Learning About a New
Technology: Pineapple in
Ghana
Wenbo Huang
Introduction
• Technology adoption is fundamental to development
• Characteristics of technology usually not transparent to
new user – an investment in learning required
• Multiple adopters in similar circumstances (often case
with innovation in agriculture):
• • process of learning may be social
• learn of characteristics from each other
• This paper investigates role of social learning in diffusion
of new agricultural technology in Ghana
• Over last decade in Akwapim South district of Ghana
• Traditional established system of maize and cassava
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intercropping for sale to urban consumers
Recently transformed into intensive production of
pineapple for export to European markets
Transformation involves adoption of new technologies
• intensive use of fertilizer and other agricultural chemicals
Measuring extent of social learning is difficult:
set of neighbours from whom can learn is difficult to
define
distinguishing learning from other phenomena that may
give similar observed outcomes
In absence of learning - act like neighbors:
• interdependent preferences, technologies • subject to
related unobservable shocks
Learning Model
•Simple model to motivate empirical specification Farmers
trying to learn about responsiveness of output y to input x
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yi,t+1=wi,t f(xi,t)+εi,t+1
•Farmers do not know function f ---- object of learning
•wi,t = positive exogenous growing conditions
•(correlated across farmers and across time – observable
to farmer but not to econometrician)
• yi,t+1= output
• xi,t = input (fertilizer)
• εi,t+1= productivity shock
• yi,t+1=wi,t f(xi,t)+εi,t+1
• Choose input x to maximize subjective
expectation of time t+1 profits for this
input choice:
• πi,t+1(x,wi,t)≡(wi,t f (x)+εi,t)− px
• where yi,t+1=wi,tf(xi,t)+εi,t+1 Choosing
inputs such that:
• E π (x*,w )≥E π (x,w ) i,t i,t+1 i,t i,t i,t
i,t+1 i,t
• all x∈sup(xi,t)
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Two main reasons for farmers' of x*i,t
to be interrelated
• First {Spatial Effect}
• Spatial or serial correlation in growing conditions induce
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correlation in farmers' fertilizer choices
-farmers face similar realizations of growing
conditions
-make similar decisions about optimal amount of
fertilizer use
Second{Learning Effect}
Farmers with related subjective information sets have
similar subjective expectations of growing-conditionsadjusted output, g.
Similar input choices when face similar prices and
growing conditions
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Implications
• Farmers tend to adjust input use toward surprisingly
successful input levels, and higher than expected profits
at the currently utilized input level will make farmers less
likely to change from that level.
• Farmers tend to adjust input use away from an input level
that was less profitable than expected.
• An oberservation of profit sufficiently above expecations
will induce a farmer to switch to that level of input use.
• The probability of changing input levels in response to a
given piece of information is decreasing in a farmers's
experience.
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Implications for Actions
• Suppose farmer faces constant growing conditions.
• New info allows him to locally learn about f at input level
X(k,t)
• With constant growing conditions--only expected profits
associated with input level X(k,t) shift
• -good news(higher than expected profits)--upward shift
• -bad news(lower than expected profits)--downward shift
• Responses to changes in expected profits different for
farmers who used X(k,t) in previous planting vs. those
who used alternative input level
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Consider farmers who used X(k,t)
• Incareases in expected profits, at input level X(k,t)
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-reinforce farmers beliefs that X(k,t) is optimal
• -continue to use it
• Sufficiently larger decrease in expected profits at input
level X(k,t)
• -may switch to alternative
• -bad news about X(k,t) provides no info about which
alternative may choose
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Consider farmers who previously used
alternative to X(k,t)
• Bad news： do not change , already thought X(k,t) was
inferior option
• Good news: may switch to X(k,t), if increase sufficientlt
large.
Data
• Two enumerators lived in or near each village
and interviewed respondents in 15 rounds at
intervals of six weeks.
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• We begin with information on pineapple being
grown on 406 plots by 132 farmers. Of these
plots, 288 were planted during oursurvey. Plot
input data are missing on 3 of these plots, leaving
285; 77 of these were planted too late in our
survey for fertilizer application to be completed
before the end of fieldwork, leaving 208 plantings.
We are missing data for some rounds on 8 of
these, leaving 200 plantings; 87 ofthese are the
first planting in our survey by particular farmers,
leaving 113 observed changes in ertilizer use.
GIS information is missing on 6 of these plots,
leaving information on 107 changes in fertilizer
use by 47 farmers.
• The Figure above shows the patttern of adoption of
pineapples in our sample villages: from less than 10
percent in 1990, pineapples spread very rapidly until more
than 46 percent of farmers were cultivating pineapples in
1997.
• For some of the analysis divide sample into 2 groups:
• experienced (adopted before 1994) and inexperienced
(adopted more recently) Pineapple cultivators are richer,
male, more likely to be in each others information
neighbourhood
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Pineapple production in Ghana
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Pineapple production in Ghana
Most salient departure from traditional techniques
is use of new chemical input.
Pineapple cultivation sequence( period t is 6
weeks):
-plot planted at time t receives crucial fertilizer
inputs during t+1 to t+4
-chemically forced flowering occurs
approximately at t+5
-pineapple harvest is complete by t+9
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Pineapple production in Ghana
• Know potential profitability of plot at time t+5, outcome is
fully known at time t+9
• Plot receives fertilizer t+1 to t+4---fertilizer inputs can be
influenced by neighbours experiences.
• X(i,t)=fertilizer applied during periods t+1 to t+4
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can be influenced during this time.
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Communication and Knowledge
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Pineapple farmers especially veteran pineapple farmers
are more likely to be in each other's information
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neighborhood than would be expected by chance.
Table 2 provides a summary of our baseline information
link distribution by experience. Over 20 percent of
veteran pineapple farmers (within each village) have
approached each other for advice about farming, while
only 2 percent of nonpineapple farmers are in each
others' information neighborhood.
A similar pattern is observed using our other
information metrics. It may be the case that these
information connections were important determinants of
the adoption process.
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Conclusion
• Evidence that social learning is important in diffusion of
knowledge regarding pineapple cultivation in Ghana.
• Take advantage of data to identify learning effects in
economy undergoing rapid technological change
• Farmers more likely to change input levels of fertilizer use
on receipt of bad about profitability of their previous level
of fertilizer use
• Less likely to change when observe bad news about
profitability of alternative levels of fertilizer
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Conclusion
• Magnitudes of innovatuions in fertilizer use:
• Farmer incareses(decreases) his use after someone with
whom he shares information achieves higher than
expected profits when useing more (less) fertilizer than he
did