growth, poverty and inequality interactions in africa: …...change in gdp and gini (early 1990s vs...

Growth, Poverty and Inequality

Interactions in Africa:

An Overview of Key Issues

Haroon BhoratDevelopment Policy Research Unit

University of Cape Town

Contact: [email protected]

Columbia/SIPA - Centre on Global Economic Governance

Seminar in collaboration with the SIPA Pan-African Network

Monday, 26 October 2015

mailto:[email protected]

Outline

• Growth, Poverty and Inequality Interactions

• Growth, Poverty and Inequality: The African Context

– The Nature, Size and Pattern of Inequality in Africa

– Africa’s Growth-Poverty-Inequality Nexus

• Inequality and Structural Change in Africa

• Drivers of Inequality in Africa: Microeconomic and Institutional

Considerations

– Natural Resources and Inequality

– Demographic Changes and the Labour Market

– Education and Human Capital Development

– Gender Dimensions of Inequality

• Summary & Policy Issues

Introduction

• Six of the world’s ten fastest growing economies (2001-

2010): In Sub-Saharan Africa

• Global sentiment around SSA changed significantly

• Current dominant global view: Africa is last of great

untapped markets, ripe for rapid growth and development.

• Supported by the Data: Six of the world’s ten fastest

growing economies during 2001-2010 were in Sub-Saharan

Africa*

• Focus: Inequality Outcomes and their Determinants in

SSA:– Understanding the Nature of Inequality in Africa

– Evolution of Inequality in Africa

– Key Drivers of Inequality in Africa

*: The countries are Angola, Nigeria, Ethiopia, Chad, Mozambique, and Rwanda

Growth, Poverty and Inequality Interactions:

Some Basics Relationships

• High level of economic growth necessary but not sufficient condition for

poverty reduction:

• Key intermediary in growth-poverty outcome: Growth-Inequality

Interaction

1. Growth accompanied by rise in income inequality reduces growth-

poverty elasticity.

2. Higher initial level of income inequality reduces growth-poverty

elasticity.

3. Income inequality-growth elasticties are inertial over time

Ravallion and Chen (1997); Kanbur(2004); Kanbur & Squire (1999). Kakwani (1993); Datt & Ravallion (1992); Ravallion (2001,

1997); Ravallion & Datt (2002); Bourguignon (2002); Kanbur (2005); Clarke (1999); Adams (2004);Li, Squire & Zou (1998); Fosu

(2009) .

The Nature, Size and Pattern of Inequality in Africa

Inequality in Africa vs. Other Developing Economies

Gini AfricaOther developing

countries

Differenc

e

Average 0.43 (8.52) 0.39 (8.54)0.04**

Median 0.41 0.38

Min0.31

(Egypt)

0.25

(Ukraine)

Max0.65

(South Africa)

0.52a

(Haiti)

Ratio of incomes:

Top 20% / Bottom 20% 10.18 8.91

Average Gini

Low-income 0.42 (7.66) 0.39 (11.84) 0.03

Lower-middle-income 0.44 (8.31) 0.40 (8.55) 0.05*

Upper-middle income 0.46 (11.2) 0.40 (8.29) 0.06*

Source: WIDER Inequality Database, 2014; World Development Indicators, 2014

Notes: 1. Other Developing Economies have been chosen according to the World Bank classification of a developing economy, which

includes a range of countries from Latin America, Asia and Eastern Europe. 2. The latest available data was used for each country (after

2000). 3. Standard deviations are shown in parenthesis.4. a The small island nation of the Federated States of Micronesia has the highest

Gini coefficient 0.61 in the ‘other developing countries’ category, which has been excluded here for comparability purposes.

5. ** significant at the 5% level, * significant at the 10% level. 6. The small sample size of other developing countries in the low income

group makes determining statistical significance difficult.

• The average Gini

coefficient for Africa

is 0.43, which is 1.1

times the coefficient

for the rest of the

developing world at

0.39

• On average, the top

20 percent of

earners in Africa

have an income that

is over 10 times that

of the bottom 20

percent


The Distribution of Gini Coefficients: Africa and Other Developing Economies

Source: WIDER Inequality Database, 2014; World Development Indicators, 2014; Own graph

Notes: 1. The latest available data was used for each country (after 2000).

2. Kolmogorov-Smirnov tests for equality of distributions are rejected at the 5% level.

0

.01

.02

.03

.04

.05

20 30 40 50 60 70Gini

Africa Other developing economies

Distribution of Gini Coefficients• An outstanding

feature of this graph is

the prevalence of

extreme inequality in

Africa, which is not

observed in other

developing economies.

• 7 outlier African

economies that have a

Gini coefficient of

above 0.55: Angola,

Central African

Republic, Botswana,

Zambia, Namibia,

Comoros and South

Africa


Movements in the Gini Over Time

Source: WIID, 2014; World Development Indicators, 2014; Own graph

Notes: 1. For the Africa average, the sample sizes per period are as follows: 27 (1990-1994), 24 (1995-1999), 38 (2000-2004), 28 (2005-

2009), 25 (2010-2013). 2. The High Inequality countries are: Angola, Botswana, Comoros, Central African Republic, Namibia, South

Africa, Zambia. The sample sizes per period are as follows: 5 (1990-1994), 2 (1995-1999), 7 (2000-2004), 3 (2005-2009), 3 (2010-2013).

40

45

50

55

60

65

Gin

i

1990-1994 1995-1999 2000-2004 2005-2009 2010-2013

Africa_all Africa_high_inequality

Africa_other

• When excluding the 7

outlier African economies,

we see that the average Gini

coefficient for the rest of

the continent declines from

0.45 in the early 1990s to a

current level of 0.40 (a 9

percent decline).

• This latter average is almost

equal to that of the rest of

the developing world


Rates of Change in Inequality in Africa


Notes: 1. For the Africa average, the sample sizes per period are as follows: 27 (1990-1994), 24 (1995-1999), 38 (2000-2004), 28 (2005-

2009), 25 (2010-2013). 2. The High Inequality countries are: Angola, Botswana, Comoros, Central African Republic, Namibia, South

Africa, Zambia. The sample sizes per period are as follows: 5 (1990-1994), 2 (1995-1999), 7 (2000-2004), 3 (2005-2009), 3 (2010-2013).

-10

-50

5

% c

han

ge

in a

ve

rag

e G

ini co

effic

ient

1994-1999 1999-2004 2004-2009 2009-2013 1994-2013

High inequality countries Africa (all)

Lower inequality countries

• After 1999, the overall

decline in inequality in Africa

has been driven

disproportionately by the

decline in inequality of the

‘low inequality’ sub-sample

of African economies.

• The cohort of ‘high

inequality’ African

economies have jointly

served to restrict the

aggregate decline in African

inequality.


Country level heterogeneity in the changes of the Gini coefficient


30

40

50

60

70

Gin

i

1990-1994 1995-1999 2000-2004 2005-2009 2010-2013

Cote D'Ivoire Madagascar

Malawi Egypt

South Africa Uganda

• Countries such as Egypt, Malawi

and Madagascar have witnessed a

narrowing of the income

distribution over time.

• Whereas Cote d’Ivoire, South

Africa and Uganda have

experienced a rise in inequality

since the 1990s.

• South Africa remains the most

unequal African country, and

indeed one of the most unequal

in the world.


Change in GDP and Gini (early 1990s vs most recent), Africa

Source: WIID, 2014; World Development Indicators, 2014; Authors have calculated the changes in the Gini coefficient and the GDP per capita

growth rates over time.

-30

-20

-10

01

0

Unit

cha

ng

e in

the

Gin

i coe

ffic

ien

t

-2 0 2 4 6 8GDP per capita growth (CAGR)

Gini (change) Fitted values (full sample)

Fitted values (high inequality) Fitted values (lower inequality)

• Fairly weak relationship

between the rate of

economic growth and the

change in the Gini

coefficient for a large

sample of African

economies.

• However, the relationship

is visibly stronger for the

subset of economies that

have an initially high Gini

coefficient, as represented

by the green fitted line.

The Nature, Size and Pattern of Inequality in Africa:

Five Key Results

• Africa: Higher mean and median level of inequality when

compared with the rest of the developing region.

• Presence of ‘African Outliers’: 7 economies exhibiting

extremely high levels of inequality. Excluding the African

Outliers - Africa’s level of inequality approximates those of

other developing economies.

• Inequality has on average declined in Africa, driven by

economies not highly unequal.

• No obvious trend around nature and pattern of African

inequality over time

• High inequality African economies: Stronger relationship

between economic growth and inequality.

Africa’s Growth-Poverty-Inequality Nexus

Poverty Rates Across Africa, LAC and South Asia, 2010

Source: World Bank, 2014, PovcalNet; Authors have calculated average poverty rates per region, using the United Nations regional classifications.

0 20 40 60 80

North Africa

LAC

South Asia

Central Africa

West Africa

Southern Africa

East Africa

Poverty Headcount Ratio (% of population)

Mean of $1.25 a day (PPP) Mean of $2 a day (PPP)

• Poverty rates and the

depth of poverty is

greater in Africa.

• Two-thirds of the

population in the four

African regions, excluding

North Africa, living below

the $2 a day poverty line,

are living in extreme

poverty.

• DRC,Ethiopia,Nigeria &

Tanzania constitute almost

50% of Africa’s poor.

Africa’s Growth-Poverty-Inequality Nexus

Growth Elasticity of Poverty

Source: World Bank (2013b) based on Christiaensen, Chuhan-Pole and Sanoh (2013)

Note: Controls include initial consumption, inequality and an indicator for a natural resource share >5% of GDP. Country fixed effects are

controlled for in all results.

-0.69

-3.07

-2.02

-3.81

-5

-4

-3

-2

-1

0

No controls With controls

SSA Rest of the World

• The estimated growth elasticity of poverty in the two decades since 1990 in SSA is -0.7, which implies that a one percent growth in consumption is estimated to reduce poverty by 0.7 percent. For the rest of the world (excl. China), this elasticity is substantially higher at -2.

• The impact of growth on poverty reduction is lower when initial inequality and mineral resource dependence are higher

Drivers of Inequity in Economic Growth Patterns

Sectoral Breakdown of Economic Activity in Africa, 1990, 2000, 2010-2012

Region Sector1990 2000 2010 2011 2012

1990-2000

change

2000-2012

change

North Africa

Agriculture (% of GDP) 21.46 18.81 14.18 14.33 14.95 -2.65 -3.87

Industry (% of GDP) 31.83 34.40 35.59 35.65 35.69 2.58 1.29

of which: Manufacturing (% of GDP) 15.17 14.28 13.87 13.93 12.89 -0.89 -1.38

Services (% of GDP) 46.71 46.78 50.24 50.02 49.36 0.07 2.58

West Africa


Industry (% of GDP) 21.82 23.41 22.37 24.47 29.18 1.59 5.77


Services (% of GDP) 43.21 42.12 47.26 47.12 43.08 -1.10 0.96

East Africa

Agriculture (% of GDP) 39.91 32.74 32.63 32.92 35.95 -7.17 3.21

Industry (% of GDP) 16.60 16.58 18.45 18.65 17.06 -0.02 0.49

of which: Manufacturing (% of GDP) 8.82 7.81 8.41 8.26 7.84 -1.01 0.03

Services (% of GDP) 43.49 50.68 48.92 48.43 46.99 7.19 -3.69

Central

Africa

Agriculture (% of GDP) 30.83 25.01 32.32 32.13 39.73 -5.83 14.72

Industry (% of GDP) 27.26 38.49 36.71 37.90 27.59 11.23 -10.90


Services (% of GDP) 41.91 36.51 30.97 29.97 32.68 -5.40 -3.83

Southern

Africa


Industry (% of GDP) 34.68 33.21 32.84 32.98 31.73 -1.47 -1.49


Services (% of GDP) 46.88 52.40 55.01 55.24 59.13 5.52 6.72

Source: Word Development Indicators, 2014 and own regional average and change calculations

Inequality and Structural Change in Africa

• Gradual shift away from Agriculture - But not toward

manufacturing.

• Services sector absorbed most of the shift away from

Agriculture, becoming the largest share in GDP for many African

Economies

• Industry in Africa: Dominated by Mining activities.

• Considerable decline in manufacturing value added since the

1990s and 2000s across the continent.

• Africa’s growth path and pattern of structural change: o One heavily dependent on natural resources

o Poor performance of the manufacturing sector (limiting

employment creation)

o Over-reliance on subsistence farming.

Inequality and Structural Change in Africa

Change in Industry and Manufacturing as Shares of GDP, percentage points (2000-2010)

Source: Word Development Indicators, 2014 and own calculations regarding the changes over time

Notes: 1. Industry comprises value added in mining, construction, electricity, water, and gas. Manufacturing has been removed from this

category and represented separately. 2. For some countries where 2010 data was not available, the latest available year after 2005 was used.

Mauritania

Libya

ZambiaSudan

Ghana

Nigeria

Burkino Faso

Chad

Equatorial Guinea

Tanzania

Sierra Leone

Botswana

AngolaGuinea

Egypt

Swaziland

Cote d'Ivore

Ethiopia

Mozambique

South Africa

Uganda

Zimbabwe

-10

-50

51

0

Ma

nu

factu

rin

g_

GD

P

-20 -10 0 10 20 30Industry_GDP

• In most African

economies – 35 out of 50

– mining and utilities have

seen a rising share in

GDP.

• Fast growing resource-

rich economies have

some of the largest shifts

of economic activity

toward these two

sectors.

• Represenative of Africa’s

lack of Structural Change.

Drivers of Inequality in Africa: Microeconomic and Institutional Considerations

• High levels of initial inequality in SSA: Related to how

natural endowments shaped nature of colonial

institutions

• Post-independence Inequality: • Small European populations (that still retained wealth)

• Small highly extractive administrations

• Focus on law & order rather than economic development.

• Independence: Wealth transferred to small group of

African elite.

• Sub-national tensions (ethnicity, religion and/or race)

further determined initial distribution of resources

• Continue to determine provision of public goods and

access to labour market opportunities.

Resource Dependence & Inequality

Source: World Bank WDI, PovcalNet; Own calculations regarding the population weighting of the Gini coefficient

Notes: 1. Kolmogorov-Smirnov tests for equality of distributions cannot be rejected at the 5% level.

2. Data weighted by population, and based on latest available Gini coefficient

Drivers of Inequality in Africa: Natural Resources and Inequality

0

.02

.04

.06

.08

Kde

nsity

30 40 50 60 70Gini Index

Resource Dependent Non Resource Dependent

• While the average levels of

inequality are relatively similar

between resource-dependent

and non-resource-dependent

economies – there are a

number of resource

dependent countries with

very high levels of inequality,

close to and above 60.

• There is a greater risk of high

inequality outcomes in

resource dependent

economies.

Resource Governance Index: Composite Scores for Developed and Developing Countries, 2013

Source: Own graph, Revenue Watch, 2013

Drivers of Inequality in Africa:

Drivers of Inequality in Resource-Rich Countries0

20

40

60

80

100

Score

(1-1

00

)

Norw

ay

United

Sta

tes

(Gu

lf of M

exi

co)

United

Kin

gdo

mA

ustr

alia

(W

este

rn A

ustr

alia

)B

razi

lM

exi

co

Can

ad

a (

Alb

ert

a)

Chile

Colo

mbia

Trin

ida

d a

nd T

oba

go

Peru

Ind

iaT

imor-

Leste

Ind

on

esia

Gh

an

aL

ibe

ria

Zam

bia

Ecu

ad

or

Kazakhsta

nV

en

ezue

laS

ou

th A

fric

aR

ussi

aP

hili

ppin

es

Boliv

iaM

oro

cco

Mo

ng

olia

Tan

zania

Aze

rbaija

nIr

aq

Bots

wan

aB

ah

rain

Ga

bo

nG

uin

ea

Ma

laysi

aS

ierr

a L

eo

ne

Chin

aY

em

en

Egypt

Pap

ua

New

Gu

ine

aN

ige

ria

Ang

ola

Kuw

ait

Vie

tna

mC

on

go

(D

RC

)A

lge

ria

Mo

zam

biq

ue

Cam

ero

on

Sau

di A

rab

iaA

fgha

nis

tan

Sou

th S

ud

an

Zim

ba

bw

eC

am

bod

iaIr

an

Qa

tar

Lib

ya

Equ

ato

rial G

uin

ea

Turk

me

nis

tan

Myan

mar


Drivers of Inequality in Resource-Rich Countries

• Number of potential channels through which a natural resource dependent economy may lead to rising inequality:

– Political capture of rents

– Ineffective and unprogressive tax systems

– Overly complicated ownership structures of extractive industry companies;

– Industrialisation and human capital upgrading strategies are poorly realised;

– States do not fully consider appropriate social welfare programmes.

• Above in turn all inextricably linked to poor governance and lack of transparency in government revenue collection and expenditure allocations.

Percentage increase in size of age groups in working-age population, 2010 to 2030 (Medium Variant)

Source: ILO (2011)

Drivers of Inequality in Africa: Demographic Changes and the Labour Market

48.7

19.5

18.4

16.2

5.7

-9

60.5

42.4

34.9

40

1

2.3

50.6

16.1

15.3

11.9

9.3

-18.7

38.7

-2.4

2.9

-7.1

7.2

-13.4

-30 -20 -10 0 10 20 30 40 50 60 70

Africa

Latin America

World

Asia

North America

Europe

Percentage increase

Age 15-24 Age 25-44

Age 45-64 Age 15-64

The Global Labor Market at a Glance, 2010 (millions)

Source: Adapted from Bhorat (2013)

Notes: 1. The data is based on the World Bank’s International Income Distribution Database (I2D2) dataset, which is a harmonized set of

household and labor force surveys drawn from a multitude of countries.


RegionWage

Employ.

Self-Empl.

Total

of which:

Self-Empl.

Agric.

of which:

Self-Empl.

Non-

Agric.

Total

Empl.Unempl.

Labor

Force

SSA61.00 236.00 181.00 55.00 297.00 23.00 320.00

(0.19) (0.74) (0.56) (0.17) (0.93) (0.07) (1.00)

Other

Non-OECD

1 118.00 1 068.00 584.00 484.00 2 186.00 134.00 2 320.00

(0.48) (0.46) (0.25) (0.21) (0.94) (0.06) (1.00)

OECD333.00 50.00 7.00 43.00 383.00 32.00 415.00

(0.80) (0.12) (0.02) (0.10) (0.92) (0.08) (1.00)

Global total1 512 1 354 772.00 581.00 2 866 189.00 3 055

(0.50) (0.44) (0.25) (0.19) (0.94) (0.06) (1.00)

Wage-Agricultural Employment and Inequality

Source: World Bank (2012); Own graph


20

30

40

50

60

Gin

i

0 20 40 60 80Wage employment share / agriculatural employment share

Fitted values Gini

• The (weakly) negative

relationship suggests that in

countries with a high ratio

of wage to agricultural

employment – i.e. where

wage employment is

sufficiently dominant –

income inequality is lower.

Enrolment Rates in Africa, 2011

Source: World Development Indicators, 2014;

Notes: 1. Latest available data 2. Gross enrolment rates can exceed 100% due to the inclusion of over-aged and under-aged students

because of early or late school entrance and grade repetition.

Drivers of Inequality in Africa: Education and Human Capital Development

Central

Africa

East

Africa

North

Africa

West

Africa

Southern

Africa

Pre-primary

(% gross)22.85 24.92 56.94 69.34 15.72

Primary (% gross) 108.55 99.31 108.57 120.23 98.84

Secondary

(% gross)32.99 43.99 69.17 51.27 45.73

Tertiary (% gross) 6.88 6.92 23.03 10.20 9.78

Percent of Schoolchildren Below Minimum Learning Threshold (Primary school, Grades 4 and 5)

Source: Center for Universal Education at Brookings, 2014; Own graph


020

40

60

80

Are

no

t le

arn

ing to

re

ad

Ta

nza

nia

Ga

bo

n

Ken

ya

Ca

mero

on

Bots

wa

na

Mau

ritiu

s

Seych

elle

s

Na

mib

ia

Buru

nd

i

Zim

ba

bw

e

Ug

an

da

Gh

an

a

Moza

mbiq

ue

Mad

ag

asca

r

Sen

eg

al

Sou

th A

fric

a

Burk

ina

Faso

Ivo

ry C

oa

st

Co

moro

s

Co

ng

o

Za

mbia

Ben

in

Ch

ad

Eth

iopia

Nig

eria

020

40

60

80

Are

no

t le

arn

ing m

ath

Mad

ag

asca

r

Ca

mero

on

Ga

bo

n

Ken

ya

Mau

ritiu

s

Ta

nza

nia

Buru

nd

i

Seych

elle

s

Sen

eg

al

Bots

wa

na

Burk

ina

Faso

Zim

ba

bw

e

Co

moro

s

Co

ng

o

Moza

mbiq

ue

Ch

ad

Ben

in

Ug

an

da

Sou

th A

fric

a

Gh

an

a

Na

mib

ia

Ivo

ry C

oa

st

Nig

eria

Eth

iopia

Za

mbia

Grade 8 Science (left) and Mathematics (right) Results

Source: TIMMS, 2011; Own graph


020

40

60

Perc

enta

ge a

ch

ieved

in

each

ca

tego

ry

Gh

an

a

Moro

cco

Tu

nis

ia

Indo

ne

sia

Bots

wa

na

Sou

th A

fric

a

Mala

ysia

Ch

ile

Th

aila

nd

Tu

rkey

Hu

ng

ary

Slo

ven

ia

Kore

a

Below 400 At or above 400 but below 475

At or above 475 but below 550 At or above 550 but below 625

At or above 625

020

40

60

Perc

enta

ge a

ch

ieved

in

each

ca

tego

ry

Gh

an

a

Bots

wa

na

Moro

cco

Indo

ne

sia

Tu

nis

ia

Sou

th A

fric

a

Ch

ile

Mala

ysia

Th

aila

nd

Slo

ven

ia

Tu

rkey

Hu

ng

ary

Kore

a

Below 400 At or above 400 but below 475

At or above 475 but below 550 At or above 550 but below 625

At or above 625

Conversion Rates from Primary to Tertiary Education, 2011

Source: Bhorat (forthcoming) using data from UNESCO Institute of Statistics (2013)

Notes: 1.Primary refers to the net enrolment ratio (NER) in primary education rate of primary school aged children. 2.Secondary is

calculated as the product of the NER and the ratio of the transition from primary to secondary education for each region. 3. Tertiary is

calculated as the product of Secondary and the gross enrolment in tertiary education for each region.


0

10

20

30

40

50

60

70

80

90

100

Population ofchildren (primary

school age)

Primary Secondary Tertiary

Sub-Saharan Africa

South and West Asia

Arab States

Central Asia

East Asia and the Pacific

Latin America and the Caribbean

Central and Eastern Europe

North America and WesternEurope

• For Africa, the data

shows that for every

100 children of

primary school age,

we can expect only 4

to enter a tertiary

educational

institution.

Drivers of Inequality in Africa: Gender Dimensions of Inequality

Source: The Economist, 2013 using United Nations data

http://www.economist.com/blogs/freeexchange/2013/11/gender-inequallity

• Since the late 1990s, there

has been some progress in

equalizing access to

education for girls and boys

in SSA - predominantly at

the primary school level

• There has been no

progress on average in

achieving gender parity in

secondary schooling, whilst

there has been a widening

of gender inequality in

tertiary educational

enrolment

0.2

.4.6

.8

Ge

nd

er

Ine

qu

alit

y In

de

x

Ma

ldiv

es

Mo

ldo

va

Mo

ng

olia

Vie

t N

am

Kyrg

yzs

tan

Ta

jikis

tan

Ph

ilip

pin

es

Rw

an

da

Mya

nm

ar

El S

alv

ad

or

Na

mib

iaP

ara

gu

ay

Nic

ara

gu

aM

oro

cco

So

uth

Afr

ica

Bo

livia

Ne

pa

lH

on

du

ras

Bo

tsw

an

aB

hu

tan

Ind

on

esi

aB

uru

nd

iC

am

bo

dia

Ga

bo

nZ

imb

ab

we

Sa

mo

aG

ua

tem

ala

Gu

ya

na

Ba

ng

lad

esh

Sw

azila

nd

Ug

an

da

La

o P

DR

Se

ne

ga

lIr

aq

Eth

iop

iaK

en

ya

Gh

an

aT

an

za

nia

Syri

aL

eso

tho

Ind

iaP

akis

tan

To

go

Eg

yp

tM

ala

wi

Ha

itiB

urk

ina

Fa

so

Be

nin

Co

ng

oP

ap

ua

Ne

w G

uin

ea

Za

mb

iaC

am

ero

on

Ga

mb

iaS

ud

an

Sie

rra

Le

on

eM

au

rita

nia

Cô

te d

'Ivo

ire

C.

A.

Re

pu

blic

Lib

eri

aM

oza

mb

iqu

eD

RC

Ma

liA

fgh

an

ista

nC

ha

dN

ige

rY

em

en

Gender Inequality Index, Upper Half of the Global Distribution, 2014

http://www.economist.com/blogs/freeexchange/2013/11/gender-inequali

Conclusions

• On average, Africa has higher than average and median inequality when

compared to the rest of the developing regions

• Seven ‘African outlier’ economies exhibiting extremely high levels of

inequality – serve to drive this inequality differential with the rest of

the developing world

• Over time, based on available data, average levels of inequality have

declined in Africa, driven mostly by the economies not classified as

highly unequal

• For countries with initially high levels of inequality, there is a stronger

relationship between economic growth and inequality –confirmation of

the cross-country evidence outside of Africa.

Conclusions


1. Dependence on natural resources has deleterious impact on building

effective, transparent and accountable institutions.

2. Lack of a dynamic manufacturing sector to absorp new work-seekers

and diversify employment opportunities.

3. Labour market structure of many African economies: Large shre of

labour force involved in low-income agricultural self-employment or in

informal sector jobs - exacerbates existing inequality.

4. Low stock of human capital: Without rapid rise in supply of skilled

workers, inequality-inducing skills premia will persist in African labour

markets.

Thank you

growth, poverty and inequality interactions in africa: …...change in gdp and gini (early 1990s vs...

Documents