launch of growth and poverty in sub-saharan africa book
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The UNU-WIDER Growth and Poverty Project (GAPP)OUP Book launch at Sida, Stockholm, Sweden, April 2016
by Andy McKay and Finn Tarp
Introduction
Context• The Economist (11 May 2000): Hopeless Africa• The Independent (15 July 2009): Africa – the lost continent• The Economist (3 December 2011): The hopeful continent – Africa rising
+ Optimistic cross-country studies: Pinskovskiy and Sala-i-Martin (2014) and Young (2012)
• Recent Afrobarometer survey: ‘despite high reported growth rates, lived poverty at the grassroots remains little changed’ (Dulani et al. 2013)
• Pressing questions:– What is really happening? – What is going on at country level?– Can one make sense of all this?– Policy implications?
• The UNU-WIDER Growth and Poverty in Sub-Saharan Africa (GAPP) project
https://www.wider.unu.edu/publication/growth-and-poverty-sub-saharan-africa
Open access:
GAPP• Carried out 16 carefully designed country case studies from the 24 most populous
countries in sub-Saharan Africa:– Ethiopia, Ghana, Malawi, Rwanda, Uganda, Burkina Faso, Mozambique, Nigeria, Tanzania, Zambia,
Cameroon, Côte d’Ivoire, Kenya, Madagascar, South Africa and the DRC – Represent almost 75% of the African population and 9 of the largest 10 countries – Exact time period varies: generally the last two decades– 16 high quality teams consisting of leading local and international experts, focused on identifying and
explaining trends in monetary and non-monetary poverty and their links to growth and inequality– Synthesis and interpretation by the editors
• Key result: there is a lot to celebrate in African development (two cheers)
– But not every where, and major challenges remain (not three cheers)
Background
The African Growth Turn-Around
Child Mortality – Nearly Halved Since 1995
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201315
35
55
75
95
115
135
155
175
195
Europe & Central Asia (developing only) East Asia & Pacific (developing only)Middle East & North Africa (developing only) Sub-Saharan Africa (developing only)Latin America & Caribbean (developing only)
Mortality rate, under-5 (per 1,000 live births)
Child Malnutrition Reduced
Europe & Central Asia (develop-ing only)
East Asia & Pacific (developing only)
Middle East & North Africa (developing only)
Sub-Saharan Africa (developing only)
Latin America & Caribbean (developing only)
0
5
10
15
20
25
30
1990 1995 2000 2005 2013
Malnutrition (weight for age) prevalence (% of children under 5)
Access to Clean Water in Rural Areas – Improved Significantly Since 1990…
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 201230
40
50
60
70
80
90
Europe & Central Asia (developing only) East Asia & Pacific (developing only) Middle East & North Africa (developing only)Sub-Saharan Africa (developing only) Latin America & Caribbean (developing only)
Improved water source, rural (% of rural population with access)
Girls’ Secondary School Enrolment – Doubled Since 1995
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201320
30
40
50
60
70
80
90
100
Europe & Central Asia (developing only) East Asia & Pacific (developing only) Middle East & North Africa (developing only)Sub-Saharan Africa (developing only) Latin America & Caribbean (developing only)
School enrolment, secondary, female (% gross)
Substantial Variability: The 16 GAPP Country Cases
The GAPP Analytical Approach
The GAPP Approach (i)• Simple cross-country extrapolations from WDI data will not reveal underlying
dynamics or lack thereof in specific country contexts • The poverty, growth and inequality triangle
– Poverty, growth and inequality tend to be related in developing countries: helps inform the GAPP analytical approach
– Keeping in mind that the triangle is not an iron rule – It can break down: recall Abs = C+ I + G = GDP + M – X
• Poor people may not cross the poverty line even if there is growth• Growth may not be proportional across components of absorption• Terms of trade effects may imply imports fall relative to exports (similar effects due to reduction in
foreign aid, or debt service payments)• Mismeasurement
The GAPP Approach (ii)• Bring together:
– Available macroeconomic data– Comparable household budget surveys (at least two surveys from each country)– Demographic and Health Survey data, and – A host of other information (prices)
• Critically examine validity and consistency of existing data (triangulation)• Develop coherent country case stories• Presentation and discussion of drafts at WIDER meetings and conferences
Conference on Inclusive Growth in Africa
Country Categorisation and Selected Cases
Four Categories of Countries• Relatively rapid economic growth and corresponding poverty
reduction: Ethiopia, Ghana, Malawi, Rwanda, and Uganda • Relatively rapid economic growth and limited poverty reduction:
Burkina Faso, Mozambique, Nigeria, Tanzania, and Zambia• Uninspiring or negative economic growth with corresponding
stagnation or increasing poverty: Cameroon, Côte d’Ivoire, Kenya, Madagascar, and South Africa
• Low-information countries: DRC
Contextualising Four Selected Countries
0
200
400
600
800
1,000
1,200
Ethiopia Mozambique Tanzania Cameroon SSA developing average
GDP per capita (2005 USD), 2014
Note: significant differences
Contextualising Four Selected Countries (ii)• Though not all indicators have the same pattern …
Ethiopia: Agricultural Success Plus• Strong growth performance overall,
though with periodic shocks
• Despite multiple challenges: weather shocks, periods of high inflation, post-electoral instability in 2005
• Agricultural Development Led Industrialisation (ADLI) policy
• Impressive progress in agriculture modern inputs and big infrastructural improvements
• Widespread safety net
Ethiopia (ii)• Data shows poverty fell from 46.8% in 2000 to 46.0% in 2005 and 23.2% in
2011
• Both urban and rural poverty fell sharply; and both fell in each of the 11 regions; depth of poverty also fell
• Increased agricultural production a key actor: area and yields; but also growth in services sector
• And impressive improvements in many non-monetary indicators: infant mortality, malnutrition, education, access to public goods (electricity, drinking water, sanitation)
Mozambique: Off-track or Temporarily Side-lined?
• Significant growth 1994-2014 – But remember mega-projects (1%) and population growth (2.5%) -> 3.5%/year = doubling in 20 years
• Household surveys: (i) 1996/97, (ii) 2002/03, (iii) 2008/09, and (iv) 2014/15 being analysed => Results: poverty head counts (next slide) and non-monetary indicators
• Getting the story right: – Poverty estimates; GDP growth (the sub-story of agricultural production estimates); differential
impact across elements of absorption; terms of trade; and inequality estimates
• A formal assessment: head count rate would have been 45% in 2008 had it not been for combination of (i) low agriculture productivity growth (aggregate GDP growth effect and differential impact on consumption), and (ii) world price shifts (food and fuel), which also suggest rise in inequality (not captured under standard assumptions)
• Perspectives for the future (extrapolations)
Mozambique: Official Consumption Poverty Headcounts
Difference (% points)
Levels (%)
1996/97 to 2002/03 to
1996/97 2002/03 2008/09
2002/03 2008/09
National 69.4 54.1 54.7
-15.3 0.6
Urban 62.0 51.5 49.6
-10.5 -1.9
Rural 71.3 55.3 56.9
-16 1.6
Tanzania: Reconciling the Macro and Micro• Challenge of data: both macro and
micro • Reported fast growth since 2000
(stagnant before)• Drivers mostly investment
(pubic/private); government spending; trade deficit; slower growth in consumption
• Population growth• Slow growth in agriculture• And significant price increases
Tanzania (ii)• But poverty reduction much slower … national headcount 39% in
1992/3, 34% in 2007, 28% in 2011/12• Very low responsiveness to growth using macro measures, but not
using micro data – Because household real consumption increased relatively little between
surveys
• Poverty fell most in Dar es Salaam, limited elsewhere• However, progress across the country in non-monetary welfare
outcomes between 1992 and 2010
Cameroon: Inconsistent Growth and Spatial Diversity
• Cameroon is not a poor country on average (USD GDP p.c. around SSA average)
• But country faces difficult political economy tension of keeping peace in face of regional, ethnic, religious and linguistic diversity
• Public sector jobs and fiscal policy in part respond to this; less focus on service delivery
• Erratic growth record, with boom linked to exploitation of oil (1979-86), followed by crisis (1987-93) and then slow post devaluation recovery
Cameroon (ii)• Monetary poverty fell from 53% in 1996 to 40% in 2001, still 40% in 2007;
reduction in early post devaluation period, not sustained • Little progress in non-monetary outcomes (under five mortality, malnutrition,
education), 1991-2004; better between 2004 and 2011– Non-monetary outcomes also not good compared to SSA average
• Significant spatial differences in all of these indicators: much higher poverty and much worse non-monetary outcomes in north of country, followed by East– Under five mortality in three northern provinces twice the SSA average
• Monetary poverty also increased in north and east, while it fell in big cities and better connected locations
Findings and Lessons from GAPP Country Studies
Findings• Socio-economic progress in Sub-Saharan Africa has been
markedly better than almost anyone expected 20 years ago, but progress has not been even
• The development process without exception highly non-linear• The fragility of gains evident• The regional powerhouses of Kenya, Nigeria, and South Africa
not among the better performers in terms of growth or poverty reduction
Eight Lessons/Topic Areas• Peace and stability• Data issues• Volatility of monetary poverty measures• Importance of multi-dimensional assessments• The role of agriculture• Relative prices• Aid flows• The perils of existing cross-country studies
Conclusion
Outstanding Challenges and a Thorny Dilemma• Global demographic projections (2015-2050): from 7.3 to 9.7
billion, and Africa’s population is set to double to 2.5 billion (bigger than both China and India and Nigeria bigger than US)
• Structural transformation slow• Jobs and employment creation lagging• Agriculture and industrialization constrained• While the potential pitfalls are many, there is every reason to push
forward in African development over the next 15-20 years (remembering T x G = 69)
Policy Implications• While realistic expectations are required, the basic elements of a self-sustaining
development process, as opposed to a temporary growth spurt due to favourable circumstances, are starting to come into place on the sub-continent
• Forward looking investments to promote this growth process are merited• Policies to stimulate smallholder agriculture form an integral part of a coherent
growth, poverty reduction and industrialization strategy for most countries of SSA• While principal responsibility lies with country governments, the international
community should consider how to promote growth and poverty reduction across Africa
• Doing better on information systems in Africa is crucial to achieving development goals, not just tracking them
www.wider.unu.eduHelsinki, Finland