Post on 18-Dec-2015
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%
Rest of Developing (excl. China) China Sub-Saharan Africa
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
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.
I. An African “growth profile”
II. Effects on poverty, mediated by inequality
III. Four key policy areas1. 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’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
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.
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
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
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.
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
-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
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
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