Economic Impact Assessment of Improved
Maize Adoption on Poverty: Case Study of
Four West African Countries
Ygué Patrice Adegbola1*, Baudelaire YF Kouton Bognon2 and Pélagie M Hessavi2
1Institut National des Recherches Agricoles du Bénin(INRAB), Benin
2Centre International de Recherche et de Formation en Science Sociales (CIRFoSS), Benin
Submission: November 09, 2020; Published: November 23, 2020
*Corresponding author: Ygué Patrice Adegbola, Institut National des Recherches Agricoles du Bénin (INRAB), Benin
How to cite this article: Ygué Patrice Adegbola,Baudelaire YF KoutonBognon, Pélagie M Hessavi. Economic Impact Assessment of Improved MaizeAdoption on Poverty: Case Study of Four West African Countries. Int J Environ Sci Nat Res. 2020; 26(4): 556193. DOI:10.19080/IJESNR.2020.26.556193
This study provides a consistent estimate of the impact of the adoption of the improved maize varieties on Poverty reduction, using three econometric approach (ATE, LATE, MTE) estimation techniques. It uses cross-sectional data of 1069 farmers from four West African country. Results show that the adoption of the improved maize varieties has a positive and statistically significant impact on average annual per capita household expenditure. Specifically, the empirical results suggest that adoption of improved maize varieties helped raise household per capita household expenditure by an average of 15.72 CFA. The adoption of improved maize varieties reduced the incidence and intensity of poverty by 7.44 and 8.33 respectively in all country.
Keywords: Welfare, MTE, Impact, Maize, West Africa
Growth in agricultural output is one of the most effective means to address poverty in the developing world. Maize represents life to more than 300 million of Africa’s most vulnerable people and is Africa’s most important cereal crop . In Africa, about 70% of people and 80% of the poor live in rural areas and depend mainly on agriculture for their livelihood . The agricultural sector has seen a sharp decline in productivity rates in recent years . This situation is due to a set of constraints including the decline of soil fertility, the use of rudimentary tools, the application of unsuitable technical itineraries and the low access of producers to inputs, especially seeds of improved varieties.
Maize is the main cereal involved in the diet of populations in West Africa (Adjanohoun et al. 2012). It accounts for 42% of the dietary energy intake (FAO, 2012) , 32% of total protein consumption and 68% of the daily per capita cereal consumption (FAO, 2012).
It is also the most energetic cereal (Charcosset & Gallais, 2009) and the most economical from the point of view of production . Between 2005 and 2014, production, mainly for human and animal consumption, increased from 3,888,639 tons to 6,287,216 tons (FAO, 2016), a growth rate of nearly 62% in 10 years. However, despite all the efforts made, the maize supply is far from meeting
the ever-increasing demand and yields are becoming lower due to frequent climatic disturbances. It is therefore clear that the main constraint facing the maize industry is its low productivity. This constraint is fundamentally due to the improved varieties little used by the producers (MAEP, 2014). Thus, to contribute to the improvement of maize yield, it is necessary to substitute the local varieties which represent the essential of the plant material used (Missihoun et al. 2012) by the improved varieties with high yield potential . It necessary to demonstrate to farmers the contribution of adopting improved maize seed on human welfare. The main objective of this study is to analyze the economic impact of adopting improved maize on poverty and welfare in West African zone.
The study estimated an econometric model to assess the potential impact of improved maize adoption on farmer’s welfare in four West Africa countries (Benin, Burkina-Faso, Ivory Coast and Mali).
This paper differs from the earlier economic research on African agriculture in the two ways. First, it quantifies improved maize adoption impact on maize farmer’s welfare for four maize producer countries in West Africa zone. Second, this paper compares three econometric approach to estimate the effect IMV adoption on farmer’s welfare.
The remaining part of the paper is structured as follows.
Methodological framework is discussed in section 2. Section 3
discusses both descriptive and empirical analysis results and
section 4 concludes the paper.
Several methods are developed to assess counterfactuals,
drawing on the impact evaluation literature1. This study uses
the “potential outcomes framework” developed by Rubin  to
estimate the counterfactuals and compute the average impact of
the adoption of improved maize on the observed outcome variable,
here household expenditure. To describe the concept of “potential
results”, let us consider Yi
as the observed outcome variable, of a
producer i, who either does or does not adopt the improved maize
varieties. Let Ai
be the decision to adopt, to be taken in such a way
that Ai =1
if the producer adopted the improved maize varieties
and Ai =0
if he did not. By supposing that the equation of outcome
variable depends on the observed and non-observed factors, and
by using the notation of classical regression, the expenditure Yi
can be written as follows :
where λ, γ and β are unknown parameters to be estimated;
i x are independent variables; and εi
the error term. β is the mean
causal impact of A on Y in all observation units.
There are three main potential sources of bias in the
programme impact measurement . First of all, adopters are
likely to be different from non- adopters in the distribution of their
observed characteristics, leading to a bias due to the “selection on
A second source of bias in the impact may arise when there
is a dissemination of improved varieties knowledge within
the communities. In the presence of the dissemination, the
comparison between those adopting improved varieties and nonadopter
in the same village is likely to underestimate the impact.
A last source of bias is that those adopting improved varieties may
be different from non-adopter in the distribution of non-observed
characteristics (for example, in the capacity of the farmer that
affects at the same time the decision to use improved varieties and
the desire to look for new knowledge), which leads to the selection
on non-observables. In the absence of an appropriate instrument
to participate in the programme, it is difficult to control explicitly
the selection of non-observable factors. Observable and nonobservable
variables must be controlled, otherwise it is possible
to wrongly conclude that there is a relation of causality between
the use of improved maize varieties and the outome variables.
Estimating the impact of farmers’ participation in the activities
on the revenues is therefore likely to be biased. Thus, the correct
estimation of equation (1) needs the instrument Ai .
By admitting that the impact of adoption of improved maize
on the outcome variable (β) is heterogeneous, interaction terms
were included in the model. The impact of adoption of improved
maize varieties on the outcome variable (β) is therefore rewritten
as being a function of the independent variables x and of the nonobserved
Where x is a vector of sample means of x.
By replacing the value of β given by equation (2) in equation
(1), the estimation model is presented as follows:
Using the method of the instrumental variable to estimate the
impact of adoption of improved maize on the outcome requires
the presence of AiVi in the error term of equation (3). According
to Dimara & Skuras (2003), the decision of a producer to adopt or
not to adopt is determined by the expected utility resulting from
the difference between the expected services from the adopter
and the non-adopter. Producers, by taking into account their own
non-observed gain , and exogenous variables x may auto-select
themselves. This leads to a correlation between AiVi and z. The
conditional value expected of AiVi
given by (z, x), can be written
Wooldridge  demonstrated that:
where i ∅ (.) is a standard probability density function and the
The equation (3) can therefore be rewritten as follows:
The objective of this study is to estimate what the situation
of the producer who adopt IMV would have been if they did not
choose to adopt it. To solve the problem of selection bias and
generate estimations with fewer possible biases at the level of
the impact results, and the counterfactual approach based on the
method of instrumental variables (VI) [7,10], was used.
Learning from Wooldridge , the heterogeneous model
of the impact of adoption of improved maize varieties on the
households expenditure for instance presented by equation (6)
was estimated in two steps.
1More comprehensive and detailed surveys can be found in Blundell and Costa Dias (2000), Wooldridge (2002) and Lee (2005).
The first step consisted of estimating a probit model of the
factors that influence the probability to adopt improved maize
varieties on the exogenous variables x and z. The exogenous
variables x is common to the adoption and households expenditure
equation while the exogenous variables z belong exclusively to the
adoption equation. This “exogeneity” restriction of the variables
z is determining for the estimation of the household expenditure
model to be consistent. The specification of the model of adoption
is presented as follows:
In the second step, the impact model of the adoption of IMV
on the household of equation (6) was estimated. In this study, to
correct the problem of noncompliance we use the instrumental
variable (IV) approach. The variant of the IV approach adopted in
this study is the local average treatment effect (LATE), introduced
by Imbens & Angrist  and is estimated as follows:
Where z is an instrument, indicating the adoption status
variable, with z = 1 if the farmer was adopted IMV and z = 0
otherwise. Note that, t1 is the treatment status of a farmer who
was adopted IMV and t1 = 0 if the farmer was not adopted IMV.
LATE parameter gives the treatment effect only for those who
decide to participate because of a change in z, and they are often
referred to as compliers in the impact assessment literature
. The mean impact of the adoption of IMV on poverty in the
subpopulation of the compliers is given by Imbens & Angrist ,
Imbens & Rubin (1997). In this study, for the LATE estimation, the
estimator proposed by Abadie  was used. This estimator is a
generalization of the one proposed by Imbens & Angrist  and
for which the randomness of the instrument is not required or
instrument is independent from y1i
conditionally from x .
This estimator requires using at least an instrument z that affects
directly the status of adoption but indirectly the results y1i and y0i
once the independent variables x are controlled.
The instrumental variable, in this study, is the knowledge
the existence of improved maize varieties () with z =1 for the
producers who know of the existence of improved maize varieties
and z = 0 for the producers who don’t. In fact, the choice of this
instrument is justified by the fact that knowing the existence of
improved maize varieties can influence adoption of improved
maize varieties. It is the producers who are aware of the existence
of improved maize varieties that can be use it. On the contrary,
the fact of being aware of the existence of improved maize
varieties does not influence directly the household expenditure.
A producer may be aware of the existence of improved maize
varieties and still not adopt improved maize varieties. Thus, to
adopt improved maize varieties, it is not enough to be aware of
its existence. In summary, being aware of the existence of the
improved maize varieties may influence its adoption but does not
influence directly the household expenditure. Thus, this variable
respects the definition of the instrument as presented by Abadie
 and Heckman .
According to Abadie  and Lee , the average impact for
the sub-population of potential participants (LATE) can be used
from the function of Local Average Response Function (LARF) »
In addition to the instrumental variable introduced in the
impact model, other independent variables were introduced based
on the literature on the household expenditure determinants (for
the expenditure model).
Since the treatment is self-determined by farmers, to account
for potential endogeneity, the most plausible hypothesis is
“selection for unobservable” . Therefore, to eliminate both the
bias induced by the observable and unobservable characteristics
and the treatment variable, the MTE was used. This approach
unifies not only the literature of treatment effects but also
provides a consistent economic explanation of LATE. According
to Heckman , it is possible to define the MTE parameter as
The MTE parameter, defined by a conditional expectation, is
obtained independently of an instrument . It is defined as
the average income of adoption for individuals with observable
characteristics X = x and unobservable UA = u . For individuals with
a value close to zero, the MTE is the expected effect of treatment
on individuals who have unobservable characteristics that make
them more likely to adopt improved varieties and who would have
adopted even if UA(Z) utility is low.
The data used for this study was collected by national partners
of West and Central African Council for Agricultural Development
(WECARD) in Benin, Burkina Faso, Ivory Cost and Mali. Household
data on income, household and production characteristics
were collected from 1,068 maize framers randomly selected in
4 countries (Benin, Burkina Faso, Ivory Cost and Mali). These
countries are amongst maize producing countries in West Africa
and are all in the same agro-ecological zones. In total, the survey
was conducted in 15 districts across the four countries. In each
country, districts were chosen to get a wide representation of
farms across vulgarization zone. In each selected district, survey
was conducted on randomly selected maize farming households.
The number of surveyed households varied from country to
country. A sample of 252 households were randomly chosen in
Benin, 385 in Burkina Faso, 225 and 207 in Ivory Cost and Mali,
respectively. The number of surveyed households per adoption
status and per country is shown in Table 1. The data collected at
farm-level was for the season 2016-2017. The data include :
2 y1i and y0i represent respectively the variable of interest (the household expenditure for example) if the producer has adopted improved maize
varieties or not .
a) the socio-economic characteristics of the agricultural
b) the characteristics of the farms;
c) the quantity of inputs;
d) the value of outputs; and
e) the farmer’s adoption status.
Two models were used for estimation. At the first stage,
we introduce based on the literature on the adoption of IMV
determinants and finally the socio-economic variables. This first
model (adoption Model) is estimated with Probit regression
using Stata Software. At the second stage, based on the literature
on the expenditure we integrated determinants into the model.
The impact model are estimated using a MTE. For each of these
models, we also estimated separately for adoption and impact
The estimation model consists of two dependent variables:
adoption and household expenditure. The adoption variable
indicated whether the farmer adopted or rejected at least one
maize storage innovation. Household expenditure measured
the total amount of money which the household devoted to
consumption during the survey year. Sample means for household
expenditure for each country are computed for adopters and nonadopters
of IMV and reported in Table 2. Descriptive statistics
show that farmers who adopted IMV spent on average more on
their house consumption than did non-adopters.
The study adopted the relative poverty line3 which was
calculated as 2/3 of the mean per capita household expenditure.
The calculated poverty line for all countries is equal to z= 379.49
CFA ($0.68) per day. Households with per capita expenditure
below this poverty line are classified as poor and non-poor
Descriptive statistics by adoption status and test of
The result of the descriptive statistics for some selected
variables by adoption status is presented in Table 3. The
statistically significant differences in the mean of some of the
variables between adopter and non-adopter show that the two
groups differ in some characteristics. Hence, the use of mean
difference between the outcomes of the adopter and non-adopter
will not have any causal interpretation. It also presents the
statistical test of the average difference between the two groups.
This allows us to know the variables likely to influence the impact
indicators. Generally, these results show that the two groups
(adopter and non-adopter of IMV) presented many similarities
in socioeconomic characteristics, except for the variables where
the mean difference was statistically significant. Therefore,
we can suspect that the differences observed in the household
expenditure were due to this significant variable.
To correct these differences, these variables were introduced
in the impact adoption model of IMV in addition to other
determinants of the expenditures and poverty.
3The study uses the three poverty measures proposed by Foster et al. (1984) which is formulated as follows: ,wherezis the poverty line, ciis
the per capita consumption expenditure of farmer i, and n is the total number of farmers. According to Foster et al. , αis a policy parameter that takes on values
of 1, 2, and 3.When αis 1, it gives the headcount index, which is the proportion of poor farmers in the population. When it is 2, the result of the analysis provides the
poverty deficit or gap index. When it is 3, it gives the poverty severity index which signifies the level of income inequality in the population.
Impact of IMV adoption on household expenditure
The results of the estimation of the impact of the adoption of
at least one IMV on household expenditure are presented in Table
4. The Fisher test showed that the models are globally significant
at the 1%. In addition, the variation in household expenditure
is explained by 54% of the explanatory variables in all the subzones.
This rate was 47% and 56% respectively in Benin and Côte
d’Ivoire; 67% and 61% respectively in Burkina Faso and Mali.
The results of the estimation show that variables such as,
adoption status, secondary level, distance from village to city,
Years of experience in maize production and contact with at least
one extension service are all significant at least 10% (Table 4).
Together, they determine the variation of the adoption of IMV
on household expenditure. The average impact of the adoption
of IMV on household expenditure of a randomly selected maize
producer is 14.95% and significant at the 5% threshold for all
sub-areas. In other words, for an adopter of the improved variety
randomly selected as a whole, the relative difference in expected
household expenditure is about 14.95%. The highest average
impact was observed in Benin in the coastal sub-zone (12.32%)
and in Mali in the Sahel sub-zone (16.43%). In addition, compared
to non-adopters of improved varieties of maize, the household
expenditure of adopters increases by about 15.72% (MTT) in all
sub-areas (Table 5).
4The exchange rate at $1 to 550CFA.
Poverty analysis of the farmers before and after the
The impact of adopting at least one IMV on the poverty status
of producers was measured through the Foster-Greer-Thorbecke
(FGT)  index at the sub-coastal zone level (Benin and Côte
d’Ivoire), the Sahel sub-zone (Burkina Faso and Mali) and all
sub-zones (Table 6). The results showed that the poverty rate in
all sub-study areas is high (between 33% and 40%). Producers
who have adopted at least one improved variety of maize are less
poor than those who have not adopted it. Indeed, the adoption of
IMV reduced the incidence of poverty by 4.37% and the intensity
of poverty by 7.44% in all countries (Table 7). At the sub-zone
level, the impact of the adoption of IMV on the poverty status of
producers is more remarkable in the coastal sub-zone (9.32% for
Benin and 10.99 for Côte d’Ivoire) than in the Sahel sub-zones
(3.29% in Burkina Faso and 3.52% in Mali). The adoption of these
improved varieties increases yield, which improves producers’
incomes and thereby contributes to poverty reduction.
This study broadly assesses the impact of IMV on poverty
reduction among smallholder maize farmers in four West African
countries. Specifically, the study examines how the use of the
IMV has impacted annual household per capita expenditure and
reduces the poverty headcount index among rural smallholder
maize farmers. The results reveal that all the observed poverty
indices declined after the adoption. The adopter farmers have on
average a higher total annual household expenditure compared
to the non-adopter farmers. Results showed a significant impact
of IMV adoption on household expenditure in West African rural
households. These results confirm the potential and role of using
agricultural technologies in improving household welfare. The
use of improved technologies of maize production can be used as
an effective instrument to fight again food insecurity and poverty.
Studies on the impact of the adoption of maize on poverty in all
maize-growing areas of Benin are necessary, so that substantial
measures are taken.
The authors express their gratitude to all the staff of the
West and Central African Council for Agricultural Research
and Development (CORAF / WECARD) for the involvement and
availability they have shown in putting the present mission in the
better working conditions. Special thanks also go to the Executive
Director of WECARD and Director of the National Center of
Specialization on Corn (CNS-Maïs) for his accompaniment in the
design and implementation of this study. We would like to thank
the International Center for Research and Training in Social
That the authorities of the General Directorate of the National
Institute of Agricultural Research of Benin (INRAB), find here
the expression of our deep gratitude for various scientific and
administrative support. We would also like to thank the Burkina
Faso, Mali and Ivory Coast work teams who actively contributed to
the completion of this study.
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