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Policies and Productivity Growth in African Agriculture
Keith Fuglie and Nicholas Rada*Economic Research Service
U.S. Department of AgricultureWashington, DC
*The views expressed in this presentation are the authors’ own and not necessarily those of the Economic Research Service.
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Is agriculture in SSA taking off?
• Higher rates of agricultural GDP growth following structural adjustment– From 1.4% per year (1970-1984) to 2.9% per year (1985-2009)
• Possible reasons for higher agricultural growth– Macroeconomic & political stability (Binswanger-Mkhize & McCalla, 2009)– Improved agricultural terms of trade (Anderson and Masters, 2008)– Technology diffusion and greater productivity (Block, 1995; Nin-Pratt & Yu,
2008; Alene & Coulaby, 2009)• Aims of study:
– Has growth been primarily resource-led or productivity-led?– What are the policy drivers for agricultural growth? Especially, what is the
role of national and international agricultural research?
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Framework for Analysis: Decomposing and Explaining Growth
Yield growth
Area growth
Input intensification
TotalFactor
Productivity(TFP) growth
Out
put
grow
th
Area growth
Productivity-led growth
Research & extensionHuman capitalInstitutions & incentivesInfrastructure
Resource-led growth
Prices & costsInput policiesExchange ratesInfrastructure
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Analytical strategy
X1 X2
Y1
Y2
Output Y
Input X
Productivity-led growthTotal factor productivity = f(policy)
Resource-led growthY = f(X)
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Explaining Total Factor Productivity (TFP) Growth
CGIAR technology dissemination
CGIAR research
National research
Enabling factors
CGIAR technology dissemination
National research
Enabling factors
Agricultural TFP growth
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Agricultural Production Function Estimates
Coefficient(elasticity)
(all significant at 1% level)
Production inputs
Labor 0.248Assume constant returns to scale (elasticities sum to 1.00)
Production elasticity = input cost share under competitive market equilibrium
Land 0.315
Livestock Capital 0.357
Machinery Capital 0.024
Fertilizers 0.055
Resource quality variables
Irrigation (%) 68% Coefficient indicate % increase in yield over unfavorable rainfed cropland areaFavorable area (%) 125%
R 2 overall 0.700
R 2 between countries 0.706
R 2 within countries 0.700
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Increase in output growth has been primarily resource-led with some rise in TFP growth
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
1961-1984 1985-2009
Ave
rage
Ann
ual G
row
th
TFP
Input/Cropland
Cropland
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Agricultural TFP index for sub-Saharan Africa (1961=100)
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Agricultural TFP index for sub-Saharan Africa (1961=100)
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Agricultural TFP index for sub-Saharan Africa (1961=100)
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Can R&D investments explain TFP growth?
• International (CGIAR) agricultural research– Invests about $200 million in SSA (25% for crops)– 1200 international scientists (40% for SSA)– Improved crop technology adopted on about 20% of
cropland
• National agricultural research in SSA– $US 350 million ($PPP 960 million) – 9000 scientists (15% with PhD) in 2000– Low and declining level of research intensity
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National & international agricultural research in sub-Saharan Africa
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New technologies have impacted at least 25% of SSA cropland by 2001-05
Source: Compiled by author from case studies of technology adoption and impact. These technologies Originated primarily from CGIAR centers except NRM technologies, which are primarily farmer innovations.
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Macro and price policies have become less discriminating against agriculture since 1985
Source: Anderson and Masters (2008).
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Data coverage for policy variables
R&D, School, NRA, Roads (9+, Obs=273)
R&D, School (27, Obs=783)
R&D (31, Obs=899)
R&D, Roads (17+, Obs=611) R&D, NRA (17, Obs=467)
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Some Findings from Regression Models
• Lack of data coverage constrains analysis– Road data too limiting to draw inference
• Technology policy– CGIAR technology adoption strongly correlated with TFP growth– NARS – absolute size seems to matter (small country problem)– CGIAR raises returns to NARS
• Other factors– Economic policy and incentives matter – Schooling leads to higher adoption but not higher yield given
adoption– War and HIV/AIDS (strongly) depress growth
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Returns to Agricultural Research in SSA
Median for countries grouped by size IRR(%)
IRR without CGIAR
B/C ratio
(10% discount rate)
Large countries Output > $3 bil. 25.0 19.5 4.0
Medium-size countries Output, $1-$3 bil. 17.4 12.8 2.2
Small countries Output < $1 bil. 7.4 3.9 0.7
Returns to CGIAR in SSA 44.5 — 8.6
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Conclusions
• Agricultural growth acceleration has been primarily resource-led
• Some evidence of productivity improvement, especially in West Africa
• Robust drivers of productivity and growth:– CGIAR research & technology dissemination– NARS R&D (except for small countries)– Improved economic policy
• Implications for national R&D policy– Underinvestment by medium and large countries– Evidence for economies of size in NARS (small country problem)– Important to be open to international sources of technology