By Keith Fuglie and Nicholas Rada. Presented at the ASTI-FARA conference Agricultural R&D: Investing in Africa's Future: Analyzing Trends, Challenges, and Opportunities - Accra, Ghana, December 5-7, 2011. http://www.asti.cgiar.org/2011conf

1. Policies and Productivity
Growth in African Agriculture
Keith Fuglie and Nicholas Rada*
Economic Research Service
U.S. Department of Agriculture
Washington, DC
*The views expressed in this presentation are the authors’ own and not necessarily those of
the Economic Research Service.

2. 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?

3. Framework for Analysis:
Decomposing and Explaining Growth
Productivity-led growth
Total
Factor Research & extension
Productivity Human capital
(TFP) growth Institutions & incentives
Yield growth Infrastructure
Output growth
Input
intensification Resource-led growth
Prices & costs
Input policies
Exchange rates
Infrastructure
Area growth Area growth

4. Analytical strategy
Output Y
Y2
Productivity-led growth
Total factor productivity = f(policy)
Resource-led growth
Y1
Y = f(X)
X1 X2 Input X

5. Explaining Total Factor Productivity (TFP) Growth
CGIAR research
National research CGIAR technology
dissemination
Enabling factors
CGIAR technology
dissemination
Agricultural TFP
National research growth
Enabling factors

6. Agricultural Production Function Estimates
Coefficient (all significant at 1% level)
(elasticity)
Production inputs
Labor 0.248
Assume constant returns to scale
Land 0.315 (elasticities sum to 1.00)
Livestock Capital 0.357
Production elasticity = input cost
Machinery Capital 0.024 share under competitive market
equilibrium
Fertilizers 0.055
Resource quality variables
Irrigation (%) 68% Coefficient indicate % increase in
yield over unfavorable rainfed
Favorable area (%) 125% cropland area
R 2 overall 0.700
R 2 between countries 0.706
R 2 within countries 0.700

7. Increase in output growth has been primarily
resource-led with some rise in TFP growth
3.5%
3.0%
2.5%
Average Annual Growth
2.0%
TFP
1.5% Input/Cropland
Cropland
1.0%
0.5%
0.0%
1961-1984 1985-2009
-0.5%

8. Agricultural TFP index for sub-Saharan Africa
(1961=100)

9. Agricultural TFP index for sub-Saharan Africa
(1961=100)

10. Agricultural TFP index for sub-Saharan Africa
(1961=100)

11. 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

12. National & international agricultural research in
sub-Saharan Africa

13. 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.

14. Macro and price policies have become less
discriminating against agriculture since 1985
Source: Anderson and Masters (2008).

15. Data coverage for policy variables
R&D (31, Obs=899) R&D, School, NRA, Roads (9+, Obs=273)
R&D, School (27, Obs=783) R&D, Roads (17+, Obs=611) R&D, NRA (17, Obs=467)

16. 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

17. Returns to Agricultural Research in SSA
Median for countries grouped by size IRR IRR B/C ratio
(%)
without
CGIAR (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

18. 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