Learning About a New Technology: Pineapple in Ghana Tim Conley, University of Chicago and Chris Udry, Yale University
Learning About a New Technology: Pineapple in
Ghana
Tim Conley, University of Chicago
and
Chris Udry, Yale University
Pro
porti
on o
f Far
mer
s C
ultiv
atin
g P
inea
pple
'experienced' 'inexperienced'1990 1992 1994 1996 1998
.1
.2
.3
.4
.5
Figure 3. Sample proportion of farmers cultivating pineapple by year. Wedefine famers who adopted before 1994 as experienced.
Goal:
• Understand Learning About New Technology in
Agrarian Economy: Rural Ghana
Background
• Once Upon a Time: Farmers Grew Maize and
Cassava
• New Thing: Price of Pineapple Went Up→ Shift
to Producing Pineapple for Export
Question:
• Is Social Learning Important As Ghanaian Farm-ers Learn How to Farm Pineapple?
Pineapple Issues: Costs/Benefits
• Profitability Relative to Old Crops
• Barriers to Entry
— Capital, Land, Labor Mkt Imperfections
∗ Min. Efficient Size for Exporters
∗ Land Characteristics Matter: Goldstein andUdry (1999)
∗ Hired Labor Needed
∗ Inputs More Expensive
— Costs of Learning How to Use New Tech.
Initial Adoption and Learning Best Inputs Clearly NOT
Separate, But Focus on Latter Here
Data
• Demographic: Family Characteristics
• Geographic: Soil Characteristics, Plot Locations
• Actions: Which Crops to Plant, Use of Fertilizer,Herbicide, Pesticide
• Outcomes: Yields, Profits, Farmer Forecast Val-uation of Unharvested Crop at End
• Knowledge and Communication: Who FarmersKnow, Learned From, Talk To About a Problem
Emprically Identifying Social Learning is Hard
• Operationally Defining What Farmers Learn From:Neighbors and Observed Info
• Identify Learning v. Other Stuff: Correlated Shocks,Mimicry, etc.
Modeling Who Is My Neighbor and What Can I See?
• Whole Village, Collective Experimentation
• Sparse Network, Limited Observability
Knowledge Info
Individual’s Roster of Contacts
• Lots of Types of Interactions
Questions About Random Six Pineapple Plots
• Do You Know the Owner of this Plot?
• Do Whether It Is A Pineapple Plot?
• What Inputs Used on It?
• What Was Harvested?
Questions About Random Seven Farmers + ‘Big Three’
• Do You Know Farner A?
• When Did You Last Talk With Farmer A?
• Ever You Ever Gone to Farmer A for Advice?
Roster Connections and Average Pineapple Plot Coordinates Village 3
1 Km
Figure 1. The circles represent the geographic center of plots for eachpineapple farmer in Village 3. The lines connect pineapple farmerslisted on each other’s roster of contacts.
Ker
nel D
ensi
ty E
stim
ate
Distance in Meters
No Information Link Information Link
0 2000 4000 6000
0
.2
.4
.6
Figure 2. Kernel density estimates for the distribution of distances betweenall pineapple farmers and those listed on the roster of contacts. AnEpanechnikov kernel with a bandwidth of 300 meters was used.
Table 2: Logit Predicting Ask For Advice
Coefficient Standard Error
Either Party Holds Traditional Office -0.55 0.26Same Religion 0.04 0.33Same Clan 0.43 0.24Same Gender 1.73 0.78Same Soil Type -0.23 0.27Absolute Age Difference (years) -0.04 0.02Absolute Wealth Difference (million cedis) 0.15 0.03Distance Between Plot Centers (kilometers) 0.46 0.16Constant -2.14 0.84
Logit MLE Estimates, Sample Size = 490, Pseudo R-squared =.12.Dependent variable is one if either party answered yes to the question:Have you ever gone to _____ for advice about your farm?
Production Function: yi,t+1 = witf(xit) + εi,t+1
• yitt+1 = pineapples
• xi,t = fertilizer (discrete) with price pit
• wi,t is a weather correlated in space/time known
to farmers not us
• εi,t+1 IID productivity shock.
Myopic Farmer’s Problem: Choose the Best Input
Given Subjective Expectation:
Eit{witf(x∗i,t, εi,t+1)}− pitx
∗it ≥
Eit
nwitf(x, εi,t+1)
o− pitx
Farmer Needs to Learn Productivity of Input Levels
Eit
nf(xkt, εk,t+1)
o= Eit
nyk,t+1wkt
|xkto
Learn Expected “Weather-adjusted Output” Given X
git(xkt) ≡ Eit
nyk,t+1wkt
|xkto
• Learning = Updating g
— Using Observations of (yi,t+1, wit, xit)
— Own and Those of Info. Neighbors
• Farmer Chooses x∗ so:
Eit{witf(x∗i,t, εi,t+1)}− pitx
∗it ≥
Eit
nwitf(x, εi,t+1)
o− pitx
• Reasons for different farmers to choose same x∗
— Prices for fertilizer (Constant, Common)
— Weather shocks (Spatial, Time Correlation)
— Related Information (Can Measure Neighbors)
Local Learning Definition
Update Only One Conditional Distribution at a Time
(y, x,w)→ Update gi,t(x) not gi,t(x) for x 6= x
Update In Direction of Observations:
yw ≥ git(x)→ git+1(x) ≥ git(x)
Example: Sample Analog Updating:
git(x) =1P
j∈Ni,τ<t−11(xj,τ=x)
Pj∈Ni,τ<t−1
1(xjτ = x)yj,τ+1wj,τ
gi,t+1(x) = αit(x) · yk,t+1wkt+ (1− αit(x))git(x)
αit(x) = (1 +P
j∈Ni,τ<t−11(xjτ = x))−1
gi,t+1(x)− gi,t(x) = αit(x) ·hyk,t+1wkt
− git(x)i
Implication for Actions
• Good News About Input Level x:
nyk,t+1wkt
≥ git(x)oornπk,t+1(x) ≥ Ei,tπi,t+1(x)
ogi,t+1(x) ≥ gi,t(x) andEi,t+1πi,t+2(x) ≥ Ei,tπi,t+1(x)
Move Weakly To x
• Bad News About x
nyk,t+1wkt
< git(x)oornπk,t+1(x) < Ei,tπi,t+1(x)
oEi,t+1πi,t+2(x) < Ei,tπi,t+1(x)
Perhaps Switch Away If at x, Else Stay Put
• Random weather
Responsiveness is probabilistic when weather is ran-
dom. Let supp(x) = {H,L} with H > L. Farmer i
chooses xit = H if witgit(H) − pitH ≥ witgit(L) −pitL. xit = H if
wit ≥ w∗it =pit(H − L)
git(H)− git(L).
w∗it is strictly declining in git(H), so the probability
given git(·) and all past wkt that i will choose xit = H
is increasing in git(H). Thus, the conditional proba-
bility that i will choose xit = H is increasing in ykt.
similar pattern in the absence of learning if weather
is not properly taken into account. suppose git(·) isknown. Farmers will choose xi,t = H when
wit ≥p(H − L)
g(H)− g(L).
this will cause and yi,t+t to be high relative to its un-
conditional expectation. If wk.t is positively spatially
and serially correlated, then farmers choices of xi,tand hence yi,t+t will be positively correlated across
space and time as well. In particular, if weather shocks
at small lags in time are highly positively correlated
for physically proximate plots, then farmer k’s choices
xk,t−1 = H and associated likely-to-be-high output
yk,t will be tend to be followed by choices of H and
higher outputs of k0s physical neighbors, solely due tothe positive dynamic correlations in weather. To the
outside observer, a higher than long-run average real-
ization of yields and profits by farmer k using the high
fertilizer level tends to be followed in near future by
an increased use of that quantity of fertilizer by his
physical neighbor farmer j who also tends to achieve
a higher than long-run average profit.
• Caveats:
— Global Knowledge of Production Fn Might Change
Predictions
— Know w Role in Production Fn Implies Can
Learn From All Neighbors, Not Just Those
With Matching w
Social Learning and Experimentation
Simplest setting - two periods. Expected profits are
V2 = maxj∈{H,L}
E2³w2θj − q2 · j + ε3
´.
So the farmer chooses x2 = H if w2µ2H − q2 ·H >
w2µ2L − q2 · L. So value of H in 1 is
V1H = w1µ1H − q1 ·H + δE1V2
= w1µ1H − q1 ·H +
δE1
"max
j∈{H,L}E2
³w2θj − q2 · j + ε3
´#= w1µ1H − q1 ·H + δE1
max
"w2
Ãy2H
σ21Hσ21H + 1
+ µ1H1
σ21H + 1
!− q2 ·H ,
w2µ1L − q2 · L] .
y2H =σ21H + 1
σ21H
õ1L +
q2w2(H − L)− µ1H
1
σ21H + 1
!
V1H
= w1µ1H − q1 ·H +
δZ Z
max
"w2
Ãy2H
σ21Hσ21H + 1
+ µ1H1
σ21H + 1
!− q2 ·H
w2µ1L − q2 · L]× f(w2|w1)× φ1H(y2H)dy2Hdw2,
= w1µ1H − q1 ·H +
δZ Z ∞
y2H
Ãw2
Ãy2H
σ21Hσ21H + 1
+ µ1H1
σ21H + 1
!− q2 ·H
!×f(w2|w1)φ1H(y2H)dy2Hdw2+δ
Z Z y2H
−∞(w2µ1L − q2 · L)
×f(w2|w1)φ1H(y2H)dy2Hdw2∂V1H∂µ1H
= w1 + δZw2(1−Φ1H(y2H))f(w2|w1)dw2,
∂V1L∂µ1H
= δZw2Φ1L(y2L)f(w2|w1)dw2
Differentiate the value function to find
1
δ
∂V1L∂µ1H
=Z Z yi2L
−∞w2f(w2|w1)φ1L(y2L)dy2Ldw2 +Z
(w2µ1H − q2H)φ1L(y2L)∂y2L∂µ1H
f(w2|w1)dw2 −Z Ãw2
(y2L
σ21Lσ21L + 1
+ µ1L1
σ21L + 1
)− q2L
!
×φ1L(y2L)∂y2L∂µ1H
f(w2|w1)dw2where y2L is defined (analogously to y2H) as the crit-
ical realization of y2 given that x1 = L such that if
y2L ≥ y2L, then x2 = L and x2 = H otherwise.
Thus y2L is defined so that
(w2µ1H − q2H) =
Ãw2
(y2L
σ21Lσ21L + 1
+µ1L
σ21L + 1
)− q2L
!.
Given the definition of y2L, the difference of the sec-
ond and third lines of the derivative calculated above
is zero and equation (??) follows.
Can show
(1− Φ1H(y2H)) > Φ1L(y2L)−1
2.
(1−Φ1H(y2H)) is the probability that x2 = H given
that the farmer chooses x1 = H. So ∂V1H∂µ1H
− ∂V1L∂µ1H
= w1 + δZw2(1−Φ1H(y2H)−
Φ1L(y2L))f(w2|w1)dw2> w1 + δ
Z−12w2f(w2|w1)dw2
= w1 −δ
2E(w2|w1) > 0
• Point: Social learning implies that farmers will in-novate in their fertilizer use after the realization
of particularly high profits by their information
neighbors. However, this will also be observed
across geographic neighbors if we don’t condition
on weather. Use geographic proximity to condi-
tion for weather.
• Also Investigate
— Any Model with Learning Convergence: Up-
dates Decreasing in Some Measure of Experi-
ence
— Does Source of Information Influence Responses?
Input Responses Are Fn ofhyk,t+1wkt
− git(x)ior Profits
Analog:nπk,t+1(xk,t)−Ei,t
hπk,t+1(xk,t)|xk,t, wk,t
ioProblems:
• No Weather Data
— Scale ofhyk,t+1wkt
− git(x)inot identified
• Short Time Series Dimension
— Cannot Estimate Subjective git(·)
Solutions:
• Exploit Spatial, Temporal Correlation to IdentifySign
• Use Rational Expectations Analog of Subjectivegit(·)
Event We Care About:nπk,t+1(xk,t) > Ei,t
hπk,t+1(xk,t)|xk,t, wk,t
io
ApproximateEi,t
hπk,t+1(xk,t)|xk,t, wk,t
iwith estimate
of Ehπk,t+1(xk,t)|xk,t, wk,t
i
Estimate Ehπk,t+1(xk,t)|xk,t, wk,t
ifor unknown w
exploiting assumption that w is spatially, serially cor-
related.
Suppose info neighborhood of farmer i contains H
neighbors close to k with common weather and input
bE hπk,t+1(xk,t)|xk,t, wk,t
i= 1
H
PHh=1 πh,t+1(xh,t)
Use Event:nπk,t+1(xk,t) >
bE hπk,t+1(xk,t)|xk,t, wk,t
io
di,k,t ≡ 1nπk,t+9(xk,t, wk,t) >
bEi
hπk,t+9(xk,t, wk,t)
io.
G∗i,t1(x = xi,t0) ≡Xk∈Ni
Xτ∈[t0,t1)
ψ(t1 − τ)di,k,τ1{xi,t0 = xk,τ}.
ψ(τ ; t0, t1) ≡ (ψ(t1 − τ)− ψ(t0 − τ))
G∗∗i,t1(x = xi,t0) ≡Xk∈Ni
Xτ∈[t0−4,t1)
ψ(τ ; t0, t1)di,k,τ1{xi,t0 = xk,τ}.
Gi,t1(x = xi,t0) ≡1
TotalP lantsi,t1Xk∈Ni
Xτ∈[t0−4,t1)
ψ(τ ; t0, t1)di,k,τP lantsk,τ1{xi,t0 = xk,τ}.
1Ni
Pk∈Ni
1
Ãπk,t(xk,t−1) >bE h
πk,t(xk,t−1)|xk,t−1, wk,t
i ! hxk,t−1 − x∗i,t−1i
• Problem: Similarity in Weather Induces Similarityin Actions
• “Solution” Use Spatial Average of Actions to ProxyFor Alignment Due to Weather
1Gi
Pk∈Gi
hxk,t−1 − x∗i,t−1
i
• Problem: Similarity in financial conditions inducesimilarity in actions
• ”Solution” use average within financial neighbor-hoods:
1Fi
Pk∈Fi
hxk,t−1 − x∗i,t−1
i
Regression Specification:
∆xi,t = ω0i,tα+
β 1Ni
Pk∈Ni
1
Ãπk,t(xk,t−1) >bE h
πk,t(xk,t−1)|xk,t−1, wk,t
i ! hxk,t−1 − x∗i,t−1i
+γ 1Gi
Pk∈Gi
hxk,t−1 − x∗i,t−1
i+ δAi,t + εi,t
Estimate via LS Using Spatial SE Allowing General
Correlation
Operational Defnitions:
• Close
bE hπk,t(xk,t−1)|xk,t−1, wk,t
i: within 700 meters and
Lagged ≤ 2
Gi : within 700 meters
• Spatial Correlation Bartlett Product Weight 1.5km Cutoffs
• New Farmers Exp≤ 3 (27%)
• Big Farmers 60,000+ suckers (27 %) (Median
suckers 22,000, mean 41,000). .
More Issues:
• Nonstationarity in updating changes not here: can-not improve with short TS
• Own experience poorly measured: some may bepicked up by 1
Gi
Pk∈Gi
hxk,t − x∗i,t−1
i
• Information neighbors may also share common ac-cess to credit arrangements: can vary Ai,t.
• Endogenous neighborhoods due to something un-observed that also influence productivity
Table 4: Predicting the Change in Input Use
Dependent Variable: Indicator of a Change in Per Plant Fertilizer Use
Good News at Lagged Fertilizer Use -0.13 0.05[1.19] [1.16]
Good News at Alternative Fertilizer Use 0.18 0.37[0.97] [1.02]
Bad News at Lagged Fertilizer Use 12.32 14.41[3.72] [4.63]
Bad News at Alternative Fertilizer Use -2.98 -4.22[1.91] [2.07]
Average Absolute Deviation from 0.49 0.49Geographic Neighbors' Fertilizer Use [0.13] [0.14]
Inexperienced Farmer 1.14 1.28[0.92] [1.04]
Talks with Extension Agent -1.39[0.75]
Village 1 -2.34 -1.70[0.88] [1.06]
Village 2 -1.96 -2.55[0.99] [0.95]
Wealth (Million Cedis) -0.04 0.04[0.13] [0.11]
Clan 1 2.05 2.39[1.07] [1.02]
Clan 2 2.72 2.88[0.94] [0.84]
Church 1 -0.18 -0.68[0.91] [0.91]
BA
Logit MLE point estimates, spatial GMM (Conley 1999) standard errors in brackets allow for heteroskedasticity and correlation as a function of physical distance, see footnote 25 for details. Sample Size = 107. Pseudo R-squareds .34 and .36, columns A and B respectively. A full set of round dummies were included but not reported. Information neighborhoods defined using responses to: Have you ever gone to farmer ____ for advice about your farm?
Table 5: Predicting Innovations in Input Use, Differential Effects by Source of Information
Dependent Variable: Innovation in Per Plant Fertilizer UseA
Index of Inputs on Successful Experiments (M) 0.99[.16]
M * Inexperienced Farmer 1.09[0.22]
M * Experienced Farmer 0.10[0.32]
Inexperienced Farmer 4.01 4.20 4.22 4.19 4.12[2.62] [2.66] [2.65] [2.65] [2.77]
Index of Experiments by Inexperienced Farmers -0.13[0.37]
Index of Experiments by Experienced Farmers 1.02[0.17]
Index of Exper. by Farmers with Same Wealth 1.03[0.18]
Index of Exper. by Farmers with Different Wealth -0.41[0.32]
Index of Experiments on Big Farms 1.10[0.14]
Index of Experiments on Small Farms 0.89[0.18]
Index of Exper. by Farmers with Same Soil 1.04[0.16]
Index of Exper. by Farmers with Different Soil 0.91[0.19]
Avg. Dev. of Lagged Use From Geographic Nbrs 0.54 0.55 0.58 0.58 0.58 0.59[0.06] [0.08] [0.06] [0.06] [0.06] [0.06]
Avg. Dev. of Lagged Use From Financial Nbrs 0.53 0.45 0.40 0.43 0.22 0.24[0.58] [0.58] [0.59] [0.55] [0.61] [0.60]
Village 1 -7.62 -7.92 -8.09 -8.24 -7.81 -7.88[1.16] [1.43] [1.36] [1.43] [1.31] [1.31]
Village 2 -0.61 -1.82 -2.15 -2.17 -1.83 -1.78[1.56] [2.02] [2.03] [2.11] [2.02] [2.07]
Wealth (Million Cedis) 0.13 0.36 0.41 0.45 0.29 0.29[0.25] [0.17] [0.17] [0.17] [0.20] [0.20]
Clan 1 -2.62 -2.42 -2.68 -2.62 -2.53 -2.55[1.29] [1.21] [1.12] [1.09] [1.11] [1.15]
Clan 2 -0.40 -0.11 -0.11 -0.15 -0.31 -0.29[1.44] [1.32] [1.32] [1.32] [1.30] [1.30]
Church 1 0.26 0.67 0.76 -0.60 0.87 0.88[1.29] [1.12] [1.06] [1.11] [1.12] [1.12]
R-squared 0.70 0.73 0.71 0.71 0.71 0.71OLS point estimates, spatial GMM (Conley 1999) standard errors in brackets allow for heteroskedasticity and correlation as a function of physical distance, see footnote 20 for details. Sample Size = 107. A full set of round dummies included but not reported. Information neighborhoods defined using responses to: Have you ever gone to farmer ____ for advice about your farm?
FB C D E
Table 6: Alternate Definitions of the Information Network
Dependent Variable: Innovation in Per Plant Fertilizer Use
B
Information Neighborhood Metric
Roster of Contacts: Full
Set of Contacts
M * Inexperienced Farmer 1.50 1.49 6.34[0.28] [0.28] [1.14]
M * Experienced Farmer 0.19 0.15 4.52[0.21] [0.22] [1.80]
Inexperienced Farmer 4.66 4.65 4.01[2.84] [2.84] [2.77]
Average Deviation of Lagged Use From Geographic Neighbors' Use 0.49 0.49 0.33
[0.09] [0.09] [0.12]
Average Deviation of Lagged Use From Financial Neighbors' Use 0.50 0.51 0.59
[0.69] [0.70] [0.82]
Village 1 -7.59 -7.52 -9.25[1.64] [1.63] [1.75]
Village 2 -2.09 -2.08 -1.86[2.11] [2.10] [2.07]
Wealth (Million Cedis) 0.35 0.35 0.16[0.17] [0.17] [0.22]
Clan 1 -2.25 -2.23 -1.66[1.37] [1.37] [1.28]
Clan 2 -0.02 0.01 0.42[1.41] [1.40] [1.33]
Church 1 0.62 0.59 0.75[1.22] [1.23] [1.23]
R-squared 0.72 0.72 0.73
OLS point estimates, spatial GMM (Conley 1999) standard errors in brackets allow for heteroskedasticity and correlation as a function of physical distance, see footnote 20 for details. Sample Size = 107. A full set of round dummies were included but not reported. Alternative information neighborhoods are as defined in Section 3.2 and Appendix 2.
CRoster of Contacts: Farm Info
Only
Predicted Advice
A
Table 7: Robustness to Changes in SpecificationDependent Variable: Innovation in Per Plant Fertilizer Use
F
Lagged Fertilizer Use
M * Inexperienced Farmer 0.93 0.39 1.03 1.85 1.11 0.34[0.19] [0.15] [0.19] [0.20] [0.27] [0.13]
M * Experienced Farmer 0.16 -0.14 -0.41 0.04 -0.24 0.08[0.23] [0.24] [0.35] [0.23] [0.45] [0.31]
Inexperienced Farmer 4.38 5.83 4.01 2.87 5.94 4.05[2.73] [3.46] [2.71] [2.71] [2.72] [2.62]
Lagged Own Fertilizer Use -0.84[0.22]
0.51 0.95 0.58 0.10 0.50 0.09[0.08] [0.09] [0.08] [0.06] [0.12] [0.17]
0.36 -0.17 0.51 1.06 0.64 0.59[0.54] [0.40] [0.55] [1.16] [0.70] [0.61]
Village 1 -7.51 -7.96 -8.13 -7.48 -13.46 -3.19[1.35] [1.58] [1.51] [2.10] [3.03] [1.55]
Village 2 -2.02 -2.83 -1.93 -1.40 -1.68 -2.70[2.09] [2.71] [2.09] [2.08] [2.37] [2.11]
Wealth (Million Cedis) 0.36 0.50 0.41 0.24 0.73 0.21[0.18] [0.18] [0.17] [0.20] [0.21] [0.19]
Clan 1 -2.30 -2.58 -2.45 -2.71 -4.04 -1.17[1.16] [1.35] [1.35] [1.21] [1.87] [1.32]
Clan 2 -0.07 0.16 -0.004 -0.62 0.25 0.71[1.28] [1.31] [1.35] [1.47] [1.39] [1.29]
Church 1 0.56 1.33 0.62 0.43 1.58 0.36[1.14] [1.17] [1.13] [1.36] [1.58] [1.16]
Soil Organic Matter 0.14[0.67]
Soil pH 4.09[2.31]
Soil Type = Loam 1.40[1.14]
Soil Type = Sandy -5.78[2.72]
Sample size 107
R-squared 0.74 0.72 0.73 0.68 0.80 0.75
Avg. Dev. of Lagged Use From Geographic Neighbors' Use
Avg. Dev. of Lagged Use From Financial Neighbors' Use
A B
No Information from Plantings
at t-1
Soil Charac-teristics
C
No Information from Plantings
at t-1 or t-2
Fertilizer Categories: Zero, Med, High (High
> 80th percentile)
Geographic Neighborhood within 500m
D E
OLS point estimates, spatial GMM (Conley 1999) standard. errors in brackets allow for heteroskedasticity and correlation as a function of physical distance, see footnote 20. Round dummies included but not reported. Alternative specifications are as defined in Section 6.2.
107 89107 93 107
Table 8: Predicting Innovations in Labor for Pineapple and Maize-Cassava Plots
Dependent Variable: First Difference in Labor Inputs for Pineapple and Maize-Cassava
0.95 0.04[.37] [0.14]
0.25[0.14]
0.48 0.83[0.22] [0.14]
-0.27 0.02[0.23] [0.11]
Village 1 770.96 -195.98 -203.94[238.43] [100.54] [98.52]
Village 2 567.83 -417.61 -389.13[316.37] [119.34] [134.16]
Village 3 -126.29 -228.58[89.21] [132.30]
Wealth (Million Cedis) 47.59 1.09 -14.05[31.46] [33.42] [37.69]
Clan 1 -802.58 213.44 -794.38[358.62] [566.83] [450.66]
Clan 2 369.36 -45.33 -3.19[232.41] [82.65] [78.85]
Church 1 93.85 -37.86 -4.69[179.96] [77.80] [86.46]
Soil Organic Matter -259.11 -99.26 -72.00[202.53] [90.41] [40.58]
Soil pH 358.52 15.19 88.98[141.46] [131.66] [58.55]
Soil Type = Loam -641.90 -97.95 -105.23[197.89] [45.75] [78.08]
Soil Type = Sandy -909.36 128.06 -76.96[382.93] [66.14] [126.10]
Sample size 346R-squared 0.55 0.42 0.24
C Maize-Cassava
(labor cost in 1000 cedis per hectare)
A BPineapple Maize-Cassava
OLS point estimates, spatial GMM (Conley 1999) standard errors in brackets allow for heteroskedasticity and correlation as a function of physical distance, see footnote 20 for details. Round/season dummies included but not reported. Information neighborhood from: Have you ever gone to farmer ____ for advice about your farm?
Crop (labor cost in cedis per plant)
(labor cost in 1000 cedis per hectare)
89 346
Index of Experiments in the Geographic Neighborhood
Average Deviation of Lagged Use From Geographic Neighbors' Use
Average Deviation of Lagged Use From Financial Neighbors' Use
Index of Experiments: M-tilde
Table 1: Descriptive Statistics
Mean Std. Deviation
Fertilizer Use (cedis per sucker) 1.938 5.620
Change in Fertilizer Use -0.315 10.335Indicator of Change in Fertilizer ≠ 0 0.496 0.502Indicies of Successful Experiments: M Ask advice 0.114 4.312 M Talk frequently -0.041 4.259 M Know each other's plots -0.033 3.308 M Roster of contacts, farm info only -0.174 3.467 M Roster of contacts, full list -0.126 3.469 M Predicted ask for advice -0.020 0.940
Wealth (million cedis) 2.331 2.835Clan 1 Indicator 0.327 .Clan 2 Indicator 0.451 .Church 1 Indicator 0.487 .pH 5.952 0.738Soil Organic Matter (%) 2.927 1.110Loamy Soil Indicator 0.434 .Sandy Soil Indicator 0.053 .
Contact with Extension Agent Indicator 0.327 .
Inexperienced Farmer Indicator 0.230 .
Avg. Dev. of Lagged Use From Geographic Neighbors' UseAvg. Dev. of Lagged Use From Financial Neighbors' Use
0.498 7.790
-0.053 0.380
Table 3: Information Connections by Cohort of Pineapple Adoption
Proportion of pairs of individuals in each other's information neighborhood
Not Farming Pineapple
Inexperienced Pineapple
Farmer
Experienced Pineapple
Farmer Neighorhood Metric
Not Farming Pineapple 0.06 0.05 0.07Inexperienced Pineapple Farmer 0.05 0.09 0.13Experienced Pineapple Farmer 0.07 0.13 0.21
Response to "Have you ever gone to ____ for
advice about your farm?"