Evaluating the effects of cassava research for development approach: Household evidence from Malawi and the DRC

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Evaluating effects of cassava, R4D effects: market participation/sales,adoption, food security, ATE > per participant costs in DRC; ATE, ATT,ATU positive in Malawi, Replication studies, strong designs in future,test different contexts,ARIs.

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Evaluating the effects of cassava research for development approach: Household evidence from Malawi and the DRC

  1. 1. Evaluating the effects of cassava research for development approach: Household evidence from Malawi and the DRC J. Rusike, N.M. Mahungu, S.S. Lukombo, T. Kendenga, S.M. Bidiaka, A. Alene, A. Lema, V.M. Manyong, S.Jumbo, V. S. Sandifolo, and G. MalindiMini symposium on Outcomes and Impact Assessments R4D Week 2010 24 November 2010 www.iita.org
  2. 2. Introduction• Pressure to demonstrate research pays off and impact at scale• Shift to work at a large scale• Congealed in R4D• Debates whether and how R4D works• Does R4D have an impact on farm level outcomes? www.iita.org
  3. 3. Inputs OutputsPartnerships/ Tissue Improved crop Processingplatforms, culture, management technologiesnetworks, rapidcapacity multiplicatibuilding on, nurseries, clean planting materials, new varieties Outcomes: change agents (INERA, extension services, NGOs, farmers groups, agribusiness firms) Outcomes: Farmers Awareness, adoption, productivity, profitability Impact: food security, incomes, nutrition Impact: aggregate area, yield, production www.iita.org
  4. 4. Methods• Randomized experiments – Random sample randomly assign to treatment and control – Treatment effect = difference in means – Not done: Projects targeted to areas and households• Quasi-experiments – Village residence “as if” random – Project to villages “as if” randomly treated others not – Endogeneity and selection bias – Program evaluation theory methods: matching, regression-adjusted matching, differences-in- differences, Instrumental Variables www.iita.org
  5. 5. Data• Interviews 2009: researchers and implementers• Farm survey 2009: IITA n= 521 (Bandundu, Bas Congo, Kasai Oriental, Katanga, Kinshasa, Oriental, Sud-Kivu, Katanga)• Farm survey 2003:IITA:n=770 (Nkhotakota, Nkhatha Bay, Karonga, Mzimba, Mulanje, Lilongwe East,Lilongwe West)• NSO household surveys: 1997/98 & 2004/05• Secondary MOA data: multi-location variety trials; cassava area, production, yields; rainfall; prices (fresh cassava, maize, fertilizer) www.iita.org
  6. 6. DRC CMD Project areas www.iita.org
  7. 7. Survey villages: 2009 www.iita.org
  8. 8. Market participation and sales 60 no yes no yes 60 40 40Frequency Frequency 20 20 0 0 50 100 0 50 100 cassava % harvest sold Graphs by R4D interventions 0 0 50 100 0 50 100 cassava % harvest sold Graphs by R4D interventions www.iita.org
  9. 9. Multinomial choice regression-adjusted matching model prediction results Model predictions of number of technologies adopted by households 1.4 1.2probabilities with respect to treatmentValue of mean numerical derivative of 1 0.8 0.6 Multinomial logit 0.4 Multinomial probit 0.2 0 0 1 2 3 4 5 6 -0.2 -0.4 -0.6 Technology option: Number of technologies adopted www.iita.org
  10. 10. Heckman’s treatment effectsDependent variable Yield Gross Food (t/ha) margin security (US$/ha)Regressor Coeffic Coeffic CoefficAge of head 0.03 4.59 -0.01 *Education head -0.16 -50.57 0.01Family labor 0.16 35.86 -0.02 *Cropped area 0.08 14.05 0.01 **% area cassava -1.2 -349.5 * 0.01Farm equipment 0 -0.09 0Temperature -0.6 * -80.53 0.01Rainfall 0 0.93 * 0 ***Treatment 8.07 * 1223 * 0.59 ** Constant 23.46 2049.4 -0.35 www.iita.org
  11. 11. Malawi project areas: 1993-95 & 1999-2001 www.iita.org
  12. 12. Synthetic control matching results www.iita.org
  13. 13. Differences-in-differences matching resultsPer capita area 1998 cross- 2005 cross 1998-planted to cassava section section 2005Number of 396 1063 1459observationsCoefficient 0.010 0.079 0.053Std. Err. 0.068 0.138 0.131P>|z| 0.880 0.566 0.684 www.iita.org
  14. 14. Heckman’s treatment effectsDependent variable Months household can meet its minimum caloric requirements from home-produced maize and cassava staplesExplanatory variable Coef. Std. Err.Sex of household head -1.69Household size -2.78 ***Size of land holdings 5.25 **Area planted to maize -3.23Area planted to cassava 6.56 **Dummy indicator of exposure of 5.87 *the extension planning areato1998/1999-2001/02 cassavaplanting materials multiplicationdistribution projectDummy variable for adoption of 7.89 *improved cassava varietiesConstant 14.11 *** www.iita.org
  15. 15. Conclusion• Evaluating effects of cassava R4D important• R4D effects: market participation/sales, adoption, food security• ATE > per participant costs in DRC; ATE, ATT, ATU positive in Malawi• Replication studies, strong designs in future, test different contexts, ARIs www.iita.org

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