This document evaluates the effects of cassava research for development (R4D) approaches in Malawi and the Democratic Republic of Congo (DRC) using household data. It finds that R4D had positive impacts on market participation, technology adoption rates, food security, and yields. A cost-benefit analysis found that the average treatment effects outweighed per participant costs in the DRC. The study recommends future replication with stronger experimental designs to further test impacts across different contexts.

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. Malindi
Mini symposium on Outcomes and Impact Assessments
R4D Week 2010 24 November 2010
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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?
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3. Inputs
Outputs
Partnerships/ Tissue Improved crop Processing
platforms, culture, management technologies
networks, rapid
capacity multiplicati
building 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
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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
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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)
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6. DRC CMD Project areas
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7. Survey villages: 2009
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8. Market participation and sales
60 no yes
no yes
60
40
40
Frequency
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
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9. Multinomial choice regression-adjusted
matching model prediction results
Model predictions of number of technologies adopted by households
1.4
1.2
probabilities with respect to treatment
Value 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
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10. Heckman’s treatment effects
Dependent variable Yield Gross Food
(t/ha) margin security
(US$/ha)
Regressor Coeffic Coeffic Coeffic
Age of head 0.03 4.59 -0.01 *
Education head -0.16 -50.57 0.01
Family labor 0.16 35.86 -0.02 *
Cropped area 0.08 14.05 0.01 **
% area cassava -1.2 -349.5 * 0.01
Farm equipment 0 -0.09 0
Temperature -0.6 * -80.53 0.01
Rainfall 0 0.93 * 0 ***
Treatment 8.07 * 1223 * 0.59 **
Constant 23.46 2049.4 -0.35 www.iita.org

11. Malawi project areas: 1993-95 & 1999-2001
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12. Synthetic control matching results
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13. Differences-in-differences
matching results
Per capita area 1998 cross- 2005 cross 1998-
planted to cassava section section 2005
Number of 396 1063 1459
observations
Coefficient 0.010 0.079 0.053
Std. Err. 0.068 0.138 0.131
P>|z| 0.880 0.566 0.684
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14. Heckman’s treatment effects
Dependent variable Months household can meet its minimum caloric
requirements from home-produced maize and
cassava staples
Explanatory variable Coef. Std. Err.
Sex of household head -1.69
Household size -2.78 ***
Size of land holdings 5.25 **
Area planted to maize -3.23
Area planted to cassava 6.56 **
Dummy indicator of exposure of 5.87 *
the extension planning area
to1998/1999-2001/02 cassava
planting materials multiplication
distribution project
Dummy variable for adoption of 7.89 *
improved cassava varieties
Constant 14.11 ***
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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
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