Working Paper No. 2015/06
Agricultural Technology Adoption and Market Participation under
Learning Externality: Impact Evaluation on Small-scale Agriculture
from Rural Ethiopia
Tigist Mekonnen Melesse¹
September 2015
© The author, 2015
¹ PhD fellow, UNU-MERIT, Maastricht
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Agricultural Technology Adoption and Market Participation under
Learning Externality: Impact Evaluation on Small-scale Agriculture from
Rural Ethiopia
Abstract
Adoption of improved agricultural technologies is central to transformation of farming system and a path out of poverty in developing countries. The aim of the current
study is to provide empirical evidence on the impact of improved agricultural technologies (HYVs and chemical fertilizer) on smallholders’ output market participation. The
analysis is based on Farmer Innovation Fund (FIF) impact evaluation survey data covering around 2,675 households collected by the World Bank in 2010-2013 in Ethiopia.
Endogenous treatment effect and sample selection models are employed to account for
the self-selection bias in technology adoption and market participation. Regressions
based on matching techniques are employed for robustness check. The main results
shows that adoption of improved high-yielding varieties (HYVs) and chemical fertilizer
is found to have a positive and robust effect on smallholders’ marketed surplus. We
found evidence that adoption of improved HYVs increases surplus crop production by
757 kg, whereas adoption of chemical fertilizer increases surplus by 285 kg. When
the two technologies are adopted jointly, marketed surplus is found to increases by
635 kg, which establishes the complementarity of the two technologies. The result
also shows that farmers’ surplus crop production and market participation is determined by access to modern inputs, cereal crop price, farm size, availability of labor,
and infrastructure facility. Access to credit and training fosters technology adoption,
however, we are unable to witness learning externality from neighbors on smallholders
marketed surplus. Therefore, agriculture and rural development policy needs to focus
on supporting agricultural technology adoption.
Key words- Smallholders, market participation, technologies, treatment effect model
JEL classification- D04, O12, Q13
1
1
Introduction
One importance of studying development economics is to understand how individuals
are able to make transition out of poverty. Adoption of improved technologies may be
viewed as a means of these transition since improved agricultural technologies is central
to transformation of farming systems and a path out of poverty in developing countries
(Besley and Case, 1993). It is considered that modern agricultural inputs enable
resource poor smallholder farmers to produce marketable surplus and quality products.
In developing countries, the need to promote smallholders market participation is an
important effort necessary to bring agricultural transformation (Braun et al., 1994).
Improving productivity, profitability, and sustainability of smallholders is the main
pathway out of poverty using agriculture for development (WorldBank, 2008).
Producer’s production technology choice might affect the productivity. Increased
productivity gain is not an end by itself and the production should be linked to the
market for driving profits. Increasing farmers’ potential in productivity and marketable
surplus requires substantial diffusion of modern agricultural technologies. Thus, commercialization is the result of a simultaneous decision making behavior of farm households in production and marketing (Braun et al., 1994). Pingali and Rosegrant (1995)
argue that commercializing subsistence agriculture is an essential pathway toward economic growth and development for developing countries. On the one hand, commercial
transformation of subsistence agriculture is expected to result in positive impact on
household food security and welfare Pingali (1997); on the other hand, improving rural farmers’ access to improved technologies and productive assets might stimulate
smallholders market participation (Barrett, 2008).
Sub-Saharan African countries where agriculture is the predominant sector that
underpins the livelihood of the majority of the poor, increasing technology adoption
such as new agricultural practices, high-yielding varieties, and the associated products
such as crop insurance have the potential to contribute to economic growth and poverty
reduction among the poor (Kelsey, 2011). According to Ravallion et al. (2007) many of
the poor in SSA and South Asia are living in rural areas and they are farmers. Nearly
75 percent of those living in less than one dollar a day will remain rural until 2040.
Thus, there is a direct link between poverty reduction and increasing the productivity of
agriculture. It can also create employment opportunity for the landless wage laborers.
The pathway out of poverty trap of many SSA countries depends on growth and
development of the agricultural sector. Thus, a more dynamic and inclusive agriculture is required to accomplish the Sustainable Development Goals (SDGs). Among
others, one of the main objectives of the sustainable development goals is eradicating
extreme poverty, hunger and investing in rural areas for inclusive and sustainable rural
transformation. This is possible by increasing agricultural productivity through yield
increasing technologies in order to sustain food self-sufficient. According to Economic
Commission for Africa ECA (2005) around 46 percent of the population in SSA lived
in less than 1 dollar a day in 2003 which is slightly more than in 1980 and in 1990, the
hunger and poverty reduction require that the income of the poor people and source
from which they derive their livelihoods should be enhanced. FAO (2002) also explain
in its report that pro-poor income growth originating in agriculture development that
could reduce poverty.
2
The growth of agricultural sector in SSA determines the economic growth by creating market opportunities for other sectors directly or indirectly (Asfaw et al., 2010).
Since long time ago, agricultural economics studies indicated, development in agriculture is the most effective way out of poverty that agriculture is at the heart of the
livelihoods of poor, while constraints operating on agriculture especially in rural areas is challenging. In response to this, institutional and infrastructure development is
necessary to ensure broad-based, low cost market access, and well-functioning input
and output marketing. Thus, rural farmers must have access to productive technologies and adequate private and public goods to participate into input and output
marketing (Barrett, 2008). The key to expand marketing opportunities of developing
countries is increasing agricultural product in international, regional, and domestic
markets (WorldBank, 2007). Thus, agricultural production is closely related to marketing, market access, and market development, whereas the poorly functioning of
markets, weak domestic demand, and lack of export possibilities are the major constraints for agricultural growth (Diao and Hazell, 2004).
Agriculture in most SSA countries is subsistence and they face challenges to gain
income from marketed surplus. One of the major challenges is the use of local varieties
for crop production. For instance, for many decades, the export of agricultural product
in Ethiopia dominated by a few commodities like coffee, oil-seeds, pulses, Khat, and
livestock products (leather, live animals and meat). Largely, coffee accounts for foreign
exchange earning about 65 percent (Rashid et al., 2011). While, export from cereal
crops is negligible; weak market for agricultural commodity in Ethiopia (WorldBank,
2005). Besides, the increased production of traditional export crops has not been
translated into much growth in farm income due to the downward trends in world
prices (Diao and Hazell, 2004). This is because many exporters from Asia and Latin
American countries who have improved product diversification and quality increased
the demand of importing countries. Asfaw et al. (2010) argue that the low productive
local varieties limit farmers from market competitiveness. ECA (2007) also reported
that SSA is the developing world region that the highest dependence on exports of
traditional primary agricultural product.
Although in some African countries the potential gain from traditional crop exports
are high, for instance, the export of cocoa from Cote d’Ivoire and Ghana, tobacco
from Zimbabwe and Malawi, the region wide gains are small. According to Diao and
Hazell (2004), even though African traditional export crops regained their ground and
returned to their historic highs in terms of world market shares equivalent to annual
growth rate of 6 percent, the per capita real agricultural income for all of Africa
would grow only by 0.3 to 0.4 percent. The small income of traditional export crops
might result in small share of the total agricultural GDP; hence it may have weak
linkage to the rest of the domestic economy. Thus, production side investment and
increasing market development is the way to improve productivity and product quality
that increases farmers’ competitiveness in the domestic and international market.
Developing countries have started to promote the diversification of production and
exports through improved technologies away from traditional commodities in order to
accelerate economic growth, to create employment opportunities, and reduce poverty.
For instance, in Ethiopia since the market oriented policy reform in 1990, the government opened up for FDI into the agricultural sector. For example, the export from
3
flower farm industry experienced a boom growing from 0.3 million US dollar in 2001
to 113 million US dollars in 2007 (Joosten, 2007). Gebreeyesus and Iizuka (2012) also
reported the flower farm industry in Ethiopia is an extraordinarily fast and successful
diversification into a non-traditional export product. Within less than a decade, the
country ranks second in Africa for flower exports next to Kenya, and fifth in non-EU
exporters to the EU market. The same experience of Zambia; the export of fresh
vegetable and cut flowers grown on smallholder farm rose from 6 million US dollar in
1994 to more than 33 million US dollar in 2001 (Diao and Hazell, 2004). This accounts
for around 40 percent of total agricultural export. Thus, market oriented agricultural
production coupled with technological progress and improvements in institutional and
infrastructure development results great impact on economic development (Minten and
Barrett, 2005).
Since the export gains from horticultural crops discussed above signify a progressive
and remarkable result, replication to other commodities such as cereal crops requires
appropriate technological investment. This is because those staple food crops are
important for the growing population food security and is the major source of income
for smallholders. Besides, more than 70 percent of the rural population in Ethiopia
produce cereal crops Shiferaw and Teklewold (2007), however lack of technological
change and market imperfection have often locked smallholder farmers into subsistence
production and contributed to stagnation of the sector.
According CSA (2014) report when we compare to other horticultural crops, the
share of cereal crops is higher. From the total cropped area and production of grain
crops, the share of cereal crops is around 78 percent and 86 percent, respectively.
The statistical figure shows that more than 96 percent of the total grain production
produced by peasant farmers, while commercial grain production takes only around
4 percent. However, even though the share of grain cropped area less for cereals in
commercial farms, the highest proportion of total grain production comes from cereals.
The differences in improved input usage and farm management during the survey year
between smallholder and commercial farmers shows some differences in crop yield
level (CSA, 2014). The lack of knowledge and continuous use of low-yielding varieties
coupled with agro-ecological condition of the areas could be the major cause for lower
smallholders productivity. Thus, the low-yielding local varieties could limit farmers
from market supply and stagnate the sector into subsistence that farmers may not
even yield a ton per hectare.
For many years, the government of Ethiopia working with extension program diffuses agricultural technologies to improve smallholders’ crop productivity and farmers
income from surplus crop production. Paradoxically, recent study indicates that farmers use of main agricultural inputs such as HYVs is less than 5 percent (Taffes et al.,
2013; CSA, 2013). The low adoption rate and use can be partly explained by limited
access to input credit (ATA, 2014). Thus, there are initiatives by governmental and
non-governmental organizations among the rural households to participate in different
association such as agricultural cooperatives, farmer’s group, savings and microcredit
cooperative, other local groups. It is expected that farmers sharing and/or access information about new agricultural practices, inputs and output price information. Hence,
learning from others can play an important role in the adoption of new agricultural
inputs in rural areas. Either in a social or economic network, learning externality could
4
increase adoption of new agricultural technologies and encourage farmers’ participation
in input and output marketing.
There are studies indicating farmers can adopt new agricultural inputs from their
neighborhoods. According to Foster and Rosenzweig (1995) individuals may act like
their neighbors in the adoption of new technologies, hence friends and neighbors are
important sources of information for fertilizer adoption in rural India. This is because,
new user of technology may learn its characteristics from each other. Such learning
can influence smallholders’ decision in input choices and method of application which
enables smallholders’ marketed surplus. Conley and Udry (2010) also claim that farmers increase or decrease fertilize use when their neighbors, using more or less fertilizer
than them makes unexpectedly high profits from surplus crop production. Munshi
(2007) reported that due to sequential information flow from one neighborhood to the
next, social learning provides a natural explanation for the gradual diffusion of new
technologies even in a homogenous populations. This learning can explain the wide
variation in the response to external interventions across and otherwise within identical
communities simply as a consequence of the randomness in information signals that
the society received. Thus, learning does not takes place in isolation, rather it involves
a range of actors and networks; both in the formal and informal way (Gebreeyesus and
Iizuka, 2012).
With these background, this study analyzes the implication of improved agricultural technology adoption which in turn affects marketable surplus production in the
presence of social network externality among rural smallholders in Ethiopia. As many
countries secure high prices by raising the quality of products, establishing grading
system, and segregating different qualities for export; improvements in input usage,
market access, and credit may enable smallholder farmers to compete in the domestic
and international markets. Therefore, analyzing the implication of HYVs and chemical
fertilizer on smallholders market participation is needed to guide the type of intervention needed to facilitate the commercial transformation of subsistence agriculture.
Using household level data from Farmer Innovation Fund (FIF) impact evaluation
survey collected by the World Bank, this paper evaluate the impact of improved technology adoption on smallholders’ output market participation. The paper also assess
the issue of neighborhood effect in the adoption of improved agricultural technologies
among rural smallholders in Ethiopia. Since technologies may not be randomly assigned, households endogenously self-select by themselves. The self-selection bias in
technology adoption and market participation are captured using endogenous treatment effect model. The main results shows that farmers use of high-yielding varieties
(HYVs) and chemical fertilizer is found to have a positive and robust effect on smallholders’ marketed surplus. It indicates that HYVs increases surplus crop production
by 757 kg, whereas chemical fertilizer use increases surplus by 285 kg. When the two
inputs are adopted jointly, marketed surplus is found to increases by 635 kg, which establishes the complementarity of the two technologies. The other potential exogenous
factors which increase smallholders’ market participation are access to modern inputs,
cereal crops price, farm size, availability of labor and infrastructure. Access to credit
and training fosters technology adoption; however, we are unable to witness learning
externality from neighbors on smallholders’ marketed surplus.
The rest of the paper is structured as follows. Section 2 gives an overview of the
5
cereal crop marketing system in Ethiopia. Section 3 presents the theoretical framework
of the paper and the empirical model. Section 4 presents the detail of the data source,
and descriptive analysis of the data. The econometric results are discussed in section
5. Section 6 concludes with policy implications drawn from the results.
2
Overview of cereal crop marketing in Ethiopia
Although agricultural production system in Ethiopia is small-scale, cereal crops is the
most important food crops that the majority of the rural households produce for consumption and receive income by selling the produce. Usually commercialization is
discussed with respect to large scale farming systems and ignoring smallholders and
poor farm households participation. Pingali (1997) argued that markets allow households to increase their incomes by producing the highest returns to land and labor.
This cash can be used to buy necessary household goods rather than be constrained to
produce everything that the household needs. Considering the fact that small farmers
produce only a little surplus, the earned income may not help them to overcome the
poverty trap.
Since subsistence agriculture is not a viable option to ensure food security and
household welfare, commercializing subsistence agriculture is one of the development
strategies that Ethiopian government has adopted through Agricultural Developmentled Industrialization (ADLI) policy. After the economic reform of the country in
1991, grain marketing has been liberalized, and restrictions on grain trade have been
lifted. As a result official pricing on grain trade have been eliminated in Ethiopia.
However, according to Gebremedhin and Hoekstra (2007) smallholders are unable to
benefit from the commercialization process due to low yielding crop varieties, poor
rural infrastructure, high translation cost, and inadequate services. Spielman (2008)
also reported it is an unfortunate fact that the poor and those in remote areas have so
far gained little from commercializing subsistence agriculture. To ensure the benefit of
rural smallholders from commercializing subsistence agriculture, government and other
development agencies are confronted with the challenges of increasing participation in
input and output markets or providing exit options for employment in other sectors
(Gebremedhin et al., 2009).
Although public agricultural extension programs in terms of new technology awareness creation resulted positive effect among the rural households, output side investment is poorly functioning. Spielman (2008) also reported that education and training
of producers and traders is sadly neglected in Ethiopia, and hence detailed studies of
the training needs of various sectors of the grain marketing system are required. The
marketing routes of cereal crops in Ethiopia ranges from local assemblers and traders
to inter-regional grain traders and brokers. Due to free marketing system, traders are
more or less setting the price during transactions. Besides, the high transaction costs
force rural farmers to sell their farm output in the village market at a lower price
relative to price in the urban markets. Hence farmers are unable to take advantage
of a potentially high crop output price. To support rural farmers, central and local
government authorities support farmers to sell either to cooperative unions or other
organization.
6
The increased demand for staple food crops is linked to investments in productivity
enhancing technologies and measures to reduce marketing costs that have significant
impact on poverty reduction and economic growth (Diao and Hazell, 2004). Thus, the
operation of grain marketing system in Ethiopia needs investments on both production
and marketing fronts. According to Gebremedhin and Hoekstra (2007) creating good
marketing institutions is necessary to support sellers and buyers interactions. Shiferaw
et al. (2008) argue that the functioning and structure of marketing system specially for
staple food crops is mainly constrained by factors such as lack of grading and quality
control system, inefficient market information delivery, lack of well coordinated supply
chain, under developed infrastructure and high transaction costs.
Thus, production side investment such as agricultural input credit, rural education,
training and improving storage facilities prolonging the credit repayment period until
the seasonal crop prices rise might increase the benefit of smallholders from marketed
surplus. Kelly et al. (2003) claims that most agricultural input credit are expected
to be paid back repay at the time when farmers collect farm output. While, the
bulk ration of cereals especially during harvesting time will make farmers loose from
their marketed surplus. Besides, the high transaction costs may not offset the credit
accessed. To this end, providing agricultural inputs credit and other support services
will result in well-being of rural smallholders.
2.1
Relationship between market and technologies
Analyzing the relationship between market and technology is complex. For this study,
we have looked at the interdependence between market and technologies from households perspective. Assuming that a market is equally important as technology adoption. Households market participation decision and technology adoption decision are
mutually interdependent. It is expected that households input technology choices
might affect their market participation. On the contrary, the returns from adoption
of improved agricultural production technology choice can be influenced by the nature
of the market. The is because the increased crop production due to the use of yield
increasing technologies may face significant price risk.
Markets also create networks between farmers, public, and private agencies in buying superior technologies and selling the produced farm outputs to expand their earning
potential. Thus, markets set a legal and institutional context for economic transactions, and provide a physical place for such transactions to take place. Farmers input
choice may be linked to the physical and spatial dimensions of local markets, especially
the extent to which households in rural areas have access to markets and a range of
service providers. The importance of markets on the one hand, the opportunity for
farm households and other rural enterprises to trade or sell farm output. On the other
hand, it buy improved agricultural inputs or tap into a range of public and private
services such as extension and credit access. The more accessible the markets are,
the greater the rural populations capability to remain economically self-sufficient and
maintain food security. Similarly, greater are the options for diversifying production
and marketing that results in improving the livelihood strategies of rural population.
7
3
Theoretical Framework
There is a large body of empirical literature that documented agricultural technology
adoption reduce poverty (Evenson and Westphal, 1995; De Janvry and Sadoulet, 2002;
Trigo and Cap, 2004; Yu et al., 2011). Other studies indicated the benefit in terms of
market surplus crop production (Pingali and Rosegrant, 1995; Pingali, 1997; Timmer,
1997; Diao and Hazell, 2004; Barrett, 2008). Since an investment in new technology
is also an investment in learning about improved technologies; the introduction of
such technologies are either by farmers own experimentation or through government
intervention. It is also argued that the social networks facilitate the diffusion of new
technologies (Evenson and Westphal, 1995). However, analyzing the impact of technology adoption on smallholders’ market participation linking with rural social network
system is challenging. This is because defining the set of neighbors from whom an
individual can learn and the interdependence of technology preference to the neighbor
may be subject to unrelated risk. Conley and Udry (2010) explained that distinguishing technology learning from other phenomena that may give rise to similar observed
outcomes is problematic. This is because in the absence of learning farmers may act
like their neighbors as a result of interdependence.
Thus, empirical estimation of the impact of technology adoption on smallholders
market participation linked to the rural social networks is rare due to limitation of
data about information on interconnectedness (Conley and Udry, 2010). However,
enormous theoretical literature on social learning. Indeed, Bandiera and Rasul (2006)
report social learning as a nonlinear process demonstrated in the adoption of sunflower
in northern Mozambique. Munshi (2004) demonstrate that information flows related
to new technology are weaker in a heterogeneous population. This study however,
estimates the impact of smallholders improved technology adoption on market surplus
crop production to contribute to the empirical literature that use micro-level data measuring the intensity of market participation under learning externality. Bandiera and
Rasul (2006) indicate that given the possible unavailability of direct data on information interconnectedness, assuming the observed relationship between households’ like
geographical proximity to the unobserved flow of information about new technologies
is critical. Thus, the data set we use is at villages level survey having more information
on the rural households technology adoption behavior, it allow us to incorporate the
neighborhood effect in the adoption of HYVs and chemical fertilizers that is expected
to enhance smallholders profitability through surplus crop sale.
Suppose a farmer expected profit from adopting a new set of technologies is γxit +
ψit , where ψit = g(ψit−1 , It−1 , xit )+it . Where It−1 is farmers previous year experience
about new agricultural technologies up to t − 1 year. ψit is either farmers perception
about new technologies or their adoption behavior which may varies depending on
household wealth, access to information from neighborhood, credit availability, training, rural education, and so on. Because of farmers characteristics, xit may varies at
a time t , ψit may bias the parameter estimates of γ (shown below), but it gives us
insights about farmers characteristics are associated with ultimately accepting the new
technologies. Although the data set we use is cross-sectional in nature, the information
on the adoption process allow us to look at the dynamic structure of smallholders agricultural technology adoption by creating a discrete choice model of our sample house-
8
holds. Let’s, dit=1 , if farmer i was adopting a new technologies at time t, t ∈ [1, ..., τ ]
and zero otherwise. Thus, a probit model can be
prob{dit=1 } = φ((γxi + ρT + σ[T Xxi ])/σu )
where T is a set of τ − 1 year indicators and T Xxi are the interaction terms that
allows the effect of farmers characteristics to change over the adoption process, and ui
is an independently and normally distributed. From this the profitability of farmers
from new agricultural input and information access from rural social network system
increase their adoption behavior. Indicating that due to knowledge externality, an
individual use of new inputs could be as a result of practices or behavior of other
individuals and these externalities affected others decision and practice with respect
to farmers agricultural practices or adoption choices. Earlier the model of agricultural
technology adoption and information externality using improved cotton cultivars was
described by (Besley and Case, 1994). This model is redefined later by Foster and
Rosenzweig (1995) on wheat and rice varieties adoption study in India. Thus, the
model indicates that farmers increasing marketed surplus crop production depends on
the use of improved inputs, and therefore the rural social networks system increase
farmers adoption behavior.
3.1
Empirical model
The empirical analysis of this study is estimating the impact of agricultural technology adoption on smallholders’ output market participation. Since technologies are not
randomly assigned into households, farmers decide whether to adopt or not to adopt
and the decision to participate or not to participate into output marketing. Thus,
analyzing the impact of technology adoption and market participation may leads to
the self-selection bias. The heterogenenity of households in terms of household wealth,
institutional service, access to information and other relater factors, both the observed
and unobserved factors affects adoption and market participation decision, hence adoption and market participation both are potentially endogenous. The failure to account
for this potential selection bias may result in inconsistent estimates of the impact of
technology adoption (HYVs and chemical fertilizer) on smallholders marketed surplus
crop production.
Most empirical studies have used different econometric techniques to correct for
the selection bias. The most commonly used analytical approaches in the literature
includes the sample selection model (Winter-Nelson and Temu, 2005; Balagtas et al.,
2007; Alene et al., 2008) and switching regression model (Vance and Geoghegan, 2004;
Bwalya et al., 2013). Moreover, households participation into output market which is
the intensity of market participation may give rise to too many zeros. The traditional
approach to evaluate data that yields a censored dependent variable has been to apply
a Tobit model. In the Tobit model, all households including those censored zero values
are included without considering the source of the zeros. This is by ignoring all the
zeros that is; households decision not to participate in the market.
Assuming that all zeros that arise by non-participation decision of households could
be due to socioeconomic, demographic, institutional, information access, and other related factors (Newman et al., 2003). The two level decision of households in technology
9
adoption and how much is supplied to the market are taken to be the same in the Tobit
model, while the explaining variables may not be the same. To address the problem
associated with the censored observations generated by households non-participation
decision Heckman (1979) sample selection model is more appropriate than the Tobit
model. Unlike the Tobit model, this sample selection model considers that the censored observations arise mainly from the respondents self-selection. This is because,
the model assumes that the evaluation on the selected sub-sample which is the censored
observation results in the sample selection bias. By undertaking a two-step estimation
procedure, the sample selection model overcome the censored regression problem.
Recently, Guo and Fraser (2014) indicated that since the development of the sample selection model econometricians have formulated many new models and estimators.
One of the important developments in this direction is applying the sample selection
model to estimation of treatment effects in non-experimental data. Exceptionally,
in the case of treatment effect model, the selection equation of the dummy variable
indicates the treatment condition that directly enters into the outcome regression.
Thus, the outcome variable is observed in both cases of treated and untreated households. Following the recent empirical literature, endogenous treatment-effect models
is employed to account for the self-selection bias of adoption and market participation
decision. Regression based on sample selection model is measured to understand the
factors that determine households which do not participate in the output market (zero
values). Both endogenous treatment effect model and sample selection model allows
variables to freely vary in the second stage than variables in the first stage. Assuming
that market participation measured in volume of cereal crop sold is a linear function
of a vector of explanatory variables and a dummy adoption variable, a linear outcome
regression equation can be specified as
Yi = xi β + ti δ + i
(1)
Selection equation
t∗i = zi γ + ui , ti = 1 if t∗i > 0, andti = 0otherwise
(2)
prob(ti = 1|zi ) = Φ(zi γ)
(3)
prob(ti = 0|zi ) = 1 − Φ(zi γ)
(4)
And
Where i and ui are bi-variat normal distribution with mean zero and co-variance
matrix:


σ 2 ρσ


(5)


ρσ 1
Where, Y is the outcome variable households participate into output market, xi represent a vector of exogenous factors affecting market participation and intensity of
participation, t∗i is the latent technology adoption which is not observed, zi is the observed factors that affect households HYVs and chemical fertilizer adoption, ti is the
dummy variable whether households adopt or not adopt HYVs and chemical fertilizer,
β, δ and γ are the parameter to be estimated, and i and ui is the random disturbance
term associated with technology adoption and marketed surplus crop production which
has mean zero and constant variance.
10
As the sample selection model, the treatment effect model estimated in a two-step
procedure using maximum likelihood estimator. Let f (, u) be the joint function of i
and ui shown in equation (1) and (2). Thus, according to Maddala (1983) the joint
density of Y and t is specified as
Zzγ
f (y − δ − xβ, u)du,
g(y, t = 1) =
(6)
−∞
and
Z∞
g(y, t = 0) =
f (y − xβ, u)du
(7)
zγ
The likelihood function for household i is specified as
li = lnΦ
−z i γ+(yi −xi β−δ)ρ/σ
√
1−ρ2
2
√
− ln( 2πσ)(8)
1
2
yi −xi β−δ
σ
1
−
2
yi − xi β − δ
σ
−
for ti = 1
(
li = lnΦ
−z i γ(yi − xi β)ρ/σ
p
1 − ρ2
)
2
√
− ln( 2πσ)
(9)
f orti = 0
To understand the households that do not participate in output market (censored
dependent) because of other factors that prohibit from output market participation,
selection model is estimated by first estimating a probit in the first stage on whether
the household supplied crop output to the market. The inverse Mills’ ratio was then
generated and used in the second-stage to explain the volume of sale among crop
sellers. The probit model can be specified as
P rob(HM P = 1) = P rob(HCSOLD > 0)(10)
= F (Zm αm ) + u1 , u1 ∼ N (0, 1)
(11)
Where HM P takes the value of one if smallholders sold at lease one cereal crops during
the survey year (2010 and 2012), HCSOLD is the the total quantity supplied to the
market measured volume of sold in the above years, Zm is the potential variables that
expected to affect market participation, αm is the parameter to be estimated, u1 is a
random error term with zero mean and constant variance. Thus, a regression model
of the intensity of participation can be specified as
HCSOLD = Xm βm + γm λm + u2 , u2 ∼ N (0, σ 2 )
(12)
Where Xm is a vector of explanatory variables, βm is the parameter to be estimated,
and λm is the inverse Mills’ ratio which is calculated as λm = φ(Zm αm )/Φ(Zm αm ) in
order to account for the sample selection in the intensity of participation (of marketed
surplus), φ and Φ is the probability density function and the cumulative distribution
function respectively, γm is the the associated parameter to be estimated, and u2 is a
random disturbance term with mean zero and constant variance σ 2 .
11
3.2
Robustness Test
Since Heckman (1976) brought the sample selection model into modern economics literature, empirical studies have used this method to address the selection bias that rises
by self-selection. Our result also shows the correlation between the two error terms
in the main regression and selection equations is greater than zero (Table 3 and 4),
indicating that the unobservables that farmers increases the marketed surplus crop
production tend to occur with the unobservables that affects farmers improved agricultural technology adoption. Thus, both the endogenous treatment effect and sample
selection model address the endogenity of adoption and market participation. While,
due to a liner functional form assumptions the coefficients on the control variables
are similar for adopters and non-adopters of technologies, since the coefficients may
vary, this assumption may not hold (Becerril and Abdulai, 2010). Besides, there is a
normally distributed error terms assumption between the outcome equation and adoption equation. To assess the consistence of the result into different assumptions and
complement the above two models and check the robustness of the results, regression
based on propensity score and matching methods is used.
In evaluating the impact of HYVs and chemical fertilizer adoption on smallholders
marketed surplus crop production, the important issue is the specification of the average treatment effect in a counterfactual framework (Rosenbaum and Rubin, 1983).
The average treatment effect can be computed as
AT E = E(Y1 |D = 1) − E(Y0 |D = 1)
(13)
Equation (13) is based on the assumption that the marketed surplus that households
produced before technology adoption E(Y0 |D = 1) can reasonably approximated by
the market surplus that produced by non-adopters E(Y0 |D = 0). This is because we
do not observe E(Y0 |D = 1), we only observe E(Y1 |D = 1) and E(Y0 |D = 0). According to Rosenbaum and Rubin (1983) the ’propensity score’, conditional probability of
receiving treatment given that pre-treatment characteristics is specified as
P (X) ≡ P r{D = 1|X} = E{D|X}
(14)
where D = {0, 1} is indicator of households exposure into treatment and X is a vector
of households characteristics before treatment. Thus, estimating the treatment effect
based on the propensity score needs two assumptions. The Conditional Independence
Assumption (CIA) and Commune Support Condition (Becker and Ichino, 2002). The
Propensity Score matching (PSM) systematically estimating the counterfactuals for
the unobserved values E(Y1 |D = 0) and E(Y0 |D = 1) to estimates impacts with
the underling assumption of ignorability treatment (Rosenbaum and Rubin, 1983).
Due to the conditional independence assumption, once controlling for the observed
characteristic, the treatment assignment is as good as random. In the case of common
support condition, the average treatment effect for the treated is only defined within
the region of common support. From this assumption, households having the same
characteristics has a probability of being both treated and untreated (Heckman et al.,
1998).
12
To match similar technology adopters and non-adopters group, the commonly used
matching methods were extensively used in the literature are: Nearest Neighbor Matching (NNM) and Kernel-Based Matching KBM). The NNM method match each treated
households with the non-treated households that having the closest propensity score.
The NNM is usually applied with or without replacement of observations. In the case
of the kernel-based matching (KBM), it uses a weighted average of all households in
the non-treated group in order to construct a counterfactual. One of the main advantage of KBM is that it produces ATT estimates with lower variance due to utilizing
more information. Figure 1 shown the treated and non-treated sample households.
Above all, both the three models, endogenous treatment effect, sample selection
and matching methods solves the selection bias of adoption and market participation
decision. The sample selection model assume selection is affected by unobservable,
whereas both the selection model and matching technique assumes selection is affected
by observables. Hence, the matching methods addresses the selection on unobservables
by imposing the distributional and a liner functional form assumptions and extrapolating over regions having no common support (see table 6). This suggests that the
selection bias may be reduced by avoiding the functional form assumptions and imposing a common support condition (Heckman et al., 1998; Smith and Todd, 2005).
4
Data
The analysis is based on household level data collected by Farmer Innovation Fund
(FIF) impact evaluation survey conducted by the World Bank in 2010-2013 in Ethiopia.1
The survey covers 2,675 households drawn from two regions and fifty-one rural villages.
Since the majority of the rural households are employed in agricultural activities and
mainly receives income from this activities, the intervention of FIF was introduced in
Amhara and Tigray regions, Ethiopia. The FIF provides funds for farmers’ group to
implement innovative ideas which is developed though extension program and from
the existing knowledge.
The survey is a rich data set which contains valuable information on several factors determining technology adoption including household specific characteristic, asset
holding (farm and non-farm asset), institutional factors, indicators of infrastructure
facility, and households participation into output market. The survey also includes information on the rural social network system and membership of households in several
rural associations. Since the survey covers around fifty-one rural village, incorporating the effect of neighborhood in agricultural technology adoption in order to produce
marketable surplus crop is an important component. Bandiera and Rasul (2006) also
indicated farmers are operating agricultural activities in a competitive inputs and output market, hence it is reasonable to assume that there may have no social restrictions
in the adoption of new technologies through accessing information from neighborhood.
1
The Farmer Innovation Fund (FIF) is a component of the rural capacity building project conducted
in two regions (Tigray and Amhara), Ethiopia. The objective of FIF is to strengthen the extension
service, improving the rural households agricultural production system, marketing, and empowering
women participation in extension services. It was funded by the World Bank. The three rounds survey
collect a Baseline data in 2010, Midline survey in 2012, and Endline survey in 2013. For this study,
we used the Baseline and Midline data because of the Endline survey is unavailable.
13
This study thus, incorporate whether social learning can play an important role in
the adoption of improved agricultural technologies that can affect smallholders surplus
crop production in the process of commercializing subsistence agriculture. Based on
this data, we evaluate the impact of HYVs and chemical fertilizer adoption on smallholders output market participation under learning externality. Market participation,
measured in total cereal crop households sold in kg per year.
4.1
Descriptive statistics
Table 1 presents the summary statistics of the sample households. The study sample
households are 82 percent male and 77 percent are illiterate. Households using at least
one improved HYVs among the five cereal crops are 46 percent, whereas chemical
fertilizer users are higher, 80 percent. When we look at households using the two
technologies simultaneously, we found 41 percent of them. The percentage of our
sample households’ who participate in at least one crop output marketing is 33 percent.
On average, smallholders’ who participate in crop output market sold 324 kg per year.
As described earlier, the data set for this study is from Ethiopia Farmer Innovation Fund (FIF) impact evaluation survey conducted in Ahhara and Tigray region,
Ethiopia. The mean age of household head is 44 years, indicating that the farm
households in these two regions can therefore be describes as young and belongs to
economically active groups. This gives the opportunity that younger household heads
are more dynamic with regard to adoption of agricultural innovations. The average
land size of the rural households cultivated for cereal crop production is around 1.3
hectare per households. The rural households size found nearly 6 persons per households. The large household size may ensure adequate supply of family labor for crop
production and adoption of improved agricultural technologies.
With regard to adoption of HYVs and chemical fertilizer, Table 2 provides some
descriptive information on potential variables that is expected in determining rural
households new agricultural technologies adoption and marketable surplus crop production. Adopters category in HYVs do not seem to significantly vary in terms of age
level (around 44 years), educational status, farm size cultivated for crop production
(nearly 1.4 hectare), and use of irrigation technologies (39 percent). While they are
different on the rest of other variables in both cases of HYVs and chemical fertilizer
adoption.
Analysis of the descriptive statistics indicates that the two group of households
who adopted HYVs and chemical fertilizer are different regarding to services access
such as credit taken, participation in farmers training activity, membership in rural
associations and spending for improved inputs (HYVs and chemical fertilizer). The
differences also appeared in information access from neighborhood and the average
number of person contacted in order to get information on new agricultural input and
output marketing. These differences are likely to cause in differences in the adoption
of the two technologies that can affect farmers marketable surplus crop production.
In our sample households, farmer are members in various rural associations.2 It is
2
The rural associations that farmers participates in the villages are farmer’s group, agricultural
cooperative, savings and microcredit cooperative, and various informal local groups. The group mem-
14
expected that farmers may share information about new agricultural inputs during
the group meetings.
By comparison, technology adopters sell farm output at higher price, 991B irr3
compared to non-adopters (528B irr). Indicating that HYVs and chemical fertilizer
use increase the crop quality which enables farmers beneficial in terms of selling price.
Adopters of HYVs and chemical fertilizer spent 1,335 B irr to purchases those inputs.
The average market distance households travel to the nearest market place is around
3 hours. This may be a challenge for technology adopters to travel for long market
distance in order to sell farm output, hence farmers may forced to sell in the near-by
market at lower price. Market distance imposes a transaction cost to households and
determines the volume of crops sold. This suggests that access to market is the key
component in the adoption of new agricultural technologies. Our study sample households shows around 81.3 percent are using animal transport to carry farm output to
the nearest market place, whereas vehicle users are only 6.7 percent, and the remaining 11.9 percent are carry by themselves, Table 2. This shows the poor infrastructure
facility that rural households face in marketable agricultural production.
From the descriptive analysis above comparing the mean differences in terms of
smallholders market participation rate, the quantity supplied to the market, human
capita such as educational level, access to institutional service, and participation in
rural association between HYVs and chemical fertilizer adopters and non-adopters
shows that adopters are better off than non-adopters in output market participation.
While, comparing the mean differences between the two groups of households may not
account for the effect of households specific characteristics, socioeconomic factors and
other observed and unobserved factors. If not taken into account these factors may
confound the impact of technology adoption on marketable crop production with the
influence of other characteristics. Hence, to evaluate the effect of HYVs and chemical
fertilizer adoption on households the quantity supplied of crop output to the market,
we apply endogenous treatment effect model by accounting for the selection bias that
arise from the fact that the two group of households that adopt improved agricultural
technologies and did not adopt may be systematically different. Regression based on
sample selection model is also used to understand the factors that determine households
not to participate into crop output marketing by taking into account the endogenity
of households decision into market participation.
5
Econometric Result
The estimation results presented in this section shows the two main aims of this paper. First, the analysis evaluates the impact of agricultural technology adoption on
smallholders marketed surplus crop production and emphasis the relationship between
technology adoption and market participation. Second, the analysis identifies the main
factors that determines market participation and sales volume. Goetz (1992) explain
in his study that separating the decision of farmers whether or not to participate in the
bers have regular meeting in a weekly bases, in every two weeks, monthly, and some groups are also
meet once in every three months.
3
B irr is a currency in Ethiopia
15
market from the decision of how much is supplied to the market is appropriate. We first
estimate endogenous treatment effect model specifically to assess the impact of HYVs
and chemical fertilizer on smallholders’ marketable surplus crop production. Then, to
understand whether the decision of smallholders not to participate into output market
is because of other factors, sample selection model is estimated.
By taking into account of the endognity of market participation decision, the sample
selection model result shows that factors such as farm size, crop prices, labor use,
transport facility such as vehicle, livestock ownership, location and year dummy are
positively and significantly affect rural smallholders market participation. While the
coefficient of credit shows negative and significant in the case of the combined adoption
of the two improved inputs. This may be because of the credit repayment period
which is not to be immediately at the time when farmers collect farm output could
enhance farmers market participation through accessing cash credit for purchasing
improved inputs. The transaction cost argument we discussed earlier also indicates
rural smallholders incurring high cost due to inappropriate services accessed.
5.1
Estimation of the treatment effect
The estimation result shows that improved agricultural technology adoption is positively and significantly correlated with farmers’ surplus crop production to the market.
This is an encouraging result given the fact that farmers in Ethiopia are small-scale
and they produce cereal crops on small land holdings. Hence, sustainable intensification of modern inputs is a good option to increase food production and smallholders’
participation into output marketing. As many empirical studies indicated the effect
of improved agricultural technologies such as HYVs and chemical fertilizer positively
correlated with poverty reduction through marketed surplus crop production for the
households income. Bezu et al. (2014) found that adoption of improved maize positively correlated with the per capita income for the poorer households. The income
from surplus crop sale enables the rural poor to spend for other consumption goods
besides their own consumption. In terms of per capita expenditure and poverty rate
adopters of HYVs are better off than non-adopters (Becerril and Abdulai, 2010).
The empirical estimation result of the treatment effect model shows that usage of
high-yielding varieties increase the marketed surplus of smallholders crop production
by 758 kg per year, whereas chemical fertilizer adoption increase the quantity supplied to the market by 285 kg. The simultaneous adoption of the two technologies
jointly increases marketed surplus by 634 kg. Indicating that high-yielding varieties
and chemical fertilizer are complimentary inputs that smallholders use to produce
marketable surplus. Consistently, Asfaw et al. (2011) found the average treatment of
improved chickpea adoption contributed for farmers’ chickpea sold ranges from 16 to
20 percent. Martey et al. (2012) in Ghana found the extent of maize and Cassava
sold by smallholders are 53 and 72 percent, respectively, whereas the total agricultural
commercialization with respect to these two crops is 66 percent.
The endogenous treatment effect estimates of HYVs and chemical fertilizer adoption are presented in Table 3. The estimates of the coefficients of correlation between
the random errors in the adoption equation and outcome regression of marketable
16
surplus crop production question is significant. Indicating that the first-stage of the
adoption regression model result and second-stage of the outcome regression model
results together shows both observed and unobserved factors influence the adoption
decision and the performance of technologies given households technology adoption decision. The significance of the coefficient of correlation between the adoption equation
and marketed surplus crop production from technology adoption indicates self-selection
occurred in HYVs and chemical fertilizer adoption.
The result indicates that commercializing subsistence agriculture is based on supporting the intensification of marketable agricultural production in general, and cereal
crop production in particular for both domestic and export market. This is also evidenced by the experience of smallholders T ef4 production in Ethiopia. Because of
high demand in the market, smallholders produce this crop almost for market and the
production system is through intensification of purchased inputs such as improved seed
and chemical fertilizer. Besides, the increasing reputation of Tef as a ”super grain”
being gluten-free and other micronutrients such as calcium, iron, vitamin B and high
in protein, a promising niche market is now developing in the foreign countries such
as in Europe and America. Currently, a Dutch website is also marketing Tef under
a profit sharing contract with Ethiopian governmental authorities as ”The grain that
makes you stronger”. This indicates that the development of agricultural markets has
contributed to the adoption of improved varieties (Keleman et al., 2009).
The development of infrastructure is also a major factor in explaining improved
agricultural technology adoption and rural farmers market participation. Given the
correlation and arbitrariness in the choice between different types of infrastructure,
various studies have used different measures to include in the analysis, depending on
the information we have in our data, we use how much hours rural farmers travel to
the nearest market place to purchase HYVs and chemical fertilizer and to sale farm
output, and how farmers transport farm output to the market. We found that the
associated infrastructure facility such as access to vehicle positively contributed to
farmers market participation and new agricultural input use. Access to credit and
training shows positive impact for agricultural technology adoption. The positive
effect of credit access and rural education such as training programs is consistent with
Spielman (2008) study reported the importance of rural credit service, education and
training for smallholders in rural Ethiopia.
Regression based on propensity-score matching (PSM), nearest neighbor matching
(NNM), and kernel-based matching (KBM) methods are used to check robustness of
the result. The result of the three matching methods is consistent with result found in
the endogenous treatment effect and sample selection model. It shows that agricultural
technology adoption positively and significantly affect farmers surplus crop production.
The NNM indicates the average difference in the volume of sold between similar pairs
of households that belong to different technological status. With similar estimation
technique as NNM, the KBM shows the causal effects of the two technologies estimates
appear to be similar to those of the NNM. Indicating that both of the three matching
methods estimation result (PSM, NNM, and KBM) supports the endogenous treatment effect and sample selection model results. Thus, the causal effect of improved
agricultural technology adoption on poverty reduction is greater by improving the rural
4
T ef is a nutritious small-grained cereal.
17
households income through surplus crop sale. The result is consistent with previous
poverty analysis measuring the differential impact of agricultural technology adoption
on poverty reduction among the rural households. Becerril and Abdulai (2010) reported adoption of improved maize reduces the probability of falling below poverty
line roughly between 19 to 31 percent in the two study areas of Oaxaca and Chiapas,
Mexico. Similarly, Mendola (2007) found adoption of HYVs has a positive and robust
effect on households income and the way out of poverty in rural Bangladesh.
5.2
Determinants of market participation and sales volume
Households market participation and volume of crop sale is determined by access to
improved agricultural inputs (HYVs and chemical fertilizer), farm size, output price,
availability of labor, and infrastructure facility, and livestock ownership. The land
holding per household and availability of labor are the main factor of production
that enable households to produce surplus crop for market. Land holding measured
in hectare found positively and significantly affect smallholders market participation.
This suggests that in a scarce land access areas and high population pressure the hypothesis of induced innovation such as land use intensification policy of yield increasing
agricultural inputs per unit of land enables smallholders to produce marketable surplus crops. As an important input for agricultural activities, labor supply is positively
correlated with adoption of improved technologies and market participation. This indicates that the competitive advantages of small farms have over large commercial
farms would be using family labor to reduce the cost of production.
The price of agricultural product is expected to be positively related with adoption
of yield increasing technologies. We found that adoption of HYVs and chemical fertilizer positively correlated with output price. The finding of Alene et al. (2008) also
shows a one percent increase in maize prices increases the maize supply by 1.67 percent
among maize sellers. Although prices for similar crops varies across villages and time,
the output price we controlled is a farm-level price that households self reported. We
found evidence that crop price significantly affect smallholders yield-increasing agricultural technology adoption for surplus crop production. Indicating that marketable
surplus increases with crop price. A price elasticity of supply change with the adoption
of improved agricultural technologies is of basic importance to income of smallholder.
Thus, the benefit of farmers is that selling farm output by a higher price than the
cost of production given that farm income remains a major source of income for the
rural households in Ethiopia. Higher market price or lower transaction cost determine
farmers participation into output market and create market linkage between different
agents. Cadot et al. (2005) estimate the cost of entry into agricultural markets, they
found evidence that farmers who sell farm output average agricultural profits are 30
percent higher than subsistence farmers in Madagascar.
In the rural farm households, livestock is an assets that most researchers used for
the economic variables that is highly correlated with adoption of agricultural technologies. Farmers used livestock for both crop production and transport animals in order
to supply farm output to the market. Due to the infrastructure facility such as road
is poor in rural areas, the accessibility of vehicles is lower. Thus, households using
transport animals is around 81.3 percent, whereas vehicle users found only 6.7 percent
18
and the reaming 11.9 percent are walking an average of 3 hours to the nearest market
place (see Table 2). This might be a challenge for farmers in rural areas facing imperfect or incomplete markets for surplus crop production and market supply. Sadoulet
and De Janvry (1995) also pointed out that the main source of imperfect market that
agrarian households face includes, costs incurring from poor infrastructure, long market distance, high marketing margin, and imperfect information. To this effect their
production and consumption decision are no longer separable. Due to poor market
coordination across time and space, rural farmers are unable to take advantage of the
spatial or temporary arbitrage. Similarly, Gabre-Madhin (2001) reported market infrastructure is severely limited in Ethiopia, nearly half of the rural population does not
have access to all-weather roads, thus, around 72 percent of the grain production is
retained for on-farm uses and weak storage infrastructure results in larger storage loses
and vulnerability of the grain to moisture and pasts. Suggesting that access to market
play an important role in intensifying crop production. Farmers with poor access to
market result little or no incentive to adopt improved varieties (Alene and Manyong,
2007).
In order to account for the differences in crop production and market supply potential between Amhara and Tigray region, we included regional dummy in our regression.
Location dummy is positively and statistically significant, implying that market surplus crop production is higher in Amhara region than Tigray region. This may be area
under improved HYVs and chemical fertilizer coupled with favorable weather conditions that facilitates positive growth rate of crop production in Amhara region than
Tigray region. Thus, controlling location dummy captures many area specific characteristics like population density, fertility and/or type of soil, rainfall availability and
so on (Asfaw et al., 2011).
6
Conclusion
This study examined high-yielding cereal crops varieties and chemical fertilizer adoption and its impact on smallholders marketed surplus crop production measured in
quantity sold in two region of Ethiopia. Given the self-selectivity bias in the adoption
of technologies treatment effect model is used to account for endogenity of adoption
and market participation. The empirical estimation result indicates that adoption of
improved cereal crop varieties helped farmers to produce market surplus crop production. Specifically, the average treatment effect of HYVs adoption found to increase
farmers volume of crop sale by 758 kg whereas chemical fertilizer use contributed for
marketed surplus 285 kg. When farmers adopt the two technologies jointly increase
farmers volume of crop sale by 634 kg. This indicates targeting intensification towards
new agricultural technologies can have far reaching poverty reduction implication especially in rural areas farming is the major source of income and food production.
Developing mechanism to expand rural farmers access to HYVs and chemical fertilizer
is therefore a reasonable policy instrument to raise income of smallholders through
market surplus crop production.
The findings shows improved technologies is the priority requirement for changing rural households agricultural production and market participation system, while
19
elements of institution service such as access to market, infrastructure development
(access to vehicle), efficient input distribution system, and appropriate economic incentives such as farmers taking advantage from output price must be present. Availability
of labor is also positively contributed for new technology adoption and market surplus
crop production that confirm the labor intensive technologies which is appropriate like
countries Ethiopia where there is surplus labor. The overall finding indicates the development of a more dynamic and competitive agricultural sector in Ethiopia requires
appropriate rural institutions that respond effectively the changing agricultural technology adoption and market conditions. Recommended policy interventions to improve
rural smallholders agricultural technology adoption and market participation includes
improving infrastructure, access to credit, education and training. Reducing the adverse effect of sunk cost and price risk due to the increased marketed surplus crop
production, the availability of risk management schemes like support price, searching
price through negotiation with private companies and cooperative unions may tackle
the risk of output price. Farmers access to information about the marketing conditions
and training of the management of output is also crucial.
20
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25
Table 1. Summary statistics of the full sample
26
Variables
Description
Obs.
Mean
Std.dev
Market participation (yes, percentage)
Market participation intensity
Improved seed
Chemical fertilizer
HYVs and chemical fertilizer
Age of household head
Gender
Educational level of head
Household size
Farm size
Irrigation use
labor use
Credit access
Non-farm income
Expenditure for inputs
Farmers training
Neighborhood information
Number of persons contact
Membership of association
Market distance
Mode of transport
Selling price
Regional dummy
=1 if farmers participate in at lease one crop output market, 0 otherwise
Quantity sold (kg)
=1 if farmers adopt HYVs, 0 otherwise
=1 if farmers adopt Chemical fertilizer, 0 otherwise
=1 if farmers adopt both technologies, 0 otherwise
Number of years
=1 if the household head is Male, 0 otherwise
Year of schooling (reference- illiterate)
Household members
Farm size cultivated (hectare)
=1 if farmer use irrigation, 0 otherwise
Number of working days per year (family, hired, and labor sharing)
=1 if farmer access to credit, 0 otherwise
=1 if farmer access to off-farm income, 0 otherwise
Amount of money spent for HYVs and/or chemical fertilizer
=1 if farmers participate on farmers training, 0 otherwise
=1 if farmers access to neighborhood information,0 otherwise
Average number of persons contacted
=1 if farmers be members in at least one association, 0 otherwise
Distance from residence to nearest market center (hours)
Transporting farm output to nearest market place (reference- animals)
Average selling price per unite (kg) sold in B irr (’000)
=1 if Amhara region, 0 otherwise
1,254
1,254
3700
3700
3700
3835
3835
3196
3850
3822
3700
3454
3898
3409
3700
3314
2954
3297
3200
1,254
935
1,254
3268
0.33
342.4
0.46
0.80
0.41
44.2
0.82
76.8
5.8
1.3
0.39
332.6
0.70
0.51
867.4
0.63
0.97
3.5
0.88
2.7
74.9
821.1
0.46
0.47
957.8
0.49
0.39
0.44
12.2
0.38
0.42
2.1
4.2
0.48
213.9
0.45
0.49
1007.4
0.48
0.15
11.3
0.31
4.5
0.43
162.8
0.49
Table 2. Descriptive statistics
HYVs
Market participation
Market participation intensity
27
Improved seed adopt
Chemical fertilizer adopt
Combined adoption
Age of household head
Male
Female
Educational level- Illiterate
Read and write (1-3)
Primary school (4-8)
High school and above
Household size
Farm size cultivated
Irrigation use
labor use
Credit access
Fertilizer
Treated
Control
Mean
(SD)
Mean
(SD)
40.1
154.3
(372.0)
46.11
28.3
78.0
(704.2)
Combined
Treated
Control
Treated
Control
t/χ2
Mean
(SD)
Mean
(SD)
t/χ2
Mean
(SD)
Mean
(SD)
t/χ2
-7.5
-3.9
35.2
117.1
(322.0)
27.4
97.1
(1121.4)
-4.0
-0.8
39.8
160.8
(386.7)
23.1
97.4
(1274.7)
-7.1
-1.7
43.0
(13.1)
77.0
22.9
89.9
4.7
4.6
0.7
5.5
(2.1)
1.7
(6.2)
30.6
265.1
(214.6)
8.7
-4.1
73.0
44.4
(11.7)
86.8
13.1
75.2
11.3
10.8
2.6
5.9
(2.0)
1.3
(4.1)
38.7
374.4
(212.6)
86.9
42.6
(12.7)
74.7
25.2
90.7
3.4
5.1
0.7
5.4
(2.1)
1.8
(6.9)
27.9
220.9
(163.7)
7.5
-3.1
80.1
44.4
(12.0)
86.6
13.4
76.4
11.0
10.1
2.5
5.9
(2.1)
1.4
(4.1)
38.8
377.5
(221.1)
79.5
44.8
(12.3)
81.1
18.8
77.3
8.9
11.2
2.5
5.7
(2.0)
1.3
(4.5)
39.6
302.5
(196.7)
60.2
0.9
-4.4
0.1
-2.2
-0.1
0.5
-10.3
-12.9
45.0
(11.8)
85.3
14.7
74.0
11.1
12.0
2.9
5.9
(2.0)
1.2
(3.6)
41.4
355.6
(207.4)
84.1
-5.4
-7.6
-5.2
3.1
-5.3
-9.9
-52.3
-6.6
-6.0
-5.3
2.1
-4.5
-14.5
-50.5
Non-farm income access
Expenditure for inputs
Participation in farmers training
Neighborhood information access
Number of persons contact
Membership of association
Market distance
Selling price
28
Mode of transport by walk
Mode of transport by car
Mode of transport by Animals
52.2
1210.3
(1183.2)
72.6
98.9
4.2
(16.1)
91.6
3.0
(5.1)
965.1
(1253.8)
13.2
8.3
78.4
50.9
574.1
(706.7)
55.7
96.3
3.1
(4.3)
86.5
2.5
(3.9)
657.1
(1032.3)
20.8
8.6
70.5
-0.7
-20.1
-10.1
-4.6
-2.6
-4.5
-1.9
-4.6
-3.5
52.6
1078.7
(1020.9)
66.6
97.2
3.7
(12.4)
90.1
3.1
(4.8)
868.2
(1182.3)
14.2
5.8
79.9
46.7
19.6
(86.8)
51.5
99.2
3.2
(6.4)
83.6
1.2
(2.5)
613.3
(1074.7)
29.4
22.3
48.2
-2.6
-28.1
-6.9
2.6
-0.8
-4.4
-5.5
-2.8
-8.1
Table 2: Source: Author calculation using FIF Impact Evaluation Survey data 2010 and 2012.
53.3
1334.9
(1181.6)
73.5
98.8
4.1
(16.5)
93.0
3.2
(5.2)
990.9
(1229.7)
11.9
6.7
81.3
48.2
0
(0)
47.1
98.9
2.7
(2.5)
85.3
1.1
(2.1)
528.1
(797.8)
32.0
22.6
45.3
-1.9
-26.9
-10.5
0.1
-1.8
-4.8
-4.5
-4.1
-8.1
Table 3. Endogenous treatment effect estimation result
29
Quantity sold
HYVs
Coef.
Fertilizer
Std. Err.
Coef.
Treatment indicators
7.392
∗∗∗
∗
0.978
2.324
Age of household head
Gender of household head
Household size
Read and write (1-3)
Primary schooling (4-8)
High school and above
Farm size
Irrigation use
Credit access
Non- farm income
Labor use
Participation in farmers training
Neighborhood information access
Number of persons contact
Membership of associations
Selling price
Market distance
Mode of transport- walking
Mode of transport- car
Livestock owned
Region
Year
Constant
0.007
1.037
-0.124
-0.607
1.219
0.668
0.082
-0.054
0.038
-0.070
0.232∗
-1.553∗∗
0.831
-0.022
0.020
0.179∗∗∗
0.074
-0.882
1.548
0.415∗∗∗
1.348∗∗
2.262∗∗
-4.682
0.024
2.077
0.140
0.887
0.905
1.577
0.059
0.895
0.665
0.601
0.128
0.611
1.836
0.015
1.067
0.023
0.063
0.799
0.984
0.105
0.591
1.005
3.326
Treatment indicators
Expenditure for inputs
Age of household head
Household size
Read and write (1-3)
Primary schooling (4-8)
High school and above
Farm size
Credit access
Participation in farmers training
Number of persons contact
Selling price
Market distance
Mode of transport- walking
Mode of transport- Car
Constant
0.000∗∗∗
-0.007∗
0.022
0.111
-0.499∗∗∗
-0.122
0.009
0.074
0.595∗∗∗
0.009
0.006 ∗
-0.000
0.055
0.523∗∗∗
-0.815
Athrho
Lnsigma
Combined
Std. Err.
Coef.
Std. Err.
1.217
∗∗∗
6.013
1.009
-0.009
1.027
-0.088
-0.413
0.202
0.408
0.119∗∗
-0.181
-0.078
-0.374
0.313∗∗∗
0.089
0.803
-0.016
0.562
0.200∗∗∗
0.061
-1.171
2.618∗∗∗
0.438∗∗∗
1.639∗∗∗
2.077∗∗
-3.940
0.023
2.104
0.133
0.841
0.854
1.496
0.056
0.907
0.825
0.606
0.129
0.542
1.850
0.015
1.084
0.022
0.061
0.771
0.956
0.106
0.607
1.025
3.395
0.002
1.136
-0.094
-0.514
0.731
0.536
0.085
-0.131
-0.720
-0.020
0.251∗∗
-1.056∗
0.818
-0.016
-0.064
0.184∗∗∗
0.071
-0.793
2.360∗∗∗
0.409
1.374∗∗
2.177
-3.454
0.024
2.083
0.135
0.858
0.874
1.526
0.058
0.894
0.696
0.601
0.128
0.587
1.828
0.015
1.076
0.023
0.061
0.778
0.949
0.105
0.592
1.003
3.296
0.000
0.003
0.022
0.144
0.140
0.250
0.014
0.097
0.091
0.007
0.003
0.010
0.131
0.166
0.232
0.003∗∗∗
-0.000
-0.061
-0.004
0.133
1.006
-0.053
1.983∗∗∗
0.057
-0.001
-0.007
0.053
0.10564
0.114
-1.415∗∗∗
0.000
0.009
0.060
0.359
0.420
0.798
0.148
0.237
0.240
0.005
0.010
0.048
0.284
0.329
0.530
0.000∗∗∗
-0.007∗
0.018
0.103
-0.399∗∗∗
-0.071
0.006
0.570∗∗∗
0.565∗∗∗
0.001
0.005
0.001
0.062
0.322∗
-1.448∗∗∗
0.000
0.004
0.023
0.150
0.144
0.254
0.012
0.105
0.096
0.002
0.003
0.010
0.139
0.172
0.253
-0.519
2.104
0.079
0.029
-0.090
2.049
0.200
0.022
-0.377
2.071
0.086
0.026
Rho
Sigma
Lambda
-0.477
8.202
-3.916
0.061
0.240
0.584
-0.100
7.766
-0.699
0.199
0.174
1.548
-0.360
7.933
-2.859
0.075
0.209
0.638
LR with endogenous treatment
Estimator: maximum likelihood
Log likelihood = -3999.0155
Number of obs
Wald chi2(23)
Prob > chi2
LR test (rho=0):chi2(1)=
992
255.12
0.000
21.35
992
218.54
0.000
0.16
30
992
204.59
0.000
12.31
Table 4. Sample selection model estimation result
Market participation
HYVs
Fertilizer
Combined
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Age of household head
Gender of household head
Household size
Read and write (1-3)
Primary schooling (4-8)
High school and above
Farm size
Irrigation use
Credit access
Non-farm income
Labor use
Participation in farmers training
Neighborhood information access
Number of persons contact
Membership of associations
Selling price
Market distance
Mode of transport- walking
Mode of transport- Car
Livestock owned
Region
Year
Constants
0.002
0.931
-0.094
-0.225
1.349
1.526
0.065
0.620
-0.265
-0.352
0.219∗∗∗
-1.661
2.215
-0.026
-0.374
0.160
0.087
-1.456
1.467
0.471∗∗∗
1.195
3.212∗∗
2.900
0.036
2.721
0.209
1.275
1.453
2.281
0.073
1.263
1.082
0.921
0.180
1.056
3.007
0.019
1.929
0.034
0.088
1.286
1.401
0.158
0.907
1.518
5.181
-0.019
1.222
-0.078
-0.594
-0.088
0.486
0.121∗∗
0.218
-0.351
-0.389
0.376∗∗∗
0.127
0.465
-0.019
0.835
0.223∗∗∗
0.056
-1.409
4.552∗∗∗
0.489∗∗∗
1.630∗∗∗
2.911∗∗∗
-2.518
0.028
2.399
0.154
0.924
0.946
1.639
0.059
1.005
0.887
0.710
0.152
0.628
2.019
0.016
1.362
0.025
0.065
0.942
1.231
0.123
0.656
1.177
3.801
-0.031
1.453
0.005
-0.390
0.827
1.608
0.076
1.381
-2.358∗
-0.318
0.284
-1.105
1.798
-0.022
0.248
0.207∗∗∗
0.061
-1.250
4.633∗∗∗
0.465∗∗∗
1.442
5.109∗∗∗
1.315
0.040
2.962
0.220
1.315
1.471
2.357
0.071
1.353
1.364
1.014
0.203
1.062
3.077
0.019
2.544
0.036
0.090
1.435
1.629
0.171
0.971
1.725
5.590
Treatment indicators
Expenditure for inputs
Age of household head
Household size
Read and write (1-3)
Primary schooling (4-8)
High school and above
Farm size
Credit access
Participation in farmers training
Number of persons contact
Selling price
Market distance
Mode of transport- walking
Mode of transport- car
Constants
0.000∗∗∗
-0.006∗
0.032
0.135
-0.461∗∗∗
-0.114
0.009
0.104
0.588∗
0.013
0.008∗∗
-0.003
0.066
0.480∗∗∗
-1.000∗∗∗
0.000
0.003
0.022
0.144
0.140
0.252
0.013
0.097
0.091
0.008
0.004
0.010
0.131
0.162
0.233
0.003∗∗∗
-0.001
-0.068
0.019
0.147
0.951
-0.083
1.983∗∗∗
0.084
-0.001
-0.006
0.058
0.121
0.123
-1.375∗∗∗
0.000
0.009
0.059
0.359
0.420
0.778
0.069
0.236
0.235
0.005
0.010
0.047
0.285
0.319
0.516
0.000∗∗∗
-0.007∗
0.021
0.133
-0.356∗∗∗
-0.069
0.006
0.587∗∗∗
0.559∗∗∗
0.002
0.007∗
-0.000
0.096
0.305∗
-1.571∗∗∗
0.000
0.004
0.023
0.149
0.144
0.256
0.012
0.104
0.095
0.003
0.004
0.010
0.138
0.171
0.252
Mills
Lambda
-6.10222
1.570
-0.727
1.802
-4.798
1.347
Rho
Sigma
-0.638
9.551
-0.110
8.177
-0.514
9.334
Selection model- two-step
Number of obs
Censored obs
Uncensored obs
Wald chi2(22)
Prob > chi2
1015
456
559
69.48
0.000
1001
156
845
201.36
0.000
1018
513
505
89.89
0.000
31
Table 5. Matching methods
Matching techniques
Outcome
ATET
ATEU
Difference
S.E.
T-stat
PSM
Quantity sold
7.68
6.76
7.99
6.02
5.82
4.96
1.65
0.94
3.02
0.82
1.14
0.59
2.00
0.83
5.04
NNM
HYVs adoption
Chemical fertilizer adoption
Multiple adoption
Quantity sold
7.68
6.76
7.99
5.71
6.42
5.60
1.97
0.34
2.39
0.69
1.02
0.69
2.85
0.34
3.46
KBM
HYVs adoption
Chemical fertilizer adoption
Multiple adoption
Quantity sold
HYVs adoption
Chemical fertilizer adoption
Multiple adoption
7.68
6.76
7.99
5.81
5.22
5.63
1.86
1.53
2.35
0.59
1.03
0.59
3.15
1.49
3.96
Source: Farmer Innovation Fund (FIF) impact evaluation survey 2010 and 2012
Table 6. Common support
HYVs
Total
Chemical fertilize
Total
Multiple adoption
Total
Psmatch2:
Treatment
assignment
Psmatch2
Common
support
On suppor
Treated
Untreated
559
433
Treated
Untreated
845
147
Treated
Untreated
505
487
Total
559
433
992
845
147
992
505
487
992
Source: Farmer Innovation Fund (FIF) impact evaluation survey 2010 and 2012
32
Appendix
Appendix 1. Farmers adoption rate of the two technologies
HYV
Chemical fertilizer
Adopt
Not-adopt
Total
Adopt
1,536
170
1,706
Not-adopt
1,426
568
1,994
Total
2,962
738
3,700
33
Figure 1: Graphical representation of matching method
0
.2
.4
.6
Propensity Score
Untreated
0
.2
.2
.8
1
Treated
.4
.6
Propensity Score
Untreated
1
Treated
.4
.6
Propensity Score
Untreated
0
.8
.8
1
Treated
Source: Farmer Innovation Fund (FIF) impact evaluation survey 2010 and 2012
34