impact of agricultural technology adoption on asset ownership: the case of improved cassava...

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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)


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


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


• 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


Classification of Assets

List of Assets in Study

Hoe Phone

Machete Television set

Wheel barrow Radio

Goats Bicycle

Cattle Motorcycle

Chickens Vehicle

Sheep


Varietal Adoption

Asset Accumulation

Income flow

Cassava R&D

Poverty

Productivity

Home consumption

Cash incomes

Health/Nutrition

Asset-poverty Impact Pathway


• 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


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


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.


• Propensity Score Matching

(PSM) Approach

– Nearest neighbourhood

• Endogenous Treatment

Effects

– Exposure as instrument for

adoption

Methods


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.


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


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


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 %.


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 %


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


Thank you


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

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