Genetically Modified, Neighborhood Effects, Consumer/Household Economics, Institutional and Behavioral Economics, Research and Development/Tech Change/Emerging Technologies,

Yoo, Do-il

English

Research Papers in Economics

 
The U. S. Corn Farmers’ Genetically Modified Technology Adoption 
with Neighborhood Effects 
 
 
 
 
Do-il Yoo 
Department of Agricultural & Applied Economics 
University of Wisconsin-Madison 
scydl8@gmail.com 
 
 
 
 
Poster prepared for presentation at the Agricultural & Applied Economics Association 2010 
AAEA, CAES, & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010 
 
 
 
 
 
 
 
 
 
 
Copyright 2010 by Do-il Yoo. All rights reserved. Readers may make verbatim copies of this 
document for non-commercial purposes by any means, provided that this copyright notice 
appears on all such copies. 
 
 
 Methodology
This study depends on discrete choice models derived from random utility theory in
the sense that corn farmers choose a specific GM trait as an optimal alternative to
maximize their expected profitability (utility) in a given plot and year. By traits, we
categorize corn seed as herbicide-tolerance (HT), insect-resistance (IR), and their
combination (stacked) seed contrast to non-GM (conventional) seed.
First, we use the multinomial logit (or conditional logit) model as a basic analysis
tool assuming independence of irrelevant alternatives (IIA). Then, we adopt the
concept of the mixed multinomial logit model for more realistic analysis by relaxing
the IIA assumption. This results in the fact that stacked seeds share common trait
properties with HT or IR seeds, so that there exists potential substitution among
traits. In spite of its comprehensiveness, the mixed logit model is not so easy to be
estimated due to its computational burden from simulation. Instead, we deal with
the multinomial probit model by imposing normal density to parameters. Then, it
can provide approximation of the mixed logit model with more convenience in
running STATA.
The U. S. Corn Farmers’ Genetically Modified Technology Adoption 
with Neighborhood Effects
Do-il Yoo
Department of Agricultural & Applied Economics
University of Wisconsin - Madison
Introduction
Genetically Modified (GM) corn has been dramatically adopted by farmers within
just a recent decade since the first generation GM varieties were commercially
planted in 1996. For instance, the percentage of GM corn planted acres in 2009
amounts to 85% comparing to 25% in 2000 (USDA/ERS, 2009 [1]). Such a tremendous
diffusion of GM corn in a short history comes with a question about which
determinants have influenced corn farmers’ adoption behaviors.
Previous literatures have analyzed the impact of farmers’ characteristics on
adoption behavior such as farm size, education level, risk preference, and credit
access (Fernandez-Cornejo and Caswell, 2006 [2]). However, there are few
empirical studies dealing with externalities of social interaction due to lack of
accumulated data for GM technology. This study pays attention to neighborhood
effects, which account for the tendencies that a farmer’s adoption is affected by
his/her neighbors’ behaviors in a peer group (Brock and Durlauf, 2002 [3]). Our
research object is to develop and analyze an empirical model introducing
neighborhood effects.
Comparison among Models – for Simpler Version
Due to computational burden, we apply each model to the simpler version only.
Using STATA, we use ‘mlogit’ and ‘mprobit’ for the multinomial logit and
multinomial probit model, respectively.
Note:
The base alternative is l = 0 (CONV).
The values in parenthesis indicate standard deviation.
All estimates are statistically significant at 1% level.
Conclusions
According to the results, all the coefficients have expected sign (e. g., negative in
terms of price factor, and positive sign with respect to neighborhood effects), and
statistically significant. Neighborhood effects affects the log odds of HTonly to non
GM seed most, and that of STACK least. HHI influences the probabilities of choosing
STACK seed most. Also, when IIA is assumed, the multinomial logit model
overestimates parameters than the multinomial probit.
This study makes contribution to introducing neighborhood effects into adoption
research. Future work may be put on dynamics of learning based on interaction
over years.
References
[1] U. S. Department of Agriculture/Economic Research Service, July 2009, 
“Adoption of Genetically Engineered Crops in the U. S.: Corn Varieties,” USDA, ERS, 
Washington, D. C., from 
http://www.ers.usda.gov/data/BiotechCrops/ExtentofAdoptionTable1.htm
[2] Fernandez-Cornejo, J. and M. Caswell, April 2006, “The First Decade of 
Genetically Engineered Crops in the United States,” Economic Information Bulletin 
No. 11., USDA, ERS, Washington D. C.
[3] Brock, W. and S. Durlauf, 2002, “A Multinomial Choice Model with Neighborhood 
Effects,” American Economic Review, 92: 298-303.
Model Specification
Plot-level Utiity (Profitability)
Choice Probability & Likelihood Function
Then, we can utilize multinomial probit as an approximation of mixed logit
, which requires burdensome simulation procedure.
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≠∈
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 = ⋅⋅ =
 − 
∑ 1
 where   indicates "Neighborhood Effects" for plot 
A measure of the strength of social utility for choice   at group 
We use acre share 
{}
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of  trait is planted for each group   as a proxy.
Total number of plots in group 
Choice for plot 
Indicator function
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ii
il il il
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il i il il n n
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, where   is a deterministic (observable) component.
 is composed as the following factors:
The first, second, and third partiti
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,,
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il
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il n
farmsize
seed price l i
NE i l
HHI
−− −
−
on are  and  specific factor, respectively.
Farm size
Paid seed price for  trait corn seed at plot 
Neighborhood Effects for plot   when its choice is 
Herfind ()
()
,
, :
i
ni
il
il
n
year
ε
ε
ahl Index of GM trait companies at group 
Time variable
, where   is a latent (unobservable) component.
Multinomial Logit Model ~i.i.d. & extreme value distribution
Multinomial Probit M , :~ il ε odel  Normal distribution & correlated
( ) ( )
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Multinomial Logit Model:
Mixed Multinomial Logit Model:
For simplicity, we assume a random intercept model for each  choice.
Choice probability is rewritten as ( )( )
( ) ( )
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12 5 1 2 5
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exp '
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exp '
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Note that we have 5 explanatory variables as is described above.
where   is a p.d.f. for  .
We assume   follows nomial distribution for tractability. 
Data
This study relies on a unique panel data set about the U.S. corn seed market over
the period 2000 ~ 2007 across the United States, which includes farm level
information about corn seed prices, planted acreage, and seed types by traits:
conventional, HT, IR, and other kinds of stacked GM seeds. In order to avoid
periodical and regional bias, a crop reporting district (CRD) for each year is
assumed to correspond to a neighborhood group under the same agro-climatic
conditions.
Descriptive Statistics
Note:
The number of observation is 168766 (87 missing values among 168853 observations).
The number of possible group, defined by each CRD per year, is 1890.
1/, 2/: These variables are adjusted for the multinomial logit model in STATA, so that the difference
between base choice (CONV) is introduced into the model.
0
.
2
.
4
.
6
.
8
A
c
r
e
s
h
a
r
e
2000 2001 2002 2003 2004 2005 2006 2007
Year
GM seed Non GM seed
HT single IR - Root worm
IR - Corn Borer Double stacked seed
Triple stacked seed Quadraple stacked seed
<More Specific Version>
Average Acreshare over Years: 2000 ~ 2007
GM Corn Seed Adoption Rate by Traits
Adoption Trends
The diffusion of GM corn seed indicates two features: 1) adoption rate for GM seed
increase rapidly from 2000 through 2007. 2) the trend is switching from single trait
seed to stacked seed.
Simpler Version Specific Version
l Trait % l Trait %
0 CONV 50.59 0 CONV 50.59
1 HTonly 13.89 1 HTonly 13.89
2 IRonly 16.20 2 IRonly_RW 0.65
3 STACK 19.32 3 IRonly_CB 15.55
4 DOUBLE 14.21
5 TRIPLE 4.64
6 QUADRAPLE 0.47
Explanatory
Variable
Description Mean Std. Dev.
farmsize Farm size 612.6497 743.3472
pd_S1 Price Difference b/w l – trait 
and CONV seed (simpler)1/
52.98297 36.32256
pd_S2 Price Difference b/w l – trait 
and CONV seed (specific)1/
52.98297 36.52038
NE Neighborhood Effect Term b/w l 
– trait and CONV seed 2/
0.0909552 0.2249764
HHI Herfindahl Index of trait
companies
0.2334269 0.2220434
0
.
2
.
4
.
6
.
8
A
c
r
e
s
h
a
r
e
2000 2001 2002 2003 2004 2005 2006 2007
Year
GM seed Non GM seed
HT single IR single
Stacked seed
<Simpler Version>
Average Acreshare over Years: 2000 ~ 2007
GM Corn Seed Adoption Rate by Traits
Results
Multinomial Logit Model
Regression is done by ‘mlogit’ using STATA 10.1. As a bench mark, we run the
multinomial logit model both for simper and for specific version. Here, we shows
more specific version.
Note:
The base alternative is l = 0 (CONV).
Number of obs: 168766, Log Likelihood: -101498.58
All estimates are statistically significant at 1% level.
Choice Variables Coef. Std. Err. Choice Variables Coef. Std. Err.
1: HTonly farmsize 0.0001345 0.000024 4: DOUBLE farmsize 0.0003318 0.0000223
pd_S2 -0.1769531 0.0010975 pd_S2 -0.1496967 0.0010762
NE 8.472343 0.1962254 NE 9.793068 0.1936842
HHI -0.6651991 0.1530203 HHI -2.750114 0.1506423
year 0.2548347 0.0101827 year 0.4966211 0.0102088
_cons 6.363085 0.061201 _cons 4.909983 0.618006
2: IRonly_RW farmsize 0.0002856 0.0000447 5: TRIPLE farmsize 0.0005617 0.0000262
pd_S2 -0.1416524 0.0018599 pd_S2 -0.148208 0.0012117
NE 6.857784 0.3230526 NE -8.459649 0.2836903
HHI -3.424193 0.3769859 HHI 7.558296 0.1471589
year 0.7295871 0.231395 year 1.676857 0.249841
_cons 1.304541 0.1255832 _cons -5.177418 0.1505115
3: IRonly_CB farmsize 0.0002944 0.0000225 6: 
QUADRAPLE
farmsize 0.0005637 0.0000392
pd_S2 -0.1673809 0.0010784 pd_S2 -0.1437709 0.0020467
NE 6.663236 0.1932355 NE -8.080006 0.6160253
HHI -0.4944043 0.1474191 HHI 7.114547 0.249042
year 0.143036 0.0098224 year 3.286272 0.1592169
_cons 6.849262 0.0597514 _cons -18.4625 1.091314
Choice Variables Multinomial Logit Multinomial Probit
1: HTonly farmsize 0.0001218 0.0000606
(0.0000236) (0.0000148)
pd_S1 -0.1740552 -0.1018367
(0.0010742) (0.000507)
NE 7.025023 4.60956
(0.1788384) (0.1046804)
HHI 1.270094 0.7648978
(0.132381) (0.083231)
year 0.2146882 0.1061755
(0.0099121) (0.0060487)
_cons 5.998705 3.24636
(0.0576056) (0.0301173)
2: IRonly farmsize 0.0002811 0.0001684
(0.0000219) (0.0000139)
pd_S1 -0.1631701 -0.0957957
(0.0010478) (0.0004735)
NE 5.506584 3.48357
(0.1774311) (0.1040001)
HHI 0.9360481 0.5045319
(0.1288071) (0.0816099)
year 0.1226062 0.0305824
(0.0095273) (0.0057711)
_cons 6.528903 3.715119
(0.056255) (0.0292489)
3: STACK farmsize 0.0003442 0.0002185
(0.0000214) (0.0000134)
pd_S1 -0.1416715 -0.0819779
(0.0010146) (0.000449)
NE 4.423635 2.710534
(0.1706461) (0.098722)
HHI 3.423928 2.452767
(0.112435) (0.0694432)
year 0.5732241 0.3653961
(0.0097598) (0.0059218)
_cons 3.498774 1.504215
(0.0571771) (0.0293186)
Log Likelihood -91304.839 -93725.397
Number of Obs. 168766 168766
()
()
i
i
i
ni
n
l
l =
Plot level for each farm household
The neighborhood group that plot   belongs to.
We assume  as a Crop Report District (CRD) in a year.
Possible alternative (choice/ GM trait)
<simpler version>
0:
1:
2:
3:
l
l
l
=
=
=
 Conventional (non-GM) seed
 HT-only seed
 IR-only seed
 STACK seed
0:
1:
2:
3:
4:
5:
6:
l
l
l
or l
l
l
l
<
=
=
=
=
=
=
=
More specified version>
 Conventional (non-GM) seed
 HT-only seed
 IR-only-RW (Root worm) seed
 IR-only-CB (Corn Borer) seed
 Double Stacked Seed
 Triple Stacked Seed
 Quadraple Stacked Seed
Neighborhood Effect
Index
Neighborhood Effects
Here, neighborhood effects are defined as a plot’s expectation of the percentage of
plots which makes the same choice in a peer group

2/: These variables are adjusted for the multinomial logit model in STATA, so that the difference between base choice (CONV) is introduced into the model. 0 . 2 . 4 . 6 . 8 A c r e s h a r e

Department of Agriculture/Economic Research Service,

Model Specification Plot-level Utiity (Profitability) Choice Probability & Likelihood Function Then, we can utilize multinomial probit as an approximation of mixed logit , which requires burdensome simulation procedure.

The U. S. Corn Farmers' Genetically Modified Technology Adoption with Neighborhood Effects

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Research Papers in Economics