Growth, Inequality and Poverty Reduction in Africa

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

OUTLINE 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.

Real GDP per capita in US$ at 2005 prices (in logs) AFRICA RISING : TWO DECADES OF SUSTAINED ECONOMIC GROWTH 6.95 6.90 6.85 6.80 6.75 6.70 6.65 6.60 6.55 6.50 6.45 Actual Trend reversing two lost decades from the mid-70s to the mid-90s.

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Cumulative growth index (1995=1.0) IN MANY WAYS, THIS HAS BEEN HIGH-QUALITY GROWTH 2.8 2.6 2.4 2.2 2.0 1.8 1.6 Africa s growth has been strongly investment driven, rather than consumptiondriven. 1.4 1.2 1.0 0.8 Growth has also been associated with rapid increases in foreign trade both imports and exports. Consumption Government Spending Imports Investment Exports 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.

Real GDP growth BUT THERE ARE IMPORTANT CAVEATS: GDP growth in SSA, China and other developing countries 15% 13% 11% 9% 7% 5% 3% 1% -1% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Rest of Developing (excl. China) China Sub-Saharan Africa Source: World Development Indicators

Real GDP per capita growth Real GDP growth BUT THERE ARE IMPORTANT CAVEATS: 1. Per capita growth is less impressive due to rapid population growth GDP growth in SSA, China and other developing countries 15% 13% 11% 9% 7% 5% 3% 1% -1% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Rest of Developing (excl. China) China Sub-Saharan Africa GDP growth per capita in SSA, China and other developing countries 15% 13% 11% 9% 7% 5% 3% 1% -1% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Rest of Developing (excl. China) China Sub-Saharan Africa Source: World Development Indicators

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Cumulative growth index (1995=1.0) 2. Growth performance varies significantly across countries Fragile and conflictaffected states grow markedly more slowly than non-fragile countries. There are also substantial differences between resource-rich and nonresource-rich countries 1.6 1.5 1.4 1.3 1.2 SSA Resource rich Non-resource rich Fragile Non-fragile PER CAPITA GROWTH RATES 1995-2012: Fragile: 1.2% Non-fragile: 2.3% Non resource-rich: 1.7% Resource-rich: 2.6% 1.1 1.0 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.

ZWE BDI GNB ERI COM GAB ZAR MDG CIV GMB NER SWZ GIN TGO KEN COG CMR BEN MWI SEN ZAF ZMB STP MRT CAF SLE MLI NAM SYC LSO BWA TZA BFA SDN GHA MUS UGA TCD NGA MOZ ETH AGO RWA CPV LBR GNQ ZWE BDI GNB ERI COM GAB ZAR MDG CIV GMB NER SWZ GIN TGO KEN COG CMR BEN MWI SEN ZAF ZMB STP MRT CAF SLE MLI NAM SYC LSO BWA TZA BFA SDN GHA MUS UGA TCD NGA MOZ ETH AGO RWA CPV LBR GNQ 16 14 12 10 8 6 4 2 0-2 -4 GDP Growth per capita in SSA, 1995-2012 (resource-rich countries in orange) 16 14 12 10 8 6 4 2 0-2 -4 GDP Growth per capita in SSA, 1995-2012 (fragile and conflict-affected states in red)

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Cumulative growth index (1995=1.0) 3. Growth performance also varies significantly across sectors, within countries 3.5 3.0 2.5 Agriculture and manufacturing account for a declining share of GDP in the region. 2.0 1.5 1.0 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. Agriculture Resources Manufacturing Services 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 As a result, Africa s structural transformation is bypassing manufacturing. Fast-Growing SSA Countries, Resource Rich Fast-Growing SSA Countries, Non Resource Rich 60 60 50 50 40 40 30 30 20 20 10 10 0 Agriculture Manufacturing Resources Services 0 Agriculture Manufacturing Resources Services 1995-99 2007-11 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

$1.25 a day headcount (%) AFRICA S RECENT ECONOMIC DYNAMISM HAS REDUCED POVERTY. BUT NOT BY ENOUGH 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. 70 60 50 56.5 59.4 58.1 58.0 55.7 52.3 49.2 Sub-Saharan Africa 48.5 40 30 South Asia 31 20 10 0 East Asia and Pacific 12.5 LAC 5.5 MENA 2.4 ECA 0.6 1990 1993 1996 1999 2002 2005 2008 2010 Source: PovcalNet.

Thailand Egypt, Arab Rep. Kazakhstan Philippines Morocco Turkey Tunisia Argentina Nepal Pakistan Peru Vietnam Bangladesh Romania Other developing countries India Brazil Indonesia Mexico China Colombia Uganda South Africa Ethiopia Sub-Saharan Africa Iran, Islamic Rep. Tanzania Nigeria Ukraine Yemen, Rep. THIS REFLECTS THE REGION S LOW GROWTH ELASTICITY OF POVERTY 4 2 Growth Elasticity of Poverty Reduction, 2000-2010 Five most populous countries by region*, except Poland and Sri Lanka. 0-2 -4-6 -8-10 -12-2.02-0.7-14 * For which data is available. Source: estimates based on PovcalNet.

A PARENTHESIS ON THE GROWTH ELASTICITY OF POVERTY Recall that a Lorenz curve with parameters π is given by L p, π = 1 න ydf y μ So L p p, π = y μ And So, at the poverty line: p = L p 1 y μ, π H = L p 1 z μ, π Denoting time derivatives by dx: 1 dh H = L pp L p 1 z μ 1 dμ μ + L pπ 1 L dπ p

MLI BDI ETH NER SDN SLE GNB TZA LBR BEN CMR TGO GIN NGA TCD BFA MRT SEN GAB CIV AGO GHA MDG MWI UGA ZAR MOZ GMB COG KEN CPV RWA STP SWZ LSO CAF ZMB BWA NAM ZAF COM SYC Gini coefficient 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. 70 60 50 40 30 20 10 0 Consumption Survey Income Survey Sub Saharan Africa Source: PovcalNet, most recent survey available.

Percent growth in consumption AND A GROWTH PATTERN THAT IS OFTEN NOT INCLUSIVE In 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%. 9 Growth Incidence Curve, Malawi 2004-2010 8 7 6 5 4 3 2 1 0 1 8 16 23 31 38 46 53 61 68 76 83 91 98 Consumption expenditure percentile Source: estimates based on household surveys from Survey-based Harmonized Indicator Program (SHIP)

Niger 2006 C.A.R. 1994 Chad 2004 Mozambique 2003 Mali 2006 Burkina Faso 2010 Guinea 2005 Comoros 1996 Togo 1998 Liberia 2007 Madagascar 2008 Cote d'ivoire 2005 Rwanda 2010 Burundi 2010 Ethiopia 2011 Mauritania 2000 Senegal 2010 Benin 2006 Sierra Leone 2008 DR Congo 2007 Uganda 2011 Malawi 2010 Congo Rep. 2005 Gabon 2000 Sao Tome and Principe 2008 Cameroon 2011 Nigeria 2008 Zambia 2007 Ghana 2008 Swaziland 2006 Lesotho 2009 Tanzania 2010 Kenya 2008 Namibia 2006 South Africa 1998 Zimbabwe 2010 Proportion INEQUALITY OF OUTCOMES REFLECTS LARGE INEQUALITIES OF OPPORTUNITY Rich-Poor Gaps in Proportion of 15-19 Year Olds who have Completed Grade 6 1 0.8 0.6 0.4 0.2 0 Richest quintile Poorest quintile Average Source: DHS. http://econ.worldbank.org/projects/edattain. Most recent survey available shown.

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.

FISCAL DISCIPLINE IS WANING, WITH MANY COUNTRIES NOW RUNNING LARGE FISCAL AND CURRENT ACCOUNT - DEFICITS This is particularly worrisome at a time when external tailwinds may be turning into headwinds 0 Fiscal deficits (latest) -5-10 -10.0-9.0-8.4-7.8-6.7-6.3-5.9-5.7-15 -16.3-20 -25-21.1 Eritrea South Sudan Ghana Cabo Verde The Gambia Equatorial Guinea Zambia Senegal Tanzania Kenya Source: International Monetary Fund, World Economic Outlook, October 2014.

Korea International Average Malaysia Chile Jordan Botswana Indonesia South Africa Ghana Proportion 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) 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

IN PART BECAUSE OF SYSTEMIC FAILURES IN SERVICE DELIVERY Kenya Nigeria Senegal Tanzania Togo Uganda Classroom teacher absence rate 47% 22% 29% 53% 35.9% 57% Health absence rate worker 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 Source: DHS. http://econ.worldbank.org/projects/edattain. Most recent survey available shown.

Power International call Water Road freight Internet dial-up Mobile telephone Ratio of prices ON THE PHYSICAL CAPITAL SIDE, INFRASTRUCTURE REMAINS SCARCE AND VERY EXPENSIVE IN AFRICA 7.0 Median prices to final user in Africa (relative to South Asia) 6.0 5.0 4.0 3.0 2.0 1.0 0.0 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 5% 4% 3% 2% 1% 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. 0% -1% -2% Fast-growing SSA Slow-growing SSA Fast-growing SSA, Resource Rich Fast-growing SSA, Non- Resource Rich 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.

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. Contribution of urban and rural areas to population, poverty reduction and consumption growth in Uganda, 2005 09 100% 80% 60% Population share Share of poverty reduction Shares in total consumption growth Most of the poverty reduction comes from very specific growth sources 40% 20% 0% -20% Rural Urban Source: Kaminski and Christiaensen 2013

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. CROSS-COUNTRY REGRESSIONS SUGGEST THAT GROWTH PATTERNS DO MATTER FOR POVERTY REDUCTION Africa: agriculture and services most poverty reducing Elsewhere: industry and services most poverty reducing VARIABLES Headcount Sub-Saharan Africa Poverty Gap Sq. Poverty Gap Other Developing Countries 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

REDISTRIBUTION CAN BE EFFICIENT AS WELL AS EQUITABLE. 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; UCTs CCTs 1.0 8% 23% 41% 36% 24% 48% 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)

FAMILIES SYSTEMATICALLY INVEST PART OF WHAT THEY RECEIVE and even to save and invest in physical and financial capital as well! 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)

FAMILIES SYSTEMATICALLY INVEST PART OF WHAT THEY RECEIVE Community Based Conditional Cash Transfers in Tanzania Some Results (endline) 1.09 1 625 0.38 7% points Indigenous goats Local chickens INVESTMENT IN LIVESTOCK -1 593 Cigarettes, tobacco, snuff Insurance (car, medical, life) ANNUAL AVERAGE HOUSEHOLD NON-FOOD EXPENDITURE, LCU Shoes CHILDREN'S ASSETS 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)

CONCLUSIONS 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

THANK YOU