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Impact of Agricultural Technology Adoption on
Asset Ownership: the case of Improved Cassava
Varieties in Nigeria
Bola Amoke Awotide, Arega D. Alene, Tahirou Abdoulaye
and Victor M. Manyong
24th November 2015
(R4D Week 2015)
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Impact of Agricultural Technology
Adoption on Asset Ownership: the case
of Improved Cassava Varieties in Nigeria
Bola Amoke Awotide
Arega D. Alene
Tahirou Abdoulaye
Victor M. Manyong
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Introduction
• IITA and partners have officially developed and
disseminated more than 40 Improved Cassava
Varieties (ICVs).
• There is limited evidence on the impact of these ICVs
• This study contributes to address this knowledge gap
• The objective of this study is to assess the impact of
ICVs on asset poverty
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• Poverty is not only a lack of income or consumption but also a
lack of assets (e.g., Oliver and Shapiro, 1990; Sherraden, 1991;
Haveman and Wolff, 2000).
• Asset stocks fluctuate less widely than consumption or income
measures (Dillon and Quinones, 2011).
• Asset-poor households usually have insufficient resources to
invest in their future or to sustain household members at a
basic level during times of economic shocks (Fisher and Weber
2004).
Introduction
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Classification of Assets
List of Assets in Study
Hoe Phone
Machete Television set
Wheel barrow Radio
Goats Bicycle
Cattle Motorcycle
Chickens Vehicle
Sheep
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Varietal Adoption
Asset Accumulation
Income flow
Cassava R&D
Poverty
Productivity
Home consumption
Cash incomes
Health/Nutrition
Asset-poverty Impact Pathway
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• The data is from the DIIVA project- measurement
and assessment of the impacts of the diffusion of
improved crop varieties in Africa.
• Five out of the six States (Ekiti, Osun, Ogun, Ondo,
and Oyo) that comprise the south-west zone were
selected for the study.
Data and Sampling Framework
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A total sample of at least 841 households The survey was
carried out between August and October, 2011.
Selected State
Characteristics Ekiti Ogun Ondo Osun Oyo All
All Enumeration Areas (EAs) 11561 12754 19213 25910 31137 100575
All Local Government Areas (LGAs) 16 20 18 30 33 117
Sample LGAs 2 3 4 5 6 20
Sample EAs or communities 8 12 16 20 24 80
Sample households 88 125 175 209 244 841
Data and Sampling Framework
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Methods
• A relative asset poverty line was defined as two-thirds
of the mean per capita total household asset
ownership ( in term of value).
• The asset poverty status of each household was
determined based on the per-capita ownership of
total assets.
• The standard FGT - Foster, Greer, and Thorbecke
(1984) index was used to compute the asset poverty
indices.
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• Propensity Score Matching
(PSM) Approach
– Nearest neighbourhood
• Endogenous Treatment
Effects
– Exposure as instrument for
adoption
Methods
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Exposure and Adoption Incidence Rate
Variable Parameter Robust std.
Error
Z-value P>|Z|
Awareness 0.6811*** 0.016 42.73 0.000
Adoption 0.4825*** 0.017 28.23 0.000
Adoption among exposed 0.7084*** 0.025 28.23 0.000
Total Number of obs. 856
Note: ***significant at 1%.
• Majority of the farmers (68%) were aware of at least one ICV.
• About 48% of the farmers have been planting at least one ICV.
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Variable Total
N=850
Adopters
N=413
Non-
adopters
N=437
Mean
difference
t-test
Total assets
ownership (N)
86042.9
100563.8
72505.35
28058.46*** 4.55
Per capita total
assets ownership
(N)
20772.5
24041.23 17725.30
6315.93***
4.25
Descriptive Statistics of some Indicators of
Poverty and Welfare by Adoption Status
Note: ***, **, significant at 1 % and 5 %, respectively
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Poverty Indices Total sample Adopters Non-adopters
Asset poverty
Headcount ( %)
53 46 59
Asset Poverty
Depth (%)
35 30 40
Asset Poverty
Severity (%)
27 23 31
Estimates of asset poverty indices by
adoption status
• The relative asset poverty line, estimated at N13848.39 ($81.46) per annum
• The proportion of adopters of ICVs that was asset poor is about 46%
compared to 59% for the non-adopters.
• The depth and severity of asset poverty was also higher among the non-
adopters
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Impacts on asset ownership and
asset poverty
Nearest Neighbour Matching
Parameters Per capita asset ownership Asset poverty
Adopters Non-
Adopters
Difference Adopters Non-
Adopters
Difference
Avg. Effect on
Adopters
24143.26 18569.95 5573.32** 0.4653 0.5693 -0.1039*
Avg. Potential
Effect on Non-
Adopters
18598.77 22141.75 3542.97 0.5701 0.5036 -0.0665
Avg. Effect on
population
- 4537.23 -0.0848
Avg. Effect on
adopters in
percentage
36.17**
Note: **, * significant at 5 and 10 %.
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Factors affecting total asset value
Coefficients Std. Error
Per capita total asset value (N)
Age (year) of HH head -87.77079* 48.53894
Farm size (ha) 1619.934*** 294.8542
Household size (Number) -3620.787*** 329.6498
Formal education (year) 379.1802** 154.6572
Gender (Male=1) 9590.455*** 2719.149
Adoption (thru awareness) 13879.84** 5959.602
Note: ***, **, * significant at 1, 5 and 10 %
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Conclusion and Implications • Asset ownership as a measure of poverty has potential of
accounting for the welfare dynamics
• Asset poverty is significantly lower among adopters of ICVs
compared to non-adopters
• Adoption of ICVs has led to greater asset ownership
• Scaling R4D innovation that led to grater adoption will help in
reducing poverty
• Further work using a nationally representative sample and/or
panel datasets to better capture poverty dynamics and asset
accumulations
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Thank you
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Factors affecting total asset value
Coefficients Std. Error
Adoption
Awareness 0.7737547*** 0.0903749
Constant -0.4456826*** 0.0676553
hazard
lambda -6127.988** 3042.559
rho -0.31003
sigma 19765.851
Wald chi2 (13) 257.10
Prob>chi2 0.000