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Agricultural Technology Adoption,
Market Participation, and Price Risk in Kenya
Samuel S. Bird∗
December 18, 2017
∗Ph.D. candidate in Agricultural & Resource Economics at the University of California at Davis (email:ssbird@ucdavis.edu). I am grateful for comments on earlier drafts from Michael Carter, Travis Lybbert,Kevin Novan, and participants in the Gifford Center for Population Studies workshop series at UC Davisand the 2017 AAEA Annual Meeting. The main data set used in this study comes from the Western SeedCompany impact evaluation commissioned by Acumen, a non-profit impact investment firm, and madepossible in part by the generous support of the American people through the United States Agency forInternational Development Cooperative Agreement No. AID-OAA-L-12-00001 with the BASIS Feed theFuture Innovation Lab and the Agricultural Technology Adoption Initiative (ATAI) administered by JPALat MIT and the Bill and Melinda Gates Foundation. The data used in this work were collected and madeavailable by the Tegemeo Institute of Agricultural Policy and Development of Egerton University, Kenya.However the specific findings and recommendations remain solely the author’s and do not necessarilyreflect those of Tegemeo Institute, USAID, the US Government, or other funders.
Technology Adoption and Market Participation
Abstract
Agricultural development programs in sub-Saharan Africa often target farmers meet-
ing specific eligibility criteria in order to maximize impacts. Common eligibility cri-
teria such as land wealth and membership in a farmer group target farmers who
are likely to sell agricultural output. This paper evaluates whether this targeting
approach increases program impacts on technology adoption and welfare among
smallholder farmers. Data come from a randomized control trial that randomized
expansion of highly productive maize varieties to communities of smallholder farm-
ers in western Kenya. Randomized access to the maize varieties increases adoption
by twenty-four percentage points for typical sellers compared to thirteen percentage
points for typical non-sellers. The difference in adoption between sellers and non-
sellers is robust to controlling for other factors affecting technology adoption and
greater in magnitude than these factors. Yet welfare gains from technology adoption
may be less for typical sellers than for typical buyers due to price risk aversion. Tar-
geting programs to typical sellers may increase agricultural technology adoption but
may exclude typical buyers who would realize large welfare gains from becoming
self-sufficient food producers through technology adoption.
ii
Technology Adoption and Market Participation
Rural development strategies often promote adoption of agricultural technologies such
as hybrid seeds and fertilizers as a stimulus for economic growth. Yet technology adop-
tion remains low among many smallholder farmers, especially in sub-Saharan Africa.
Farmers face several potential constraints to adoption, including liquidity contraints and
uninsured production risk [Foster and Rosenzweig, 2010, Jack, 2011]. Constraints to
adoption can be relaxed by programs such as agricultural input subsidies. Policymakers
may achieve greater success when they consider potential constraints to adoption when
selecting households for a program. Assessing the extent that technology adoption is
constrained is critical to improving the design and targeting of technology adoption
programs.
Programs often attempt to improve output market access of smallholder farmers or
target farmers whose adoption is not constrained by output market access. The con-
ceptual framework behind this approach is that transactions costs limit market access
of farmers and lead to declining marginal value of output for farmers unable to enter
the market. The implication is that interventions to increase agricultural output should
target households that reveal ability to access the market by selling agricultural output.
When this implication translates into actual program implementation, a program evalua-
tor is unable to test whether the program would be more effective if it had been targeted
differently. Testing targeting effectiveness therefore requires data from an untargeted
program, such as the program that I study in this paper.
This paper evaluates whether targeting increases program impacts on technology
adoption and welfare among smallholder farmers. The study uses data come from a
randomized control trial that randomized expansion of highly productive maize vari-
eties to communities of smallholder farmers in western Kenya. Randomized access to
the maize varieties increases adoption by twenty-four percentage points for typical sell-
ers compared to thirteen percentage points for typical non-sellers. The difference in
adoption between typical sellers and typical non-sellers is virtually unchanged when
1
Technology Adoption and Market Participation
controlling for other factors affecting technology adoption such as expected profitability,
farm size, gender of household head, credit constraint status, and past technology adop-
tion. Furthermore the increase in adoption due to these factors is smaller in magnitude
than the increase in adoption for typical sellers. The results support the hypothesis that
interventions lead to greater agricultural technology adoption when targeted to house-
holds that are typical sellers of that crop.
Yet welfare gains from technology adoption may be less for net sellers than for net
buyers due to price risk aversion. The distinction between net sellers and net buyers
of output is important because market characteristics that are welfare-reducing for net
sellers may be welfare-improving for net buyers. For example, output price risk may re-
duce gains from welfare technology adoption for net sellers but may increase gains from
technology adoption for net buyers. This result is formalized by this paper’s theoretical
model of technology adoption by an agricultural household facing staple price risk.
To investigate whether the theoretical predictions hold empirically, I combine the
randomized control trial data with panel data from the study area. The randomized
control trial provides experimental variation in technology adoption and panel data pro-
vide temporal variation to identify the effects. The advantage of this approach is using
exogenous changes to technology adoption while also capturing year-to-year change in
household behavior that most data from randomized control trials would lack. Other
empirical studies of price risk in smallholder agriculture have relied on observational
data that is either cross-sectional [Barrett, 1996] or panel [Bellemare et al., 2013].
The results suggest that targeting programs to net sellers may increase agricultural
technology adoption but may exclude net buyers who would realize large welfare gains
from becoming self-sufficient food producers through technology adoption. Such ten-
sion between maximizing impacts on technology adoption or welfare creates a program
design dilemma for policymakers. Some countries resolve the targeting tension by im-
plementing separate programs for wealth and poor farmers. But when only one program
2
Technology Adoption and Market Participation
is implemented that targets wealthier commercial farmers, the poorest farmers are ex-
cluded as a result.
Common policy approaches for targeting as well as the literature on the inverse re-
lationship between farm size and productivity inform my analytical framework. This
study is most closely related to two studies from that literature showing how endoge-
nous participation in markets prior to a program can characterize heterogeneous im-
pacts of agricultural programs. Carter and Yao [2002] estimate that the area of land that
Chinese agricultural households rented in or rented out affected their benefits from a
program to improve land transfer rights. Henderson and Isaac [2016] use a general equi-
librium theoretical model to show that the same credit constraints that drive land poor
farmers to have high land productivity also prevent them from taking on the fixed costs
of contract farming. My study adds to this literature by showing that exposure to price
risk may affect agricultural technology adoption by smallholder farmers.
While this paper studies the static welfare impacts of technology adoption on house-
hold exposure to output price risk as a net seller or a net buyer, the impacts also have
dynamic implications for development. At the household level, self-sufficiency in staple
production is linked to longer planning horizons and asset accumulation of smallholder
farmers [Laajaj, 2017]. At the regional level, technology adoption can spur dynamic eco-
nomic growth [McArthur and McCord, 2017]. Tapping into this development potential
requires a firm understanding of the key market imperfections in the economy and their
relationships with technology adoption.
Section 1 provides evidence that technology adoption is greater for typical sellers
than for typical non-sellers. Section 2 develops a theoretical model of technology adop-
tion by an agricultural household facing staple price risk. Section 3 provides evidence
supporting the theory that price risk motivates agricultural technology adoption by land
poor farmers. Section 4 concludes.
3
Technology Adoption and Market Participation
1 Technology Adoption by Output Market Participation
I first estimate differences in adoption between typical sellers and typical non-sellers
using data from the randomized control trial. Randomized control trial data from a
representative sample of agricultural households in western Kenya were collected as
part of the Western Seed Company impact evaluation from 2012-2015. The study sample
includes 1200 households in western Kenya, where adoption of hybrid maize varieties
lags behind other regions of the country [Carter et al., 2017]. Hybrid maize from Western
Seed Company is new to this area of Kenya and is well-suited to the local growing
conditions. Randomized interventions to encourage adoption of maize hybrids from
Western Seed Company were 1) a seed information treatment that provided agronomic
information in 2013 and a direct delivery program in 2015 and 2) a fertilizer provision
treatment in 2014 to relieve fertilizer costs as a constraint to adoption in the early stages
of the study. Figure 1 shows the timeline for the impact evaluation. Surveys collected
data on baseline charateristics in 2013, midline impacts in 2015, and endline impacts in
2016.
To test whether agricultural technology adoption is greatest for typical sellers of
maize, I regress an indicator of adoption of Western Seed maize hybrids in 2015 on
treatment indicators as well as interactions of the seed treatment with baseline house-
hold characteristics. The model for farmer i in village v is
adoptioniv = β · seedv + ρ · seed ′vXiv +α ′Xiv + γ ′pairv + erroriv(1)
where Xiv is a column vector of baseline household characteristics and pairv is an indi-
cator controlling for pairwise stratification of randomization units.
The data provide evidence that adoption is greater among typical sellers, as shown
in table 1. The seed treatment increases adoption of Western Seed maize hybrids by 16
percentage points on average (column (1)). The treatment increases adoption by 24 per-
4
Technology Adoption and Market Participation
centage points among households that sold at baseline, compared with a 13 percentage
point increase among households that did not sell maize at baseline (column (2)).
If market participation has no effect on adoption but its positive estimate is driven
by omitted variables, controlling for these variables would reduce the estimated effect of
market participation to zero. I test for omitted variable bias by controlling for baseline
indicators that proxy for drivers of adoption identified by Jack [2011]: midaltitude agro-
climatic zone proxies for greater expected profitability, land wealth proxies for lesser
exposure to land market inefficiencies, male household head proxies for lesser expo-
sure to labor market inefficiencies, credit unconstrained proxies for lesser exposure to
financial market inefficiencies, and past hybrid use proxies for lesser exposure to infor-
mational inefficiencies. Table 1, columns (3) through (9) show the point estimates after
adding these indicators as regressors in (1). Adding regressors explains away the main
effect of the seed treatment as expected, but the treatment effect for households that sell
maize at baseline is large and qualitatively unchanged when controlling for additional
regressors.
Figure 2 plots point estimates and confidence intervals of treatment effects from col-
umn (9). The indicator for being a typical seller has the greatest effect on adoption among
the regressors and is the only one that is different from zero with statistical significance
at the 90-percent level.
Given the high rates of adoption for typical sellers, the question remains as to how
welfare gains from technology adoption differ for typical sellers and typical buyers. This
question is studied in the remainder of the paper.
2 A Model of Technology Adoption with Price Risk
I study an agricultural household that chooses its production and consumption to max-
imize utility from consuming a bundle of goods, with a subset being staple goods both
5
Technology Adoption and Market Participation
produced and consumed by the household. Household utility is affected by three se-
quential events. First, the household makes production decisions at a time when the
staple price is unknown. Second, the staple price is realized. Third, the household
makes consumption decisions in the harvest season. Thus production decisions cannot
adjust to realized prices but consumption decisions can adjust to realized prices, as in
the temporal uncertainty models described by Chavas and Larson [1994]. Solving the
problem recursively, households adopt the technology based on its expected impact on
household income and marketed surplus.
2.1 The Agricultural Household Model
Consider a household that maximizes expected utility from consuming a staple good
c and a non-staple composite good n. Assume marginal utility from consuming each
good is strictly positive at all consumption levels, strictly decreases with consumption,
approaches infinity as consumption of that good approaches zero, and increases with
consumption of the other good.
Consumption is constrained by the household’s budget. I focus on the household’s
consumption problem in the harvest season and denote the harvest season unit price of
staple consumption p relative to the numeraire non-staple consumption good. I model
full household income as being the sum of three income sources.
1. Value of its own staple production: The household’s full income includes the
product of the staple price and household staple production p · q. Staple output
comes from a production process represented by a function f(x, T) that is stepwise
and increasing in the production technology xε {0, 1}, increasing in land wealth T ,
and zero when land wealth is zero. I assume the household does not carry over
staple stocks from previous harvests, which is consistent with little to no carry over
maize stocks in the empirical setting in western Kenya.
6
Technology Adoption and Market Participation
2. Cash on hand carried over from the planting season: The household has initial
cash on hand in the planting season z relative to the numeraire that it can carry
over into the harvest season if it is not spent on technology adoption in the planting
season. The price of the technology is px relative to the numeraire.
3. Cash income in the harvest season: The household has a known income flow i
relative to the numeraire in the harvest season representing harvest season income
that does not change with the household’s technology adoption decision or the
realization of the staple price.
The household’s indirect utility function from harvest season consumption is V(p,y|x
)≡
maxc,n>0 u(c,n)
subject to n+ p · [c− f(x, T)] = i+ z− px · x. The household chooses
technology adoption xε {0, 1} to maximize expected utility
Ep
{V(p,y|x
)}
given full income y ≡ p · f(x, T) + i + z − px · x and subject to its liquidity constraint
0 6 z − px · x. This representation of the household problem is the entry point for
analyzing the welfare effects of technology adoption for net sellers and net buyers.
2.2 Technology Adoption with Staple Price Risk
I isolate the role of price risk in technology adoption’s welfare impacts by expressing
household utility as a function of household exposure to price risk. Finkelshtain and
Chalfant [1997] derive a family of household valuations of price risk from a theoreti-
cal model, but my goal to link the theoretical model to empirically testable conditions
requires an empirical valuation. Therefore I adopt a valuation measure suited for em-
pirical analysis derived by Bellemare et al. [2013]: the household’s willingness to pay to
stabilize the price of the staple WTP. Willingness to pay is positve when risk decreases
utility and willingness to pay is positive when risk increases utility.
7
Technology Adoption and Market Participation
I define willingness to pay conditional on the technology adoption decision with the
expression
Ep
{V(p,y|x
)}≡ V
(µ,y−WTP|x
)(2)
where y is exogenous income that is uncorrelated with stochastic prices and µ is the
mean price, following Bellemare et al. [2013].
I study the effect of technology adoption on price risk by approximating the right-
hand side of (2) with a first-order Taylor series expansion
V(µ,y−WTP|x
)≈ V
(µ,y|x
)− Vy
(µ,y|x
)·WTP|x(3)
Vy is the partial derivative of the indirect utility function with respect to income and
willingness to pay is conditioned by the technology adoption decision.
The first term in (3) represents welfare in a world without price risk. The second
term represents the effect of household exposure to price risk on welfare. The remainder
of this section studies each of these components of technology adoption impacts.
2.2.1 Preferences Over Prices Motivates Technology Adoption
Without price risk, preferences over the prices of staple goods may change when adopt-
ing technologies that increase staple output. Note that utility increases with price for net
sellers and decreases with price for net buyers, since Roy’s identity implies
Vp
(p,y
)= Vy
(p,y
)·m(p,y)(4)
Furthermore, differentating (4) with respect to price and evaluating at mean price
gives
Vpp
(µ,y
)= −Vy
(µ,y
)·A(µ,y)(5)
where A(µ,y) is the absolute price risk aversion function defined by Barrett [1996]. I
8
Technology Adoption and Market Participation
derive the function from (4) and write the function as
A(µ,y) =[[R− η(µ,y) ·
|m(µ,y)|m(µ,y)
]·β(µ,y) − ε(µ,y) ·
|m(µ,y)|m(µ,y)
]· m(µ,y)
µ(6)
Absolute price risk aversion is a function of two measures of household market partici-
pation: the net volume of staples sold in the market m(µ,y) and the net value of staples
sold on the market as a share of household income β(µ,y) = µ·m(µ,y)/y; the latter is iden-
tical to the household’s net benefit ratio defined by Deaton [1989]. Additionally, price
risk aversion is a function of the household’s Arrow-Pratt coefficient of relative income
risk aversion R = −y·Vyy(µ,y)/Vy(µ,y). The final components of price risk aversion capture
how household net marketed surplus changes with income and prices as measured by
the elasticities of net marketed surplus with respect to income η(µ,y) =∂m(µ,y)∂y
y|m(µ,y)|
and price ε(µ,y) = ∂m(µ,y)∂p
µ|m(µ,y)| .
These components determine the sign of the coefficient of absolute price risk aversion
and thus the curvature of the indirect utility function with respect to price. To sign
these variables I assume that an autarkic household exists and make two additional
assumptions about household market participation.
• Assumption 1. The income elasticity of net marketed surplus is inelastic relative to
the coefficient of relative income risk aversion: η < R.
• Assumption 2. The staple is an ordinary good: ε ∈ (0, 1).
Under these assumptions I consider values of absolute price risk aversion under three
special cases: i) R 6= 0,η = 0, ε = 0; ii) R = 0,η 6= 0, ε = 0; iii) R = 0,η = 0, ε 6= 0. More
generally absolute price risk aversion is the sum of these three special cases.
To study the relative magnitudes of the special cases, I estimate parameters using
data from western Kenya. I use the parameter estimates to plot the three special cases
along with the sum of the three cases as a function of land wealth in figure 3. For land
9
Technology Adoption and Market Participation
poor households that buy large quantities of the staple, price increases decrease income
and increase income risk aversion assuming constant relative income risk aversion; thus
relative income risk aversion leads utility to decrease at an increasing absolute rate in
prices. Households with greater land wealth that buy small quantities of staples are
better able to offset income effects of price changes by adjusting their market participa-
tion; thus price responsiveness leads utility to decrease at an decreasing absolute rate
in prices. For households with greater land wealth that sell small quantities of staples,
price increases increase sales and exposure to risk but households offset these effects by
adjusting their market participation; thus price responsiveness leads utility to increase
at an increasing rate in prices. Land rich households that sell large quantities of the
staple are less able to offset income effects of price changes by adjusting their market
participation; thus relative income risk aversion leads utility to increase in prices at a
decreasing rate.
Fixing land wealth and studying the change in utility with prices shows that there
are four distinct price regimes:
• At the lowest prices, households buy large quantities so that utility decreases with
price at an increasing absolute rate;
• At higher prices, households buy small quantities so that utility decreases with
price at a decreasing absolute rate;
• At even higher prices, households sell small quantities so that utility increases with
price at an increasing rate;
• At the highest prices, households sell large quantities so that utility increases with
price at a decreasing rate.
Thus the household’s indirect utility function is convex in prices for for households near
autarky with price risk affinity (A(µ,y) < 0). Utility is concave in prices for households
that buy or sell in large quantities with price risk aversion (A(µ,y) > 0).
10
Technology Adoption and Market Participation
2.2.2 Price Risk Motivates Technology Adoption
If price risk exists, household exposure to price risk may change with technology adop-
tion. I formally approximate willingness to pay by approximating both sides of (2) with
Taylor series expansions, following the approach used by Bellemare et al. [2013].
WTP = 0.5 · σ2 ·A(µ,y)(7)
is the approximation where σ2 is the variance of the staple price.
The shape of the indirect utility function explains how willingness to pay for price
stabilization varies with land wealth. Since willingness to pay is evaluated at the mean
price, households with land wealth such that they are autarkic when the mean price is
realized are indifferent toward price risk. Households with slightly greater land wealth
have price risk affinity because positive deviations from mean price increase utility more
than negative deviations decrease utility. Similarly, households with land wealth just be-
low autarky land wealth have price risk affinity because negative deviations from mean
price increase utility more than positive deviations decrease utility. Land rich house-
holds are price risk averse because negative deviations in price decrease utility more
than positive deviations increase utility. Finally, land poor households are price risk
averse because negative deviations in price increase utility less than positive deviations
decrease utility.
Figure 4 illustrates the potential effect of technology adoption on price risk exposure
in a stylized example with parameters estimated using data from western Kenya. The
vertical axis is willingness to pay to stabilize the staple price as a percent of income and
the horizontal axis is land wealth. In this illustration, technology adoption decreases
exposure to price risk for land poor households. This leads to the question of whether
technology adoption has this effect on price exposure empirically. The remainder of the
paper provides empirical evidence of this in western Kenya.
11
Technology Adoption and Market Participation
3 Technology Adoption, Price Preferences, and Price Risk
The theoretical model showed that variation in prices affects net sellers and net buyers
of maize differently. Empirically, market participation is closely related to land wealth
so that variation in prices likely have different effects on the land poor and the land rich.
The land poor are more likely to be buyers, as shown in figure 5. Land rich farmers
are more likely to sell maize than buy maize. Thus land wealth may modify technology
adoption’s effects on household exposure to price risk.
The mechanism through which technology adoption affects exposure to price risk
is changes in market participation. On the extensive margin, the seed treatment has
no effect on the number of households buying maize at endline among baseline non-
sellers and causes a 7 percent decrease in households that buy maize at endline among
baseline sellers, as shown in table 2. The seed treatment causes a 5 percent increase
in households that sell maize at endline among baseline non-sellers and a 10 percent
increase in households that sell maize at endline among baseline sellers.
Technology adoption may also change the intensity of market participation by house-
holds. Maize purchases and sales are plotted against land wealth for treatment and con-
trol groups in figure 6. Seed treatment decreases purchases and increases sales at all
levels of land wealth.
The remainder of the section studies the implications of changing marketing partici-
pation on household welfare in order to better understand the underlying mechanisms
motivating technology adoption.
3.1 Price Preferences
I first study how price preferences vary with market participation. The first step is to
test my assumptions about how market participation responds to changes in income and
own price (assumptions 1 and 2). Income and price are yearly measures in the theoretical
12
Technology Adoption and Market Participation
model. Thus year-to-year variation identifies the relevant elasticity estimates.
The randomized control trial has only one round of maize purchase data, so I use a
separate panel data set to estimate how household market participation responds to year-
to-year variation in income and prices. Panel data come from the Tegemeo Agricultural
Policy Research and Analysis Project (TAPRA). TAPRA is a four-round panel household
survey of a representative sample of Kenyan farm households in 2000, 2004, 2007 and
2010 and is led by the Tegemeo Institute and Michigan State University. I use TAPRA
data from the survey rounds when purchase prices for maize were collected in the farm
household survey (2004, 2007, and 2010). The sub-sample of interest is households in
the former provinces of Nyanza and Western, which overlap geographically with the
Western Seed Company impact evaluation study sample in western Kenya. In these
areas 536 households were surveyed in more than one survey out of the three surveys
conducted in 2004, 2007, and 2010.
I estimate elasticities using a reduced form marketed surplus function for farmer i in
district d in year t
midt = η · yidt + ε · pidt + γi +αdt + uidt(8)
where midt is net marketed surplus, yidt is household income, pidt is the staple price, γi
is a household fixed effect, αdt is a district-year fixed effect, and uidt is an error term. I
transform net marketed surplus, household income, and price by the inverse hyperbolic
sine transformation IHST(x) = ln(x + [x2 + 1]1/2
)to estimate elasticities and reduce
the influence of outliers on my estimates. Coefficients are interpreted as elasticities
as defined by Strauss [1984] such that η = ∂m∂y
y|m|
and ε(p,y) = ∂m∂p
p|m|
. This is the
correct interpretation of the elasticity estimates and it would be incorrect to interpret the
coefficients as conventional elasticities with the level of net marketed surplus as divisor
rather than its absolute value, which I illustrate with a numerical example in table 3.
Elasticity estimates using nominal income and prices from the TAPRA panel data set
13
Technology Adoption and Market Participation
are given in table 4. The elasticity of maize marketed surplus with respect to household
income is inelastic and positive, as shown in column (1). Thus assumption 1 holds even
for households with low levels of income risk aversion. The elasticity of maize marketed
surplus with respect to household income is inelastic and positive, satisfying assumption
2. Columns (2) and (3) show that households cultivating fewer maize acres per capita
than the median from the Western Seed sample have maize marketed surplus that is
slightly more responsive to prices and less responsive to income than likely net sellers
of maize. I ignore these differences and use estimates from the full sample in column
(1) to construct the coefficient of absolute price risk aversion for maize. Assuming ho-
mogeneous elasticities for the full sample prevents endogenous differences in marketing
behavior to drive differences in my measure of price risk exposure.
The indirect utility function is concave in the staple price when the coefficient of
absolute price risk aversion is positive. The coefficient is
A =
[[R− η(µ,y) ·
|m(µ,y)|m(µ,y)
]·β(µ,y) − ε(µ,y) ·
|m(µ,y)|m(µ,y)
]· m(µ,y)
µ(9)
Estimated elasticities of net marketed surplus with respect to income (η) and price (ε)
come from table 5. Mean village price (µ), net marketed surplus (m), and net marketed
surplus’s share of household income (β) are calculated from the randomized control
trial data described in sub-section 1. The value of the Arrow-Pratt coefficient of relative
income risk aversion R must be assumed. Barrett [1996] assumes this coefficient is in
the range of 1.5 to 2.5 whereas Bellemare et al. [2013] assume the coefficient equals 1. I
consider the set of these values such that Rε{1.0, 1.5, 2.5}.
I use these parameter estimates and variables to construct a coefficient of absolute
price risk aversion for each household. Figure 7 plots the coefficient against land wealth
for treatment and control groups. Households at almost all levels of land wealth are
affine to price risk (A < 0) on average so that the indirect utility function is convex in
prices with a global minimum at the price that makes the average household autarkic.
14
Technology Adoption and Market Participation
Households are averse to price risk only when they have relatively high relative income
risk aversion (e.g. R = 2.5) and land wealth greater than 5 acres in the treatment group
and 7 acres in the control group; under these conditions, indirect utility is concave in
prices.
These results suggest that greater adoption by sellers may be due to price preferences.
When adopting the technology and increasing staple production, sellers have a lower
price at which they would be autarkic. Since utility is convex in prices, utility increases at
an increasing rate as prices increase from the autarkic price to the mean price. Therefore
technology adoption allows sellers to realize higher utility at the mean price than they
would have without technology adoption; that is, the first term in (3) is increasing in
technology adoption for sellers.
3.2 Price Risk Between Years
The measure of price risk exposure is
WTP = 0.5 · σ2 ·A(10)
The randomized control trial and panel data do not allow me to estimate the year-to-
year variance of maize prices in Kenya. To do this, I use time series data from the Food
and Agriculture Organization of the United Nations. Data are available for Kenya on
both nominal annual producer prices for maize and a monthly consumer price index
from 2000-2015. For those years I divide the annual maize price in Kenyan shillings per
kilogram by the annual average consumer price index factor relative to 2015 to obtain
annual maize prices in 2015 values.
Figure 8 plots willingness to pay against land wealth for treatment and control
groups. when When households have low relative income risk aversion (e.g. R =
1, 1.5), technology adoption decreases household exposure to price risk regardless of
15
Technology Adoption and Market Participation
land wealth. When households have high relative income risk aversion (e.g. R = 2.5),
technology adoption decreases exposure to price risk for households with land wealth
less than 3 acres and increases exposure to price risk for households with land wealth
greater than 3 acres.
Figure 9 shows the results are consistent with technology adoption improving welfare
of land poor, net buyer households by decreasing their exposure to price risk. Technol-
ogy adoption decreases purchases by households with less than 1.5 acres, decreasing
household exposure to price risk. Technology adoption increases sales by households
with more than 3 acres, increasing household exposure to price risk. This leads to the
question of when this effect motivates technology adoption by households.
Indirect evidence that price risk motivates technology adoption by the land poor
comes from households that purchase maize in a typical year that also were assigned
to receive randomized treatments. Table 5 summarizes the results. In the control group
typical purchasers are less likely to adopt Western Seed Company maize hybrids relative
to typical non-purchasers by 5 percentage points (column 1). Yet the treatment effect on
adoption is 4 percentage points greater for typical purchasers of maize so that they are
just as likely to adopt the hybrids as typical non-purchasers. A potential explanation
for this effect is that typical purchasers in the treatment group were motivated to adopt
in order to purchase less maize. In the control group 20 percent of typical purchasers
expected to purchase less maize at endline and treatment increased this number by 14
percentage points (column 2). Of the households expecting fewer purchases, approxi-
mately 80 percent expected this to be because of changes in maize harvests.
To summarize, treatment caused 15 percent of typical purchasers of maize to adopt
Western Seed Company maize hybrids. Of these households, 80 percent (0.12/0.15)
expected to decrease purchases over the following year. Thus households are aware
of the potential impact of technology adoption on maize purchases. The results are
consistent with price risk motivating technology adoption by the land poor.
16
Technology Adoption and Market Participation
4 Conclusion
This paper evaluates whether targeting typical sellers for a technology adoption program
would increase adoption and welfare impacts of the program. I use data from a field
experiment promoting a new maize hybrid in western Kenya, where maize is the main
staple food. Technology adoption is greater for typical sellers of maize than for typical
non-sellers of maize. Yet welfare gains from technology adoption may be less for typical
sellers than for typical buyers due to price risk aversion. Targeting programs to typical
sellers may increase agricultural technology adoption but may exclude typical buyers
who would realize large welfare gains from becoming self-sufficient food producers
through technology adoption.
This research has implications for researchers studying agricultural technology adop-
tion and its impacts. The utility of agricultural households is generally affected by staple
price risk, as shown in the paper’s theoretical model, and in particular this is true when
the coefficient of absolute price risk aversion is non-zero. Sufficient conditions for this to
be true are that the household participates in the market and adjusts its participation to
changes in income or price (assumptions 1 and 2). Thus there is a theoretical basis for
studying exposure to price risk as an outcome of technology adoption and for consider-
ing price risk as a factor affecting technology adoption decisions. Market participation
appears to be an important factor for researching technology adoption.
17
Technology Adoption and Market Participation
References
May 2017. URL http://www.fao.org/faostat/en/#data.
C.B. Barrett. On price risk and the inverse farm size-productivity relationship. Journal of
Development Economics, 51:194–215, 1996.
M.F. Bellemare, C.B. Barrett, and D.R. Just. The welfare impacts of commodity price
volatility: Evidence from rural Ethiopia. American Journal of Agricultural Economics, 95
(4):877–899, 2013.
M.R. Carter and Y. Yao. Local versus global separability in agricultural household mod-
els: The factor price equalization effect of land transfer rights. American Journal of
Agricultural Economics, 84(3):702–715, 2002.
M.R. Carter, M. Mathenge, S.S. Bird, T.J. Lybbert, T. Njagi, and E. Tjernstrom. Local seed
company fills a niche to increase maize productivity in Kenya. Policy Brief 2017-01,
Innovation Lab for Assets and Market Access, University of California at Davis, Feb
2017.
J. Chavas and B. Larson. Economic behavior under temporal uncertainty. Southern Eco-
nomic Journal, 61(2):465–477, October 1994.
A. Deaton. Household survey data and pricing policies in developing countries. The
World Bank Economic Review, 3(2):183–210, May 1989.
I. Finkelshtain and J. Chalfant. Commodity price stabilization in a peasant economy.
American Journal of Agricultural Economics, 79(4):1208–1217, November 1997.
A. Foster and M. Rosenzweig. Microeconomics of technology adoption. Annual Review
of Economics, 2:395–424, 2010.
H. Henderson and I.G. Isaac. Modern value chains and the organization of agrarian
production. American Journal of Agricultural Economics, October 2016.
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Technology Adoption and Market Participation
B.K. Jack. Constraints on adoption of agricultural technologies in developing countries.
White paper, Agricultural Technology Adoption Initiative, J-PAL (MIT) and CEGA (UC Berke-
ley), 2011.
R. Laajaj. Endogenous time horizon and behavioral poverty trap: Theory and evidence
from Mozambique. Journal of Development Economics, 127:187–208, 2017.
J.W. McArthur and G.C. McCord. Fertilizing growth: Agricultural inputs and their
effects in economic development. Journal of Development Economics, 127:133–152, 2017.
J. Strauss. Marketed surpluses of agricultural households in Sierra Leone. American
Journal of Agricultural Economics, 66(3):321–331, August 1984.
19
Technology Adoption and Market Participation
Table 1: Treatment effects on Western Seed hybrid maize adoption
(1) (2) (3) (4) (5) (6) (7) (8) (9)Seed treatment 0.16 0.13 0.13 0.09 0.07 0.07 0.06 0.02 0.01
Sells maize 0.11 0.11 0.12 0.11 0.11 0.10 0.10 0.10
Purchases maize 0.01 0.01 0.02 0.02 0.02 0.02 0.02
Midaltitude zone 0.06 0.06 0.06 0.06 0.07 0.07
Maize acres 0.02 0.01 0.01 0.01 0.01
Monocrop maize acres 0.04 0.04 0.04 0.04
Male 0.02 0.02 0.02
Credit unconstrained 0.06 0.06
Hybrid user -0.00
Fertilizer treatment -0.01 -0.01 -0.01 -0.01 -0.02 -0.02 -0.01 -0.01 -0.02
Main effectsSells maize 0.00 -0.00 -0.01 -0.02 -0.02 -0.02 -0.02 -0.02
Purchases maize -0.04 -0.04 -0.03 -0.03 -0.03 -0.03 -0.03
Midaltitude zone 0.02 0.05 0.04 -0.11 -0.10 0.02
Maize acres 0.02 0.02 0.02 0.02 0.02
Monocrop maize acres -0.02 -0.02 -0.02 -0.02
Male 0.04 0.04 0.03
Credit unconstrained -0.00 -0.00
Hybrid user 0.06
Fertilizer treatment -0.02 -0.02 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.02
1089 observations. Regression includes pair indicators as control variables..09 is the mean of the dependent variable in the control group.
20
Technology Adoption and Market Participation
Table 2: Treatment effects by market participation
Adoption Maize Total Market participation(1) (2) (3) (4) (5) (6)0/1 Acres Labor Acres Buyer Seller
Seed treatment 0.13 -0.12 -473.24 -0.13 -0.00 0.05
(0.04) (0.10) (408.21) (0.17) (0.04) (0.05)Treatment interactions
Sells maize 0.11 0.16 809.07 0.27 -0.07 0.05
(0.05) (0.13) (638.36) (0.20) (0.06) (0.06)Fertilizer treatment -0.01 -0.12 140.26 -0.29 0.04 -0.08
(0.04) (0.10) (464.29) (0.17) (0.06) (0.06)Main effects
Sells maize 0.00 0.11 685.30 0.13 -0.15 0.15
(0.03) (0.08) (457.14) (0.13) (0.04) (0.05)Fertilizer treatment -0.02 0.12 73.63 0.25 -0.06 0.10
(0.03) (0.07) (335.98) (0.12) (0.04) (0.04)Dep. var. mean, control 0.10 1.25 2434.31 1.71 0.39 0.29
1089 observations. Regression includes pair indicators as control variables.Labor costs in Kenyan shillings. Standard errors clustered by village in parentheses.
21
Technology Adoption and Market Participation
Table 3: Elasticity estimates from an IHST-IHST specification
p IHST(p) m IHST(m)dIHST(m)dIHST(p)
dmdp ⋅
pm
dmdp ⋅
pm
1.00 0.38 -500.00 -3.00 - - -2.00 0.63 -450.00 -2.95 0.19 0.22 -0.223.00 0.79 -400.00 -2.90 0.31 0.38 -0.384.00 0.91 -350.00 -2.85 0.48 0.57 -0.575.00 1.00 -300.00 -2.78 0.71 0.83 -0.836.00 1.08 -250.00 -2.70 1.02 1.20 -1.207.00 1.15 -200.00 -2.60 1.46 1.75 -1.758.00 1.21 -150.00 -2.48 2.17 2.67 -2.679.00 1.26 -100.00 -2.30 3.47 4.50 -4.5010.00 1.30 -50.00 -2.00 6.61 10.00 -10.0011.00 1.34 0.00 0.00 48.54 48.54 48.5412.00 1.38 50.00 2.00 53.13 12.00 12.0013.00 1.42 100.00 2.30 8.69 6.50 6.5014.00 1.45 150.00 2.48 5.49 4.67 4.6715.00 1.48 200.00 2.60 4.18 3.75 3.7516.00 1.51 250.00 2.70 3.46 3.20 3.2017.00 1.53 300.00 2.78 3.01 2.83 2.8318.00 1.56 350.00 2.85 2.70 2.57 2.5719.00 1.58 400.00 2.90 2.47 2.38 2.3820.00 1.60 450.00 2.95 2.30 2.22 2.22
AVERAGE 7.92 5.83 3.50
22
Technology Adoption and Market Participation
Table 4: Maize income and price elasticity estimates from fixed effects
Maize Acres per Capita(1) (2) (3)All Small Large
Income 0.06** 0.03 0.06**(0.02) (0.06) (0.02)
Maize price 0.54*** 0.63 0.49**(0.20) (0.41) (0.22)
Observations 1538 482 1056
R-squared 0.01 0.01 0.02
F-statistic 6.75 1.38 6.20
All specifications include district-round fixedeffects as controls. Income in Kenyan shillings.Maize price in Kenyan shillings/kilogram. Maizeprice is household average weighted by volumefor sellers and district average weighted byvolume for non-sellers. All variablestransformed by inverse hyperbolic sine function.
Table 5: Treatment effects on adoption and expected decreases in purchases
(1) (2)Adoption Expectation
Treatment 0.11** -0.02
(0.05) (0.01)Treatment·Purchaser 0.04 0.14**
(0.07) (0.06)Purchaser -0.05 0.20***
(0.03) (0.04)Treatment+Treatment·Purchaser 0.15 0.12
Adoption was 11% among typical non-purchasers in the control group.Pair indicator variables included as controls (standard errors clustered by village).549 observations. * = 10% significance, ** = 5% significance, *** = 1% significance
23
Technology Adoption and Market Participation
Figure 1: Timeline of treatments and surveys (baseline, midline, and endline)
2013
Jan
Feb
Mar
Ap
r M
ay
Jun
Jul
Aug
Sep
Oct
N
ov
Dec
2014
Jan
Feb
Mar
Ap
r M
ay
Jun
Jul
Aug
Sep
Oct
N
ov
Dec
2015
Jan
Feb
Mar
Ap
r M
ay
Jun
Jul
Aug
Sep
Oct
N
ov
Dec
2016
Jan
Feb
Mar
Ap
r M
ay
Jun
Jul
Aug
Sep
Oct
N
ov
Dec
Baseline Fertilizer provision Seed information
EndlineSeed delivery Midline
24
Technology Adoption and Market Participation
Figure 2: Direct and interaction effects of treatment on Western Seed adoption
Seed treatment
Purchases maize
Sells maize
Midaltitude zone
Maize acres
Monocrop maize acres
Male
Credit unconstrained
Hybrid user
-.2 -.1 0 .1 .2 .3
Notes: The figure graphs treatment effect point estimates from table 1, column (9) with 99-, 95-, and90-percent confidence intervals using standard errors clustered at the village-level.
25
Technology Adoption and Market Participation
Figure 3: Decomposing the coefficient of absolute price risk aversion
-5
0
5
Pric
e ris
k av
ersi
on
Land wealth
Relative income risk aversionIncome responsePrice responseAbsolute price risk aversion
Notes: The coefficient of absolute price risk aversion is the sum of price risk aversion due to: 1) relativeincome risk aversion; 2) income responsiveness of net marketed surplus; 3) price responsiveness of netmarketed surplus. This figure illustrates these effects using parameters estimated using data from westernKenya. For land poor households, price risk aversion is driven by relative income risk aversion. For landrich households, risk aversion due to relative income risk aversion is offset by risk loving due to theresponse of net marketed surplus to changes in own price. The response of net marketed surplus tochanges in income does not have a large effect on absolute price risk aversion.
26
Technology Adoption and Market Participation
Figure 4: Technology adoption decreases price risk exposure of land poor households
-0.25
0
0.25
0.5
0.75
1
Land wealth
Without technology adoption With technology adoption
Notes: The vertical axis is willingness to pay to stabilize the staple price as a percent of income andthe horizontal axis is land wealth. In this illustration, technology adoption increases staple production.For land poor households, technology adoption decreases their dependence on the market for stapleconsumption and thereby decreases their exposure to price risk. For land rich households, technologyadoption increases their dependence on the market for offloading surplus staple production and therebyincreases their exposure to price risk.
27
Technology Adoption and Market Participation
Figure 5: Selling and buying of maize
0.0
0.5
1.0 Buying Selling
0 1 2 3 4 5 6 7 8 9 10Land wealth (Acres cultivated at baseline)
Control group only: 258 observations (1 not shown with land wealth above 10 acres).
The bottom panel is a strip plot showing how households are distributed across the land wealth contin-uum; each circle represents a household and the rectangles contain the second and third quartiles.
28
Technology Adoption and Market Participation
Figure 6: Maize transactions (in kilograms) as a function of baseline land wealth
0250500
Control Treatment
A. Net marketed surplus
0
250
500 B. Purchases
0250
500
C. Sales
0 1 2 3 4 5 6 7 8 9 10Land wealth (Acres farmed at baseline)
558 observations (1 not shown with land wealth greater than ten acres)
Notes: Net marketed surplus is sales minus purchases. The bottom panel is a strip plot showing howhouseholds are distributed across the land wealth continuum; each circle represents a household and therectangles contain the second and third quartiles.
29
Technology Adoption and Market Participation
Figure 7: Absolute price risk aversion as a function of baseline land wealth
-20
24
Control Treatment
A. R=2.5-4
-20
B. R=1.5
-8-6
-4-2
0 2 4 6 8 10
C. R=1.0
548 observations (10 not shown due to outliers)
Notes: The vertical axis measures the coefficient of absolute price risk aversion for maize.
30
Technology Adoption and Market Participation
Figure 8: Price risk exposure as a function of baseline land wealth
-50
050
100
Control Treatment
A. R=2.5
-150
-100
-50
0
B. R=1.5
-200
-150
-100
-50
0 2 4 6 8 10
C. R=1.0
548 observations (10 not shown due to outliers)
Notes: The vertical axis measures willingness to pay to stabilize the maize price in shillings.
31
Technology Adoption and Market Participation
Figure 9: Price risk exposure and net marketed surplus as a function of land wealth
-50
050
100
Control Treatment
A. R=2.5
-.10
.1.2
0 2 4 6 8 10
B. Beta
548 observations (10 not shown due to outliers)
Notes: The Panel A vertical axis measures willingness to pay to stabilize the maize price in shillings. ThePanel B vertical axis measures beta, the household’s net marketed surplus as a share of household income.
32
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