policies and productivity growth in african agriculture
DESCRIPTION
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/2011confTRANSCRIPT
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.
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?
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
Analytical strategy
X1 X2
Y1
Y2
Output Y
Input X
Productivity-led growthTotal factor productivity = f(policy)
Resource-led growthY = f(X)
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
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
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
Agricultural TFP index for sub-Saharan Africa (1961=100)
Agricultural TFP index for sub-Saharan Africa (1961=100)
Agricultural TFP index for sub-Saharan Africa (1961=100)
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
National & international agricultural research in sub-Saharan Africa
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.
Macro and price policies have become less discriminating against agriculture since 1985
Source: Anderson and Masters (2008).
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)
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
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
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