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Impact of Agricultural Research in Sub-Saharan Africa

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Poverty trends in SSA,Productivity Growth in African Agriculture,Maize Research in WCA,Maize research benefits attributable to IITA

Poverty trends in SSA,Productivity Growth in African Agriculture,Maize Research in WCA,Maize research benefits attributable to IITA

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  • 1. Impact of Agricultural Research in Sub-Saharan Africa Arega D. AleneR4D Planning Meeting, 23 November 2009, Ibadan, Nigeria International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 2. Pathway: R&D Productivity Poverty R&D Technology Productivity On-farm consumption Incomes Health/NutritionLabor demand & wages Aggregate production Non-farm earnings Consumer prices Economy-wide effects Poverty International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 3. Source: Alston et al. (2006) Beintema and Stads (2006) Maredia and Raitzer (2006) Expenditure (millions of 2000 $) Expenditure (millions of 2000 $) 0 10 20 30 40 50 60 1970 400 425 450 475 500 525 575 600 550 1971 1972 1980 1973 1981 1974 1975 1982 1976 1983 1977 1984 1978 1979 1985 1980 1986 1981 1987 1982 1988 1983 1984 1989 1985 1990 1986 1991 1987 1988 1992 1989 1993 1990 1994 R&D Investments 1991 1992 1995 1993 1996 1994 1997 1995 1996 1998 1997 1999 1998 2000 1999 2001 2000 2001 2002 2002 2003 2003International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 4. Poverty trends in SSA 50 350 49 325 48 % Poor 300 Number of poor (million)) Poverty incidence (%) 47 275 46 250 45 225 44 # of Poor 200 43 175 42 150 41 125 40 100 1981 1984 1987 1990 1993 1996 1999 2002 2004 Year 80 Benin 70 Nigeria Poverty incidence (%) Burkina Faso 60 Cameroon 50 Cote dIvoire 40 Ghana Senegal 30 Mali 20 10 0Source: Chen and Ravallion (2007). 1981 1984 1987 1990 1993 1996 1999 2000 2001 2002 2004 Year International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 5. Measuring Total Factor Productivity Non-parametric distance functions—GAMS (>7,000 equations) 1/ 2 t t t 1 t 1 Dot 1 ( yt 1 , xt 1 ) Dot ( yt 1 , xt 1 ) Dot ( y t , xt ) Mo (y , x , y , x ) Dot ( yt , xt ) Dot 1 ( yt 1 , xt 1 ) Dot 1 ( yt , xt ) Eff. change Tech. progressContemporaneous Sequential Source: Nin et al. (2003), Journal of Development Economics. International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 6. Productivity Growth in African Agriculture Conventional vs. Improved estimates 20 70 TFP growth 60 15 50Cumulative growth (%)) 10 40 Cumulative growth (%) 5 Technical progress 30 20 TFP growth 0 10 Efficiency change -5 0 Technical progress -10 -10 Efficiency change -15 -20 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Conventional estimates 0.3% per year New estimates Africa –1.8% per year SSA – 1.6% per year Stronger recovery of agric. productivity than conventional measures would suggest International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 7. Explaining total factor productivity L ln(TFP) 0 + 1( j ) ln(R&D)t j 1 ln(Weather) j 0 2 ln(Labor/ha) 3 ln(Labor/ha)2 4 ln(Literacy) 5 ln(Trade)t 1 6 (t ) 7 (t ) 2 e Explicit modelling of the length & shape of R&D lag Polynomial Distributed Lag Vs. Trapezoid L 16 j ln(R&D/ha) t j ( 0 1 j 2 j 2 ) ln(R&D/ha) t j j 0 j 0 16 16 = 0 ln(R&D/ha) t j 1 j ln(R&D/ha) t j j 0 j 0 16 2 j2 ln(R&D/ha) t j j 0 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 8. Explaining agricultural productivity (N=15) Alternative R&D lag specifications (Fixed Effects: Hausman rejects RE) Variable Trapezoid Constrained PDL Unconstrained PDL (2─16) (0─16) (3─16) Coefficient t-value Coefficient t-value Coefficient t-value R&Dt 0.0019 1.65* R&Dt–1 0.0030 1.65* R&Dt–2 0 0.0041 1.65* R&Dt–3 0.0025 1.83* 0.0050 1.65* –0.0104 –1.09 R&Dt–4 0.0050 1.83* 0.0058 1.65* –0.0016 –0.39 R&Dt–5 0.0075 1.83* 0.0065 1.65* 0.0060 0.61 R&Dt–6 0.0100 1.83* 0.0071 1.65* 0.0124 1.52 R&Dt–7 0.0125 1.83* 0.0076 1.65* 0.0176 2.05** R&Dt–8 0.0125 1.83* 0.0080 1.65* 0.0216 2.33** R&D R&Dt–9 0.0125 1.83* 0.0082 1.65* 0.0244 2.48**ε = 0.20 R&Dt–10 R&Dt–11 0.0125 0.0125 1.83* 1.83* 0.0084 0.0084 1.65* 1.65* 0.0260 0.0264 2.58** 2.64** R&Dt–12 0.0100 1.83* 0.0084 1.65* 0.0256 2.63** R&Dt–13 0.0075 1.83* 0.0082 1.65* 0.0236 2.43** R&Dt–14 0.0050 1.83* 0.0079 1.65* 0.0204 1.79* R&Dt–15 0.0025 1.83* 0.0075 1.65* 0.0160 0.78 R&Dt–16 0 0.0070 1.65* 0.0104 0.01Weather ∑j R&Dt–j 0.11 0.10 0.20ε = 0.17 Weather Labor/ha 0.166 0.319 2.82*** 1.15 0.161 0.290 2.74*** 1.05 0.167 0.389 2.82*** 1.39 (Labor/ha)2 0.035 0.71 0.036 0.72 0.044 0.87 Literacy 0.016 0.18 0.014 0.16 0.022 0.25 Trade 0.188 3.31*** 0.192 3.37*** 0.176 3.08***Reforms Time trend –0.003 –0.11 –0.003 –0.12 –0.002 –0.07 (Time trend)2 –0.001 –0.18 –0.001 –0.14 –0.001 –0.29ε = 0.18 Constant –1.854 –2.31** –1.856 –2.31** –1.745 –2.18** R2 0.80 0.80 0.88 Log likelihood 168 168 170 AIC –318 –317 –318 SBIC –288 –288 –282 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 9. Weather and agricultural productivity Rainfall (mm/year) TFP Index1100 1.0501050 1.0351000 1.020 950 1.005 900 0.990 TFP R&D (t−10) Rainfall 850 TFP 0.975 800 0.960 1971 1972 1973 1974 1984 1985 1986 1996 1997 1998 1999 1975 1976 1977 1978 1979 1980 1981 1982 1983 1987 1988 1989 1990 1991 1992 1993 1994 1995 2000 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 10. R&D and Productivity TFP growth (%) R&D growth (%)5 2.54 2.0 R&D (t−10)3 1.52 1.0 TFP1 0.50 0.0 1985 1987 1992 1994 1999 2001 1986 1988 1989 1990 1991 1993 1995 1996 1997 1998 2000 2002 2003-1 -0.5 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 11. Aggregate Rate of Return Lag weights Model G. lag Elasticity ROR0.030 (years) (%)0.025 3 0.11 27 Trapezoid0.020 1 0.10 340.015 3 0.10 310.010 C. PDL0.005 0 0.10 440.000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 3 0.20 33 Years UC. PDL Unconstrained PDL 3–16 Constrained PDL 3–16 0 0.10 39 Trapezoid 2–16 Constrained PDL 0–16 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 12. System of EquationsPathway: R&D... Productivity… Incomes... Poverty R&D: Polynomial Distributed Lag International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 13. Simultaneous equation system estimates of the impact of R&D in SSAEquation/variable Parameter Estimate (t-value)Value added per hectare: α1j + α2jResearch expenditures (t) α1(0) 0.003 (2.34)** 0.007 (4.14)***Research expenditures (t –1) α1(1) 0.006 (2.34)** 0.012 (4.14)***Research expenditures (t –2) α1(2) 0.008 (2.34)** 0.018 (4.14)***Research expenditures (t –3) α1(3) 0.010 (2.34)** 0.022 (4.14)***Research expenditures (t –4) α1(4) 0.012 (2.34)** 0.025 (4.14)***Research expenditures (t –5) α1(5) 0.013 (2.34)** 0.028 (4.14)***Research expenditures (t –6) α1(6) 0.014 (2.34)** 0.030 (4.14)***Research expenditures (t –7) α1(7) 0.014 (2.34)** 0.031 (4.14)***Research expenditures (t –8) α1(8) 0.015 (2.34)** 0.032 (4.14)***Research expenditures (t –9) α1(9) 0.014 (2.34)** 0.031 (4.14)***Research expenditures (t –10) α1(10) 0.014 (2.34)** 0.030 (4.14)***Research expenditures (t –11) α1(11) 0.013 (2.34)** 0.028 (4.14)***Research expenditures (t –12) α1(12) 0.012 (2.34)** 0.025 (4.14)***Research expenditures (t –13) α1(13) 0.010 (2.34)** 0.022 (4.14)***Research expenditures (t –14) α1(14) 0.008 (2.34)** 0.018 (4.14)***Research expenditures (t –15) α1(15) 0.006 (2.34)** 0.012 (4.14)***Research expenditures (t–16) α1(16) 0.003 (2.34)** 0.007 (4.14)*** α1(RE) 0.38 (4.14)*** (αRE)Total elasticity 0.17 (2.34)**Fertilizer (kg per hectare) αFR 0.086 (1.89)*Labor (workers per hectare) αLB 0.699 (10.01)***Machinery (tractors per hectare) αMA 0.235 (3.31)***Irrigation (% of crop land) αIR 0.012 (0.23)West and Central Africa αWCA 0.457 (3.57)***Constant δ0 5.161 (23.27)***R2 0.76GDP per capita:Value added per hectare βVA 0.954 (8.49)***Land (hectares per worker) βLA 1.008 (9.82)***Government expenditures (% of GDP) βGE 0.215 (1.78)*Fixed capital investment (% of GDP) βFI –0.005 (–0.05)Rural population (% of total) βPR –0.653 (–2.28)**West and Central Africa βWCA –0.313 (–3.35)***Constant β0 0.638 (0.95)***R2 0.76Poverty:Gini coefficient of inequality γGC 1.740 (3.78)***GDP per capita γGDP –0.593 (–5.81)***Government expenditures (% of GDP) γGE 0.226 (1.18)Fixed capital investment (% of GDP) γFI 0.078 (0.51)Population growth (% per year) γPG 0.229 (1.43)West and Central Africa γWCA –0.076 (–0.59)Constant γ0 8.903 (8.47)***R2 0.31 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 14. Elasticity MeasuresElasticity of—with respect to Estimate(1) Productivity — R&D 0.38(2) GDP per capita — Productivity 0.95(3) Poverty — GDP per capita –0.60(4) GDP per capita — R&D = (1) (2) 0.36(5) Poverty — Productivity = (2) (3) –0.58(6) Poverty — R&D = (1) (2) (3) –0.22 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 15. Poverty impacts of R&D in SSA Annual poverty reduction = 2.3 million (0.8% of the poor) Poverty reduction due to IITA: 0.5 to 1 million per year Doubling R&D investments, given more efficient support services: o Poverty reduction of 2 percentage points per year o Equivalent to 4.8% of the poor International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 16. April 2009International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 17. Case Study Evidence: Maize Research in WCA Maize Area & Yields 10 2.5Area (million ha) 8 2.0 Area Yield (t/ha) 6 1.5 4 Yield 1.0 2 0.5 0 0.0 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 18. Maize varieties released in WCA, 1965–2006Country Maize varieties releasedNigeria 82Burkina Faso 59Benin 29Cameroon 27Ghana 36Togo 27Senegal 47Mali 18Côte d’Ivoire 8Chad 14D.R. Congo 20Guinea 12Total 379 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 19. Adoption of improved maize varieties in WCA 100 Country Area under improved varieties (000 ha) % maize area Nigeria 90 Mali 1981 2005 2005 Burkina Faso 80 Nigeria 27 2180 66Adoption rate (% area) Cameroon 70 Ghana Mali 2 163 38 60 Senegal Burkina Faso 5 333 76 Benin 50 Togo Cameroon 36 244 46 40 Cote dIvoire Ghana 5 664 88 Aggregate Senegal 3 136 95 30 Benin 10 236 42 20 Togo 3 213 50 10 Côte d’Ivoire 20 69 53 0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 WCA 111 4238 62 Year IITA varieties account for 43% of area under MVs International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 20. Production (million tons) 0 2 4 6 8 10 12 14 16 1981 1982 1983 1984 1985 1986 Maize 1987 1988 Actual 1989 1990 1991 Counterfactual 1992 Production (million tons) 1993 0 2 4 6 8 10 12 14 16 1994 1981 1995 1982 1996 1983 1997 1998 Millet 1984 1999 1985 2000 1986 2001 1987 2002 1988 2003 Actual 1989 2004 1990 2005 1991 Counterfactual Production (million tons) 0 2 4 6 10 12 14 8 1992 1993 1981 1994 1982 1995 1983 1984 1996 1985 1997 1986 Sorghum 1998 1987 1999 1988 1989 Counterfactual Actual 2000 1990 2001 1991 2002 1992 1993 2003 1994 2004 1995 2005 1996 Production with & without maize research 1997 1998 1999 2000 2001 2002 2003 2004 2005International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 21. Maize Research Benefits & Costs Summary Measures Annual benefits Benefit–Cost Rate of Return (US$ million) ratio (%)Nigeria 194 84 74Mali 6 11 37Burkina Faso 10 14 39Cameroon 15 18 69Ghana 24 12 40Senegal 5 12 28Benin 8 28 64Togo 5 10 31Côte d’Ivoire 13 20 63Aggregate 274 21 43 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 22. Maize research benefits attributable to IITA (US$ million)250200 US$90–126 m/year150100 50 0 1991 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2004 2005 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 23. Poverty reduction due to maize research at IITA (‘000) 600 500Poverty reduction (000) 400 230–326,000/year 300 35–50,000/$1 million 200 100 0 International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 24. September 2009International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 25. Conclusions & Recommendations Stronger recovery of agricultural productivity after mid-1980s Results confirm the key roles of R&D, weather, and policy reforms R&D has contributed to productivity growth and poverty reduction Potential impacts are greater (fix inefficiencies outside R&D system)  SSA spends half of India’s & a quarter of US R&D expenditures  Increased R&D investments would bring about greater poverty reduction  Increased investments in extension/credit/input supply systems o Infrastructure, institutions, credit, subsidies, etc o Greater physical and economic access to seed & fertilizerEnhancing poverty reduction through resource reallocation (…) o Current allocations are suboptimal International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 26. Benefits to Poor Households Resource allocation based on expected impact 50 45Cumulative benefits to the poor (% of total research benefits) 40 35 30 25 Equity Efficiency 20 Actual 15 10 5 0 0 10 20 30 40 50 60 70 80 90 100 Cumulative research costs (% of total budget) International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 27. Implied Resource Re-allocation International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 28. January 2009International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org
  • 29. Thank You International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org