Growth, Poverty and Inequality Interactions in Africa: An Overview of Key Issues Haroon Bhorat, Karmen Naidoo and Kavisha Pillay Development Policy Research Unit School of Economics University of Cape Town Contact: [email protected] UNU-WIDER 30TH Anniversary Conference Mapping the Future of Development Economics 17-19 September,2015,Helsinki,Finland

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 (20012010): 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 growthpoverty elasticity. 2. Higher initial level of income inequality reduces growth-poverty elasticity. 3. Income inequality-growth eleasticties 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

Africa

Other developing countries

Average

0.43 (8.52)

0.39 (8.54)

Median

0.41

0.38

Min

0.31 (Egypt)

0.25 (Ukraine)

Max

0.65 (South Africa)

0.52a (Haiti)

10.18

8.91

Ratio of incomes: Top 20% / Bottom 20%

Differenc e 0.04**

•

Average Gini Low-income Lower-middle-income Upper-middle income

0.42 (7.66) 0.44 (8.31) 0.46 (11.2)

0.39 (11.84) 0.40 (8.55) 0.40 (8.29)

•

0.03 0.05* 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 Nature, Size and Pattern of Inequality in Africa The Distribution of Gini Coefficients: Africa and Other Developing Economies •

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

0

.01

.02

.03

.04

.05

Distribution of Gini Coefficients

20

30

40

50

60

Gini Africa

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.

70

The Nature, Size and Pattern of Inequality in Africa •

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

40

45

50

Gini

55

60

65

Movements in the Gini Over Time

1990-1994

1995-1999

Africa_all Africa_other

2000-2004

2005-2009

2010-2013

Africa_high_inequality

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 (20052009), 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).

The Nature, Size and Pattern of Inequality in Africa Rates of Change in Inequality in Africa 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.

-10

-5

0

5

•

1994-1999

1999-2004

2004-2009

High inequality countries Lower inequality countries

2009-2013

1994-2013

Africa (all)

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 (20052009), 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).

The Nature, Size and Pattern of Inequality in Africa •

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.

50 40 30

Gini

60

70

Country level heterogeneity in the changes of the Gini coefficient

1990-1994

1995-1999

2000-2004

Cote D'Ivoire Malawi South Africa

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

2005-2009

Madagascar Egypt Uganda

2010-2013

The Nature, Size and Pattern of Inequality in Africa Change in GDP and Gini (early 1990s vs most recent), Africa

-30

-20

-10

0

10

• Fairly weak relationship between the rate of economic growth and the change in the Gini coefficient for a large sample of African economies.

-2

0

4 2 GDP per capita growth (CAGR)

Gini (change) Fitted values (high inequality)

6

8

Fitted values (full sample) Fitted values (lower inequality)

• 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.

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.

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

• 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.

Poverty Headcount Ratio (% of population) East Africa

Southern Africa

West Africa

Central Africa

South Asia

LAC

North Africa

0

20

40

Mean of $1.25 a day (PPP)

60

80

Mean of $2 a day (PPP)

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

Africa’s Growth-Poverty-Inequality Nexus Growth Elasticity of Poverty 0 -1

-0.69

-2 -2.02 -3 -3.07

-4

-3.81

-5

No controls SSA

With controls 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

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.

Drivers of Inequity in Economic Growth Patterns Sectoral Breakdown of Economic Activity in Africa, 1990, 2000, 2010-2012 Region

Sector

Agriculture (% of GDP) Industry (% of GDP) North Africa of which: Manufacturing (% of GDP) Services (% of GDP) Agriculture (% of GDP) Industry (% of GDP) West Africa of which: Manufacturing (% of GDP) Services (% of GDP) Agriculture (% of GDP) Industry (% of GDP) East Africa of which: Manufacturing (% of GDP) Services (% of GDP) Agriculture (% of GDP) Central Industry (% of GDP) of which: Manufacturing (% of GDP) Africa Services (% of GDP) Agriculture (% of GDP) Southern Industry (% of GDP) of which: Manufacturing (% of GDP) Africa Services (% of GDP)

1990

2000

2010

2011

2012

21.46 31.83 15.17 46.71 34.97 21.82 9.56 43.21 39.91 16.60 8.82 43.49 30.83 27.26 10.97 41.91 18.44 34.68 17.92 46.88

18.81 34.40 14.28 46.78 34.47 23.41 8.91 42.12 32.74 16.58 7.81 50.68 25.01 38.49 7.05 36.51 14.68 33.21 15.39 52.40

14.18 35.59 13.87 50.24 31.27 22.37 6.00 47.26 32.63 18.45 8.41 48.92 32.32 36.71 4.06 30.97 12.15 32.84 14.78 55.01

14.33 35.65 13.93 50.02 29.54 24.47 5.87 47.12 32.92 18.65 8.26 48.43 32.13 37.90 4.13 29.97 11.78 32.98 14.16 55.24

14.95 35.69 12.89 49.36 28.83 29.18 5.99 43.08 35.95 17.06 7.84 46.99 39.73 27.59 4.35 32.68 9.15 31.73 11.44 59.13

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

1990-2000 change

2000-2012 change

-2.65

-3.87 2.58

-0.89

1.29 -1.38

0.07 -0.50

2.58 -5.64

1.59 -0.65 -1.10 -7.17 -0.02 -1.01

5.77 -2.92 0.96 3.21 0.49 0.03

7.19 -5.83 11.23 -3.91 -5.40 -3.76 -1.47 -2.53 5.52

-3.69 14.72 -10.90 -2.71 -3.83 -5.54 -1.49 -3.95 6.72

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 10

Change in Industry and Manufacturing as Shares of GDP, percentage points (2000-2010) • In most African economies – 35 out of 50 – mining and utilities have seen a rising share in GDP. • Fast growing resourcerich economies have some of the largest shifts of economic activity toward these two sectors. • Represenative of Africa’s lack of Structural Change.

Swaziland

5

Equatorial Guinea Angola Guinea Zimbabwe

Mozambique Botswana

Tanzania

0 Sierra Leone

Libya

Uganda

Nigeria Ethiopia

Sudan Cote d'Ivore

-5

Ghana

Zambia

Egypt

South Africa

Chad Mauritania

-10

Burkino Faso

-20

-10

0 10 Industry_GDP

20

30

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.

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.

Drivers of Inequality in Africa: Natural Resources and Inequality

0

.02

.04

.06

.08

Resource Dependence & Inequality

30

40

50 Gini Index

Resource Dependent

60 Non Resource Dependent

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

70

• 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.

Norway United States (Gulf of Mexico) United Kingdom Australia (Western Australia) Brazil Mexico Canada (Alberta) Chile Colombia Trinidad and Tobago Peru India Timor-Leste Indonesia Ghana Liberia Zambia Ecuador Kazakhstan Venezuela South Africa Russia Philippines Bolivia Morocco Mongolia Tanzania Azerbaijan Iraq Botswana Bahrain Gabon Guinea Malaysia Sierra Leone China Yemen Egypt Papua New Guinea Nigeria Angola Kuwait Vietnam Congo (DRC) Algeria Mozambique Cameroon Saudi Arabia Afghanistan South Sudan Zimbabwe Cambodia Iran Qatar Libya Equatorial Guinea Turkmenistan Myanmar

0

20

40

60

80

100

Drivers of Inequality in Africa: Drivers of Inequality in Resource-Rich Countries

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 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.

Drivers of Inequality in Africa: Demographic Changes and the Labour Market Percentage increase in size of age groups in working-age population, 2010 to 2030 (Medium Variant) -18.7

Europe

-13.4

Age 15-24

Age 25-44

Age 45-64

Age 15-64

2.3

-9

7.2

North America

1

9.3

5.7

-7.1

11.9

Asia

40

16.2 2.9

15.3

World

34.9

18.4 -2.4

16.1

Latin America

42.4

19.5 38.7

50.6

Africa

60.5

48.7

-30

Source: ILO (2011)

-20

-10

0

10

20 Percentage increase

30

40

50

60

70

Drivers of Inequality in Africa: Demographic Changes and the Labour Market The Global Labor Market at a Glance, 2010 (millions)

Region

SSA Other Non-OECD OECD Global total

Wage Employ.

of which: of which: Self-Empl. Self-Empl. Self-Empl. Total NonAgric. Agric.

Total Empl.

Unempl.

Labor Force

61.00

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)

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)

333.00

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)

1 512

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)

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.

Drivers of Inequality in Africa: Demographic Changes and the Labour Market Wage-Agricultural Employment and Inequality

40 30 20

Gini

50

60

• 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.

0

20 40 60 Wage employment share / agriculatural employment share Fitted values

Source: World Bank (2012); Own graph

Gini

80

Drivers of Inequality in Africa: Education and Human Capital Development Enrolment Rates in Africa, 2011

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

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.

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

Madagascar Cameroon Gabon Kenya Mauritius Tanzania Burundi Seychelles Senegal Botswana Burkina Faso Zimbabwe Comoros Congo Mozambique Chad Benin Uganda South Africa Ghana Namibia Ivory Coast Nigeria Ethiopia Zambia

Tanzania Gabon Kenya Cameroon Botswana Mauritius Seychelles Namibia Burundi Zimbabwe Uganda Ghana Mozambique Madagascar Senegal South Africa Burkina Faso Ivory Coast Comoros Congo Zambia Benin Chad Ethiopia Nigeria

0

0

40

60

40

60

Are not learning math

20

20

80

80

Drivers of Inequality in Africa:

Education and Human Capital Development

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

Drivers of Inequality in Africa: Education and Human Capital Development 60 40 20

Korea

Hungary

Turkey

Slovenia

Thailand

Malaysia

Chile

South Africa

Tunisia

Indonesia

Morocco

Botswana

Ghana

0

Korea

Slovenia

Hungary

Turkey

Thailand

Chile

Malaysia

South Africa

Botswana Percentage achieved in each category

Indonesia

Tunisia

Morocco

Ghana

0

20

40

60

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

Below 400

At or above 400 but below 475

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 475 but below 550

At or above 550 but below 625

At or above 625

Source: TIMMS, 2011; Own graph

At or above 625

Drivers of Inequality in Africa: Education and Human Capital Development Conversion Rates from Primary to Tertiary Education, 2011 100 90

Sub-Saharan Africa

80

South and West Asia

70

Arab States

60

Central Asia

50 East Asia and the Pacific

40

Latin America and the Caribbean

30 20

Central and Eastern Europe

10

North America and Western Europe

0 Population of children (primary school age)

Primary

Secondary

Tertiary

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.

• 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

Maldives Moldova Mongolia Viet Nam Kyrgyzstan Tajikistan Philippines Rwanda Myanmar El Salvador Namibia Paraguay Nicaragua Morocco South Africa Bolivia Nepal Honduras Botswana Bhutan Indonesia Burundi Cambodia Gabon Zimbabwe Samoa Guatemala Guyana Bangladesh Swaziland Uganda Lao PDR Senegal Iraq Ethiopia Kenya Ghana Tanzania Syria Lesotho India Pakistan Togo Egypt Malawi Haiti Burkina Faso Benin Congo Papua New Guinea Zambia Cameroon Gambia Sudan Sierra Leone Mauritania Côte d'Ivoire C. A. Republic Liberia Mozambique DRC Mali Afghanistan Chad Niger Yemen

0

.2

.4

.6

.8

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

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

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 Drivers of Inequality in Africa: 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.

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