agricultural technology adoption, market participation ... · section 1 provides evidence that...

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:[email protected]). 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


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


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

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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.

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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


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


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.

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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


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


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

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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).

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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.

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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


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


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


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


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


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


References

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C.B. Barrett. On price risk and the inverse farm size-productivity relationship. Journal of

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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.

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production. American Journal of Agricultural Economics, October 2016.

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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.

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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


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


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


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


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


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


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


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


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


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


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


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


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


Notes: The vertical axis measures willingness to pay to stabilize the maize price in shillings.

31


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


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

agricultural technology adoption, market participation ... · section 1 provides evidence that...

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