Policies and Productivity Growth in African Agriculture


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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

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Policies and Productivity Growth in African Agriculture

  1. 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 ofthe Economic Research Service.
  2. 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. 3. Framework for Analysis: Decomposing and Explaining Growth Productivity-led growth Total Factor Research & extension Productivity Human capital (TFP) growth Institutions & incentives Yield growth InfrastructureOutput growth Input intensification Resource-led growth Prices & costs Input policies Exchange rates Infrastructure Area growth Area growth
  4. 4. Analytical strategyOutput Y Y2 Productivity-led growth Total factor productivity = f(policy) Resource-led growth Y1 Y = f(X) X1 X2 Input X
  5. 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. 6. Agricultural Production Function Estimates Coefficient (all significant at 1% level) (elasticity)Production inputsLabor 0.248 Assume constant returns to scaleLand 0.315 (elasticities sum to 1.00)Livestock Capital 0.357 Production elasticity = input costMachinery Capital 0.024 share under competitive market equilibriumFertilizers 0.055Resource quality variablesIrrigation (%) 68% Coefficient indicate % increase in yield over unfavorable rainfedFavorable area (%) 125% cropland areaR 2 overall 0.700R 2 between countries 0.706R 2 within countries 0.700
  7. 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. 8. Agricultural TFP index for sub-Saharan Africa (1961=100)
  9. 9. Agricultural TFP index for sub-Saharan Africa (1961=100)
  10. 10. Agricultural TFP index for sub-Saharan Africa (1961=100)
  11. 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. 12. National & international agricultural research in sub-Saharan Africa
  13. 13. New technologies have impacted at least 25% of SSA cropland by 2001-05Source: Compiled by author from case studies of technology adoption and impact. These technologiesOriginated primarily from CGIAR centers except NRM technologies, which are primarily farmer innovations.
  14. 14. Macro and price policies have become lessdiscriminating against agriculture since 1985Source: Anderson and Masters (2008).
  15. 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. 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. 17. Returns to Agricultural Research in SSAMedian for countries grouped by size IRR IRR B/C ratio (%) without CGIAR (10% discount rate)Large countries Output > $3 bil. 25.0 19.5 4.0Medium-size countries Output, $1-$3 bil. 17.4 12.8 2.2Small countries Output < $1 bil. 7.4 3.9 0.7Returns to CGIAR in SSA 44.5 — 8.6
  18. 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