Download pdf - STRUCTURAL TRANSFORMATION AND POVERTY REDUCTION IN … · 2020. 9. 16. · POVERTY REDUCTION IN AFRICA STATE OF THE AFRICA REGION WORLD BANK – IMF ANNUAL MEETINGS 2014 Francisco

STRUCTURAL TRANSFORMATION AND

POVERTY REDUCTION IN AFRICA

STATE OF THE AFRICA REGION WORLD BANK – IMF ANNUAL MEETINGS 2014

Francisco H. G. Ferreira Chief Economist, Africa Region

The World Bank

OUTLINE

1. Current economic performance – a bird’s eye view

2. Africa’s uneven growth

3. Structural transformation and poverty

4. Risks – old and new

WITH GDP GROWTH STABLE AT 4.6% P.A. , SSA IS THE WORLD’S THIRD

FASTEST GROWING REGION IN 2013-14

-2

0

2

4

6

8

East Asia &Pacific

Europe &Central Asia

Latin America &Caribbean

Middle East &North Africa

South Asia Sub-SaharanAfrica

Perc

ent

2013 2014

Source: Global Economic Prospects (World Bank)

Annual growth in GDP, 2013 and 2014: selected country groupings

PER CAPITA GDP IS GROWING AT 2.1% PER YEAR – AND HAS BEEN

RISING STEADILY FOR TWO DECADES…

-2

0

2

4

6

8

East Asia &Pacific





Perc

ent

2013 2014

Source: Global Economic Prospects, and Health Nutrition and Population Statistics (World Bank)

Annual growth in GDP per capita, 2013 and 2014: selected country groupings

… IN A CONTEXT OF BROADLY STABLE INFLATION RATES (IN MOST

CASES).

0

5

10

15

20

25

2010M01 2011M01 2012M01 2013M01 2014M01

Ghana KenyaNigeria South AfricaZambia Sub-Saharan Africa

Percent, y/y

Source: World Bank

POVERTY HAS FALLEN OVER THE LAST FIFTEEN YEARS, BUT MORE SLOWLY

THAN ELSEWHERE, AND THAN NEEDED TO MEET MDG1

Source: World Bank, Africa’s Pulse vol. 10. PovcalNet (2014)

0%

10%

20%

30%

40%

50%

60%

70%

1990 1993 1996 1999 2002 2005 2008 2010 2011

Headcount ($1.25 a day)

SSASSA Path to MDG 2015Rest of the worldRest of the world Path to MDG 2015

0

0.1

0.2

0.3

1990 1993 1996 1999 2002 2005 2008 2010 2011

Poverty Gap ($1.25 a day)

SSA Rest of the World

THIS REFLECTS THE REGION’S LOW GROWTH ELASTICITY OF POVERTY

REDUCTION

Source: estimates based on PovcalNet (2014). *For which data is available

Growth elasticity of poverty reduction, 2000-2010 – Five most populous countries by region*, except Poland and Sri Lanka

-2.02

-0.7

-14

-12

-10

-8

-6

-4

-2

0

2

4

Thai

lan

d

Egyp

t, A

rab

Rep

.

Kaz

akh

stan

Ph

ilip

pin

es

Mo

rocc

o

Turk

ey

Tun

isia

Arg

enti

na

Nep

al

Pak

ista

n

Per

u

Vie

tnam

Ban

glad

esh

Ro

man

ia

Oth

er d

evel

op

ing

cou

ntr

ies

Ind

ia

Bra

zil

Ind

on

esia

Mex

ico

Ch

ina

Co

lom

bia

Uga

nd

a

Sou

th A

fric

a

Eth

iop

ia

Sub

-Sah

aran

Afr

ica

Iran

, Isl

amic

Rep

.

Tan

zan

ia

Nig

eria

Ukr

ain

e

Yem

en, R

ep.

OUTLINE





Source: World Bank, Africa’s Pulse vol. 10. World Development Indicators (2014) Note: The index presented in this figure depicts the cumulative growth in real per capita GDP from 1995 to 2013 in Sub-Saharan Africa and sub-groups. We use GDP in U.S. dollars at 2005 prices from the World Development Indicators ,for 45 countries in SSA (16 resource rich, 29 non resource rich)

THE WEAK LINK FROM GROWTH TO POVERTY IS RELATED TO THE UNEVEN

NATURE OF THE REGION’S GROWTH

1.4

1.7

1.2

1

1.2

1.4

1.6

1.8

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Ou

tpu

t p

er c

apit

a in

dex

(1

99

5=1

)

Sub-Saharan Africa Resource-rich Non-resource-rich

Growth in GDP per capita in SSA by country groups, 1995-2013 (Output per capita index, 1995=1)

BOTH ACROSS COUNTRIES, AND ACROSS SECTORS AND REGIONS

WITHIN COUNTRIES

Source: staff estimates based on WDI (2014). Note: Subset of 36 SSA countries for which sectoral value added data is available.

1.1

1.5

1.6

0.9

1.1

1.3

1.5

1.7

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Ou

tpu

t p

er c

apit

a in

dex

(1

99

5=1

)

Sub-Saharan Africa Agriculture Industry Services

Growth in GDP per capita by sector, 1995-2011 (Output per capita index, 1995=1)

SERVICES AND THE NATURAL RESOURCE SECTOR ARE GROWING

MUCH FASTER THAN AGRICULTURE AND MANUFACTURING

Source: staff estimates based on WDI (2014). Note: Subset of 32 SSA countries for which sectoral value added data can be decomposed into manufacturing and other industry.

1.4

1.1 1.1

1.5

0.9

1.1

1.3

1.5

1.7

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Ou

tpu

t p

er c

apit

a in

dex

(1

99

5=1

)

Sub-Saharan Africa Agriculture Manufacturing Services

Growth in GDP per capita by sector, 1995-2011 (Output per capita index, 1995=1)

COMPARED TO OTHER LDCS, AFRICA’S GROWTH IS RELATIVELY MORE

DEPENDENT ON EXTRACTIVES, AND MUCH LESS ON MANUFACTURING

Sectoral contribution to total cumulative growth

Source: staff estimates based on World Development Indicators (2014) Note: Subset of 32 SSA and 56 developing countries for which sectoral value added data can be decomposed into manufacturing and other industry.

12% 7% -2%

17%

27%

18%

63% 58%

Sub-Saharan Africa Other Developing Countries

Sectoral contribution to average growth in GDP per capita (1995-2011)

Agriculture Manufacturing

Other industry Services

Avg. growth: 2%

Avg. growth: 4.4%

-1%

0%

1%

2%

3%

4%

5%

Sub-Saharan Africa Other DevelopingCountries

Average growth in GDP per capita by sector (1995-2011)

Agriculture Manufacturing

Other industry Services

THE HUGE IMPORTANCE OF SERVICES IN AFRICA IS NOT EXCEPTIONAL

15

55

10

20

-

20

40

60

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

20

11

Val

ue

add

ed (

%G

DP

)

Sub-Saharan Africa

Agriculture ServicesManufacturing Other industry

10

52

23

15

0

10

20

30

40

50

60

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

20

11

Val

ue

add

ed (

%G

DP

)

Other developing countries

Agriculture Services

Manufacturing Other industry

Source: : staff estimates based on WDI (2014). Note: Subset of 32 SSA and 56 developing countries for which sectoral value added data can be decomposed into manufacturing and other industry.

The high share of extractives (and agriculture) and the low share of manufacturing are.

THE SECTORAL COMPOSITION OF LABOR ALSO DIFFERS GREATLY IN AFRICA

AND OTHER DEVELOPING COUNTRIES

Source: World Bank, Africa’s Pulse vol. 10. International Income Distribution Database. Notes: The numbers correspond to working age (15-65) population weighted averages of the most recent survey between 2002 and 2012. Average of 33 (20) SSA countries and 66 (41) other developing countries for total working (working poor).

59.2

35.0

8.7

24.1

32.1 40.8

Sub-Saharan Africa Other developingcountries

Total working

Agriculture Industry Services

THE SECTORAL COMPOSITION OF LABOR ALSO DIFFERS GREATLY IN AFRICA

AND OTHER DEVELOPING COUNTRIES… ESPECIALLY AMONG THE POOR

Source: World Bank, Africa’s Pulse vol. 10. International Income Distribution Database. Notes: The numbers correspond to working age (15-65) population weighted averages of the most recent survey between 2002 and 2012. Average of 33 (20) SSA countries and 66 (41) other developing countries for total working (working poor).

59.2

35.0

8.7

24.1

32.1 40.8


Total working


78.2

59.7

5.4

18.5

16.4 21.8


Working poor


OCCUPATIONAL COMPOSITION ALONG THE INCOME DISTRIBUTION

Source: World Bank, Africa’s Pulse vol. 10. Calculations using SHIP data. (z=$1.25 /day)

CHANGING SECTORAL COMPOSITION OF LABOR: RWANDA

Source: World Bank, Africa’s Pulse vol. 10. Calculations using SHIP data. (z=$1.25/day)

OCCUPATIONAL COMPOSITION ALONG THE INCOME DISTRIBUTION

CHANGING SECTORAL COMPOSITION OF LABOR: SENEGAL

OUTLINE





DO SUCH DIFFERENCES IN THE PATTERN OF GROWTH – OR IN THE NATURE OF

STRUCTURAL TRANSFORMATION – MATTER FOR POVERTY REDUCTION?

International evidence on sector-specific growth impacts

Source: World Bank, Africa’s Pulse vol. 10 (adapted from Ferreira et al. (2010), Ravallion and Chen (2007), an Ravallion and Datt (1996) Notes: The results refer to time periods of analysis from 1985 to 2004 for Brazil, 1981-2001 for China, and 1951 to 1991 for India. National poverty lines are used for all countries. Vertical axis measures regression coefficients (unadjusted by sector shares)

-10

-8

-6

-4

-2

0

2

4

Brazil China India

Effe

ct o

f gr

ow

th o

n p

ove

rty

red

uct

ion

Agriculture

Industry

Services

THERE IS SOME EVIDENCE THAT THE PATTERN OF GROWTH ALSO

MATTERS IN AFRICA

In Ethiopia, growth in agriculture has contributed most to poverty reduction since at least 2000

-10

-8

-6

-4

-2

0

2

1996 - 2000 2000 - 2005 2005 - 2011

Sectoral contribution to poverty reduction (% points)

Agriculture Manufacturing Construction

Service Other

Source: Hill & Tsehaye, 2014, Growth, Safety Nets and Poverty-Assessing Progress in Ethiopia from 1996 to 2011

CROSS-COUNTRY REGRESSIONS SUGGEST THAT GROWTH PATTERNS

DO MATTER FOR POVERTY REDUCTION

Africa: agriculture and services most poverty reducing

Source: World Bank, Africa’s Pulse vol. 10. Data from WDI (2014) on sectoral value added as a share of GDP and poverty data PovcalNet (2014) from 1990 to 2010.

Note: The null hypothesis that the sectoral composition of growth does not matter is rejected at the 1% level for all the poverty measures (Headcount, Poverty Gap and Squared Poverty Gap). This is robust to the inclusion of controls .


VARIABLES Headcount Poverty Gap

Sq. Poverty

Gap Headcount Poverty Gap

Sq. Poverty

Gap

Agriculture -0.668*** -1.025*** -1.322*** -1.224 -0.752 -2.411*

(0.209) (0.318) (0.417) (1.268) (1.799) (1.333)

Industry -0.086 -0.078 -0.115 -1.864*** -2.595*** -3.079***

(0.301) (0.371) (0.434) (0.483) (0.624) (0.787)

Services -0.963*** -1.233*** -1.493*** -1.881*** -1.899*** -1.195*

(0.193) (0.254) (0.310) (0.507) (0.681) (0.683)

Observations 228 228 228 240 240 239

Countries 29 29 29 31 31 31

R-squared 0.280 0.309 0.319 0.367 0.344 0.377 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Elsewhere: industry and services most poverty reducing

THIS IS ROBUST TO THE INCLUSION OF CONTROLS

Source: World Bank, Africa’s Pulse vol. 10. Data from WDI (2014) on sectoral value added as a share of GDP and poverty data PovcalNet (2014) from 1990 to 2010.

Note: The null hypothesis that the sectoral composition of growth does not matter is rejected at the 1% level for all the poverty measures (Headcount, Poverty Gap and Squared Poverty Gap). This is robust to the inclusion of controls .


VARIABLES Headcount

Poverty

Gap

Sq. Poverty

Gap Headcount

Poverty

Gap

Sq. Poverty

Gap

Agriculture -0.673*** -1.024*** -1.316*** -1.033 -0.545 -2.321* (0.227) (0.337) (0.436) (1.305) (1.837) (1.338)

Industry -0.084 -0.088 -0.140 -1.934*** -2.665*** -3.156*** (0.341) (0.415) (0.479) (0.476) (0.618) (0.772)

Services -0.940*** -1.229*** -1.516*** -1.931*** -1.963*** -1.226* (0.207) (0.271) (0.329) (0.526) (0.705) (0.717)

CPI 0.015 0.000 -0.021 0.008 0.013 0.015

(0.039) (0.048) (0.060) (0.017) (0.024) (0.032)

Infant Mortality 0.022 0.007 -0.005 -0.532 -0.676 -0.498

(0.171) (0.250) (0.327) (0.800) (1.116) (1.429)

Lag GDP per capita -0.008 -0.007 -0.005 -0.067 -0.069 -0.120**

(0.026) (0.035) (0.044) (0.040) (0.053) (0.050)

Observations 228 228 228 240 240 239

Countries 29 29 29 31 31 31

R-squared 0.281 0.309 0.320 0.380 0.352 0.391 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

DIGGING DEEPER INTO THE INDUSTRY PUZZLE

As the poverty line is raised, agriculture matters less and less, while industry gains importance (ever so slightly)

Source: World Bank, Africa’s Pulse vol. 10 .

Note: the solid bars represent significant effects at 10% of significance or lower. Industry is not significant and agriculture loses its power as the poverty line increases (not significant for $4 a day).

-0.5 -0.4 -0.3 -0.2 -0.1 0

1.25

2

2.5

4

Growth elasticity of poverty reduction

Po

verty line

(USD

per d

ay)

Sub-Saharan Africa


The “service elevator” dominates throughout

Promoting agricultural productivity growth remains paramount

• 60% of Africa’s labor force, and almost 80% of the working poor are in agriculture.

• When it takes place, agricultural growth is effective at reducing poverty. But it takes place all too slowly.

• The evidence is suggestive of a “service elevator” out of poverty:

The services sector has grown strongly.

AND it has large effects on poverty.

How do people get on to it, and how can policy

help?

• The manufacturing sector is in relative decline, and not effective against poverty.

• Africa’s manufacturing weakness is not pre-ordained, but endogenous

Three horizontal paths to giving African

manufacturing a break

DO WE NEED TO RETHINK HOW WE VIEW STRUCTURAL

TRANSFORMATION?

1. PROVIDE A SKILLED LABOR FORCE

Source: staff estimates based on Education Statistics, 2014. Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com

0

2

4

6

8

10

12

East Asia &Pacific


Latin America& Caribbean



Average years of schooling (age 15+)

2005 2010

2. PROVIDE RELIABLE AND AFFORDABLE POWER

Source: preliminary numbers for SSA from Electricity Subsidies Study, 2014. Comparison countries from Readiness for Investment in Sustainable Energy (RISE) 2014.

0.51

0.02

0.28

0.22

0.07

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Lib

eria

Cap

e V

erd

e

Ch

ad

Sen

egal

Bu

run

di

Bu

rkin

a Fa

so

Mad

agas

car

Gam

bia

, Th

e

Ken

ya

An

gola

Be

nin

Co

mo

ros

Cam

ero

on

Leso

tho

Mal

awi

Gh

ana

Cô

te d

'Ivo

ire

Gu

inea

Bo

tsw

ana

Zim

bab

we

Mau

riti

us

Mo

zam

biq

ue

Mau

rita

nia

Eth

iop

ia

Ho

nd

ura

s

Ch

ile

Ind

ia

Un

ited

Sta

tes

Yem

en, R

ep.

Nep

al

Mo

ngo

lia

Arm

enia

US¢

per

kW

h

Africa (10 MWH consumption / 100 kW capacity)

Africa (750 MWH consumption / 2,000 kW capacity)

Comparison countries (10MWH consumption, 100 kVA capacity)

Industrial tariffs (US¢ per kWh) inclusive of demand charges (where applicable), energy charges and applicable taxes and fees

3. LOWER TRANSPORT, TRADING AND TRANSACTION COSTS

Source: Logistics Performance Index, World Bank,2014. Note: regional numbers are simple averages of country specific scores.

2.85

2.76 2.74

2.61

2.5

2.46

East Asia & Pacific Europe & CentralAsia


South Asia Middle East &North Africa

Sub-Saharan Africa

Logistics Performance Index

The LPI captures the quality of transport infrastructure, timeliness of shipments, the efficiency of border clearance processes, etc. It is based on a survey of 1000 respondents from 143 countries

(69 in SSA), in Oct-Dec 2013.

OUTLINE





BESIDE THE STRUCTURAL AGENDA OF MAKING GROWTH MORE INCLUSIVE,

A NUMBER OF SHORT-TERM RISKS NEED WATCHING

Fiscal discipline is waning – at a time when external tailwinds may be turning into headwinds

Source: International Monetary Fund, World Economic Outlook, October 2014.

-21.1

-16.3

-10.0 -9.0 -8.4

-7.8 -6.7 -6.3 -5.9 -5.7

-25

-20

-15

-10

-5

0

Eritrea South Sudan Ghana Cabo Verde The Gambia EquatorialGuinea

Zambia Senegal Tanzania Kenya

Fiscal deficits (latest)

PUBLIC HEALTH SHOCKS, SUCH AS THE CURRENT EBOLA EPIDEMIC

IN WEST AFRICA

Source: Petrolum Directorate, Sierra Leone.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14

Holiday VFR Business

Conference Other Total

Source: Sierra Leone Immigration Department

Sierra Leone – Visitor arrivals by air (2014) Sierra Leone – Diesel Fuel Sales Volume In Liters (2014)

15

17

19

21

23

25

27

Jan

20

14

Feb

20

14

Mar

20

14

Ap

r 2

01

4

May

20

14

Jun

e 2

01

4

July

20

14

Au

g 2

01

4

millions

Economic activity is slowing markedly in the core three countries, largely due to aversion behavior

Fiscal impacts in 2014 -- from reduced taxes and increased spending – are at US$113 million for Liberia, US$95 million for Sierra Leone, and $120 million for Guinea. These will rise. Note: All estimates are in 2013 dollars.

Estimated Impact of Ebola over the short and medium term (Millions of dollars in lost GDP)

-130

-66

-163

-359

43

-113 -59

-129 -142

-234

-439

-815 -900

-800

-700

-600

-500

-400

-300

-200

-100

0

100

Guinea Liberia Sierra LeoneCore ThreeCountries

2014 2015 (Low Ebola) 2015 (High Ebola)

FORGONE OUTPUT IN 2014 AND 2015 UNDER TWO SCENARIOS

THERE IS A RISK OF BROADER REGIONAL CONTAGION – BOTH

EPIDEMIOLOGICAL AND ECONOMIC

Estimated Impacts of Ebola on GDP and Annual Growth Rates for West Africa under two scenarios, 2014-2015

NEW FORMS OF VIOLENT CONFLICT, INCLUDING SECTARIAN

TERRORISM

Boko Haram and the evolution of social violence in Nigeria

Source: Nigeria Social Violence Project.

Fatalities in Nigeria by Category, 1998-2014

CONCLUSIONS

1. Africa continues to grow robustly – above 4.5% per year in GDP, and above 2.0% in per capita terms.

2. That growth continues to be relatively ineffective in reducing poverty.

3. That is because most of Africa’s poor people work in agriculture, but most of the growth takes place elsewhere.

4. Services sector growth is poverty-reducing and should be promoted – and better understood.

5. Faster gains in agricultural productivity and a level playing field for African manufacturing are needed.

6. So are solid systems and institutions to face risks in health, macroeconomics and violent conflict.

THANK YOU

MERCI

OBRIGADO

GRACIAS