Growth, Inequality and Poverty Reduction in Africa Francisco H. G. Ferreira The World Bank and IZA...

Growth, Inequality and Poverty Reduction in Africa

Francisco H. G. FerreiraThe World Bank and IZA

Lusaka - 19 September 2014

I. An African “growth profile”

II. Effects on poverty, mediated by inequality

III. Four key policy areas

1. Macroeconomic management under receding tailwinds

2. Investment in human and physical capital

3. Promoting growth in the places and sectors where the poor are…

4. … and creating social protection and promotion systems that enable them to share in growth elsewhere.

OUTLINE

“AFRICA RISING”: TWO DECADES OF SUSTAINED ECONOMIC GROWTH

…reversing two lost decades from the mid-70s to the mid-90s.

19601962

19641966

19681970

19721974

19761978

19801982

19841986

19881990

19921994

19961998

20002002

20042006

20082010

20126.45

6.50

6.55

6.60

6.65

6.70

6.75

6.80

6.85

6.90

6.95

Actual Trend

Real

GD

P pe

r ca

pita

in U

S$ a

t 20

05 p

rice

s (in

logs

)

ALTHOUGH REVERSING ABSOLUTE DIVERGENCE WILL TAKE LONGER

Standard of living levels remain very low, relative to developed countries.

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.03

0.05

0.02

Sub-Saharan Africa (SSA) Slow-growing SSA Fast-growing SSA

Inco

me

per c

apita

ratio

rela

tive

to th

e Eu

ro A

rea

“Fast-growing SSA” are defined as countries that experienced at least one growth spurt (3.5% p.a. growth in real GDP per capita, for at least five contiguous years) in 1995-2012

IN MANY WAYS, THIS HAS BEEN “HIGH-QUALITY” GROWTH…

• Africa’s growth has been strongly investment driven, rather than consumption-driven.

• Growth has also been associated with rapid increases in foreign trade – both imports and exports. 19

95

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

Consumption Investment Government Spending

Exports Imports

Cum

ulati

ve g

row

th in

dex

(199

5=1.

0)

Note: The figure depicts the median cumulative growth of the different components of aggregate demand for a sample of 44 Sub-Saharan African countries over the period 1995-2012. The data on the components of aggregate demand (household consumption, investment, government consumption expenditure, exports and imports) was originally computed in US dollars at 2005 prices. Source: World Bank, Africa’s Pulse, Volume 9.

BUT THERE ARE IMPORTANT CAVEATS:

Source: World Development Indicators

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012-1%1%3%5%7%9%

11%13%15%

Rest of Developing (excl. China) China Sub-Saharan Africa

Real

GDP

gro

wth

GDP growth in SSA, China and other developing countries

BUT THERE ARE IMPORTANT CAVEATS:

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012-1%

1%

3%

5%

7%

9%

11%

13%

15%


Source: World Development Indicators

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012-1%1%3%5%7%9%

11%13%15%


Real

GDP

gro

wth

GDP growth in SSA, China and other developing countries

Real

GD

P pe

r cap

ita g

row

th

1. Per capita growth is less impressive due to rapid population growth

GDP growth per capita in SSA, China and other developing countries

2. Growth performance varies significantly across countries

19951996

19971998

19992000

20012002

20032004

20052006

20072008

20092010

20112012

1.0

1.1

1.2

1.3

1.4

1.5

1.6

SSA

Resource rich

Non-resource rich

Fragile

Non-fragile

Cum

ulati

ve g

row

th in

dex

(199

5=1.

0)

• Fragile and conflict-affected states grow markedly more slowly than non-fragile countries.

• There are also substantial differences between resource-rich and non-resource-rich countries

Note: The Index presented in this figure depicts the cumulative growth in real per capita GDP from 1995 to 2012 across 44 Sub-Saharan Africa. Resource rich countries (9 oil and 12 non-oil) are those with average rents from natural resources (excluding forests) that exceed 5% of GDP in 2006-2011. Fragile countries (16) are defined as having either a harmonized CPIA rating of 3.2 or less, or presence of UN and/or regional peacekeeping or peacebuilding missions during the past 3 years. Source: World Bank, Africa’s Pulse, Volume 9.

PER CAPITA GROWTH RATES 1995-2012:

Fragile: 1.2%Non-fragile: 2.3%Non resource-rich: 1.7%Resource-rich: 2.6%

ZWE

GNBCOM ZA

RCIV NER GIN KEN CM

RM

WI

ZAF

STP

CAFM

LI SYC

BWA

BFA GHAUGA

NGAET

HRW

ALB

R-4

-2

0

2

4

6

8

10

12

14

16

GDP Growth per capita in SSA, 1995-2012 (resource-rich countries in orange)

ZWE

GNBCOM ZA

RCIV NER GIN KEN CM

RM

WI

ZAF

STP

CAFM

LI SYC

BWA

BFA GHAUGA

NGAET

HRW

ALB

R-4

-2

0

2

4

6

8

10

12

14

16

GDP Growth per capita in SSA, 1995-2012 (fragile and conflict-affected states in red)

3. Growth performance also varies significantly across sectors, within countries

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

1.0

1.5

2.0

2.5

3.0

3.5

Agriculture ManufacturingResources Services

Cum

ulati

ve g

row

th in

dex

(199

5=1.

0)

• Agriculture and manufacturing account for a declining share of GDP in the region.

• The natural resource sector (which includes mining as well as construction) and the services sector have grown faster than the economy on average, and account for increasing shares.

Note: The resources sector includes construction, mining and quarrying, and gas, electricity and water. Sectoral value added information is in US dollars at 2005 prices from the World Development Indicators. The figure shows the median cumulative growth of the value added of the different sectors for a 44 Sub-Saharan African countries over the period 1995-2012. Source: World Bank, Africa’s Pulse, Volume 9.

ECONOMIC TRANSFORMATION

Fast-Growing SSA Countries, Resource Rich Fast-Growing SSA Countries, Non Resource Rich

Agriculture Manufacturing Resources Services0

10

20

30

40

50

60

1995-99 2007-11

Agriculture Manufacturing Resources Services0

10

20

30

40

50

60

1995-99 2007-11

Source: World Bank, Africa’s Pulse vol. 9 Note: Resources sector includes mining and quarrying, construction and electricity, gas and water

As a result, Africa’s structural transformation is bypassing manufacturing.



III. Four key policy areas1. Macroeconomic management under receding tailwinds




OUTLINE

AFRICA’S RECENT ECONOMIC DYNAMISM HAS REDUCED POVERTY. BUT NOT BY ENOUGH…

1990 1993 1996 1999 2002 2005 2008 20100

10

20

30

40

50

60

70

East Asia and Pacific12.5

ECA 0.6

LAC 5.5MENA 2.4

South Asia31

56.5 59.4 58.1 58.055.7

52.349.2 Sub-Saharan Africa

48.5

$1.2

5 a

day

head

coun

t (%

)

Source: PovcalNet.

• SSA continues to account for one third of those classified as “extremely poor” globally• In the last twenty years, extreme poverty fell by 8% in Africa, compared to 44% in East

Asia.

THIS REFLECTS THE REGION’S LOW GROWTH ELASTICITY OF POVERTY…

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

Growth Elasticity of Poverty Reduction, 2000-2010Five most populous countries by region*, except Poland and Sri Lanka.

Thai

land

Egyp

t, Ar

ab R

ep.

Phili

ppin

esRo

man

iaUk

rain

eKa

zakh

stan

Mor

occo

Yem

en, R

ep.

Tuni

siaTu

rkey

Nep

alPa

kist

anVi

etna

mBa

ngla

desh

Viet

nam

Arge

ntina

Rest

of t

he W

orld

Peru

Indi

aBr

azil

Indo

nesia

Ethi

opia

Colo

mbi

aN

iger

iaUg

anda

Mex

ico

Chin

aSu

b Sa

hara

n Af

rica

Iran,

Isla

mic

Rep

.Ta

nzan

iaSo

uth

Afric

a

-20

-16

-12

-8

-4

0

-2.02

-0.7

Recall that a Lorenz curve with parameters π is given by

So

And

So, at the poverty line:

Denoting time derivatives by dx:

A PARENTHESIS ON THE GROWTH ELASTICITY OF POVERTY

… WHICH IN TURN REFLECTS HIGH INEQUALITY…

Most African countries have high levels of consumption or income inequality, relative to the rest of the world. Seven of the ten most unequal countries in the world today are in SSA.

Source: PovcalNet, most recent survey available.

MLI

NER

TZA

CMR

BFA

MRT

AGO

MDG CO

G

CPV

STP

BWA

NAM

0

10

20

30

40

50

60

70

Gin

i coe

ffici

ent

Consumption Survey Income Survey Sub Saharan Africa

…AND A GROWTH PATTERN THAT IS OFTEN NOT INCLUSIVEIn Malawi, average p.c. household consumption grew by 6.5% between 2004-2010. But whereas the top 5% of the population experienced annual growth rates of almost 8%, the bottom 5% grew by between 1% and 3%.

0.5 5 9.5 14 18.5 23 27.5 32 36.5 41 45.5 50 54.5 59 63.5 68 72.5 77 81.5 86 90.5 95 99.50

1

2

3

4

5

6

7

8

9Growth Incidence Curve, Malawi 2004-2010

Consumption expenditure percentile

Perc

ent g

row

th in

con

sum

ption

Source: estimates based on household surveys from “Survey-based Harmonized Indicator Program (SHIP)”

INEQUALITY OF OUTCOMES REFLECTS LARGE INEQUALITIES OF OPPORTUNITY…

Niger 2006

Chad 2004

Mali 2

006

Guinea 2005

Togo 1998

Madagasca

r 2008

Rwanda 2010

Ethiopia 2011

Senegal 2

010

Sierra

Leone 2008

Uganda 2011

Congo Rep. 2005

Sao Tome and Prin

cipe 2008

Nigeria 2008

Ghana 2008

Leso

tho 2009

Kenya 2008

South Afri

ca 1998

0

0.2

0.4

0.6

0.8

1

Richest quintile Poorest quintile Average

Prop

ortio

n

Source: DHS. http://econ.worldbank.org/projects/edattain. Most recent survey available shown.

Rich-Poor Gaps in Proportion of 15-19 Year Olds who have Completed Grade 6

http://econ.worldbank.org/projects/edattain

WHAT DOES THIS MEAN FOR THE FUTURE?

BY 2030, AFRICA’S POVERTY CAN BE REDUCED FROM A HALF TO BETWEEN A QUARTER AND A THIRD OF THE POPULATION

1990 1993 1996 1999 2002 2005 2008 2010 20300

10

20

30

40

50

60

70

0

50

100

150

200

250

300

350

400

450

289.7 330 349.4 376.8 390.2 394.8 399.3 413.7

26.4(356.1)

29.9(403.8)

Headcount(Number of poor (mil.))

Scenario 1 Scenario 2Historical Headcount (Number of poor (mil.) in bars)

Num

ber

of p

oor

(mil.

)

$1.2

5-da

y h

eadc

ount

(%)

* Scenario 1 projects each country’s historical GDP per capita growth rate over the 2000-2010 period forward to 2030. Scenario 2 does the same using mean household survey incomes. Both scenarios assume distribution-neutral growth.

Source: PovcalNet and staff estimates based on Chen, Kleineberg, Kraay, Lanjouw (2013).

1990 1993 1996 1999 2002 2005 2008 2010 20300

10

20

30

40

50

60

70

0

50

100

150

200

250

300

350

400

450

289.7 330 349.4 376.8 390.2 394.8 399.3 413.7

26.4(356.1)

29.9(403.8)

Headcount(Number of poor (mil.))

Scenario 1 Scenario 2Historical Headcount (Number of poor (mil.) in bars)

Num

ber

of p

oor

(mil.

)

$1.2

5-da

y h

eadc

ount

(%)

* Scenario 1 projects each country’s historical GDP per capita growth rate over the 2000-2010 period forward to 2030. Scenario 2 does the same using mean household survey incomes. Both scenarios assume distribution-neutral growth.

BUT WITH SIMILAR ASSUMPTIONS HOLDING ELSEWHERE, MOST OF THE WORLD’S POOR WOULD THEN LIVE IN AFRICA

63% of the world’s poor in Africa

78% of the world’s poor in Africa

Source: PovcalNet and staff estimates based on Chen, Kleineberg, Kraay, Lanjouw (2013).

TO SUM UP

• Poverty in Africa remains high, and the pace of reduction remains slow

• Sustained economic growth in the next two decades is essential for progress

• But it is not sufficient:– Growth must be more inclusive– With falling inequality – in outcomes and

opportunities.








OUTLINE

THE EXTERNAL TAILWINDS THAT SUPPORTED THE REGION IN THE 2000s ARE NOW RECEDING…

• Metal and mineral prices rose by 99.4% between 2000 and 2013. Since December 2010, they have fallen by 25%.

36526365573658636617366473667836708367393677036800368313686136892369233695136982370123704337073371043713537165371963722637257372883731637347373773740837438374693750037530375613759137622376533768137712377423777337803378343786537895379263795637987380183804738078381083813938169382003823138261382923832238353383843841238443384733850438534385653859638626386573868738718387493877738808388383886938899389303896138991390223905239083391143914239173392033923439264392953932639356393873941739448394793950839539395693960039630396613969239722397533978339814398453987339904399343996539995400264005740087401184014840179402104023840269402994033040360403914042240452404834051340544405754060340634406644069540725407564078740817408484087840909409404096941000410304106141091411224115341183412144124441275413064133441365413954142641456414870

50

100

150

200

250

Agriculture Energy Metals and mineralsPrice index, 2005=100

Source: Development Prospects Group

…WHILE FISCAL AND CURRENT ACCOUNT BALANCES HAVE GENERALLY WORSENED IN THE LAST DECADE

If terms of trade deteriorate and capital flows become less abundant, countries with low fiscal and current account deficits will fare much better.

20

10

0

-10

-20

Chan

ge in

Cur

rent

Acc

ount

Bal

ance

, % G

DP

Change in Current Account Balance (% GDP) and Fiscal Balance (% GDP), 2000 - 2012

-300 -200 -100 0Change in Fiscal Balance, % GDP

Resource Rich Resource Poor

Source: World Economic Outlook, International Monetary Fund

HUMAN CAPITAL: LEARNING OUTCOMES COMPARE POORLY WITH THOSE ACHIEVED ELSEWHERE

Proportions of Grade 8 students scoring at “low”; “intermediate / high”; and “advanced” benchmarks (Math, TIMSS 2011)

Kore

a

Inte

rnati

onal

Ave

rage

Mal

aysi

a

Chile

Jord

an

Bots

wan

a

Indo

nesi

a

Sout

h Af

rica

Gha

na

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

>625 400-625 <400

Prop

ortio

n

IN PART BECAUSE OF SYSTEMIC FAILURES IN SERVICE DELIVERY

Source: DHS. http://econ.worldbank.org/projects/edattain. Most recent survey available shown.

Kenya Nigeria Senegal Tanzania Togo Uganda

Classroom teacher absence rate 47% 22% 29% 53% 35.9% 57%

Health worker absence rate 29% 29% 20% 21% 53%

Notes: Tanzania and Senegal data from 2010; Kenya and Uganda from 2013.

Source: World Bank Service Delivery Indicators projects www.sdindicators.org

http://econ.worldbank.org/projects/edattain

ON THE PHYSICAL CAPITAL SIDE, INFRASTRUCTURE REMAINS SCARCE AND VERY EXPENSIVE IN AFRICA

Pow

er

Inte

rnati

onal

cal

l

Wat

er

Road

frei

ght

Inte

rnet

dia

l-up

Mob

ile te

leph

one

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Ratio

of p

rice

s

Median prices to final user in Africa (relative to South Asia)

Causes include lack of scale economies, geographic fragmentation and lack of competition.

Consequences include higher costs transmitted downstream to infrastructure users, and hence reduced competitiveness and diversification.

Source: Africa Infrastructure Country Diagnostic, 2010

BEYOND “BETTER” FACTOR ACCUMULATION, PRODUCTIVITY GROWTH ALSO NEEDS LOOKING AT…

-2%

-1%

0%

1%

2%

3%

4%

5% Total Factor Productivity

Human Capital

Physical Capital

• Except in fast-growing resource-rich countries, Africa’s growth has been driven more by factor accumulation than by productivity growth.

• Promoting productivity growth across all economic sectors is a key priority, including in agriculture, where 60-70% of people work.

Note: Fast-growing countries are those that experienced at least one growth spurt (3.5 percent per annum growth in real GDP per capita for at least 5 contiguous years) in 1995-2012. The criteria identifies 22 fast-growing countries in the region (13 resource rich and 9 non-resource rich). Source: World Bank, Africa’s Pulse, Volume 9.








OUTLINE

-30-20-1001020300%

10%

21%

31%

41%

52%

62%

72%

CAG: 3.6%

An iso-growth line in poverty/inequality spaceHeadcount 2010: 74.4%

Source: estimates based on household surveys from “Survey-based Harmonized Indicator Program (SHIP)”

47%

$1.2

5-da

y he

adco

unt 2

030

Inequality change (%)Gini 2010: 57.4

CHANGING THE DISTRIBUTIONAL PROFILE OF AFRICA’S GROWTH CAN HAVE LARGE IMPACTS ON POVERTY –

Nic

arag

ua

Boliv

ia

Ecua

dor

Arge

ntina

El S

alva

dor

Mex

ico

Vene

zuel

a

Braz

il

Peru

Dom

. Rep

.

Pana

ma

Chile

Cost

a Ri

ca

Para

guay

Uru

guay

Gua

tem

ala

Hon

dura

s

LAC-

17

Chin

a

Sout

h Af

rica

Indi

a

USA

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

-2.64

-2.05-1.99

-1.30-1.24-1.17-1.07-1.03-0.91-0.79-0.74-0.72-0.47-0.39-0.20-0.10

0.61

-0.95

2.12

0.82 0.770.40

Source: Lustig (2013)

CHANGING THE DISTRIBUTIONAL PROFILE OF AFRICA’S GROWTH CAN HAVE LARGE IMPACTS ON POVERTY – AND IT CAN BE DONE!

Latin America: Declining income inequality by country: 2000-2011Annual change of Gini in %

A 10 percent fall in the Gini coefficient over a 20 year period can yield as much as 6 percent additional poverty reduction.

Rural Urban

-20%

0%

20%

40%

60%

80%

100%

GROWTH THAT TAKES PLACE IN RURAL AREAS – WHERE MOST OF THE POOR LIVE – IS MOST EFFECTIVE IN REDUCING POVERTY• Econometric evidence from

China, India and Brazil suggests that the geographic and sector composition of growth affects the overall growth elasticity of poverty.

• In Africa, most of the growth has not been coming from the places and sectors where the poor are.

• Most of the poverty reduction comes from very specific growth sources

Contribution of urban and rural areas to population, poverty reduction and consumption growth in Uganda, 2005–09

Source: Kaminski and Christiaensen 2013

Share of poverty reduction Population share

Shares in total consumption growth








OUTLINE

GROWTH THAT TAKES PLACE “AWAY FROM THE POOR” MUST ALSO BE HARNESSED

• The natural resource sector is seldom directly pro-poor• The rents it generates should be re-invested

– Building human and physical capital to replace the natural capital being depleted

– Cash transfers targeted to the poor must have a place in that investment portfolio

Equatorial Guinea*

Angola Republic of Congo

Mozambique Nigeria Uganda Tanzania

836.6

326.3

178.8

64.2 29.8 5.3 4.9

Note: number of poor for Equatorial Guinea calculated using national poverty line.Source: Estimates of natural resource fiscal revenues from Devarajan and Giugale (2013)

Annual transfer to each poor person ($1.25 a day) from 10% of natural-resource fiscal revenues in selected countries.

THE “CCT REVOLUTION” IN SOCIAL PROTECTION…

Since the late 1990s, conditional cash transfers have shown that:(i) Good targeting is possible(ii) Transfers increase family incomes and reduce poverty(iii) Households use the transfers to improve nutrition…

…IS COMING TO AFRICA.

…increase investments in human capital;

Average impact of cash transfers on the odds of school enrollment (and 95% confidence interval)

Results from a systematic review of 8 unconditional and 27 conditional cash transfer interventions (Baird et al. , 2014)

UCTs

CCTs

1.0

24% 48%

36%

23%8% 41%

FAMILIES SYSTEMATICALLY INVEST PART OF WHAT THEY RECEIVE

“Five years ago when my oldest daughter was in school and we received money from PROGRESA, we saved 600 pesos to buy wood and the other materials for building a chicken coop, and with what was left we bought a few chickens. Since then, we have raised many chickens that we sometimes sell, and we collect 10 to 15 eggs per week that we eat ourselves.”

- Oportunidades beneficiary in rural Mexico, August 2004, cited in Gertler, Martinez and Rubio-Codina (AEJ: Applied Economics, 2012)

…and even to save and invest in physical and financial capital as well!

FAMILIES SYSTEMATICALLY INVEST PART OF WHAT THEY RECEIVE

Community Based Conditional Cash Transfers in Tanzania – Some Results (endline)

Indigenous goats Local chickensINVESTMENT IN LIVESTOCK

0.38

1.09

Cigarettes, tobacco, snuff Insurance (car, medical, life)ANNUAL AVERAGE HOUSEHOLD NON-FOOD EXPENDITURE,

LCU

-1,593

1,625

CHILDREN'S ASSETS...

7% points

Note: Investment in livestock refers to additional goats and chickens purchased by treatment households. Additional annual average non-food expenditure by treatment households measured in Tanzanian Shilling (TSH). Children’s assets refers to the higher likelihood of treated children to own shoes in percentage points.

Source: charts based on results from Evans, Hausladen, Kosec and Reese (2013 )

• Sustained economic growth is essential to the fight against poverty in Africa

– Macroeconomic prudence reduces vulnerability to receding tailwinds

– Investment in human and physical capital must become more effective (quality as well as quantity)

• But growth is not sufficient: shared prosperity will require a reduction in inequality (of outcomes and opportunities)

– Boost productivity in agricultural and rural off-farm jobs

– Cash transfers are a real policy option

CONCLUSIONS

THANK YOU

Growth, Inequality and Poverty Reduction in Africa Francisco H. G. Ferreira The World Bank and IZA...

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Transcript of Growth, Inequality and Poverty Reduction in Africa Francisco H. G. Ferreira The World Bank and IZA...