities?

•

How can better debt management help reduce
LIC vulnerabilities?

•

How can complementary policy measures
reduce LIC vulnerabilities?

Key characteristics of therecent rise in LIC debt
A recent sharp rise. Debt relief under the Heavily
Indebted Poor Countries (HIPC) initiative andthe Multilateral Debt Relief Initiative (MDRI)
helped to reduce public debt among LICs from a
median debt-to-GDP ratio of close to 100 percent
in the early 2000s to a median of just over 30
percent in 2013.2 This downward trend reversed
sharply thereafter, with the median debt ratio
rising to above 50 percent by 2017 (Figure 4.1.1).
The increase was large relative to other EMDEs,
whose median debt rose by less than 11 percentage
points of GDP from 2013 to 2017, compared to
20 percentage points for LICs. It was also broadbased: debt ratios rose in almost 90 percent of
LICs, and a third experienced debt increases ofmore than 20 percentage points.

2 Most LICs—27 out of 33—benefited from one or both of the
HIPC and MDRI programs.

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FIGURE 4.1.1 LIC government finances
LIC government debt ratios have risen since 2013, in part because ofwidening fiscal deficits, but still remain well below levels in the early 2000s.
A. Gross government debt

B. Gross government debt by LIC
category

C. Primary fiscal balance

D. LICs with largest increase ingovernment debt

Source: International Monetary Fund, World Bank.
A. Dashed blue lines denote the interquartile range, while the solid blue line is the median.
A.B.C. The sample includes 30 low-income countries, of which 2 are oil exporters, 8 metals exporters,
and the remaining 20 are non-resource-intensive. It excludes Somalia, South Sudan, and Syria due todata restrictions.
A.B. Figure shows median gross government debt in percent of GDP.
B.C.D. LICs= Low-income countries.
Click here to download data and charts.

The key role of fiscal deficits. Primary fiscal
deficits had largely been closed among LICs by
2006, but widened steadily following the global
financial crisis, especially among commodity
exporters suffering from falling commodity prices.
Rising deficits may also be the result of LICs’
increased ability to borrow as a result of HIPC
and MDRI debt relief (Bayraktar and Fofack
2011; Marcelino and Hakobyan 2014). The
primary balance of most LICs has been negative
since the mid-2000s, and all but five (of 31 withavailable data) LICs had primary deficits in 2017,with a third carrying a primary deficit exceeding 3
percent of GDP.
Uses of borrowed funds. A rising debt burden is
typically less of a reason for concern if it is used tofinance investment that raises a country’s potential
output, and therefore its ability to repay loans in
the future (World Bank 2017). In some LICs,

growing deficits reflected a push to finance public
investment, as suggested by the doubling of
median LIC public investment as a share of GDP
from 3 percent in 2000 to 6 percent in 2015. This
was the experience of Guinea-Bissau, Madagascar,
Mali, and Nepal, where wider fiscal deficits werematched by higher public investment (IMF
2018a). These countries form a minority,
however, as a substantial part of LIC borrowing
has been used to finance a rise in current
consumption. In resource-intensive countries in
Sub-Saharan Africa, for example, the bulk of
increased spending enabled by a rise incommodity prices went to public sector wages(World Bank 2018a). Some borrowing may also
have been redirected toward the accumulation ofprivate assets stored abroad.3
Dependence on external debt. Given their
typically small local creditor base, a significant
share of LIC borrowing comes from abroad and isdenominated in foreign currencies. The resulting
currency mismatch poses a challenge to LICs, as adepreciating currency can lead to a rise in thedomestic value of the country’s debt burden andinterest payments. This challenge is more severe incountries with a significant share of external debt
priced at market rates, and less so for countries
benefiting from the low interest rates on
concessional debt.
The median LIC carries external debt, including
both public and private debt, equivalent to 28
percent of GDP and almost half of total debt.
Median external debt as a share of GDP has risen
about 3 percentage points since 2012, with several
important outliers. Commercial debt issuances
have contributed to especially sharp rises inexternal debt burdens in Mozambique andTajikistan. In Uganda, external debt as a share of
GDP has more than doubled since 2012, to morethan 40 percent of GDP in 2017. The maturity
composition of LIC external debt has remained
broadly stable—short-term debt remainedmoderate at 5 percent of total external debt in2016.

3 Ndikumana and Boyce (2011) find that for every dollar inexternal loans to Sub-Saharan Africa, capital outflows increased byroughly 60 cents in the same year.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

Shift toward non-traditional creditors. The
composition of public debt has shifted over thelast decade, becoming increasingly nonconcessional as LICs have increased their relianceon financing from non-traditional sources (Figure
4.1.2). The median share of non-concessional debt
in public debt rose to 55 percent in 2016 (thelatest year for which data are available), an increaseof nearly 8 percentage points since 2013, and 15
percentage points compared with a decade earlier.
Commercial creditors have become an importantsource of credit for some countries (World Bankand IMF 2018a). Ethiopia, Mozambique,Rwanda, Senegal, Tajikistan, and Tanzania haveall issued commercial public debt since 2010,
generally denominated in U.S. dollars.4
Non-Paris Club creditors, notably China, havealso become a more important source of financing
over the past decade, especially in Sub-SaharanAfrica (World Bank 2015a). In 2016, non-Paris
Club debt accounted for more than a fifth of themedian LIC’s external debt, and about 13 percentof their public debt (World Bank 2018b). Major
recipients of lending from non-Paris Club
creditors include the Democratic Republic ofCongo, Ethiopia, Mozambique, Tanzania,
Uganda, and Zimbabwe (Atkins et al. 2017; Eom,
Brautigam and Benabdallah 2018).
Lending arrangements for commercial and nonParis Club debt are often not public, and they canbe complex and varied (World Bank and IMF
2018b). Some of this debt is collateralized, which
could reduce budget flexibility by earmarking
revenues, could weaken the creditor’s incentive toassess the borrower’s debt sustainability, and (iflarge) could increase funding costs from othercreditors who may reassess the probability of being
repaid. Moreover, increased exposure to non-Paris
Club and commercial creditors may pose
coordination challenges for debt resolutions in the
future, making the consequences of debt distresseven more disruptive, especially if debt iscollateralized (World Bank and IMF 2018c).

4 Of 11 LIC debt issuances since 2010, all were denominated in
U.S. dollars, with the exception of one of Senegal’s two issuances in
2018, which was euro-denominated.

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201

FIGURE 4.1.2 Public debt in LICs
Higher debt, and the shift from concessional to market financing, makes
LICs more vulnerable to rising interest rates.
A. Change in creditor compositionof public and publicly guaranteed
external debt, 2007-16

B. Share of non-concessional debt

C. Interest payments

D. Share of LICs in debt distress or athigh risk of distress

Source: International Monetary Fund, World Bank.
A. 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 fromautonomous 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 andintergovernmental agencies. It excludes loans from funds administered by an internationalorganization on behalf of a single donor government. “Bonds” include PPG bonds that are either
publicly issued or privately placed. “Commercial” includes PPG debt from commercial bank loans
from private banks and other private financial institutions, as well as export and supplier credits.
B.C. 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.
D. Figure shows the percent of low-income countries eligible to access the IMF’s concessional
lending facilities that are either at high risk of, or in, debt distress. A country is considered to be in
debt distress if it is experiencing difficulties in servicing its debt, as evidenced, for example, by theexistence of arrears, ongoing or impending debt restructuring, or if there are indications that a future
debt distress event is probable. The sample includes 30 low-income countries.
Click here to download data and charts.

Rising cost of debt service. As debt loads havegrown and become less concessional, interest
payments have absorbed a growing share ofgovernment revenues. Among LICs, the medianinterest payments-to-revenue ratio rose to over 5
percent in 2017, up from just over 3 percent in2013. The increase in the ratio was due to rapidly
rising interest payments, with median interest
payments among LICs having grown by over 128
percent versus 31 percent growth in government
revenues.
Drivers of rising debt. Countries with the fastest
rise in debt were often fragile and affected by a

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019combination of conflict, weak governance, orcommodity-dependence (World Bank 2018c). In
The Gambia, government debt increased fromnearly 60 percent of GDP in 2013 to an estimated88 percent in 2017, with interest payments
absorbing 42 percent of revenue. The rise in debt
was a result of loose fiscal policy, bailouts of stateowned enterprises, and widespread mismanagement by the previous government prior to atransition to democracy in early 2017 (IMF
2018b).

LIC vulnerabilities are reflected by the fact that
almost all LICs have the lowest or second lowest
grade in the OECD’s country credit risk
classification.6 Because of rising arrears or the needfor debt restructuring, eleven LICs were assessed as
being in debt distress or at a high risk of debt
distress as of November 2018, compared to only
six in 2015.7 For LICs assessed at low or moderaterisk of debt distress, safety margins have eroded.

In Mozambique, the government debt-to-GDP
ratio has increased by close to 50 percentage
points since 2013, reaching an estimated 102
percent in 2018, with interest payments risingfrom 2.6 percent of revenues to 16.5 percent overthe same period. The deterioration wasunderpinned by rising deficits as fiscal policy
remained loose amid lower commodity prices andsubdued growth, and was aggravated by the
inclusion of previously undisclosed external
commercial debt in 2016 (IMF 2018c). The
country is in debt distress, and several payments toexternal borrowers have been missed.

Private debt. Due to shallow domestic capital
markets and limited access to international
finance, the median LIC has total private debt
equivalent to only 18 percent of GDP,significantly less than the 41 percent ratio for the
median non-LIC EMDE (Figure 4.1.3).8
Nonetheless, LIC private sector debt has been on asteady upward trend since 2005, rising by almost
8 percentage points. Excess private debt cansometimes be transformed into public debt, either
directly through bailouts or indirectly throughcountercyclical government spending in response
to private deleveraging, suggesting that the line
between public and private debt can blur (Mbaye,
Badia and Chae 2018).

Zimbabwe is also classified as being in debt
distress. Over the last five years, government debt
has risen substantially from just over 48 percent of
GDP in 2013 to an estimated 82 percent in 2017.
Persistently large fiscal deficits have partly been
the result of an elevated public wage bill, which
absorbed 90 percent of revenues in 2017 (IMF
2017). In addition, revenues remain subdued
amid weak growth and structural rigidities, whiletransfers to the agricultural sector have kept nonwage expenditure elevated. Moreover, the deficits
have partly been financed through an overdraft
facility at the Reserve Bank of Zimbabwe that,given insufficient reserves, has led to money
creation and exacerbated foreign-currency
shortages.
Risk of debt distress. Higher levels of public debt,
much of it external, and an increased reliance oncommercial loans make many LICs vulnerable tocurrency, interest rate, and refinancing risks(Devarajan 2018; Gill and Karakülah 2018a,b).5
5 Separately, some countries such as The Gambia are vulnerable torollover risk because of heavy reliance on short-term domestic debt
(IMF 2018d).

Other LIC vulnerabilities

Growth subject to downside risks. Growth in
LICs is expected to remain resilient, supporting
their ability to service debt, but risks are tilted tothe downside. LIC growth is expected to average
5.6 percent in 2018 and accelerate to just over 6
percent in 2019-20, supported by rising
agricultural output and continued infrastructure
investment (Chapter 1). However, over the next
6 There is one exception: The credit rating for Senegal has
improved recently in the OECD credit risk classification, improvingfrom 6 to 5 in a 0-7 rating system, with a higher number indicating
higher credit risk (OECD 2018).
7 A country is considered to be in debt distress if it is experiencing
difficulties in servicing its debt, as evidenced, for example, by theexistence of arrears, ongoing or impending debt restructuring, or if
there are indications that a future debt distress event is probable.
LICs in debt distress are The Gambia, Mozambique, South Sudan,
and Zimbabwe. LICs at high risk of debt distress are Afghanistan,
Burundi, Central African Republic, Chad, Ethiopia, Haiti, andTajikistan. There is a total of 30 LICs who have a debt sustainability
analysis (DSA) available under the Joint World Bank / IMF debt
sustainability framework (DSF).
8 Private sector debt refers to the sum of commercial banks’ andother financial corporations’ claims on the non-financial private
sector, in percent of GDP.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019decade, weaker growth in major emerging markets
may slow global demand for metals, whichdampens growth prospects for LICs that dependon metals for government and export revenues(World Bank 2018c). Downside risks to thisoutlook predominate and include the possibility ofa faster-than-expected slowdown among major
trading partners (including China, a majorcommodity consumer); a renewed plunge incommodity prices; a deterioration in international
financial conditions; and the possibility of natural
disasters, conflict, or severe weather events.
Elevated debt, lower investment growth,
increased risks. Rising levels of non-concessional
public debt, often at variable rates, make some
LICs susceptible to a sudden increase inborrowing costs, especially when they havesubstantial refinancing needs in coming years or
have borrowed in foreign currencies. As advanced
economies continue to withdraw monetary policy
accommodation, new debt issuances and debt
rollovers may become more expensive, resulting in
rising LIC debt service costs that could weaken
investment and lower medium-term growth(World Bank 2015b, 2016, and 2017). Fiscal
consolidation, while often necessary, can also
dampen growth in the short term.
In the absence of sufficient lending made available
at concessional terms, there is a risk that highpublic debt will lead to higher interest rates,
crowding out private investment and slowing
growth.9 Similarly, rising interest payments todomestic creditors may encourage policymakers toengage in financial repression—using administrative or other means to channel domestic
savings toward the purchase of public debt—
which can dampen private sector investment andlimit the development of domestic financial
markets (Fry 1997).
Substantial current account deficits. Almost all
LICs carry persistent, substantial current accountdeficits, with an estimated median of 6.8 percent
of GDP in 2017 (Figure 4.1.4). Forty percent of

9 Bevan (2012) argues that although evidence in the literature forthe crowding out effect on investment in LICs is weak, it may be
more important where financial depth is low.

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203

FIGURE 4.1.3 Risks to LIC debt sustainability
Private sector debt has risen alongside public debt in LICs. LIC exports
tend to be concentrated in a few products, generally commodities. LIC
growth is accelerating but risks are tilted to the downside. Over the
medium term, demand for many commodities is expected to slow, which
may pose a challenge for exporters.
A. Private sector debt

B. Median export concentration

C. Growth

D. Global commodity demand growth

Source: BP Statistical Review, Haver Analytics, United Nations, United States Department ofAgriculture, World Bank, World Bureau of Metals Statistics.
A. Domestic credit to the non-financial private sector provided by commercial banks and, if data are
available, by other financial corporations. Median debt, based on 148 EMDEs and 29 LICs.
A.B. Non-LIC EMDEs= Emerging market and developing economies excluding LICs; LICs= Lowincome countries.
B. Orange lines indicate interquartile ranges of Herfindahl-Hirschmann concentration index, whichmeasures the degree of product concentration, with values closer to 100 indicating a country’s
exports are highly concentrated in a few products.
D. To ensure comparability, 2010-16 is model-predicted commodity demand growth.
Click here to download data and charts.

LICs had current account deficits that widened byat least 3 percentage points of GDP over the last
decade. Among metals exporters, rising deficits
reflected the pickup in import-intensive mining
investment, while in non-resource-intensive
countries it often reflected high public investment.
Countries relying on capital inflows to finance alarge and persistent current account can be more
vulnerable to currency crises, as weaker investor
confidence can result in a slowdown in capital
inflows, leading to higher borrowing costs,
downward currency pressures, difficulties inrolling over debt, and possible macroeconomic
and financial market stress (Roubini and Wachtel
1999). Current account deficits in LICs, however,are typically financed by capital inflows from

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development assistance, remittances, foreign
lending, and foreign direct investment. The stable,
long-term and often concessional nature of thisfinancing mitigates some of the risks usuallyassociated with large current account deficits.
Foreign direct investment and development
assistance flows were generally more than adequate
to finance LICs’ current account deficits—themedian LIC received inflows of these types 1.6
times as large as its current account deficit. In
more than half of LICs, development assistancealone was greater than the total current accountdeficit. Median FDI inflows were equal to about
half the current account, except for metals
exporters where it was considerably more.
FDI flows to LICs, however, are particularly
sensitive to fluctuations in global growth andliquidity (Burger and Ianchovichina 2017).
Among these countries, commodity exporters,
particularly metals exporters, are particularly
vulnerable to sudden swings in FDI flows that
accompany changes in the external environment—
FDI flows are more than twice as volatile in metalexporting LICs than in other EMDEs. While
external vulnerabilities can be mitigated by astrong foreign reserve position, more than 40
percent of LICs with available data have reserves
close to or below three months of imports.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019minimize fiscal risks stemming from contingent
liabilities, such as guarantees or on-lending tostate-owned enterprises or through public-private
partnerships, through effective monitoring andreporting.
The benefits of sound debt management are
fourfold:•

Lowers debt servicing costs. In many LICs,
debt service payments absorb a significant
share of public revenues (notably in Burundi,
the Central African Republic, and Chad),reflecting a combination of low revenue bases,
sizable debt loads, and a shift toward nonconcessionalterms.
Effective
debt
management can help avoid excessive debt
service costs by increasing awareness of thefinancial options available, enabling countries
to borrow at competitive costs with a prudent
degree of risk.

•

Supports financial sector development. Moredeveloped local-currency bond markets canpromote economic stability by reducing thereliance on external debt, facilitating theimplementation of counter-cyclical fiscal
policies, and enhancing resilience to sudden
reversals of capital flows. Public debt
instruments can serve as a benchmark forpricing of private sector debt instruments.
Local-currency bond markets can enable
diversification from bank financing andprovide a savings vehicle for a variety ofinvestors to support growth (World Bank and
IMF 2014).

•

Reduces economic volatility. Effective debt
management can reduce economic volatility
by selecting debt instruments that help
insulate the government balance sheet fromuncertainties. Both currency and interest rate
shocks can be mitigated in this fashion,
making a country less susceptible to contagion
and financial risks, and supporting cheaperand more stable funding for the private sector.

•

Enhances public sector transparency andmedium-term planning. A key element ofsound public debt management is the publicand comprehensive reporting of government
debt, which improves the capacity ofRole of better debt
management
Goal of sound debt management. In most LICs,
government debt is the largest domestic financial
portfolio, and debt management operations can besubstantial relative to public spending andeconomic activity. A sound macro-fiscal policy
framework requires that public debt is sustainable
and can be serviced under a wide range ofcircumstances at reasonable costs. While ex ante
the level of debt is mainly determined by fiscal
policy, ex post the composition of debt can play
an important role in safeguarding debt
sustainability. Effective debt management plays acritical role in funding the government’s financing
needs in a timely fashion, helping ensure low debt
servicing costs at an acceptable degree of risk, andsupporting the development of domestic securities
markets. In addition, debt management can help

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

policymakers and the broader public to assess
the fiscal position and appropriately weighpublic balance sheet risks alongside spending
and revenue priorities.
Evolution of debt management in LICs. Despitesome improvements, debt management in LICs
still suffers from substantial deficiencies.
Weaknesses in debt transparency, notably inmonitoring and reporting, are pervasive. Mediumterm debt strategies are becoming more common
but have shortcomings in quality andimplementation. Capacity and institutional set-up
are often lagging.
••

Debt transparency. Better compilation andmonitoring of public debt and guarantees areneeded to ensure that risks are detected before
they materialize (World Bank 2007). Recent
examples of hidden debt and discrepancies
among debt statistics point to continued low
debt recording capacity, weak legal
frameworks, and governance challenges. Debt
Management
Performance
Assessments(DeMPA) suggest that, of the seventeen LICs
with available data, minimum requirements indebt recording are met by only eight, andmonitoring guarantees are met by only four.
Due to shortcomings in accuracy, timeliness,coverage and completeness of debt records,
only four of these seventeen countries met theminimum requirements for debt reportingand evaluation (Figure 4.1.5). Only a third of
the 59 countries eligible for International
Development Association borrowing reportprivate sector external debt statistics (WorldBank and IMF 2018d).
Debt management strategies. A growing
number of countries are producing mediumterm debt management strategies. However,their quality varies significantly, andimplementation is often lagging. Few
countries are aligning the processes for
managing medium-term debt with theirbudget process.10

10 The World Bank, in partnership with the IMF, has been
supporting increasing debt management capacity in LICs through its
Debt Management Facility (DMF). Building on the progressachieved and on lessons learned in recent years, this involves

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FIGURE 4.1.4 External positions in LICs
LICs carry persistent current account deficits, largely financed bydevelopment assistance and FDI. However, FDI flows can be volatile,especially for metals exporters. Modest foreign exchange reserves makesome countries vulnerable to changes in foreign investor sentiment.

A. Current account balance

B. Current account funding

C. Foreign direct investment

D. Median foreign reserves

Source: International Monetary Fund, World Bank.
A. LICs= Low-income countries. Figure shows median current account balance in percent of GDP.
The sample represents a total of 30 low-income countries, of which 2 are oil exporters, 8 are metals
exporters, and the remaining 20 are non-resource-intensive. It excludes Somalia, South Sudan, andSyria due to data restrictions.
B. The sample represents a total of 21 low-income countries, including 6 metals-exporting LICs, withcurrent account deficits in 2016.
C. EMDEs= Emerging market and developing economies. Standard deviation represents the medianstandard deviation of foreign direct investment in percent of GDP from 2000 to 2017.
D. LIDCs=Low-income developing countries; LICs=Low-income countries. See Annex 41 for details.
Orange lines indicate interquartile range. Data is as of the last reported year, mostly 2016.
Click here to download data and charts.

•

Broader issues. Some of the most pressing
challenges include insufficient legal frameworks, weak capacity, lack of coordination
between fiscal and monetary policy, inefficient

supporting further improvements in debt recording and monitoring,increasing debt transparency, and adding to debt management
capacity. The DMF also seeks to strengthen macro-fiscal frameworks,
including through improved domestic revenue mobilization, and toadvance the implementation of growth-enhancing structural reforms.
Since 2009, the DMF has supported over 280 Technical Assistancemissions in 75 countries and 14 subnational governments, trainedclient practitioners and hosted around 40 debt management
practitioners. A growing number of countries prepare and publish
debt management strategies, the quality of debt records in many LICs
has improved, and many countries have well-structured debt
management offices. Several countries have strengthened their legal
framework and improved their operational risks management withthe support of the DMF.

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FIGURE 4.1.5 LIC policy frameworks
Debt management capacity in many LICs is low, especially in the areas ofdebt reporting and monitoring. Policy frameworks have improved in LICs,
with several countries adopting flexible exchange rates and strengthening
their central banks’ independence.
A. Countries meeting DeMPA
minimum requirements, selectcategories

C. Central bank transparency index

B. Countries meeting DeMPA
minimum requirements, selectcategories

D. Exchange rate regimes

Source: Bloomberg, Debt Management Performance Assessments (DeMPA), Dincer andEichengreen (2014), International Monetary Fund, Shambaugh (2004), World Bank.
A.B. BCP=Business Continuity Planning; CBM=Cash Balance Management; CFF=Cash Flow
Forecasting; DA=Debt Administration; DMS=Debt Management Strategy; DS=Data Security;
FP=Fiscal Policy; LGLD=Loan Guarantees, On lending Derivatives; MP=Monetary Policy;
SD=Segregation of Duties; SC=Staff Capacity. Sample covers 17 low-income countries.
C. Unweighted averages. The range of the index is from 0-15, 0=least transparent and 15=mosttransparent.
D. De facto exchange rate regime from the Exchange Rate Regime Classification of Shambaugh(2004) is used to determine whether a country has a pegged or flexible exchange rate. The originalclassification has four categories: “1” reflects no fluctuation at all, “2” indicates movements within
1 percent bands, “3” indicates movements within 2 percent bands, and “4” indicates a one-time
devaluation with 0 change in the remaining 11 months of the year. Shambaugh (2004) assesses
these movements against relevant base currencies. The constructed dummy variable indicating a
pegged exchange rate regime was defined to equal 1 for countries classified as 1, 2, 3, or 4. A valueof 0 is assigned to flexible exchange rates—i.e., exchange rates that routinely fluctuate outside a 2
percent band. Based on 31 LICs.
Click here to download data and charts.

management of cash and fiscal risks, and poor
audit and risk control procedures.

Complementary policy
measures
Domestic resource mobilization. Among LICs,
there is considerable scope to enhance tax revenues
and reduce the need to rely on debt financing(Baum et al. 2017). In the median LIC,
government revenues accounted for only 19
percent of GDP in 2017, well below 28 percent of

GDP in the median non-LIC EMDE, reflecting
the prevalence of informal activity (Chapter 3).
This highlights the need to broaden tax bases,
especially for higher-income households, in a waythat minimizes economic distortions and that
carefully manages trade-offs between efficiency
and equity (World Bank 2018d). Unexpected
revenue windfalls from sudden improvements in acountry’s terms of trade can be set aside to reduce
fiscal deficits and debt.
Improving spending efficiency. LICs havesignificant infrastructure needs that require debt
financing. However, debt sustainability concerns
associated with the financing of infrastructure may
be lessened if these expenditures are accompaniedby stronger long-term growth and better macrofiscal, budgeting, and financing frameworks.
There may also be room to cut unproductive
spending (often subsidies) in order to allow for
more growth-enhancing or better-targeted
programs.11 Debt used to finance projects thatgenerate a revenue stream is less likely to beunsustainable. There is also often considerable
scope to improve the efficiency of investment
spending by improving the institutions andprocedures
governingproject
appraisal,procurement, and monitoring. By one estimate, acountry moving from the lowest quartile to thehighest quartile in the efficiency of public
investment could double the impact of thatinvestment on growth (IMF 2015).
Development of local financial markets. Relianceon external funding means that there is often acurrency mismatch in LIC borrowing andrevenues, leaving countries vulnerable to swings in
the value of the currency. The development oflocal currency bond markets can help mitigate thisrisk, though they are often a relatively high-cost
option. These markets require a functional money
market, primary and secondary markets, a diversebase of investors, a stable regulatory system which
includes reliable custody and settlement systems,

11 Credible and well-designed institutional arrangements—such as
fiscal rules, stabilization funds, and medium-term expenditureframeworks—can help build fiscal space, improve the management ofrevenue windfalls, and strengthen policy outcomes (Huidrom, Kose,
and Ohnsorge 2016).

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CHAPTER 4

and a significant improvement in debt
management capacity. Sound macroeconomic
policy and financial sector stability are also critical,
as is transparent and effective communication by
the government. Alongside improved debt
management, growing local financial markets canhelp countries graduate from concessional lendingby mitigating some of the costs and risks
associated with non-concessional debt.

debt vulnerabilities. The Addis Ababa Action
Agenda calls for debtors and creditors to work
together to prevent and resolve unsustainable debt
situations. Creditors can aim for good practice inlending, drawing on principles for sustainablelending such as those being championed by G20
countries (G20 2018).

Better data collection. Transparency aboutbalance sheets is a pre-requisite for sound debt
management. Among other gaps, there is often
limited data on contingent liabilities (especiallythose arising from state-owned enterprises andpublic-private partnerships) and the assets held by
LIC governments. These data limitations areespecially acute for debt issued by commercial andnon-Paris Club creditors. Improving datacollection practices for LIC debt would help
policymakers make informed and appropriate
borrowing decisions and allow the public to holdthe government accountable for its fiscal
management (World Bank and IMF 2018d).

In recent years, a broad-based rise in borrowing
has increased public debt vulnerabilities in LICs.
The composition of debt has also shifted, as many
LICs have increased their exposure to non-Paris
Club creditors and market-based debt, which may
pose coordination challenges for any future debt
resolution. While increased access to market
funding has provided LICs with opportunities toaddress development needs, it has also exposedsome countries to currency, interest rate, andrefinancing risks.

Monetary policy and exchange rate regimes.
More resilient monetary policy frameworks andforeign reserve buffers can help mitigate theimpact of terms-of-trade and other shocks,
including on the fiscal position (Adler, Magud,and Werner 2017). More LICs could join thegrowing number of EMDEs where improvementsin the monetary policy regime have reduced
inflation and, where appropriate, allow greater
exchange rate flexibility to absorb shocks.
Rigorous and transparent lending standards.
Creditors also have a role to play in containing

Conclusion

The number of LICs at high risk of debt distressor in debt distress has increased significantly, andsafety margins in many LICs currently assessed at
low or moderate risks of debt distress have eroded.
External gross financing needs are likely to risefurther as current account deficits widen and large
international bonds fall due. By increasing theeffectiveness of resource mobilization, public
spending, and debt management—supported bybetter data collection—LICs can reduce theprobability of costly defaults, enhance debt
transparency, support sustainable financial sector
development, and reduce economic volatility.

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ANNEX 4.1 Comparison of LIDCs and LICs
This essay discusses LICs following the World Bank Group definition of countries with per capita gross national
income below $995 per year. This group includes 33 countries. It differs from other reports (such as IMF 2018a andWorld Bank and IMF 2018c), which include additional middle-income countries following the IMF definition of
low-income developing countries (LIDCs). The term “LIDC” refers to countries with low per capita gross national
income and comparatively weak socioeconomic indicators.
ANNEX TABLE 4.1 List of countries in LIDCs and LICs
Low-income developing
countries (LIDCs)
1 Afghanistan

Low-income countries(LICs)
1 Afghanistan

2 Bangladesh
3 Benin

2 Benin

Low-income developing
countries (LIDCs)

Low-income countries(LICs)

31 Malawi

17 Malawi

32 Mali

18 Mali

33 Mauritania

4 Bhutan

34 Moldova

5 Burkina Faso

3 Burkina Faso

35 Mozambique

6 Burundi

4 Burundi

36 Myanmar

7 Cambodia

37 Nepal

8 Cameroon

38 Nicaragua

9 Central African Republic

5 Central African Republic

39 Niger

10 Chad6 Chad40 Nigeria

11 Comoros

7 Comoros

41 Papua New Guinea

12 Congo, Dem. Rep. of

8 Congo, Dem. Rep. of

42 Rwanda

19 Mozambique
20 Nepal
21 Niger

22 Rwanda

13 Congo, Republic of

43 São Tomé and Príncipe

14 Côte d’Ivoire

44 Senegal

23 Senegal

15 Djibouti

45 Sierra Leone

24 Sierra Leone

16 Eritrea

9 Eritrea

46 Solomon Islands

17 Ethiopia

10 Ethiopia

47 Somalia

25 Somalia

18 Gambia, The

11 Gambia, The

48 South Sudan

26 South Sudan

20 Guinea

12 Guinea

49 Sudan

21 Guinea-Bissau

13 Guinea-Bissau

50 Tajikistan

28 Tajikistan

22 Haiti

14 Haiti

51 Tanzania, United Rep. of

29 Tanzania, United Rep. of

19 Ghana

27 Syrian Arab Republic

23 Honduras

52 Timor-Leste

24 Kenya

53 Togo30 Togo25 Kiribati

54 Uganda

31 Uganda

26 Kyrgyz Republic

55 Uzbekistan

27 Lao P.D.R.

56 Vietnam

28 Lesotho

57 Yemen, Rep. of

29 Liberia

15 Liberia

58 Zambia

30 Madagascar

16 Madagascar

59 Zimbabwe(continues in the next column)

32 Yemen, Rep. of
33 Zimbabwe

Source: International Monetary Fund, World Bank.

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CHAPTER 4

Poverty Impact of Food Price Shocks and Policies
In the event of large swings in world food prices, countries often intervene to dampen the impact ofinternational food price spikes on domestic prices and to lessen the burden of adjustment on vulnerable
population groups. While individual countries can succeed at insulating their domestic markets from short-term
fluctuations in global food prices, the collective intervention of many countries may exacerbate the volatility ofworld prices. Insulating policies introduced during the 2010-11 food price spike may have accounted for 40
percent of the increase in the world price of wheat and one-quarter of the increase in the world price of maize.
Combined with government policy responses, the 2010-11 food price spike tipped 8.3 million people (almost 1
percent of the world’s poor) into poverty. Instead of trade policies, targeted safety net interventions such as cashtransfers, food and in-kind transfers, and risk management instruments can be more effective in mitigating thenegative effects of food price shocks on poor households.

Introduction
In August 2011, nominal international food prices
hit an all-time high.1 This followed shortly after
the 2007-08 food price spike, which pushed an
estimated 105 million people into extreme poverty(Ivanic and Martin 2008). This event also
prompted widespread concerns about the foodsecurity of the poorest and fears over a potential
world food crisis. Although food prices havedeclined considerably since then, in real terms,
they are still significantly above their lows in 2000
(Figure 4.2.1).
Food price spikes such as in 2010-11 may
materialize again as the growing frequency of
extreme weather events increases the risk of
disruption to food production, setbacks in foodavailability and access to food. World hunger andsevere food insecurity rose during 2014-17,
reversing the decline of the previous decade. In
2017, the number of undernourished people
reached 821 million, up by 5 percent since 2014
and a setback in achieving the Sustainable
Development Goal of eradicating hunger by 2030
(FAO et al. 2018). G20 policy makers haverecently reiterated the urgency of tackling thechallenges to achieving food security (G20 2018).
While agricultural and food prices are expected torise only moderately in 2019, significant upside

Note: This essay was prepared by David Laborde, Csilla Lakatos,and Will Martin. Research assistance was provided by Xinyue Wangand Heqing Zhao.
1 Unless otherwise stated, the concept of food prices as used in thisessay refers to the commodity price of major staple foods such as rice,wheat, and maize.

risks could materialize as a result of higher-thanexpected energy prices, El Niño events, or tradetensions. First, higher-than-expected energy prices,a key input in the production of most agriculturalcommodities, could raise grain and oilseed prices.
Energy prices affect agricultural production costs
directly (through fuel use) and indirectly (throughfertilizer and other chemicals use and an incentive
to shift production to biofuels). Second, an El
Niño event is expected with an 80 percent
probability during December 2018-February
2019. Should this materialize, heavier-thanexpected rains could occur in Central Asia, SouthAmerica, and East Africa, while drier-than-normal
conditions could affect Central America, theCaribbean, and Southern Africa, affecting theprices of many agricultural commodities. Finally,
although the escalation of existing trade frictions
represents a downside risk for the price ofagricultural commodities, policy measures
introduced by major producers and exporters in
response to higher tariffs could also affect prices(World Bank 2018e).
Several forces have raised food prices during the
2000s. A dramatic increase in demand forfeedstock for biofuel production in the early 2000s
put considerable pressure on markets for grain andcontributed to a rundown in stocks (Akiyama etal. 2001; Wright 2014). Population growth andurbanization, as well as a shift in diets toward
animal-based foods, created demand pressuresdespite an increase in agricultural productivity inemerging market and developing economies(EMDEs; Fukase and Martin 2017). Slowing
yield growth and declining availability ofagricultural land constrained food production

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FIGURE 4.2.1 Global food prices
In August 2011, shortly after the 2007-08 food price spike, international
nominal food prices hit an all-time high. Although food prices havedeclined considerably since then, in real terms, they are still significantly
above their lows in the 2000s. Evidence points to a rise in world hunger
and severe food insecurity between 2014 and 2017, reversing thedeclining trend observed in the previous decade.

A. Global food prices

C. Undernourished people

B. Global food price volatility

D. Prevalence of undernourishedSource: Food and Agriculture Organization of the United Nations, World Bank.
A. Based on yearly commodity price indexes between 1960-2017.
B. Based on monthly nominal commodity price indexes between January 1960 – November 2017.
C.D. Undernourishment is defined a state, lasting for at least one year, of inability to acquire enoughfood, defined as a level of food intake insufficient to meet dietary energy requirements.
Click here to download data and charts.

growth. Extreme climate events (e.g., El Niño,
droughts, and natural disasters), particularly when
agricultural stocks are low, and the financializationof agricultural futures markets have also
contributed to food price volatility.
Food price increases have important macro- andmicroeconomic impacts through several channels.
At the macroeconomic level, food price increases
result in higher inflation, which can reduce
household real incomes. For food-importing
countries, high food prices can also result interms of trade shocks that lower growth andreduce government policy space.
The microeconomic impact of food price increaseson poverty and inequality depends on the net foodseller/buyer status of the poorest households. For
households that are net sellers of food products(such as farmers, agricultural workers, and small
land owners), rising food prices increase real
incomes. By contrast, they lower the real incomesof households that are net buyers of food. On
average, sharp increases in food prices raisepoverty, reduce nutrition, and curtail the
consumption of essential services such as
education and health care (World Bank 2011).2
Countries often use policy interventions todampen the domestic impact of international foodprice spikes and lessen the burden on vulnerable
population groups. For example, during the 200708 food price spike, close to three-quarters of
EMDEs took policy action to insulate theirdomestic prices from the sharp increase ininternational food prices (World Bank 2009). In
the event of food price spikes, net food-importing
countries usually intervene by lowering tradeprotection (typically tariffs) on food items, while
net food-exporting countries impose export
restrictions or bans. These policies are often
complemented with social safety net programs
such as cash transfers or school feeding programs.
To the extent that policy interventions reduce thetransmission of international price spikes todomestic markets, they may appear to be
successful for individual countries. However, thecombined intervention of many countries raises
international prices. These insulating policies tend
to encourage consumption and reduce production
during price spikes. This, in turn, results in higher
import demand and reduced export supply thatfurther drive up global prices. During priceplunges, government interventions encourage
greater exports and greater global supply thatfurther depresses prices. Only countries that
insulate themselves to an above-average degree canreduce price volatility in their domestic markets(Anderson, Martin, and Ivanic 2017).
The international community has recognized theimportance of ensuring the stability andavailability of food supplies as key to addressing
several development objectives. The Sustainable
2 In the longer term, once producers and consumers have adjusted
to the increases and wage rates have responded, sustained increases infood prices may lower poverty by raising incomes of poor foodproducing households (Ivanic and Martin 2014a; Gillson and Fouad2014).

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CHAPTER 4

Development Goals (SDGs) give food security ahigh priority: the second SDG sets out explicitly
the goal to “end hunger, achieve food security andimproved nutrition, and promote sustainableagriculture.” Other SDGs are stronglyinterconnected: food, agriculture and nutritionplay an important role in SDGs on ending
poverty, improving health, fostering sustainableconsumption and production, and encouraging
climate change adaptation and mitigation.
In this context, this essay addresses the following
questions:•

How do food price shocks affect EMDEs?

•

How do countries intervene to reduce theimpact of food price shocks?

•

What was the impact of the 2010-11 foodprice shock on poverty?

The essay presents the following findings:•

At the macroeconomic level, a high share of
agriculture and food in total output,
consumption, employment, trade, andgovernment revenues heighten countries’
vulnerability to volatility in international foodprices. At the microeconomic level, food pricespikes are felt most severely by the poorest
segments of the population who tend to be
net food buyers.

•

Governments in EMDEs tend to respondparticularly strongly to sharp changes in world
prices for staple foods—such as rice, wheat
and maize—to smooth volatility. Domestic
food prices are considerably less volatile thanworld food prices in the short run, but overthe longer term, there is a tendency fordomestic and world prices to return to their
original relationship. In the short run, a 1
percent increase in the world price of rice,wheat and maize is associated with an increasein domestic prices by 0.6 percent, 0.7 percent,and 0.8 percent, respectively.

•

While individual countries can succeed atinsulating their domestic markets from shortterm fluctuations in global food prices, their

combined interventions make global foodprices more volatile. Insulating policies
introduced during the 2010-11 food pricespike accounted for 40 percent of the increasein the world price of wheat and one-quarter ofthe increase in the world price of maize. In
contrast, a reversal of earlier government
interventions in rice markets dampened thedegree to which world prices increased byabout 50 percent.
•The 2010-11 food price spike, and the widespreadgovernment
intervention
that
accompanied it, increased the number of poor
living on less than $1.90 per day by almost 1
percent or 8.3 million.

Food price shocks andtheir effects
At the macroeconomic level, a high share of
agriculture and food in total output, consumption,
employment, trade, and government revenues
heighten countries’ vulnerability to volatility ininternational food prices. At the microeconomic
level, a high share of net food buyers among thepoorest segments of society heightens the adverse
effects of food price spikes on poverty and income
inequality.
Macroeconomic channels
Reliance on food imports and production.
Agriculture accounts for close to one-third of total
value added and two-thirds of total employment
in LICs. This is almost three times their shares in
the average EMDE (Figure 4.2.2; Aksoy andBeghin 2004). For example, in Burkina Faso and
Burundi, agriculture accounts for more than fourfifths of total employment. In Chad and Sierra
Leone, it accounts for more than half of domestic
value added. In addition, more than three-quartersof LICs are net food importers compared to only
half of EMDEs.3 In these net food-importing
LICs, net food imports amount to 5.4 percent ofprivate consumption. Benin and Gambia are
3 High trade costs, such as tariffs and border delays, can bias
downwards estimates of the share of food imports (Tombe 2015).

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FIGURE 4.2.2 Macroeconomic channels of transmission
from global food prices
At the macroeconomic level, a high share of agriculture and food in total
output, consumption, employment, trade, and government revenues
heighten countries’ vulnerability to volatility in international food prices.
A. Share of agriculture in economy

B. Net food imports and exports

C. Inflation in LICs

D. Contribution of food prices toinflation

E. Terms of trade in LICs

F. Fiscal balance in LICs

Source: Kose et al. (2017), World Bank.
A. Based on a sample of 93 non-LIC EMDEs and 21 LICs. Averages for 2010-16.
B. Blue bars show the share of non-LIC EMDEs or LICs in which food imports exceed food exports(“Net food importers”) or food imports fall short of food exports (“Net food exporters”). Red bars show
net food imports relative to consumption in non-LIC EMDE and LIC food exporters and importers.
C. Average inflation based on a sample of 12 LICs.
D. Share of inflation accounted for by food price inflation. Yellow line indicates half.
E. Net barter terms-of-trade index, 100=2000.
F. Median based on a sample of 26 LICs.
Click here to download data and charts.

particularly vulnerable to high food prices, with
net food imports adding up to more than 10
percent of private consumption.4

4 Conversely,
heavy reliance on food exports heightens
vulnerability to food price declines. For example, in Malawi, net foodexports amount to 12 percent of total private consumption.

Inflation. A surge in food prices increases
consumer price inflation. For example, the 200708 and 2010-11, LIC inflation more thandoubled, from 7 to 15 percent during 2007-2008
and from 5 to 11 percent during 2010-2011. The
increase in EMDE inflation was less pronounced,
from 7 to 11 percent during 2007-2008 and from
5 to 6 percent during 2010-2011. Food prices
accounted disproportionately for these increases in
inflation—for about two-thirds in LICs and morethan half in EMDEs. In vulnerable LICs such as
Benin and Niger, where net food imports amountto 15 and 7 percent of household consumption,
respectively, inflation surged from 1 percent to 8
percent and 0.2 percent to 11 percent,respectively, during the 2007-08 food price spike.
Terms of trade. Sharp increases in food prices canconstitute significant adverse terms of trade shocks
that lower growth, especially in countries that arelarge net importers of food. More than threequarters of LICs are net food importers. The
median LIC’s terms of trade declined by 2 percent
and 4 percent during the 2007-08 and 2010-11
food price spikes, respectively. In some, thedeterioration was much steeper. For example, theterms of trade of Sierra Leone, a LIC highly reliant
on food imports, weakened by 10 percent during
each of these food price spike episodes.5 In heavy
food importers, the exchange rate depreciation
typically associated with adverse terms of trade
shocks can compel central banks to tightenmonetary policy and further lower growth.
Indeed, during the 2007-08 food price spike, close
to half of EMDE central banks responded torising inflation and depreciation by tightening
monetary policy.6
Fiscal policy constraints. Absent stabilizing fiscalarrangements, heavy reliance on food andagricultural trade can contribute to volatility inpublic finances and erode fiscal sustainability:
rising food prices may increase tax revenues fromthe agricultural sector and encourage governments
to spend. Conversely, when food prices fall,
5 Severe terms of trade shocks are considerably more common in
LICs than in advanced economies and, of all possible external shocks,
tend to have the most severe output cost in LICs (IMF 2011; Becker
and Mauro 2006).
6 Based on a sample of 54 EMDEs.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

revenue losses in the agricultural sector are
exacerbated by political pressures to subsidize foodproduction. During the sharp rise in food prices in
2007-08, LICs’ fiscal balances deteriorated, on
average, by close to 1 percentage point of GDP, inpart due to higher food import bills. Food pricespikes may also cause sociopolitical instability,
including political unrest and food riots (Barrett2013).

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213

FIGURE 4.2.3 Microeconomic channels of transmission
from global food prices
At the microeconomic level, a high share of net food buyers among thepoorest segments of the population heightens the adverse effects of foodprice spikes on income distribution and poverty.
A. Share of food in total consumption
expenditure

B. Consumption expenditure by
product of the poorest households

C. Share of net food sellers

D. Share of income generated by foodin the income of the poor

Microeconomic channels
Rising food prices impact households throughprice and income effects. They reduce households’
purchasing power but raise income generated fromfood production. The overall impact on poverty
and income inequality depends on the relative
magnitude of these effects for households in
different segments of the income distribution.
In LICs, households spend on average close to 60
percent of their income on food, more than onethird more than in EMDEs (Figure 4.2.3). In
countries such as Burundi and Guinea, the shareof food expenditures is even higher, accounting for
more than 70 percent of total consumption ofhouseholds. In LICs, more than one-third ofhouseholds’ consumption expenditure on food isspent on staple foods such as cereals andvegetables. These staple foods are considerably
more exposed to international price volatility thandomestically processed food products (Figure
4.2.1).
For households that are net sellers of agriculturaland food products (e.g., farmers), rising foodprices raise incomes. More than one-fifth ofhouseholds around and below the poverty line of$1.90 per day are net food sellers in the average
EMDE and LIC. Households around and below
the poverty line in these countries tend to generate
about one-quarter of their incomes from foodproduction. In contrast, poor urban households
are typically net buyers of food that spend a large
share of their consumption expenditure on food(Aksoy and Hoekman 2010).
On average, many of the poor in EMDEs and
LICs are net buyers of food. As a result, food pricespikes tend to raise poverty, reduce nutrition andSource: International Food Policy Research Institute, World Bank.
A. Based on data from the Global Consumption Database reflecting on the share of food in total
consumption expenditure of households. Data is available for 63 non-LIC EMDEs and 25 LICs. Thebase year of the household surveys differs but the data has been converted to a common reference
year, 2010. The share of income spent on food is likely to be different.
B. Based on data from the Global Consumption Database on the share of products in total household
consumption expenditure. Data is available for 63 non-LIC EMDEs and 25 LICs. The base year of thehousehold surveys differs but the data has been converted to a common reference year, 2010. The
share of income spent on food is likely to be different.
C.D. Averages weighted by the number of poor for a sample of 22 non-LIC EMDEs and 7 LICs.
Poverty line is defined as $1.90/day.
Click here to download data and charts.

cut consumption of essential services such as
education and health care.7 For example, the2007-08 rise in food prices is estimated to haveraised the number of poor by 105 million(10 percent of the people living on less than a one
dollar a day; Ivanic and Martin 2008). In extreme
cases, food price spikes can lead to food insecurity
and hunger, with severely adverse long-term
impacts on human capital.

7 Vulnerable groups such as women and children, are more likely
to be disproportionately affected.

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Government interventions
during food price shocks
In the event of large swings in global food prices,
governments are confronted with difficult policy
choices. One option is to allow domestic prices toadjust to world food price changes, exposing
domestic consumers and producers to changes intheir real incomes. Even if a sizable non-tradeable
service component in the cost of providing
consumers with food such as transportation,
storage and retail dampens the pass-through ofworld food price shocks into domestic markets,allowing domestic food prices to adjust may raise
inflation in the short run and, in countries where
inflation expectations are poorly anchored, in themedium to long run.8
Alternatively, governments can spare consumers or
producers from these losses by reducing thetransmission of international food price shocks todomestic markets.9 As measured in this essay,
policy intervention is reflected in the ratio ofdomestic to world prices—the “protection rate.”
During a period of rising world prices, theprotection rate declines when a country seeks toinsulate its domestic markets from the increase inworld prices. If the protection rate rises,
policymakers are compounding the increase inworld prices.
In practice, during the 2007-08 food price spike,close to three-quarters of EMDEs took policy
action to insulate their economies from the sharp
increase in international food prices (World Bank

8 The decline in real incomes associated with higher inflation
would entail welfare losses, especially when consumers are loss- andrisk-averse (Gouel and Jean 2015; Freund and Ozden 2008;
Giordani, Rocha, and Ruta 2016; Easterly and Fischer 2001). In
principle, monetary policy tightening can offset inflationary effects
from rising global food prices to ensure that rising food prices remaina purely relative price change and do not become entrenched inhigher inflation. However, this would come at the cost of reduced
economic activity (Lustig 2009).
9 Policymakers may also have a longer-term goal to protect (or totax) domestic agents (Grossman and Helpman 1994). In empirical
work based on political economy models, protection rates vary to
reduce both the costs associated with adjusting prices and the costs ofproviding a rate of protection that differs from the long-run politicalequilibrium (Anderson and Nelgen 2011; Ivanic and Martin 2014b).
The less than perfect pass-through world price shocks into domestic
markets is explicitly considered.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 20192009). The most commonly used interventions
were reductions in taxes, including import duties
and consumer taxes (Figure 4.2.4).10 Net
importers frequently intervened by lowering
import tariffs or even by introducing importsubsidies, while net exporters imposed export
restrictions or bans to dampen the increase indomestic prices.11
Domestic and world food price dynamics
Domestic food prices are considerably less volatile
than global food prices in the short run, but overthe longer term, there is a tendency for domestic
prices to return to their original relationship withinternational prices (Figure 4.2.5). This does notnecessarily imply that protection rates become
zero, but that they return to their pre-spike levels.
Governments in EMDEs tend to respondparticularly strongly to sharp changes in the world
prices of staple foods—such as rice, wheat andmaize—to reduce the volatility of domestic prices.
For staple foods, domestic price movements candiverge substantially from international pricemovements in the short run, but converge in thelonger term.
The movements of world and domestic staples
food prices during the latest two food price spikes(2007-08 and 2010-11) resembled similar earlier
episodes: world prices rose rapidly, while domestic
prices rose only gradually. However, the 2010-11
spike was different from previous episodes inseveral aspects. The 2007-08 increase in foodprices came after a long period of stability in foodprices. In 2007-08, world prices of all staple foods
increased steeply, led by the strong increase in theworld price of rice. Most countries reacted
strongly by introducing insulating policies. In
contrast, the 2010-11 episode occurred whenworld markets and policies were still normalizing
from the 2007-08 episode. Government
10 If countries are insulating primarily through subsidies and are
fiscally constrained, their ability to insulate will be limited(Ianchovichina, Leoning, and Wood 2014).
11 For net importers, untargeted food subsidies have implications
for government revenues and fiscal space. If financed by aid, theimpact on fiscal space is limited. Alternatively, targeted transfers may
be more effective in protecting vulnerable groups with limited
macroeconomic repercussions.

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interventions
differed considerably acrosscountries and across commodities. On average,
government interventions (or the unwinding ofearlier interventions) actually contributed to a
decline in the world price of rice.
Rice. Rice was the staple food with the largest priceincrease during the 2007-08 food price spike.
Between January 2007 and May 2008, world rice
prices almost tripled.12 This sharp increasereflected export restrictions introduced by major
producers (e.g., India and Vietnam) motivated byfood security concerns, panic buying by several
large importers, a weak dollar, and record highprices of oil, which is a major input into foodproduction (Childs and Kiawu 2009). During this
episode, domestic markets were largely insulated
from this global rice price spike (Ivanic andMartin 2008). By contrast, during the 2010-11
price spike, rice prices increased much less, by
about 30 percent between June 2010 and May
2012. In some countries, adverse supply
conditions combined with changes in non-tariff
trade policies resulted in domestic rice prices rising
above world prices.13 Instead of insulating policies,
on average, EMDEs implemented policies thatraised domestic prices relative to world prices(Figure 4.2.5).
Wheat. Between February 2007 and March 2008,
world wheat prices more than doubled, partly inresponse to lower-than-anticipated wheat
production caused by drought in Australia,
Ukraine and other major exporters.14 Strong
policy intervention partially insulated domestic
markets from the global wheat price spike andtheir subsequent collapse in the aftermath of the
global financial crisis in 2009-10. Similarly,
during the 2010-11 event, world wheat prices
more than doubled between June 2010 and May
2011.15 This time, the increase in world prices was

12 The world price of 5 percent broken white Thai rice increased
from $313/mt to $902/mt.
13 In Vietnam, for instance, domestic rice prices rose by 41 percent
between July-October 2010 due to lower-than-expected production,prior commitments on exports, and high inflation from adepreciating currency.
14 The world price of U.S. Hard Red Wheat (HRW) increased
from $196/mt to $440/mt.
15 The world price of U.S. Hard Red Wheat (HRW) increased
from $158/mt to $355/mt.

CHAPTER 4215

FIGURE 4.2.4 Food-related government policies
Countries often use policy interventions to dampen the domestic impact ofinternational food price spikes and lessen the burden on vulnerable
population groups. In the short run, domestic markets for key staple foods,
such as rice and wheat, are highly insulated from global food price swings.
Insulation policies undertaken during the 2010-11 episode exacerbated thevolatility of world prices and accounted for about 40 percent of theincrease in the world price of wheat and one-quarter of the increase in theworld price of maize.
A. Interventions in agricultural
markets

B. Policy interventions during the
2007-08 food price spike

C. Insulation and correction
coefficients

D. Increase in world prices, 2010-11

Source: Ag-Incentives Database, Ivanic and Martin (2014b), World Bank.
A. Nominal Rate of Protection (NRP) is computed as the price difference between the farm gate pricereceived by producers and an undistorted reference price at the farm gate level. The reference priceat the farm gate level is defined as the net price of the product when it leaves the farm, aftermarketing costs have been subtracted. The undistorted farm gate price is defined as the priceprevailing in competitive world markets.
B. Percent of respondents based on a survey of 80 EMDEs.
C. Estimates based on an Error Correction Model described in Annex 4.2.1. The coefficient of priceinsulation ranges from 0 for countries that do not insulate against the rise in world prices, to -1 forcountries that adopt policies that fully insulate domestic markets. The error correction term represents
the cost of being out of equilibrium or the speed with which polices achieve the target level ofprotection or at which policymakers move back toward this equilibrium after being forced away from it
by a shock to world prices. Based on data for 82 countries, of which 26 advanced economies, 44 nonLIC EMDEs, and 12 LICs for the period 1955-2011.
D. Real terms. Estimates derived based on the methodology described in Annex 4.2.1.
Click here to download data and charts.

partly driven by lower-than-expected productionand exports in Kazakhstan, Russia, and Ukraine
and excessive rains in Australia that damaged
wheat crops (World Bank 2010). Large orders
from major wheat importers in the Middle Eastand North Africa added to price pressures. Since
2011, global and domestic wheat prices havefluctuated, broadly synchronously.
Maize. During the 2007-08 food price spike, theworld price of maize almost doubled, partly as a

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FIGURE 4.2.5 Domestic and global food prices
Domestic food prices tend to be less volatile than global food prices. This
partly reflects a sizeable services component in the cost of providing
domestic consumers with food, but also policy intervention.
A. Price of staple foods

In contrast, many countries in Sub-Saharan Africa
benefitted from excellent maize harvests, which incombination with unpredictable trade policies led
to sharp falls in domestic prices.

B. Rice prices

Insulation of domestic food markets

C. Wheat prices

D. Maize prices

Measuring the insulation of domestic markets.
The degree of insulation of domestic markets fromworld food price swings can be quantified using an
Error Correction Model (Annex 4.2.1). The
model regresses the log of the protection rate on
the log of world prices and the deviation fromlong-term “equilibrium” food prices. The model
estimates the degree of insulation to global pricechanges in both the short run (specifically, anegative coefficient on short-term changes in
global food prices) and long run (specifically, anegative coefficient on the long-term relationshipbetween domestic and global food prices). The
sample used here includes annual data for 8 foodcommodity prices in 82 countries, of which 44 are
EMDEs and 12 are LICs, during 1955-2011.

E. Domestic and global staple foodprices during 2007-08 and 2010-11

F. Average increase in world anddomestic price index, 2010-11

Source: Ivanic and Martin (2014b), World Bank.
Note: Trade-weighted averages.
A. Rice, wheat, maize, oil, and sugar prices.
E. Event study based on monthly cross-country average domestic staples prices (average of wheat,rice and maize prices) and global staples prices (average of wheat, rice and maize) during 2007-08
and 2010-11. Period 0 represents the month of the peak of the world food price spike.
F. Average percent increase in the price index.
Click here to download data and charts.

result of increasing U.S. demand for maize
stimulated by mandatory targets for ethanol
production.16 Similarly, during the 2010-11
episode, the world price of maize increased
significantly. As in the case of wheat, adverse
weather-related events in major maize exporting
countries contributed to the spike in world prices.
16 Between January 2007 and June 2008, the world price of maize
increased from $165/mt to $287/mt.

Estimates of short-term insulation. Estimates
point to considerable short-term insulation inmarkets for key staple foods such as rice and wheat(Figure 4.2.4). Among these key staples, insulationis the highest for rice. In the short run, a 1 percent
increase in global rice, wheat, and maize prices is
associated with an increase in domestic prices of
0.6 percent, 0.7 percent, and 0.8 percent,respectively.
Effectiveness of insulating policy measures.
Certain types of interventions in markets for staple
foods have raised volatility in domestic markets.
For example, during the 2008-09 food price spike,several African countries intervened using foodpricing, marketing, and trade policies to stabilize
domestic maize markets. Countries thatintervened most intensively experienced thehighest domestic price volatility, mostly because of
the ad hoc and unpredictable nature of theseinterventions (Chapoto and Jayne 2009).17 The
17 After abstaining from the use of interventions in staple foodmarkets for several years, policymakers in Eastern and Southern
Africa used extensively pricing, marketing, and trade policy tools
during the 2015-16 agricultural season to contain the impact of an El
Niño-induced decline in output and food security (Al-Mamun et al.
2017; Tschirley and Jayne 2010).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019use of an export ban during food price spikes,
possibly related to a domestic drought, illustrates
the trade off between different policy instruments:•

Ensuring food security. By restricting the saleof food for exports, an export ban increases
domestic supply and dampens domestic foodprice increases. This can help net-food buyers
access food.

•Alleviating poverty. Net food-selling farmers
are likely to be hardest-hit by a drought. An
export ban reduces their ability to mitigate
their production losses with higher incomesfrom higher prices. If these farmers are among
the poorer segments of the income
distribution, the export ban will likely increase
poverty, as it did in Zambia during the 201617 El Niño event (Al-Mamun et al. 2017).

•

Volatility. While export bans may alleviatepressures during a specific situation, theyheighten domestic price volatility by
preventing domestic shocks from being
dissipated through changes in trade. If bansare backed up by stockholding measures suchas those used in India, they can be consistent
with domestic price stabilization, although thefiscal costs of this policy approach tends to behigh relative to that of price insulation(Gouel, Gautam, and Martin 2016).

Synchronous policy measures. While individual
countries can succeed at insulating their domestic
markets from short-term fluctuations in global
food prices, their combined policies may makeglobal food prices more volatile. Government
interventions tend to increase consumption andreduce production during price spikes and support
production and discourage consumption during
price plunges. During price spikes, this results inhigher import demand and, hence, higher global
demand that further drives up global prices.
During price plunges, it encourages greater exports
from each country and, hence, greater global
supply that further depresses prices. Only
countries that insulate themselves to an aboveaverage degree are able to reduce the transmissionof international price volatility to their domestic
markets (Anderson, Martin and Ivanic 2017;

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Martin and Anderson 2012; Ivanic and Martin
2014b).18

Poverty impact of the
2010-11 food price shock
The impact of the 2010-11 food price shock onpoverty is quantified in two steps. The first step
estimates the degree of policy intervention bycountries (Anderson, Ivanic, and Martin 2014). In
the second step, these estimates are fed into a
computable general equilibrium (CGE) model in
combination with household models for 285,000
households from 31 countries to determine theimpact of policy interventions on poverty (Annex
4.2.1; Laborde, Robichaud and Tokgoz 2013).
Two scenarios are compared. In the first scenario,
the impact of countries’ own interventions onpoverty is considered. In the second scenario, thecombined effect of all policy interventions on
global food markets and their feedback todomestic poverty is quantified.
Impact of policy interventions on global anddomestic prices
Quantifying policy interventions. A primaryshock, such as a weather shock, is assumed togenerate initial production shortfalls that are
calibrated to match the observed changes inprotection rates and world prices shown in Figure
4.2.6.19 In attempting to insulate domestic
markets from the increase in world prices,
governments take offsetting trade measures, suchas the introduction of export bans (food exporters)or the reduction of import duties (foodimporters). These policy responses are calibrated
to match the observed protection rates and world
price increases in 2010-11. As the model
distinguishes between domestic and imported
goods, two potential policy instruments are
considered—an import duty (or subsidy) and an

18 Consistent with Martin and Anderson (2012) and Anderson,
Ivanic, and Martin (2014).
19 For example, a negative production shock of 55 percent for rice,
27 percent for wheat, and 35 percent for maize in advanced
economies and Russia generates an increase of 10 percent in averageworld prices for these commodities.

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FIGURE 4.2.6 The extent of government interventions
during the 2010-11 food price spike
Some countries reduced trade barriers to insulate themselves fromincreasing world prices. Others resorted to policy interventions that
ultimately raised domestic prices more than the increase in world prices.
A. Decline in protection rates,
2010-11

C. Change in EMDE protection rates,
2010-11

B. Increase in protection rates,
2010-11

D. Change in LIC protection rates,
2010-11

Source: Ag-Incentives Database.
Note: Estimates based on the methodology described in Annex 4.2.1. Changes in the rates ofprotection are presented in the form: Ti = ∆t/(1+t 0), where t is the initial rate of protection (positive if
an import tariff or export subsidy) and ∆t is the change in this rate of protection. If the change in therate of protection is negative during a period of rising world prices, countries are seeking to insulate
their markets from the increase in prices. If it is positive, policymakers are compounding the increase
in world prices with an increase in protection, which may be due to the correction of past “errors”:
If domestic prices fall below policymakers’ desired long-run level of protection, or if a policy that
insulated the domestic market from world markets and a subsequent exogenous shock—such as aharvest shortfall—has caused the domestic price to rise relative to the world price.
C.D. Median and interquartile range in the change for protection rates for rice, wheat, and maize in
non-LIC EMDEs (C) and LICs (D).
Click here to download data and charts.

export subsidy (or tax).20 These measures, in turn,reinforce the original shock to world prices. The
data used for quantifying the extent of trade policy
interventions are taken primarily from the AgIncentives Consortium database reflecting changes

in domestic and world prices for 57 countries and68 agricultural and food commodities during
2005-2015.21 Where data from the Ag-Incentives
database were unavailable, alternative data wereused from FAOSTAT, GIEWS and Fewsnet.
Overall, this analysis covers 24 major foodproducing and consuming countries, using dataon household income sources and spending
patterns from 2011. Of these, 18 are EMDEs and6 are LICs.
Impact of policy interventions on global prices.
During the food price spike of 2010-11, world
prices of maize, wheat and rice rose by 44, 39, and6 percent, respectively, but domestic prices
considerably less (Figure 4.2.4). Model results
suggest that the combined action of government
policies amplified global wheat and maize priceincreases, accounting for about 40 percent of theincrease in world price of wheat and one-quarterof the increase in the price of maize. In contrast,
combined policy action reduced the rice pricesurge compared to a non-action scenario.22
Wheat. Most EMDEs took measures to offset theincrease in global wheat prices in 2010-11,
broadly similar to those employed during the spikein wheat prices in 2007-08. Policymakers justified
efforts to dampen the impact of the global wheat
price spike by noting that the world wheat pricespike partly reflected a catching up with risingdomestic wheat prices.23 The combinedintervention of countries accounted for close to 50
percent of the increase in the world price of wheat.
Maize. Although most countries insulated theirdomestic maize markets against maize priceincrease during 2010-11, there was considerable
The data is available at www.ag-incentives.org.
This primarily reflects the elimination of export restrictions inIndia and the increased import protection in Pakistan, Indonesia,
Uganda, and Yemen.
23 Ethiopia is an exception, where domestic wheat prices rose
28 percentage points more than world prices during 2010-11.
This reflected domestic supply shocks, combined with limitedaccess to global wheat markets to alleviate shortages. In particular,
wheat output fell by 10 percent in 2010-11 as a result of afungus that destroyed the wheat harvest and lowered stocks in 2011.
Wheat imports rose but were constrained by tight foreign exchange
controls, effectively stopping private sector imports and ensuingthat all grain imports are channeled through the state-owned
Ethiopian Grain Trade Enterprise (Wakeyo and Lanos 2014; Negassa
and Jayne 1997).
21
22

20 Many countries typically put in place flanking policies. In 200708, for example, Indonesia subsidized imports of wheat and rice,
respectively, to hold down domestic consumer prices. To avoid
subsidizing exports of the same goods, export restrictions were also
introduced. Because rice, wheat, and maize are bulk commodities thatare less strongly differentiated than manufactured products, two-way
trade in these goods is unusual—except when there are regional
differences in varieties (for example, Indian exports of Basmati rice
and imports of Jasmine rice). Models of differentiated products areneeded to adequately capture actual bilateral trade flows in thesecommodities (Thursby, Johnson, and Grennes 1986).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

heterogeneity in policy responses. In Bangladesh,
Ecuador, Malawi, Tanzania, and Zambia,
protection rates fell, fully offsetting the rise inglobal maize prices. Ethiopia, Uganda, and Yemen
increased protection rates or used policies that, in
combination with domestic output shocks,
amplified the increase in domestic prices.
Rice. Some countries (e.g., Bangladesh, Nepal,
Panama, Tanzania and Zambia) reduced tradebarriers to partially offset the rise in world rice
prices. However, important net rice exporters such
as India, Pakistan, and Yemen implemented policy
interventions that, ultimately, raised domestic rice
prices more than the increase in world prices. In
India, the world’s second-largest rice producer,
quantitative restrictions imposed in 2007 initiallyprevented domestic price increases. However, thesubsequent abolition of export quotas in
September 2011 resulted in a surge in exports anda rise in domestic prices. In Pakistan, heavy
summer flooding that affected one-fifth of thecountry’s land area and inflicted extensive damage
to crops raised domestic rice prices relative to theworld price over the same period. A large increasein domestic prices relative to external prices
occurred in Yemen, amid persistent water
shortages and a shift to less water-intensive nonstaple crops and, in Ethiopia and Uganda, amid
drought. The combined intervention of all
countries dampened the increase in the world
price of rice by about 50 percent compared to a
scenario without insulation policies.
Impact of policy intervention on poverty
Poverty impact of hypothetical food price spikes
without policy intervention. A hypothetical 10
percent surge in rice, wheat, and maize prices
raises the number of extreme poor living on lessthan $1.90 per day by 0.22 percent or 2.1 million.
Among staple foods, an increase in wheat prices
raises the number of poor most (by 0.01
percentage points for a 10 percent wheat priceincrease). Rice price increases cause particularly
large increases in the number of poor in SubSaharan Africa (0.13 percentage points). Finally,maize price increases tend to have a lesser impact
on the number of poor.

CHAPTER 4

219

FIGURE 4.2.7 Poverty impact of policies implemented
during the 2010-11 food price spikeThe 2010-11 food price spike raised global poverty. The combined impactof all government interventions raised poverty worldwide, except in a few
countries. Due to the dampening effect of interventions on the world priceof rice, however, the impact of the combined interventions is found to haveraised poverty about 14 percent less than individual action.
A. Regional poverty impact of a 10
percent rice, wheat, and maize priceincrease

B. Global poverty impact of a 10
percent rice, wheat, and maize priceincrease

C. Regional poverty impact of the
2010-11 food price shock

D. Global poverty impact of policy
responses to the 2010-11 food priceshock

Source: World Bank.
Note: Based on estimates using the MIRAGRODEP computable general equilibrium model describedin Annex 4.2.1.
A. Change in the poverty headcount measured at $1.90 per day.
B. Poverty impact measured at $1.90 per day.
A.C. EAP = East Asia and Pacific; LAC = Latin America and the Caribbean; MNA = Middle East andNorth Africa; SAR = South Asia; and SSA = Sub-Saharan Africa.
C.D. Assuming increases in the price of maize, rice, and wheat as represented in Figure 4.4.D andbased on a poverty line of $1.90/day.
Click here to download data and charts.

Poverty impact of 2010-11 food price spike with
policy intervention. When incorporating theeffects of government intervention to reduce thepass-through of rising global to domestic prices,model results suggest that the food price spikes of
2010-11 still raised poverty in most countries(Figure 4.2.7). On average, the share of extreme
poor living on less than $1.90 per day increased by
0.12 percentage point from 13.7 percent. This isequivalent to an additional 8.3 million, or a 1
percent increase in the number of extreme poor.
Heterogeneity in poverty impact. The increase inworld food prices, combined with government
intervention, was most strongly felt in countries

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such as India and Uganda, where the extreme poor
tend to be net food-buyers whose real incomes
declined.24 The poverty impact of the 2010-11
food price spike on some regions such as East Asiaand the Pacific (EAP), and Latin America and the
Caribbean (LAC) is estimated to have been
limited: low rates of poverty combined with thebenefits of the price increase for countries that areheavy exporters of rice (EAP) or maize (LAC)
offset some of the losses incurred due to theincrease in prices. Even in Sub-Saharan Africa—
the region that accounts for two-thirds of the
global increase in poverty—countries like Ethiopia
and Nigeria implemented insulation policies that
reduced poverty.
Comparison with 2007-08 food price shock.
These poverty impacts are less pronounced thanthose induced by the 2007-08 food price shock.
The 2007-08 food price shocks may haveincreased extreme poverty by 105 million (Ivanicand Martin 2008). Government policies reduced
poverty impacts and their combined effect wasclose to zero (Anderson, Ivanic, and Martin 2014).
The difference in poverty impacts reflects thegreater severity of the 2007-08 price shocks, thestronger transmission of price changes from world
to domestic markets and higher initial poverty
rates (the poverty headcount in India, for instance,
fell from 31 percent in 2009 to 21 percent in2011).25 While the 2007-08 event was led by rice
prices, exacerbated by export restrictions imposedby major rice producers, the 2010-11 food pricesurge was led by maize and wheat prices, triggeredby adverse weather events in major wheat andmaize producers in Australia and the Black Sea
Basin. During 2007-08, large rice consumers, such

24 Results reported here do not take into account the impact ofsafety-net programs such as India’s Public Distribution System,
which distributes food to poor households at fixed prices and so
automatically makes larger transfer to the poor when food prices rise.
25 World Bank (2012) estimate that the 2010-11 food price spikeincreased the number of poor by 50 million in the short run, and by
34 million in the long run. These higher estimates do not explicitly
account for insulation policies and consider price increases of a wider
range of food commodities (also beef, chicken, dairy, vegetable oils
and soybean prices). In addition, there is uncertainty around poverty
estimates due to systematic measurement errors in household surveys
that may bias the poor’s dependence on food purchases (Headey andMartin 2016). Finally, Jacoby (2016) and Jacoby and Dasgupta(2018) highlight the importance of accounting for the endogenous
agricultural wage response and spillover effects to non-agricultural
wages (also accounted for in this essay).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019as India, imposed export restrictions to containdomestic rice price increases. These were gradually
unwound over the following years. In 2010-11,
some large wheat and maize producers, such as
Russia and Ukraine, introduced export restrictions
and import bans to contain domestic pricepressures.

Conclusions
During the 2010-11 food price shock, coming inshort succession after the 2007-08 surge in foodprices, many countries used trade policies toinsulate domestic markets from the increase inworld prices. While each country’s policies candampen domestic price movements, the combined
use of policies by many countries increases global
food price volatility. For example, widespread
insulation policies accounted for 40 percent of theincrease in world wheat prices and one-quarter forworld maize prices. The increase in food prices
combined with government policy responses in2010-11 raised global poverty by almost 1 percent(8.3 million).
These findings highlight that the use of trade
policy interventions to insulate domestic markets
from food price shocks compounds the volatilityof international prices and may not be effective inprotecting the most vulnerable populationsgroups. Instead, targeted safety net interventions
such as cash transfers, food and in-kind transfers,
school feeding and public works programs canmitigate the negative impact of food price shocks
on poor households. Measures such as crop andweather insurance, warehouse receipt systems,commodity exchanges and futures markets could
also be used as risk management instruments.
Additional policy interventions such as targeted
nutrition and health programs can contribute toimproving health outcomes in the medium term,
while regulatory interventions (taxing unhealthyfood) can improve health outcomes in the longer
term.
More generally, in addition to targetedinterventions it is important to ensure thatcountries have detailed strategic framework forfood crisis response in place and that these
programs are sufficiently resourced withadministrative budgets. International financial

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

institutions (IFIs) can assist countries to bettertarget the people most vulnerable to a food pricecrisis. IFIs can also help countries identify
practical mechanisms (including indicators) for
monitoring nutritional and welfare outcomes, inmeasuring the impacts of food crises andmitigation programs, and work with them to
implement those mechanisms. The private sector
can play a crucial role in enhancing investments infood supply in the short and medium term (WorldBank 2013), while better collaboration among
public and private stakeholders can improve risk
management and provide effective responses toreduce the impacts of extreme weather on
agriculture (G20 2018).

ANNEX 4.2 Methodology
Error Correction Model
The analytical framework used to represent theimperfect transmission of changes in international
prices into domestic markets relies on an Error
Correction Model (ECM) as described in Ivanicand Martin (2014a). As noted by Nickell (1985),
this model represents a situation in which
policymakers seek to reduce both the costs ofchange, and the costs of being out of equilibrium.
A simplified version model used by Ivanic andMartin (2014a), expressed in logs, is:Δτα pw - pwt-1

β pt-1 - γ pwt-1 ,where p represents domestic prices; pw world
prices; τ the rate of protection, approximated by
p-pw ; α, α 0, the coefficient of price insulation
ranging from 0 for countries that do no insulate
against the rise in world prices, to -1 for countries
that adopt policies that fully insulate domestic
markets;β, β0, the cost of being out of equilibrium or
the speed with which polices achieve the target
level of protection or at which policymakers moveback toward this equilibrium after being forced
away from it by a shock to world prices; γdetermines the long-run relationship between acountry’s protection and the global level ofagricultural protection; and pt-1 - γ pwt-1 is thedeviation from the political-economy equilibrium.
It depends on factors like income levels,

CHAPTER 4

exportable/importable status, the elasticity ofimport demand, and the share of real incomesgains from higher protection that will accrue topolitically organized producers (Anderson 1995;
Grossman and Helpman 1994).
The database on Distortions to Agricultural
Incentives (Anderson and Valenzuela 2008;
Anderson and Nelgen 2013) is the main data
source for estimating the ECM model. It includes
estimates of domestic and world price levels, which
also determine the level of protection. The pricedata used in the model capture natural shocks (oilprices, weather events) and the impact of trade
policy interventions, the separate impact of which
is not possible to disentangle. The model isestimated for eight food commodities with data for
82 countries, of which 26 are advanced economies,
44 EMDEs, and 12 LICs.
Measuring trade policy interventions
The approach to quantify the extent of trade
policy interventions builds on that used inAnderson, Ivanic, and Martin (2014). It is assumed
that a primary shock, such as weather shock,generates an initial change in domestic and world
prices. In attempting to insulate consumers andproducers from price increases, governments makeoffsetting changes in protection measures, such as
the introduction of export bans or reduction in
import duties. These measures, in turn, reinforce
the original shock to world prices. When a country
imposes an export restriction, the availability offood to the rest of the world is reduced, what tends
to push up world price. Similarly, when an
importing country reduces its import tariffs, it
increases the demand for imports and hence puts
upward pressure on the world price.
The impact of the changes in trade policies can be
distinguished from those of the primary shocks byin the following equation:
ΣiSi

i

i

Σi Di

i

,

where Si is supply in region i; Di is demand inregion i; i
* 1
t i is the domestic price; *is the world price; ti is a country-specific tradebarrier, such as a proportional tariff; and i is arandom production shift variable for region i.
Totally differentiating the equation above,

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rearranging, and expressing the results inpercentage changes yields an expression of the
impact of a set of changes in trade distortions on
the world price:̂ *=Σi (Gi i - Hi γi)
Σi Hi ̂i + Σi (Hiγi - Gi i) Ti

where ̂* is the proportional change in theinternational price; î is an exogenous output
shock such as might result from good or badseasonal conditions; i is the elasticity of demandin market i; γi is the elasticity of supply in marketi; Gi is the share at world prices of country i inglobal demand; Hi is the share of country i inglobal production, and T" i
1 ti .
In other words, the impact on the world price of achange in trade policies in country is given as aweighted average of the changes in tradedistortions in different markets, with the weighton region i depending on the importance of thatcountry in global supply and demand, as well as
the responsiveness of its production andconsumption to price changes in the country, as
represented by γi and i .
It is thus assumed that elasticities of demand areequal between countries, i.e., that imported anddomestic goods are perfect substitutes, and that
there are no supply responses. Alternatively, one
could allow for differentiation between imported
and domestic products, as well as a limited supply
response (Jensen and Anderson 2017). The result
would be an expression with weights that depend
on, for instance, the shares of imports in
consumption in each market. However, the overall
result is similar in expressing the change in world
prices as a weighted sum of changes in tradedistortions.
To avoid having to deal with difficult-to-interpret
interaction terms, all proportional changes areconverted into log changes in Ti , i’s, and as:
î

̂

T" i

Changes in relative prices are measured as in the
Agricultural Incentives database and capture awide range of policy measures used to assess
agricultural trade distortions—including tariffs,
export subsidies, export taxes, export bans andimport subsidies.

If products are homogeneous, and a country issmall, the change in ∆t represents the change inthe domestic price of the good. Additionally, if
T" i is negative in a period of rising world prices,
countries are seeking to insulate their markets
from the increase in prices. If it is positive,
policymakers are compounding the increase inworld prices with an increase in protection. Thismay be due to the correction of past “errors”.
This might occur if domestic prices fall below
policymakers’ desired long-run level, or if
policy insulated the domestic market from world
markets and an exogenous shock—such a harvest
shortfall—has caused the domestic price torise relative to the world price. Such insulation
patterns have been observed in the maizemarkets in many African countries (Chapoto andJayne 2009).
The MIRAGRODEP model
The analytical framework to measure the poverty
implications of the 2010-11 food price spike relieson the MIRAGRODEP model (Laborde,
Robichaud, and Tokgoz 2013) complemented
with household surveys for more than 31 countries
and285,000
representative
households.
MIRAGRODEP is a dynamic, multi-country, andmulti-sector computable general equilibrium(CGE) model. The model relies on GTAP 9, a
global database for 2011. The GTAP databaseincludes input-output tables linked by bilateral
trade flows for 140 regions (countries or country
aggregates) and 57 sectors. For the purposes of thesimulations these countries and sectors were
aggregated into 31 countries/regions and 15
sectors among which rice, wheat, and maize arerepresented separately.
On the supply side, the production function is a
Leontief function of value-added and intermediate
inputs. The intermediate inputs are represented bya nested, two-level constant elasticity of
substitution (CES) function of all goods. Based on
this, substitutability exists between intermediate
goods, but these are more substitutable when they
are in a same category (such as agricultural inputs
or service inputs). Value-added is also represented
by a nested structure of CES functions of
unskilled labor, land, natural resources, skilledlabor, and capital. This nesting allows the modeler

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2019

to incorporate some intermediate goods that aresubstitutes of factors, such as energy or fertilizers.
On the demand side, a representative consumer isassumed to have a constant propensity to save.
The remaining national income is used for thepurchase of consumption goods. Consumers’
preferences are represented by a linear expendituresystem–constant elasticity of substitution (LES–
CES) function, calibrated based on the U.S.
Department of Agriculture Economic ResearchService income and price elasticities to best reflect
non-homothetic demand patterns with changes inrevenue. Given an increase in the price staple
foods, consumers substitute away to consume
other food products. Armington elasticities, whichmeasure the elasticity of substitution between
products of different countries, are drawn fromthe GTAP database and are assumed to be thesame across regions.
Factor endowments are assumed to be fully
employed. The supply of capital goods is modified
each year because of depreciation and investment.
New capital is allocated among sectors according
to an investment function. Growth rates of labor
supply are fixed exogenously. Land supply is
endogenous and depends on the real remunerationof land. Skilled labor is the only factor that isperfectly mobile; unskilled labor is imperfectly
mobile between agricultural and nonagricultural
sectors according to a constant elasticity oftransformation (CET) function. Unskilled labor’s
remuneration in agricultural activities is different
from that of nonagricultural activities. The only
factor whose supply is constant is the natural
resources factor. It is, however, possible toendogenously change the factor endowment in thebaseline in order to reflect long-term depletion of
resources with respect to a price trajectory.
The poverty impact is captured through a topdown approach using a data set of household
surveys for more than 31 countries and 285,000
representative households. The impact of a policy
shock on poverty depends on price changes, therelative reliance of households on the
consumption of individual staple foods and the
net food buying status of households in different
segments of the distribution (Deaton 1989).

CHAPTER 4

Beyond the standard features of a global dynamic
CGE model, the MIRAGRODEP model includes
several improvements: sub-national land markets(agro-ecological zones or administrative districts)
and endogenous land supply; poverty analysis
through either a top-down approach for global
coverage or a bottom-up approach (for a subset ofcountries); dual-dual approach for formal/informal
and rural/urban labor markets (Stifel andThorbecke 2003); a consistent aggregator for trade
policies (Laborde, Martin, and van der
Mensbrugghe 2017); differentiated data sets on
actual trade and farm policies and existing policy
space for scenario design and endogenous policy
responses; macro nutrient (calories, fats, proteins)accounting system based on FAOSTAT foodbalance sheets and a global Input-Output matrix;
and sensitivity analysis framework based onMonte-Carlo simulations.
While the elasticities of substitution for
rice, wheat, and maize used in this model, arehigher than for manufactured goods, they are notinfinite as assumed using the perfect substitutes
model (Thursby, Johnson, and Grennes 1986).
This specification has important implications forboth the economy-wide analysis and at thehousehold level. Given these assumptions, anincrease in the price of an imported good has amuted impact on the domestic consumer price ofthat good. Since, with the Armington
assumption—imported goods differentiated basedon their country of origin—, the composite priceof the consumer good is weighted by the shares ofdomestic and imported goods, the impact of aunit change in the world price, or in trade policy,
is given by the share of imports in total
consumption. Because the share of imports intotal consumption of staple foods is typicallysmall, the impact of trade policy on consumer
prices is much more muted than under theassumption of perfect substitution used inAnderson, Ivanic, and Martin (2014). On the
production side, the assumption that eachcountry’s export product is the same as
the products sold domestically means that changes
in export trade policies will have a more direct
impact on producer prices if the country is
an exporter and not too large in the markets
it supplies.

223

224

CHAPTER 4

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227

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 1 9

231

Real GDP growth
Annual estimates and forecasts1

Quarterly growth2

2016

2017

2018e 2019f

2020f

2021f

17Q2

17Q3

17Q4

World

2.4

3.1

3.0

2.9

2.8

2.8

2.9

3.2

3.2

3.0

3.2

2.9

Advanced economies

1.7

2.3

2.2

2.0

1.6

1.5

2.1

2.5

2.4

2.2

2.4

2.0

United States

1.6

2.2

2.9

2.5

1.7

1.6

2.1

2.3

2.5

2.6

2.9

3.0

Euro Area

1.9

2.4

1.9

1.6

1.5

1.3

2.5

2.8

2.7

2.4

2.2

1.6

Japan

0.6

1.9

0.8

0.9

0.7

0.6

1.8

2.1

2.4

1.2

1.4

0.0

United Kingdom

1.8

1.7

1.3

1.4

1.7

1.8

1.9

2.0

1.6

1.3

1.4

1.5

3.7

4.3

4.2

4.2

4.5

4.6

4.4

4.7

4.7

4.7

4.8

4.5

Emerging market and developing economies
East Asia and Pacific

18Q1 18Q2 18Q3e

6.3

6.6

6.3

6.0

6.0

5.8

6.6

6.6

6.5

6.6

6.4

6.2

Cambodia

6.9

7.0

7.1

6.8

6.8

6.7

..

..

..

..

..

..

China

6.7

6.9

6.5

6.2

6.2

6.0

6.9

6.8

6.8

6.8

6.7

6.5

Fiji

0.4

3.8

3.5

3.4

3.3

3.3

..

..

..

..

..

..

Indonesia

5.0

5.1

5.2

5.2

5.3

5.3

5.0

5.1

5.2

5.1

5.3

5.2

Lao PDR

7.0

6.9

6.5

6.6

6.7

6.6

..

..

..

..

..

..

Malaysia

4.2

5.9

4.7

4.7

4.6

4.6

5.8

6.2

5.9

5.4

4.5

4.4

Mongolia

1.4

5.4

5.9

6.6

6.3

6.2

6.2

5.3

6.3

6.3

6.1

6.6

Myanmar

5.9

6.8

6.2

6.5

6.6

6.8

..

..

..

..

..

..

Papua New Guinea

2.6

2.8

0.3

5.1

3.1

3.4

..

..

..

..

..

..

Philippines

6.9

6.7

6.4

6.5

6.6

6.6

6.6

7.2

6.5

6.6

6.2

6.1

Solomon Islands

3.5

3.5

3.4

2.9

2.8

2.7

..

..

..

..

..

..

Thailand

3.3

3.9

4.1

3.8

3.9

3.9

3.9

4.3

4.0

4.9

4.6

3.3

Timor-Leste

5.3

-4.7

0.8

3.3

4.9

5.0

..

..

..

..

..

..

Vietnam

6.2

6.8

6.8

6.6

6.5

6.5

6.2

7.5

7.7

7.4

6.7

6.9

1.7

4.0

3.1

2.3

2.7

2.9

3.3

3.5

2.5

2.7

3.1

3.0

Albania

3.4

3.8

4.0

3.6

3.5

3.5

4.2

3.6

3.6

4.5

4.3

..

Armenia

0.2

7.5

5.3

4.3

4.6

4.6

..

..

..

..

..

..

Azerbaijan

-3.1

0.1

1.1

3.6

3.3

2.7

..

..

..

..

..

..

Belarus

-2.5

2.4

3.4

2.7

2.5

2.5

1.7

3.0

4.3

5.2

4.0

..

Bosnia and Herzegovina7

3.1

3.0

3.2

3.4

3.9

4.0

3.2

2.8

3.3

2.0

3.4

..

Bulgaria

3.9

3.8

3.3

3.1

3.0

2.8

3.8

4.3

3.3

3.5

3.2

2.7

Croatia

3.5

2.9

2.7

2.8

2.8

2.6

3.2

3.4

2.2

2.5

2.9

2.8

Georgia

2.8

4.8

5.3

5.0

5.0

5.0

4.8

4.0

5.3

5.2

5.6

3.7

Hungary

2.3

4.1

4.6

3.2

2.8

2.4

3.5

4.1

4.5

4.5

4.9

4.9

Kazakhstan

1.1

4.1

3.8

3.5

3.2

3.2

5.3

4.2

3.1

4.1

4.3

..

Kosovo

4.1

4.2

4.2

4.5

4.5

4.5

..

..

..

..

..

..

Kyrgyz Republic

4.3

4.6

3.1

3.4

3.9

4.0

..

..

..

..

..

..

Macedonia, FYR

2.8

0.2

2.5

2.9

3.2

3.3

-1.8

0.1

1.6

0.9

3.0

3.0

Moldova

4.5

4.5

4.8

3.8

3.5

3.2

..

..

..

..

..

..

Montenegro5

2.9

4.7

3.8

2.8

2.5

2.5

5.0

4.8

3.9

4.5

4.9

..

Poland

3.1

4.8

5.0

4.0

3.6

3.3

4.4

5.4

4.5

5.1

5.1

5.1

Romania

4.8

6.9

4.1

3.5

3.1

2.8

6.1

8.8

6.7

4.0

4.1

4.3

Russia

-0.2

1.5

1.6

1.5

1.8

1.8

2.5

2.2

0.9

1.3

1.9

1.5

Serbia

2.8

1.9

3.5

3.5

4.0

4.0

1.8

2.2

2.5

4.8

4.9

3.8

Tajikistan

6.9

7.1

6.0

6.0

6.0

6.0

..

..

..

..

..

..

Turkey

3.2

7.4

3.5

1.6

3.0

4.2

5.3

11.5

7.3

7.2

5.3

1.6

Turkmenistan

6.2

6.5

6.2

5.6

5.1

4.9

..

..

..

..

..

..

Ukraine

2.3

2.5

3.5

2.9

3.4

3.8

2.6

2.5

2.3

3.1

3.8

2.8

Uzbekistan

7.8

5.3

5.0

5.1

5.5

6.0

..

..

..

..

..

..

Europe and Central Asia

232

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 1 9Real GDP growth (continued)
Annual estimates and forecasts1

Quarterly growth2

2016

2017

2018e

2019f

2020f

2021f

17Q2

17Q3

17Q4

18Q1

18Q2

18Q3e

-1.5

0.8

0.6

1.7

2.4

2.5

1.5

1.9

2.3

1.9

1.6

1.6

Argentina

-1.8

2.9

-2.8

-1.7

2.7

3.1

3.0

3.8

3.9

3.9

-4.0

-3.5

Belize

-0.5

1.2

1.5

1.9

1.7

1.7

..

..

..

..

..

..

Bolivia

4.3

4.2

4.5

4.3

3.8

3.4

3.8

4.3

5.2

4.4

4.4

..

Brazil

-3.3

1.1

1.2

2.2

2.4

2.4

0.6

1.4

2.2

1.2

0.9

1.3

Chile

1.3

1.5

3.9

3.5

3.3

3.2

0.5

2.5

3.3

4.5

5.4

2.8

Colombia

2.0

1.8

2.7

3.3

3.7

3.6

1.7

1.7

1.8

2.2

2.8

2.7

Costa Rica

4.2

3.3

2.7

2.7

2.8

3.0

3.7

2.7

3.0

2.8

3.6

..

Dominican Republic

6.6

4.6

5.8

5.1

5.0

4.8

3.1

3.1

6.5

6.4

7.1

..

Ecuador

-1.2

2.4

1.0

0.7

0.7

1.2

2.1

2.9

2.8

1.6

0.9

..

El Salvador

2.6

2.3

2.8

2.5

2.4

2.4

0.3

3.2

2.5

2.7

2.9

..

Grenada

3.7

5.1

5.2

4.2

2.8

2.8

..

..

..

..

..

..

Guatemala

3.1

2.8

2.7

2.9

3.0

3.1

2.2

2.7

2.9

2.0

3.3

..

Guyana

2.6

2.1

3.4

4.6

30.0

24.8

..

..

..

..

..

..

3

Haiti

1.5

1.2

1.6

2.3

2.4

2.5

..

..

..

..

..

..

Honduras

3.8

4.8

3.6

3.8

3.8

3.7

3.5

5.9

4.2

3.0

4.0

..

Jamaica

1.4

1.0

1.7

1.8

2.0

2.0

0.1

1.0

1.2

1.4

2.2

..

Mexico

2.9

2.1

2.1

2.0

2.4

2.4

1.9

1.5

1.5

1.2

2.6

2.5

Nicaragua

4.7

4.9

-3.8

-0.5

2.6

3.6

4.1

3.2

4.7

2.5

-4.4

..

Panama

5.0

5.3

4.0

6.0

5.4

5.2

5.1

5.2

4.4

4.0

3.1

3.6

Paraguay

4.3

5.0

4.0

3.9

4.0

4.0

2.8

5.8

5.4

4.7

6.2

...

Peru

4.0

2.5

3.9

3.8

3.8

3.7

2.5

2.9

2.4

3.2

5.5

2.3

St. Lucia

3.4

3.8

1.5

2.7

2.8

2.3

..

..

..

..

..

..

St. Vincent and the Grenadines

1.3

0.5

1.2

1.6

1.6

2.0

..

..

..

..

..

..

Suriname

-5.6

1.7

1.4

1.6

1.8

1.9

..

..

..

..

..

..

Trinidad and Tobago

-6.1

-2.6

1.0

0.9

1.2

1.2

..

..

..

..

..

..

Uruguay

1.7

2.7

2.1

2.1

2.3

2.5

2.8

1.9

2.0

2.2

2.6

2.1

Venezuela

-16.5

-14.5

-18.0

-8.0

-5.0

-4.0

..

..

..

..

..

..

5.1

1.2

1.7

1.9

2.7

2.7

1.3

2.3

1.1

2.3

2.6

2.6

Algeria

3.2

1.4

2.5

2.3

1.8

1.8

..

..

..

..

..

..

Bahrain

3.2

3.9

3.2

2.6

2.8

2.8

..

..

..

..

..

..

Djibouti

8.6

5.7

6.7

7.3

7.5

7.5

..

..

..

..

..

..

Egypt

4.3

4.2

5.3

5.6

5.8

6.0

5.0

5.2

5.3

5.4

5.4

..

Iran

13.4

3.8

-1.5

-3.6

1.1

1.1

3.5

6.1

2.4

2.9

2.5

..

Iraq

13.0

-2.1

1.9

6.2

2.9

2.8

..

..

..

..

..

..

Jordan

2.0

2.0

2.1

2.3

2.4

2.7

..

..

..

..

..

..

Kuwait

2.9

-3.5

1.7

3.6

3.6

3.6

-2.9

-3.6

-2.7

-0.5

1.9

..

Lebanon

1.7

1.5

1.0

1.3

1.5

1.5

..

..

..

..

..

..

Morocco

1.1

4.1

3.2

2.9

3.5

3.5

..

..

..

..

..

..

Oman

5.0

-0.9

1.9

3.4

2.8

2.8

..

..

..

..

..

..

Qatar

2.1

1.6

2.3

2.7

3.0

3.0

0.8

0.7

3.3

2.0

2.5

..

Saudi Arabia

1.7

-0.9

2.0

2.1

2.2

2.2

-0.8

-0.3

-1.4

1.2

1.6

..

Tunisia

1.1

2.0

2.6

2.9

3.4

3.6

1.8

2.1

2.0

2.3

2.9

2.6

United Arab Emirates

3.0

0.8

2.0

3.0

3.2

3.2

..

..

..

..

..

..

West Bank and Gaza

4.7

3.1

1.7

1.9

1.9

1.9

..

..

..

..

..

..

Latin America and the Caribbean

Middle East and North Africa

3

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 1 9

233

Real GDP growth (continued)
Annual estimates and forecasts1

Quarterly growth2

2016

2017

2018e

2019f

2020f

2021f

17Q2

17Q3

17Q4

18Q1

18Q2

18Q3e

7.5

6.2

6.9

7.1

7.1

7.1

5.5

6.2

6.8

7.6

8.1

7.0

Afghanistan

2.4

2.7

2.4

2.7

3.2

3.2

..

..

..

..

..

..

Bangladesh3,4

7.1

7.3

7.9

7.0

6.8

6.8

..

..

..

..

..

..

Bhutan

7.4

5.8

4.6

7.6

6.4

6.4

..

..

..

..

..

..

India

7.1

6.7

7.3

7.5

7.5

7.5

5.6

6.3

7.0

7.7

8.2

7.1

Maldives

6.2

7.1

8.0

6.3

5.6

5.6

..

..

..

..

..

..

Nepal3,4

0.6

7.9

6.3

5.9

6.0

6.0

..

..

..

..

..

..

Pakistan3,4

4.6

5.4

5.8

3.7

4.2

4.8

..

..

..

..

..

..

Sri Lanka

4.5

3.3

3.9

4.0

4.1

4.1

3.0

3.2

3.5

3.4

3.6

2.9

1.3

2.6

2.7

3.4

3.6

3.7

1.7

2.0

2.5

2.3

1.9

2.3

Angola

-2.6

-0.1

-1.8

2.9

2.6

2.8

..

..

..

..

..

..

Benin

4.0

5.8

6.0

6.2

6.5

6.6

..

..

..

..

..

..

Botswana3

4.3

2.4

4.4

3.9

4.1

4.1

1.0

3.5

6.3

4.5

5.1

4.2

Burkina Faso

5.9

6.3

6.0

6.0

6.0

6.0

..

..

..

..

..

..

Burundi

-0.6

0.5

1.9

2.3

2.5

2.8

..

..

..

..

..

..

Cabo Verde

4.7

4.0

4.5

4.7

4.9

4.9

..

..

..

..

..

..

Cameroon

4.6

3.5

3.8

4.2

4.5

4.5

..

..

..

..

..

..

Chad

-6.3

-3.0

3.1

4.6

6.1

4.9

..

..

..

..

..

..

Comoros

2.2

2.7

2.7

3.1

3.1

3.1

..

..

..

..

..

..

Congo, Dem. Rep.

2.4

3.4

4.1

4.6

5.5

5.9

..

..

..

..

..

..

Congo, Rep.

-2.8

-3.1

1.0

3.2

-0.1

-1.5

..

..

..

..

..

..

Côte d'Ivoire

8.0

7.7

7.5

7.3

7.4

6.8

..

..

..

..

..

..

Equatorial Guinea

-8.5

-4.9

-8.8

-2.1

-5.8

-5.8

..

..

..

..

..

..

Eswatini

3.2

1.9

-0.6

1.7

1.8

1.8

..

..

..

..

..

..

Ethiopia3

8.0

10.1

7.7

8.8

8.9

8.9

..

..

..

..

..

..

Gabon

2.1

0.5

2.0

3.0

3.7

3.7

..

..

..

..

..

..

Gambia, The

0.4

4.6

5.3

5.4

5.2

5.2

..

..

..

..

..

..

Ghana6

3.7

8.5

6.5

7.3

6.0

6.0

..

..

..

..

..

..

Guinea

10.5

8.2

5.8

5.9

6.0

6.0

..

..

..

..

..

..

Guinea-Bissau

5.8

5.9

3.9

4.2

4.4

4.5

..

..

..

..

..

..

Kenya

5.9

4.9

5.7

5.8

6.0

6.0

4.7

4.7

5.4

5.7

6.3

..

Lesotho

3.1

-1.7

1.2

1.2

0.2

1.8

..

..

..

..

..

..

Liberia

-1.6

2.5

3.0

4.5

4.8

4.8

..

..

..

..

..

..

Madagascar

4.2

4.2

5.2

5.4

5.3

5.3

..

..

..

..

..

..

Malawi

2.5

4.0

3.5

4.3

5.3

5.5

..

..

..

..

..

..

Mali

5.8

5.4

4.9

5.0

4.9

4.8

..

..

..

..

..

..

Mauritania

2.0

3.5

3.0

4.9

6.9

6.9

..

..

..

..

..

..

Mauritius

3.8

3.9

3.9

4.0

3.6

3.6

..

..

..

..

..

..

Mozambique

3.8

3.7

3.3

3.5

4.1

4.1

..

..

..

..

..

..

Namibia

0.6

-0.9

0.7

1.8

2.1

2.1

..

..

..

..

..

..

Niger

4.9

4.9

5.2

6.5

6.0

5.6

..

..

..

..

..

..

Nigeria

-1.6

0.8

1.9

2.2

2.4

2.4

0.7

1.2

2.0

2.1

1.5

1.8

Rwanda

6.0

6.1

7.2

7.8

8.0

8.0

..

..

..

..

..

..

Senegal

6.2

7.2

6.6

6.6

6.8

6.9

..

..

..

..

..

..

Seychelles

4.5

5.3

3.6

3.4

3.3

2.9

..

..

..

..

..

..

South Asia

3,4

3,4

Sub-Saharan Africa

234

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 1 9Real GDP growth (continued)
Annual estimates and forecasts1
2016

Quarterly growth2

2017

2018e

2019f

2020f

2021f

17Q2

17Q3

17Q4

18Q1

18Q2

18Q3e

Sub-Saharan Africa (continued)
Sierra Leone

6.3

3.7

3.7

5.1

6.3

6.3

..

..

..

..

..

..

South Africa

0.6

1.3

0.9

1.3

1.7

1.8

1.2

1.6

1.4

0.8

0.4

1.1

Sudan

4.7

4.3

3.1

3.6

3.8

3.8

..

..

..

..

..

..

Tanzania

7.0

7.1

6.6

6.8

7.0

7.0

7.8

6.8

8.4

..

..

..

Togo

5.1

4.4

4.5

4.8

5.1

5.1

..

..

..

..

..

..

Uganda3

4.8

3.9

6.1

6.0

6.4

6.5

6.4

7.0

6.1

6.2

5.0

..

Zambia

3.8

3.5

3.3

3.6

3.8

3.8

3.4

5.3

4.1

2.7

3.9

..

Zimbabwe

0.6

3.2

3.0

3.7

4.0

4.0

..

..

..

..

..

..

Source: World Bank and Haver Analytics.
Note: e = estimate; f = forecast.
1. Aggregate growth rates calculated using constant 2010 U.S. dollars GDP weights.
2. Year-over-year quarterly growth of not-seasonally-adjusted real GDP, except for Ecuador, the Euro Area, Tunisia, and the United Kingdom.
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, FYR 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,Paraguay, Peru, and Uruguay.
Middle East and North Africa: Egypt, Iran, Kuwait, Qatar, Saudi Arabia, and Tunisia.
South Asia: India and Sri Lanka.
Sub-Saharan Africa: Botswana, 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 2017 refers to FY2016/17. For India, the column labeled 2016 refers to
FY2016/17.
5. Quarterly data are preliminary.
6. Growth rates reflect GDP data prior to recent rebasing.
7. Growth rates based on the production approach.
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 1 9

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.

House Prices Indicators, IMF Balance of Payments Statistics, and IMF International Financial
Statistics.

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

Forecast Process. The process starts with initialassumptions about advanced-economy growthand commodity 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. Throughout
the forecasting process, staff use macroeconometric models that allow the combinationof judgement and consistency with model-based
insights.

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-specificgrowth rates. Income groups are defined as in theWorld Bank’s classification of country groups.

235

236

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 1 9

Global Economic Prospects: Selected Topics, 2015-19
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

The great disinflation

January 2019, Box 1.1

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

Weak investment in uncertain times: Causes, implications and policy responses

January 2017, Chapter 3

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

Inflation

Growth prospects

Global output gap

Potential growth

Investment slowdown

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 1 9

S E L E CTE D TO P I C S

Global Economic Prospects: Selected Topics, 2015-19
Growth and Business Cycles
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

January 2017, Box 2.5.1

Sub-Saharan Africa

January 2017, Box 2.6.1

Quantifying uncertainties in global growth forecasts

June 2016, SF 2

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, SF 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

Forecast uncertainty

Cross-border spillovers

Fiscal space

Other topics

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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 1 9

Global Economic Prospects: Selected Topics, 2015-19
Commodity Markets
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, SF1

From commodity discovery to production: Vulnerabilities and policies in LICs

January 2016, Chapter 1

After the commodities boom: What next for low-income countries?

June 2015, Chapter 1, SF

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

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, SF

The U.S. economy and the world

January 2017, SF

Regulatory convergence in mega-regional trade agreements

January 2016, Box 4.1.1

Can remittances help promote consumption stability?

January 2016, Chapter 4

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

What lies behind the global trade slowdown?

January 2015, Chapter 4

Monetary and Exchange Rate Policies
The great disinflation

January 2019, Box 1.1

Corporate debt: Financial stability and investment implications

June 2018, SF2

Recent credit surge in historical context

June 2016, SF1

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, SF1.1

Countercyclical monetary policy in emerging markets: Review and evidence

January 2015, Box 1.2

Fiscal Policies
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, SF

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 1 9

S E L E CTE D TO P I C SDevelopment Economics Prospects Group (DECPG):
Selected Other Publications on the Global Economy, 2015-19
Commodity Markets Outlook

Column1

The implications of tariffs for commodity markets

October 2018, Box

The changing of the guard: Shifts in industrial commodity demand

October 2018, SF

Oil exporters: Policies and challenges

April, 2018, SF

Investment weakness in commodity exporters

January 2017, SF

OPEC in historical context: Commodity agreements and market fundamentals

October 2016, SF

Energy and food prices: Moving in tandem?

July 2016, SF

Resource development in an era of cheap commodities

April 2016, SF

Weak growth in emerging market economies: What does it imply for commodity markets?

January 2016, SF

Understanding El Niño: What does it mean for commodity markets?

October 2015, SF

How important are China and India in global commodity consumption

July 2015, SF

Anatomy of the last four oil price crashes

April 2015, SF

Putting the recent plunge in oil prices in perspective

January 2015, SF

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

High-Frequency Monitoring

Column1

Global Monthly newsletter
Global Weekly newsletter

239

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G

lobal economic prospects have darkened. Financing

conditions have tightened, industrial production has
moderated, and trade tensions remain elevated. The
recovery in emerging market and developing economies has
stalled, and some countries have experienced significant
financial stress. Downside risks have increased, including
the possibility of disorderly financial market movements
and escalating trade disputes. It is thus critical to rebuild
policy buffers while fostering potential growth by boosting
human capital, promoting trade integration, and addressing
informality.
In addition to discussing global and regional economic
developments and prospects, this edition of Global

Economic Prospects includes a chapter on the challenges
posed by informality and associated policy options. The
report also contains pieces on the remarkable decline in
EMDE inflation over the past four to five decades, risingdebt vulnerabilities in low-income countries, and theimplications of large spikes in food prices for poverty.

Global Economic Prospects is a World Bank Group Flagship
Report that examines global economic developments andprospects, with a special focus on emerging market anddeveloping countries, on a semiannual basis (in January andJune). The January edition includes in-depth analyses of
topical policy challenges faced by these economies, while
the June edition contains shorter analytical pieces.
ISBN 987-1-4648-1343-6

SKU 211163