THE IMPACT OF ETHANOL PRODUCTION
ON LOCAL CORN BASIS
Kathleen Behnke
A Thesis
Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
at the
University of Wisconsin
June 2010
ABSTRACT OF THESIS
The Impact of Ethanol Plants on Local Corn Basis
by Kathleen Behnke
As the United States searches for a sustainable source of fuel, corn-based ethanol has
emerged as an early leader. Over the past decade, ethanol production has risen from 1.5 million
gallons in 1999 to 10.6 million gallons in 2009. This growth was primarily fueled by the growth
and expansion of starch-based ethanol plants. Accordingly, this resulted in increased demand for
corn and today almost 35 percent of U.S. corn production is used for ethanol.
The aim of this thesis is to examine the impact local ethanol plants have on corn basis.
The basis is the difference between the local cash price and the nearby futures contract price, and
it accounts for variation in the supply and demand in the local market relative to the national
market. It is predicted the entrance of an ethanol plant into a local cash market will increase corn
demand, resulting in an increased cash price.
The data set contains cash corn prices from 153 grain buyers in eight different
Midwestern states. The data ranges from Fall 1999 through Summer 2009. In addition to being
affected by ethanol production, it is predicted basis is influenced to by the ratio of local to
national corn production, transportation costs, storage opportunity costs, and seasonal factors.
To estimate the impact these variables have on corn basis a spatial error component model is
used, which accounts for both the spatial dependencies and panel data structure.
The empirical results were plausible and consistent with theoretical expectations. Results
show that ethanol production in a 50-mile region of a county centroid has a small yet positive
impact on local corn prices. The estimated impact of a 50 million gallon per year plant is a 0.425
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cent per bushel increase in basis. These findings are smaller than the impacts found in previous
work so the impacts were further investigated and shown to be consistent when directly
compared to others’ findings.
This study concludes local ethanol plants do have a positive price impact; however the
research also suggests the price impacts of ethanol production may be felt well beyond the
county borders. Additionally, there is evidence the long-term price impacts are much less than
the initial short-term price response.
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ACKNOWLEDGEMENTS
I would like to express my gratitude to my advisor, Professor Randy Fortenbery, for his
support and guidance during this study as well as my academic career. I would also like to
extend thanks to Professor Steve Deller and Professor Brent Hueth for serving on my thesis
committee. Additionally, I am thankful for the programming support I received from Professor
Brian Gould.
I owe a special thanks to Kevin McNew and CashGrainBids.com for generously
providing me with the data necessary for this project.
Finally, I would like to thank the professors, staff, and graduate students in the
Department of Agricultural & Applied Economics for their support and assistance throughout
this process.
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TABLE OF CONTENTS
ABSTRACT OF THESIS ............................................................................................................. ii
ACKNOWLEDGEMENTS…………………………………………………………….………iv
TABLE OF CONTENTS ............................................................................................................. v
LIST OF FIGURES .................................................................................................................... vii
LIST OF TABLES ..................................................................................................................... viii
CHAPTER 1: INTRODUCTION ................................................................................................ 1
1.1 Rationale................................................................................................................................ 1
1.2 Objectives .............................................................................................................................. 3
1.3 Scope of the Study................................................................................................................. 4
1.4 Organization of the Study ..................................................................................................... 4
CHAPTER 2: BACKGROUND AND LITERATURE REVIEW ........................................... 6
2.1 Background ........................................................................................................................... 6
2.1.1 Corn Background ............................................................................................................ 6
2.1.2 Ethanol History and Policy ............................................................................................. 8
2.1.3 Ethanol Production Process .......................................................................................... 11
2.1.4 Ethanol’s Future ........................................................................................................... 12
2.2 Literature Review ................................................................................................................ 13
CHAPTER 3: ANALYTICAL FRAMEWORK ...................................................................... 17
3.1 Conceptual Model of Estimating Basis ............................................................................... 17
3.2 Empirical Model .................................................................................................................. 23
3.2.1 Panel Data ..................................................................................................................... 24
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3.2.2 Spatial Methods ............................................................................................................ 24
3.2.3 Spatial Panel Model ...................................................................................................... 26
3.3 Data Sources ........................................................................................................................ 28
CHAPTER 4: EMPIRICAL RESULTS ................................................................................... 32
4.1 Model Validation................................................................................................................. 32
4.1.1 Hausman Test ............................................................................................................... 32
4.1.2 Lagrange Multiplier Tests to Select Model .................................................................. 32
4.1.3 Tests for Spatial Error Correlation and Random Region Effects ................................. 34
4.3 Alternative Models............................................................................................................................ 35
4.3 Parameter Estimates ............................................................................................................ 35
4.3.1 Weights Matrix ............................................................................................................. 40
4.4 Time Comparison ................................................................................................................ 42
4.5 Comparison to Literature .................................................................................................... 44
CHAPTER 5: CONCLUSION................................................................................................... 54
5.1 Summary ............................................................................................................................. 54
5.2 Conclusions ......................................................................................................................... 55
5.3 Suggestions for Further Research ....................................................................................... 56
APPENDIX A ............................................................................................................................... 57
APPENDIX B ............................................................................................................................... 59
APPENDIX C ............................................................................................................................... 63
APPENDIX D ............................................................................................................................... 67
SOURCES..................................................................................................................................... 73
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LIST OF FIGURES
Figure 1.1 Historical Ethanol Production …………………………………………………….…. 2
Figure 2.1 U.S. Corn Production………………………………………………………...…….….7
Figure 2.2 Percentage of U.S. Corn Production Used for Ethanol and Exports…………………..7
Figure 3.1 Corn Basis Map……………………………………………………………………....19
Figure 3.2 Average Annual Corn Basis………………………………………………………….20
Figure 3.3 Counties with Corn Price Observations…………………………………………...…29
Figure 3.4 Counties with Ethanol Plants…………………………………………………………29
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LIST OF TABLES
Table 3.1 Summary Statistics ……………………………………………………………….…..31
Table 4.1 Alternative Model Specifications……………………………………………………36
Table 4.2 Model Estimates ……………………………………………………………………38
Table 4.3 Spatial Weight Variations……………………………………………………………..41
Table 4.4 Compare Monthly and Quarterly Data………………………………………………..43
Table 4.5 Compare Full Sample and Sub-sample Estimates…………………………………….47
Table 4.6 Ethanol Impacts …………………………………………………………………...….49
Table 4.7 Compare Full Sample and Sub-sample Estimates with Interest…………………...….51
Table 4.8 Ethanol Impacts with Interest………………………………………………………....53
Table A.1 Renewable Fuel Standard Program Mandates………………………………………..58
Table B.1 Ethanol Plants Included in the Sample…………..……………………………………59
Table C.1 Grain Elevators………………………………………………………………………..63
Table D.1 Summary Statistics by State…………………………………………………………..67
Table D.2 Summary Statistics by Year…………………………………………………………..69
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CHAPTER 1
INTRODUCTION
1.1 RATIONALE
More than decade ago the United States began an aggressive search to find a practical
source of renewable fuel to meet our insatiable energy demands. Alternative fuels such as
starch-based ethanol, cellulosic ethanol, and biodiesel are all considered to be potential solutions
in a national effort to reduce gasoline usage by 20 percent over the next ten years (Bush, 2007).
Corn-based ethanol emerged as an early leader due to the abundance of corn and the popularity
of ethanol-gasoline mixes.
The national ethanol industry has expanded dramatically over the past 10 years.
According to the Renewable Fuels Association (RFA), today there are more than 200 production
plants with the capacity to produce almost 13.5 billion gallons. This is up from just 54
biorefineries with a production capacity of 1.7 billion gallons in 2000. The historical increase in
production can be seen in Figure 1.1. RFA also reports the ethanol industry supported 400,000
jobs in 2009 and contributed $53.3 billion to the nation’s Gross Domestic Product (GDP).
Furthermore, they calculate that despite the tax credit to ethanol producers the industry still
contributed a tax surplus of $3.4 billion to the federal treasury.
The rise of ethanol in the US has been largely driven by government mandates, tax
incentives, and the push to lessen America’s dependence of foreign oil. The government has
supported the use of ethanol as a policy to reduce dependence on foreign oil since the 1970’s. In
1
Figure 1.1: Historical Ethanol Production
Historical Ethanol Production
12000
Millions of Gallons
10000
8000
6000
4000
2000
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
0
Source: Renewable Fuels Association
the 1990’s it became popular to blend ethanol as an oxygenate in conventional gasoline to reduce
smog. Ethanol production standards were set in place by the Energy Policy Act of 2005, and
then updated as part of the Energy Independence and Security Act of 2007. Currently, ethanol
production is scheduled to reach 36 billion gallons by 2022 and in the short-term there are plans
to increase production by another 1.4 billion gallons in 2010. Furthermore, according to the
2010 Ethanol Industry Outlook, the 2009 production of 10.6 billion gallons of ethanol reduced
the demand for oil by 364 million barrels.
These government mandates, coupled with the high crude oil prices, have pushed the
biofuels sector to center stage in the discussion of future U.S. energy policy. However, this
conversation must consider the implications of energy policy on the agricultural sector.
2
Diverting corn and soy to produce ethanol and biodiesel has an impact on these commodity
prices, which in turn affects many other factors in the traditional agriculture markets. Several
economic issues are important to stakeholders in the both the ethanol and corn industries.
Questions about the how much corn will be needed to continue the growth of the ethanol sector
and how the increased demand for corn will affect prices on both a local and national level have
increased in importance as the industry continues to expand.
1.2 OBJECTIVES
The ever strengthening relationship between the food and fuel markets clearly raises the
question of how the biofuels industry affects the price producers receive for corn.
The overall
purpose of this study is to examine the magnitude of this impact at the local level and measure
the extent to which the effect is maintained over time. To this end the specific objectives of are
to:
1. develop and estimate a spatial panel model of corn basis;
2. assess the impacts of ethanol plants on the local corn basis; and
3. determine if these impacts are consistent with the short-run impacts found in previous
studies.
Objective 1 involves the construction and validation of a spatial error components model
to analyze the impact ethanol production and other independent variables have on corn basis.
Objective 2 requires the implementation of the model to estimate the impact local ethanol
production has on local corn price. This study specifically examines the price impact of ethanol
production within 50 miles of a county centroid. Finally, objective 3 compares the findings of
3
this study to other work to determine if the impacts previously found are maintained in this more
long-run setting.
While the topic has been studied before, this particular work is important because it
increases both the scope and the depth of the data used. Additionally, by accounting for spatial
dependencies and the panel nature of the data more validity can be given to the results. Most
importantly, this study will provide greater understanding of the impact of ethanol production on
local corn prices.
1.3 SCOPE OF THE STUDY
In order to estimate the impact of ethanol plants on local corn basis the study includes
data from Illinois, Indiana, Iowa, Kansas, Minnesota, Nebraska, South Dakota, and Wisconsin.
Together these Midwestern states account for more than 75 percent of the nation’s corn
production (NASS) and are home to more than 70 percent of ethanol production plants (RFA)
making them the ideal sample space.
To estimate basis changes over time the sample period ranges from October 1999 through
September 2009. This allows for estimation from the beginning of the period of rapid ethanol
plant expansion. The data is aggregated by season to account for the variation throughout the
year. Overall, there are observations from 153 different locations and 40 time periods included
in the sample.
1.4 ORGANIZATION OF THE STUDY
This chapter has presented the question this thesis addresses as well as objectives, and the
general approach to the study. The next chapter provides a more detailed background of the
4
problem and a literature review of the topic. Chapter 3 presents the analytical framework
designed to measure the changes in basis, as well as the spatial-panel model needed to properly
frame the question. Chapter 4 presents the empirical results and discussion and Chapter 5
concludes with the summary, conclusions, and suggestions for further research.
5
CHAPTER 2
BACKGROUND AND LITERATURE REVIEW
2.1 BACKGROUND
Before consumers are able to purchase starch-based ethanol at the pump there are many
important production steps. The corn must be planted, harvested, and transported to market.
Then the processing plant must buy inputs, produce ethanol, and send it to a blending facility
before it can be distributed. This chapter begins by providing a background of the U.S. corn
industry. It continues with a background of the ethanol industry, including an examination of
government policies, production practices, and a look towards the future. This chapter will
conclude with a review of the economic literature investigating the impact ethanol production
has on corn prices and this study’s economic contribution.
2.1.1 Corn Background
Corn has been an important part of agriculture in the U.S. since it was first introduced
from Central America. Not only is corn a food source for humans and animals, it can also be
converted to sugar, starch, beverage, or fuel. Previously the low cost of substitute fuels limited
the conversion of corn to ethanol, but in response to changing prices and regulatory conditions it
has found a place as a source of renewable biofuel.
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Figure 2.1: U.S. Corn Production
U.S. Corn Production
14000
Millions of Bushels
12000
10000
8000
6000
4000
2000
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Source: USDA National Agricultural Statistics Service
Figure 2.2: Percentage of U.S. Corn Production Used for Ethanol and Exports
Percentage of U.S. Corn Production Used for Ethanol
and Exports
40.0%
35.0%
30.0%
25.0%
Ethanol
20.0%
Exports
15.0%
10.0%
5.0%
0.0%
1990
1992
1994
1996
1998
2000
2002
Source: USDA Economic Research Service
7
2004
2006
2008
According to the Economic Research Service of the United States Department of
Agriculture (USDA), national corn production has increased over the past decade due to greater
demand. Figure 2.1 shows changes in production from 1990 through 2009. Production levels
fluctuate in response to acres planted, weather conditions, and improved plant technology that
allows for greater yields. Additionally, the USDA predicts higher net future returns to corn
relative to other crops which provides an economic incentive to expand corn acreage in the
coming years.
In 2009, 34 percent of national corn production went into ethanol production, 15 percent
was exported, 41 percent fell into the category of feed and residual use, and the rest was used for
food, sugar, seeds, or was carried over as stocks in 2010. Over the past two decades the relative
shares of corn use have shifted, as shown in Figure 2.2. From 1990 to 2009 the amount of corn
exported as percent of total production decreased by seven percentage points, despite an increase
in total export volume. In contrast, corn used for ethanol as a percentage of total production
increased from four percent to 34 percent, becoming the second largest use category. The
growth rate in this category is expected to stabilize as fewer new corn-based ethanol plants are
built, but the demand for corn in ethanol production will continue to be large.
2.1.2 Ethanol History and Policy
The use of ethanol has always been linked to the automotive industry. According to the
U.S. Energy Information Association (EIA), Henry Ford built his first vehicle, the Quadricycle,
to run on pure ethanol. However this automobile was quickly pushed to the side in favor of
vehicles powered by gasoline, a less expensive alternative. This, coupled with the impending
Prohibition, began America’s love affair with oil. Post World War II, the commercial ethanol
8
market almost disappeared and did not begin to resurge until the oil crisis of the 1970’s (U.S.
EIA).
As an effort to decrease dependence of foreign oil, the government has created policies to
increase the use of ethanol fuel since 1978. The Energy Tax Act of 1978 provided a tax credit of
$0.40 for every gallon of ethanol blended into gasoline at the 10 percent level. The number of
ethanol plants began to increase and the Tax Reform Act of 1984 increased the blending credit to
$0.60 per gallon. Despite these subsidies many of the new ethanol plants went out of business in
the late 1980’s.
Additional support for ethanol came from the United States Environmental Protection
Agency (EPA). As a response to growing air pollution in 1995, the EPA required an oxygenate
be added to gasoline in ten major smog producing regions of the country. This mandated that
gasoline be mixed with a 10 percent oxygenating agent. Initially methyl tert-butyl ether (MTBE)
was the popular choice, but after groundwater contamination scares from MTBE, ethanol has
come to dominate the market for oxygenates (EIA).
Another important component in the growth of the ethanol industry has been tax credits
such as the Volumetric Ethanol Excise Tax Credit (VEECT) and the small ethanol producer
credit. The VEECT was signed into law as part of the American Jobs Creation Act of 2004 and
provided gasoline blenders a $0.51 excise tax credit per gallon of ethanol blended with gasoline.
The 2008 Farm Bill reduced the credit to $0.45 and it is set to expire December 31, 2010.
Additionally, the small ethanol producer credit provides an income tax credit of $0.10 per gallon
for the first 15 million gallons of ethanol produced for plants with a capacity of less than 60
9
million gallons per year. Both of these credits have played an important role in encouraging
growth in ethanol production and use.
To promote even greater ethanol utilization, the government passed the Energy Policy
Act of 2005. It mandated the production and sale of four billion gallons of ethanol in 2006, with
incremental increases resulting in production of 7.5 billion gallons in 2012. This legislation led
to a huge increase in ethanol production and plants were quickly producing a much greater
volume than the mandates required. As a response the Energy Independence and Security Act of
2007 updated the mandates requiring almost 13 billion gallons in 2010 and setting the target at
36 billion gallons in 2022. However, the directive specifies that only 15 billion gallons can be
corn-based ethanol, the rest will be cellulosic and advanced biofuels. For a deeper look into the
Renewable Fuel Standard see Appendix A.
The future expansion of the ethanol industry may depend on EPA’s approval of
increasing the ethanol content in gasoline from 10 percent (E10) to 15 percent (E15). With
RFAs current supply and demand predictions, if 100 percent of gasoline is sold as E10, by 2011
the supply of ethanol would exceed volume needed to create the E10 blend. According to
Nurenberg of Ethanol Producer Magazine (2009), this blend wall will make it difficult to reach
the Renewable Fuels Standard of 36 billion gallons by 2022. The EPA may allow E15 sometime
later this year, but without a change the growth of the industry may be hindered by suppressed
demand.
It should also be noted that ethanol production and use receives support from state
governments. All of the states in the sample have some sort of program supporting biofuels and
some even have their own renewable fuels mandates. Other programs include production tax
10
incentives, plant loan assistance, blender’s tax credits, state money for ethanol research and
promotion, and mandates for state fleets to shift to renewable fuels (International Institute for
Sustainable Development, 2006).
2.1.3 Ethanol Production Process
Ethanol is a sugar-based bio-fuel that produces energy when burned. Though its energy
content is less than that of pure gasoline, ethanol can reduce tailpipe carbon monoxide emissions
by as much as 30 percent (RFA). This is a result of ethanol being composed of 35 percent
oxygen, which results in more complete fuel combustion (RFA). Ethanol is commonly blended
with gasoline at the 10 percent level (E10) to serve as an oxygenate. Furthermore, flex fuel
vehicles can use E85, a blend of 85 percent ethanol and 15 percent gasoline.
The majority of American-made ethanol is produced from corn, while a small amount is
produced from cheese whey, wood waste, or other grains. Corn-based ethanol plants represent
92 percent of all plants, but almost 99 percent of all U.S. ethanol production. Depending on
plant technology, an average of 2.8 gallons of ethanol can be produced from one bushel of corn
(RFA). As technology improves, plants are continually striving to produce more ethanol with
fewer inputs to improve efficiency and reduce costs.
In 2000, plants which produced 40 million gallons of ethanol per year were standard, but
by 2005 the new plants were being built with capacities of 50 or 100 million gallons per year
(Sneller & Durante, 2006). Today the average plant capacity is slightly less than 70 million
gallons of production per year (RFA). According to Eidman (2007) the returns to scale in
ethanol production increased between 2003 and 2005, leading to an increase in the size of new
plants. In late 2006, an average 60 million gallon per year plant had an investment cost of
11
$1.875 per gallon of output, whereas a 120 million gallon per year plant had a cost of $1.50 per
gallon of output. Other important components in determining the profitability of a plant include:
the price the plant receives for its outputs, efficiency of the firm, cost of capital and labor, the
cost of natural gas to power the plant, and of course, the cost of the main input, corn.
In addition to ethanol, a traditional dry mill ethanol plant produces a number of coproducts such as dried distillers’ grains with solubles (DDGS), and carbon dioxide (CO2). On
average a plant can produce 18 pounds of DDGS per bushel of corn used in ethanol production
and most plants are able to sell it to the livestock industry as a high value feed source. If there is
a market opportunity, some plants are able to sell the CO2 to the food processing and bottling
industries. Also, ethanol wet mills can produce corn gluten meal, corn gluten feed, sweeteners
and corn oil which can also be sold to their respective industries.
2.1.4 Ethanol’s Future
The future of ethanol production remains uncertain. The initial rapid growth in the
industry was challenged as the price of corn began to rise in 2007. High input costs put a strain
on many ethanol producers, and in 2008 a hedging strategy gone wrong caused the bankruptcy of
the nation’s largest ethanol producer, Verasun (Taylor & Dorsey, 2010). As the corn-based
ethanol industry still faces challenges, the race is on to develop new methods of ethanol
production. According to the RFA, there are currently 28 new cellulosic ethanol plants under
development and construction which will use wood, switch grass, sugarcane, or other biomaterials.
Work completed by De La Torre Ugarte, English, and Jensen (2007) examined the future
implications of ethanol production and expansion. Their estimates are based upon the standards
12
set by The Energy Policy Act of 2005 and The Biofuels Security Act of 2007, as proposed at the
time of writing, which set ethanol production levels at 10 billion gallons in 2010, 30 billion
gallons in 2020, and 60 billion gallons in 2030. Mindful of improved technologies for cellulosic
ethanol being developed, the authors examine three scenarios for the future of ethanol
production. The first scenario projects cellulose-to-ethanol technology to be commercially
available by 2012, thus new ethanol plants would adopt this technology and existing corn-toethanol plants would continue to use corn as their main input. The second scenario examines the
impacts of corn-based plants adopting cellulose technologies in 2012 and the third scenario
envisions the switch happening in 2015. When compared to the USDA 2006 baseline, all three
scenarios estimate the price of corn to increase by at least $0.86 per bushel by 2010. By 2030,
the corn price impact is estimated to be $0.62 in the first scenario, $0.52 in the second scenario,
and $0.59 in the third scenario. They also estimate the 2030 price of soybeans to increase by
$0.89 to $1.23 per bushel and the price of wheat to increase by $0.36 to $0.53 per bushel as a
result of competing for acres with higher valued corn. From 2007 to 2030 they project a
cumulative net farm income increase of $210 billion and an $8.7 billion reduction in government
payments as a result of current ethanol policy.
2.2 LITERATURE REVIEW
The record growth of the ethanol industry has generated a wide body of economic
literature investigating ethanol’s impacts on crop, land, and fuels prices, its ability to create
community economic development, the industry’s potential for long-term sustainability, and
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more. While all of these pieces are important in understanding the broad impact ethanol has on a
national level, here the focus is on the impact ethanol plants have on local corn prices.
There is rich literature on the link between the food and fuel markets and the impact
ethanol has on national corn prices. A study by Luchansky and Monks (2008) found an
interesting shift in the relationship between corn price and ethanol production as the market has
evolved. Their findings indicate that on the supply side, ethanol production is not significantly
related to corn prices. In contrast, they cite a 1998 study by Rask which found that corn prices
strongly influence ethanol production levels, but they conclude that due to government mandates
and clean air requirements, production is no longer being heavily influenced by input costs.
Rather, it now appears ethanol production levels are playing a role in determining the price of
corn.
Fortenbery and Park (2008) measured the effect of ethanol production on U.S. corn prices
at a national level and found that a one percent increase in ethanol production will cause a 0.16
percent increase in the short-run corn price. The results also show that the great increase in corn
price in 2007 is not fully explained by the impact of ethanol production. They conclude that
some of the price increase can be contributed to ethanol production, but other supply and demand
factors also played a large role.
Rather than investigating the impact of ethanol on the national corn price, it is the goal of
this study to estimate the effect an ethanol production plant has on the local corn price. Early
work on measuring the impact of ethanol plants on local grain prices was conducted by McNew
and Griffith (2005) in a study which estimated the impact of 12 ethanol plants that opened in
2001 and 2002. The found, on average, corn prices increased by 5.9 cents in the region and
14
positive price impacts could be felt up to 68 miles away. The price impact at the ethanol plant
sites ranged from 4.6 to 19.3 cents per bushel, depending on the local corn supply. In theory,
areas with high corn demand or low corn supply experience price impacts that are relatively
greater than in areas with less demand or excess supply.
The findings of an increased local corn price were supported by a Henderson and Gloy
(2009) investigation of ethanol plant impacts on cropland values. Their results indicate an
annual impact of 2.3 to 6.4 cents per bushel. They conclude the change is a result of the
decreased transportation costs.
However, the estimation of positive price impacts due to a local ethanol plant is not a
universal finding. In Kansas, O’Brien (2009) found corn prices at elevators located within 60
miles of an ethanol plant were significantly lower than elevators further than 60 miles from a
plant. In addition, a similar effect was found in Katchova (2009). In his model, farmers located
in the same zip code as an ethanol plant actually received a price 10.9 cents lower than other
farmers in the sample. This does not mean ethanol plants negatively affect corn prices, but
merely that spatial differences play an important role in determining prices and close proximity
to an ethanol plant does not secure higher prices.
A study of Iowa ethanol plants by Gallagher, Wisner, and Burbacker (2005) found the
corn price impacts were dependent on the location of the plant. They observed nine market areas
in Iowa and found evidence that an ethanol plants tends to increase the local corn price.
However, in locations where there were already many grain buyers, such as the northwest region
of the state, the introduction of an ethanol plant had no statistically significant price impact.
Thus the results were mixed and dependent upon pre-existing market conditions. They also
15
found evidence that corn prices declined as distance increased from the Mississippi River on
Iowa’s eastern border.
Olson, Klein, and Taylor (2007) build a strong theoretical model for basis analysis. The
variables include futures price, corn production, corn usage in ethanol, a storage measure, and
variables dealing with transportation. McNew and Griffith (2005) include state and national corn
production, monthly dummy variables, and an ethanol dummy variable, diesel price, and a
sophisticated distance term. In other basis literature, not specific to ethanol production, the
relationship between local and national prices in modeled by a production ratio, rather than
absolute production values (Fortenbery, 2002). Also, Fortenbery (2002), Kahl & Curtis (1986),
and Garcia & Good (1983) stress the importance of including a storage cost or opportunity cost
measure in basis analysis. Finally, Kahl & Curtis (1986) also include a price measure. The
specific variables used in this study will be further discussed in Chapter 3.
The analysis in this thesis is consistent with the current literature’s attempt to assess the
economic impacts of ethanol production on local corn prices. This study expands upon the
current literature by increasing both the time span and the scope of the data set. This
investigation is able to better predict the long term price changes as a result of ethanol production
due to the use of a 10 year data set. Also, this study expands the data set to include counties with
and without ethanol plants to truly measure how much a nearby ethanol plant actually affects
basis.
Taking a broad look across time and space at an ethanol production plant’s price impact
is this study’s contribution.
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CHAPTER 3
ANALYTICAL FRAMEWORK
Shepherd wrote “within the limits of the perfect market, prices should differ among
locations by no more than the cost of transportation; among time periods by no more than the
cost of storage; and among product forms by no more than the cost of transformation” (as cited
in Davis & Hill, 1974, p. 135). Yet it is found that a simple calculation of transport, storage, and
processing costs are not enough to explain corn price differentials across space and time. Factors
such as supply and demand on the local, national, and international level also play a huge role in
determining the corn basis. This chapter examines these factors, describes the empirical model
to be implemented and concludes with a description of the data sources.
3.1 CONCEPTUAL MODEL OF ESTIMATING BASIS
The rise in US ethanol production has many important economic consequences.
Diverting corn away from its traditional feed, food, and export markets leads to fundamental
shifts in agriculture production decisions.
Corn is a commodity, thus corn producers are price takers. This means they have no
direct influence over the prices they receive. Moreover, most producers sell directly to the local
grain elevator, thus the most important price to a producer is the price being offered at a specific
location and time. The pricing of corn is further complicated by production and use patterns.
Corn is produced only once per year, but there is a relatively constant demand throughout the
year. Prices vary across time in response to storage costs, and vary across space in response to
whether specific regions have a corn surplus or deficit.
17
In addition to the cash price, another important pricing component is the basis. It is
defined as:
Basis = Local Cash Price – Nearby Futures Price.
(3.1)
The local cash price is defined as the price a corn producer would receive for corn on a specified
day and location. The nearby futures price is defined as the price of the nearby futures corn
contract as traded through the CME Group. The contract trades 5,000 bushels of #2 yellow corn
in the months of March, May, July, September and December. The nearby contract describes the
contract closest to expiration, not including the current month.
When the corn price basis increases, or strengthens, in a local grain market, it indicates
the local corn price has increased relative to the futures price. It is important to note the basis
merely measures the relationship between the local price and futures price; it indicates nothing
about the actual price levels. For example, the basis can be strengthening but the corn price may
be dropping.
There are a variety of factors influencing the basis at any particular location at any given
point in time. Figure 3.1 is a map of the corn basis throughout the Midwest on May 28, 2010.
The basis varies across space in response to supply and demand conditions, as well as in regard
to transportation costs. The map more or less shows how basis weakens as distance increases
from south-western Illinois.
The basis also varies over time in regards to changing market conditions. Figure 3.2
shows the basis over the past decade for individual states, plus the full sample.
18
Figure 3.1: Corn Basis Map
Source: Center for Agricultural and Rural Development, Iowa State University
19
Figure 3.2: Average Annual Corn Basis
Corn Basis
0
1999
2000
2001
2002
2003
2004
2005
-10
2006
2007
2008
Illinois
Cents per bushel
Iowa
-20
Indiana
Kansas
-30
Minnesota
Nebraska
South Dakota
-40
Wisconsin
Full Sample
-50
-60
Source: CashGrainBids.com, Calculations by Author
Fortunately, many of the factors which influence basis fit into three specified categories
provided by Garcia and Good (1983). They determined the magnitude of basis is influenced by
cost, stock, and flow factors. Cost factors include storage and transportation costs. The stock
factor is the supply measure and includes the amount of corn in storage. Finally, flow factors
measure demand and include the rate of market consumption. The model employed here will use
historical data that accounts for the cost, flow, and stock factors identified by Garcia and Good.
Specifically, the variables of interest are:
Production Ratio
The basis is determined by both the local cash and futures prices, thus the ratio of local to
national production is important in determining price relationships. It is expected that if
20
local stocks make up a relatively greater share of the national stocks, the basis will be
lower because corn will need to be transported out of the region. Conversely, if local
production is making up relatively less of the national production, it is expected the local
cash price will be higher relative to the futures price.
Local ethanol
Theoretically an ethanol plant has the power to strengthen the local basis is two ways.
First, an ethanol plant increases the demand for corn in its region. Second, as Davis and
Hill (1973) note, due to the spatial nature of the elevator industry, market structure theory
indicates some elements of geographical monopsony may exist. If this occurs, a single
firm in a particular region may then exhibit market power and have the ability to
influence price. Thus, an ethanol plant represents a new entrant to the market and may
dilute market power held by the grain elevator.
Diesel
Transportation costs play a major role in determining basis because all surplus grain from
the local market must be moved to the national or international market. Following
economic theory and Gallagher, Wisner, and Burbacker (2005) the local market price is a
function of the exogenous national market price less the cost to transport the commodity
to market. Hence, for excess supply markets the decline in local price occurs with greater
distance to the market, matching the increased transport costs. To account for the cost of
transport the average Midwest diesel price is used as a proxy for grain trucking costs.
21
Storage
There is a substantial demand for corn year-round, but the commodity is only produced
one time per year. This means the price will fluctuate throughout the year in relation to
storage. Garcia and Good (1983) note storage costs can include warehouse charges,
interest, or insurance. In the model, storage is accounted for by including the prime
interest rate, which mimics opportunity costs. Specifically, if the opportunity cost of
holding grain is high, producers are expected to sell. This would amount to an increased
supply on the cash market, thus a high opportunity cost is expected to cause a decrease in
basis.
Seasonality
Another important consideration, and one which is closely linked to storage, is
seasonality. A higher cash price is expected as time increases from harvest as a method
to compensate producers for storing the grain. In grain surplus areas, it is expected the
basis will be the weakest at harvest and will strengthen throughout the year as the local
market reduces its overall supply of grain. As the excess grain is moved out of the
market the local cash price is expected to converge with the futures price (Kahl & Curtis,
1986).
A model which can be used to explain local corn price basis can be built by combining
these factors. The model used in this study is specified as:
22
Basis = β0 + β1 * (Production Ratio) + β2*(Ethanol Production) + β3*(Interest Rate) +
β4*(Diesel Price) + ∑7𝑖=1 𝛽𝑖6 * (State) + ∑3𝑖=1 𝛽𝑖7 * (Season)
(3.2)
As noted in Chapter 2, Kahl & Curtis (1986) include price in their basis model and find it
to be statistically significant in grain surplus markets. Higher prices may induce producers to
sell, thus weakening the basis. The model will also be estimated with this variable.
Selling grain on the local cash market is inherently risky because of the high level of
price volatility in the market. In order to reduce risk, producers are able to enter into a hedge and
trade the price risk for basis risk. Typically, basis risk is less than price risk. As ethanol plants
enter a local grain market the increased demand for corn has the potential to increase the local
cash price relative to the futures price, thus strengthening the basis. Investigating the impact
ethanol has on corn basis is important so producers can adjust their price expectations. Above
all, having a proper prediction of the basis is vital to enable producers to make effective risk
management decisions.
3.2 EMPIRICAL MODEL
The observations in the dataset vary over time and space. This provides the opportunity
to observe changes which occur through time, as well as those that occur across locations. Panel
data has an advantage over pure cross-section or pure time-series data in detecting and measuring
effects, as it is able to look at a broader picture (Gujarati, 2003). Most importantly, it allows for
the investigation of more complicated behavior models. However, this expansive dataset also
presents many significant econometric considerations. As OLS is not an appropriate method for
panel data, a fixed or random effects approach must be employed (Elhorst, 2003). Additionally,
potential spatial dependency must be accounted for in the model. The following sections
23
describe methods for addressing panel data, incorporating spatial methods, and finally combining
the two.
3.2.1 Panel Data
In order to select the best approach for dealing with panel data both a fixed effects and
random effects model must be estimated. Based on prior assumptions of the model, the fixed
effects approach uses dummy variables to represent time periods, cross-sections, or a
combination of both to account for omitted explanatory variables. Alternatively, the random
effects approach represents the lack of knowledge of the true model through the disturbance
term. To choose between the two approaches the Hausman test may be used (Gujarati, 2003).
3.2.2 Spatial Methods
When using spatial data it is important to account for spatial dependencies. Ignoring
these relationships can lead to inefficient and biased estimates, invalid inference procedures, and
ultimately lead to drawing the wrong conclusions from the data analysis (Cliff & Ord, 1981).
Spatial dependency arises in a sample for a variety of reasons. In this work, the spatial
dependency is a result of spill-over effects and spatial externalities. Additionally, the arbitrary
county boundaries make it necessary to aggregate data over space to get a full view of the supply
and demand conditions in a particular location. Others have used time-series methods to deal
with the changes in basis, but Anselin (1988) writes that these specifications are not appropriate
due to the multidirectional nature of dependence in space as opposed to the one-directional time
movement. Thus it is necessary to account for spatial dependency in basis analysis.
24
3.2.2.1 Spatial Models
There are two main types of models to deal with spatial dependency. The first is a spatial
lag model which is used when the spatial correlation pertains to the dependent variable and is
generally specified as:
y = ρWy + Xβ + ε.
(3.3)
The second is the spatial error model, which is used when the spatial correlation effects the error
term. It is typically specified as:
y = Xβ + ε
ε = λWε + u
(3.4)
In both models, y is a n x 1 vector of observations of the dependent variable, W is an n x n
spatial weights matrix, ρ and λ are spatial autoregressive parameters, X in an n x k matrix of
observations of the independent variables, β is the k x 1 vector of regression coefficients, and ε
and u are error term vectors.
3.2.2.2 Spatial Weights Matrix
The spatial weights matrix, W, is an N x N positive matrix that specifies the
neighborhood set for each observation. Each location observation appears as both a row and
column. If location i and location j are considered to be neighbors wij will have a non-zero
value, and if the locations are not neighbors wij = 0. Also, by convention a location is not
considered to be its own neighbor thus the diagonal elements wii = 0. Generally, the weights are
standardized so that the elements of each row sum to one, or 𝑤𝑖𝑗𝑠 = 𝑤𝑖𝑗 / ∑𝑗 𝑤𝑖𝑗 . This row
25
standardization allows for the interpretation of the weights by constructing a weighted average of
the neighboring values through a spatial lag operator, which can then be applied to the error term
Wε (Stakhovych and Bijmolt).
3.2.3 Spatial Panel Model
To analyze the effects of time and space, following Anselin, Le Gallo & Jayet (2008), we
begin with a basic pooled linear regression model:
yit=xitβ + uit ,
(3.5)
For the model i is the cross-sectional index, with i = 1….N and t the time index, with t = 1….T.
The total number of observations is NxT. The dependent variable is yit, where each unique
observation is denoted at both i and t. The observations of the exogenous variables are contained
in1xK vector xit, β is a Kx1 vector of the regression coefficients, and uit is the error term.
To properly analyze spatial effects the observations are stacked first by the time period
t = 1….T and then by the cross-section i = 1….N which leads to y’ = (y11,….y1N,…yT1,…yTN).
The error term consists of spatially autocorrelated residuals, as well as random disturbances.
Following Baltagi et al. (2003), the error vector for time t is represented as
ut = µ + εt
(3.6)
εt = λWεt + vt.
(3.7)
with
where ut’ = (ut1, …,utN), εt’ = (εt1,…, εtN) and μ’ = (μ1,…, μN) denote the vector of random region
effects which are assumed to be IIN(0, σ2μ) . Using the W-matrix we are able to find λ, the
26
spatial autoregressive coefficient, which will have a positive value less than one. In a panel
setting the spatial weights matrix and the spatial autoregressive coefficient are assumed to
remain constant over time (Anselin, 1988). Finally, vt’ = (vt1, …, vtN) where vti is i.i.d. over i and
t and is assumed to be N(0, σ2v). εt can be rewritten as
εt = (IN – λW)-1vt = B-1vt,
(3.8)
where B=IN – λW and IN is an identity matrix of dimension N.
Once the data is stacked the pooled regression can be written as:
y = Xβ + u,
(3.9)
where y is a NT x 1 vector, X is a NT x K matrix, and ε is a NT x 1 vector. The error vector
takes the form:
u = (iT ⊗IN)μ + (IT ⊗ B-1)v,
(3.10)
where iT is a vector of ones with dimension T and IT is an identity matrix of dimension T. From
this the covariance matrix for u can be written:
Ωu = σ2μ (JT ⊗ IN) + σ2v (IT ⊗(B’B)-1),
(3.11)
where JT is a matrix of ones with dimension T. From this we can rewrite the matrix as:
Ωu = σ2v [KT ⊗ (T φIN + (B’B)-1) + ET ⊗ (B’B)-1] = σ2v Σu,
(3.12)
Where φ= σ2μ/ σ2v, KT = JT/T, ET = IT – KT, and Σu = [KT ⊗ (T φIN + (B’B)-1) + ET ⊗ (B’B)-1]
thus:
Σu-1 = KT ⊗ (T φIN + (B’B)-1)-1 + ET ⊗ (B’B)
27
(3.13)
and:
|Σu| = |T φIN + (B’B)-1| *|(B’B)-1|T-1.
(3.14)
Using these results, Anselin derived the log-likelihood function which was for the model:
L= −
NT
1
T−1
1
ln 2πσ2v − ln [ |TϕIN + (B′ B)−1 |] +
ln |B′ B| − 2 u′ Σu−1 u
2
2
2
2σv
where u = y- Xβ.
(3.15)
3.3 DATA SOURCES
The data set contains information on monthly corn prices, futures prices, diesel prices,
interest rates, and ethanol production. Additionally, there is information at an annual level on
local and national corn production and quarterly information about national and state stock
levels.
The variable of interest in the model is the corn basis. The local price data is a collection
of daily corn prices compiled by CashGrainBids.com from 153 grain elevators over 129 months.
The data ranges from 1999 to present, and the sample used ranges from October 1999 to
September 2009. The daily data were aggregated to monthly data. Missing observations, as well
months with partially missing data, where interpolated from neighboring counties. In the
sample, missing observations represent approximately two percent of the data points. In order to
calculate the basis, the local corn price is subtracted from the nearby futures price. All counties
with an observation are shown in purple in Figure 3.3.
28
Figure 3.3: Counties with Corn Price Observations
Figure 3.4: Counties with Ethanol Plants
29
All information about ethanol production, such as plant location and nameplate capacity
comes from the Renewable Fuels Association (RFA). To determine the date production began at
the plant, data was used from Ethanol Producer Magazine. If the date was unavailable, the
plant’s website was used or the plant was called for the information. Figure 3.4 is a map
displaying all counties with at least one ethanol plant in green.
The data of national and county level corn production, as well as state and national corn
stocks, comes from the USDA National Agricultural Statistics Service (NASS). Any missing
data points in the level of county corn production where obtained by using the existing data from
the county, and the average percentage change in production from around the state.
Data on corn production is available on a county level and information about ethanol
production is available at a point location, but to truly understand the local supply and demand
conditions which determine the price at a grain elevator a more broad measure is needed. In an
effort to get more complete information of these factors a 50 mile buffer ring was drawn around
the centroid of each county. From this, to determine the local conditions, data from the county
was summed with data from any other counties whose centroid fell within the 50 mile buffer.
In the model, diesel prices are used as a proxy for transportation costs. The price used is
the average monthly Midwest retail price as provided by the U.S. Energy Information
Administration. The prime interest rate acts as a proxy for the storage opportunity cost in the
model. This information comes from the Federal Reserve Bank Statistical Release.
For much of the analysis the data was aggregated to quarterly time periods. O’Brien
(2009) also uses this approach in his analysis as a method to reflect the seasonality of grain
marketing. He defines October through December as the fall quarter. At harvest time the final
30
size of the new crop is known and producers begin to make decisions about the use of their crop.
January through March is the winter quarter. At this time of year producers are continuing to
store or selling to elevators or other grain buyers. Spring includes April through June and is the
planting season. Prices are driven by acreage decisions and crop planting conditions, as well as
grain stocks and demand. Finally, the summer months of July through September are the period
of crop development, where weather and yield predictions dominate the market prices.
This method is supported in other basis literature. Garcia and Good (1983) aggregated
monthly prices into seasonal observations in an effort to increase the variability among
independent variables. Davis and Hill (1974) also separated their data into seasons to estimate
basis. Even the when using monthly data, Kahl and Curtis (1986) use seasonal dummy variables
to reflect the seasonality of grain pricing. Thus, the aggregation of data into quarterly pieces will
reduce some variation, but it is a method with precedence in the literature.
Table 3.1 provides summary statistics of the data set. Summary statistics are also
available by individual state or year in Appendix D.
Table 3.1: Summary Statistics
Variable
Mean
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price (cents)
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Interest Rate
-28.14
227.28
136.32
211.46
10.65
2.14
155.03
5.10
3.71
6.12
31
Std. Dev.
14.52
109.11
155.49
80.86
1.26
0.99
92.38
2.79
2.35
1.95
Min
-92
7
0
115.40
8.97
0.07
2.51
0.96
0.08
3.25
Max
16
544
1026
434.20
13.04
4.34
543.99
10.28
13.70
9.50
CHAPTER 4
EMPIRICAL RESULTS
The goal of this study is to measure the impact ethanol plants have on local corn basis.
This chapter presents a set of results which empirically quantify these effects. First, the model’s
validity is assessed and then estimates of the impact are reported. Next, results from varying
model specifications are presented and compared to the model’s estimates. Finally, the model is
compared against other models in the literature.
4.1 MODEL VALIDATION
4.1.1 Hausman Test
As noted in section 3.2.1, when using panel data the Hausman test is necessary to pick
between a fixed- or random-effects estimator. To conduct this test both models are estimated
and the parameters are compared. The null hypothesis is that there are no omitted variables,
which would mean the fixed effects and random effects estimators do not differ substantially.
The Hausman test returns a value of 5.96. The test statistic is distributed as chi-squared with six
degrees of freedom, which leads to a failure to reject the null hypothesis. Hence, the test does
not indicate a serious problem with omitted variables, thus a random-effects estimator is most
appropriate.
4.1.2 Lagrange Multiplier Tests to Select Model
When using spatial data, an important first step is determining the proper model
specification. In order to test the spatial lag model against the spatial error model two Lagrange
Multiplier tests are used. LMerror is used to evaluate if the spatial error model is necessary and
the LMlag test examines if the spatial lag model should be used (Stakhovych & Bijmolt, 2008).
32
The null hypothesis is no spatial modeling and the tests are chi-square distributed with one
degree of freedom. The tests are:
𝐿𝑀𝑒𝑟𝑟𝑜𝑟 =
𝐿𝑀𝑙𝑎𝑔 =
𝑒′ 𝑊𝑒 2
)^
̂2
𝜎
(
(4.1)
𝑇
𝑒′ 𝑊𝑦 2
)^
̂2
𝜎
(
(4.2)
𝑛𝐽
where
𝑇 = 𝑇𝑟[(𝑊 ′ + 𝑊)𝑊]
(4.3)
′
1
𝐽 = 𝑛∗𝜎̂2 [(𝑊𝑋𝛽̂ ) 𝑀(𝑊𝑋𝛽̂ ) + 𝑇𝜎̂ 2 ]
(4.4)
𝑀 = 𝐼 − 𝑋(𝑋 ′ 𝑋)−1 𝑋 ′
(4.5)
e is the vector of OLS residuals, 𝜎̂ 2 = e’e/N, I is an identity matrix of dimensions n x n, and 𝐵̂are
the OLS parameter estimates.
When running the LM tests for the corn basis data it is found that both the LMerror test
and LMlag test reject the null hypothesis of no spatial dependency. Stakhovych and Bijmolt
recommend that if both Lagrange Multiplier test statistics are found to be significant the
specification associated with the more significant test is the correct. The test statistics are LMerror
= 100.29 and LMlag = 93.63, thus the spatial error is used from this point forward. It should also
be noted the spatial error model coincides with previous literature on the topic. (Note: These
tests are for spatial data, but not developed to account the time series nature of the data. The LM
tests were conducted from the final time observation in the data set, but were found to also hold
in other time periods.)
33
4.1.3 Tests for Spatial Error Correlation and Random Region Effects
The spatial distribution of the data suggests there may be some spatial dependency in the
sample. As noted in Baltagi, Song, and Koh (2003), it is important to conduct a Lagrange
Multiplier (LM) test because ignoring the spatial correlation and heterogeneity due to random
region effects will result in inefficient estimates and misleading inference. The LM test
developed in Baltagi et al. (2003) is conducted to simultaneously test for the existence of spatial
error correlation and random region effects. In order to test the joint hypothesis H0: λ = 𝜎𝜇2 = 0,
the test statistic:
𝑁𝑇
𝐿𝑀𝑗 = 2(𝑇−1) 𝐺 2 +
𝑁2 𝑇
𝑏
𝐻2
is used, where using the OLS residuals, u, 𝐺 =
(4.6)
𝑢′ (𝐽𝑇 ⊗𝐼𝑁 )𝑢
𝑢′ 𝑢
− 1 , 𝐻 = 𝑢′ (𝐼𝑇 ⊗
𝑊+𝑊 ′
2
) 𝑢/𝑢′𝑢 and
𝑏 = 𝑡𝑟(𝑊 2 + 𝑊 ′ 𝑊) . This test returns a value of 18825.02 which implies we are able to reject
the joint null of no spatial effects and no random effects.
Additionally, a marginal LM test is conducted to detect the presence of spatial effects
assuming there are no random effects (Baltagi et al., 2003). The test statistic for H0: λ=0
(assuming 𝜎𝜇2 = 0) is
𝑁2 𝑇
𝐿𝑀2 = √
𝑏
𝐻
(4.7)
which returns a value of 93.63. The statistic is asymptotically distributed 𝜒12 thus this null
hypothesis is also rejected.
34
4.2 ALTERNATIVE MODELS
The tests in Section 4.1 indicate that the use of a spatial error components model is
necessary to obtain efficient and unbiased estimates. The data is entered into the maximum
likelihood function developed by Anselin (1988) and keeping with the literature (McNew &
Griffith, 2005) the W-matrix specifies that any observations within 50-miles of one another will
have correlated errors.
In the literature there are a variety of model specifications used to estimate basis. The
theoretical model discussed in Chapter 3 included a production ratio, transportation costs, storage
costs, ethanol production, and state and seasonal dummy variables. The results for this model,
along with alternative specifications are displayed in Table 4.1.
The theoretical model described above is labeled Model A. Model B uses the ratio of
local stocks to national stocks, rather than the production ratio. Model C omits the measure of
storage opportunity cost, Model D omits the transportation proxy variable, and Model E omits
the seasonal dummy variables. Model F includes the nearby futures price as an additional
variable.
35
Table 4.1 Alternative Specifications
Intercept
Prod Ratio
Stocks Ratio
Diesel
Ethanol
Interest
Futures
Illinois
Iowa
Kansas
Minnesota
Nebraska
South Dakota
Wisconsin
Fall
Spring
Summer
𝛔𝟐𝐯
𝛔𝟐𝛍
λ
Likelihood
Parameters
AIC
BIC
Model A
20.32 **
-1.29 *
-0.10 **
0.0085 **
-2.16 **
Model B
4.18
-2.20
-0.11
0.0084
-2.19
Model C
5.99
-1.11
*
**
**
**
Model D
0.93
-1.18 *
-0.12 **
0.0083 **
-6.46 **
6.20 **
-6.88
-13.12 **
12.89 **
-11.03
-7.48 *
3.98
-6.88
-19.72 **
-6.42 **
-17.21
-8.36 *
4.53
-6.20
-22.10 **
-8.83 **
-19.55
-24.35 **
-11.00 **
-22.98
-2.39
2.39
3.13
2.25
5.52
6.37
-1.38
2.97
2.17
11.66 **
11.65 **
11.64
17.91 **
17.83 **
16.76
0.95 **
0.95 **
0.96
-18447.8
-18464.7
-18682.2
15
15
14
36926
36959
37392
36898
36932
37367
*Notes significance at the five percent level
**Notes significance at the one percent level
36
**
**
**
**
**
**
**
**
0.0083 **
-2.40 **
-6.60
-13.02
-7.39
-19.55
-8.16
-21.85
-24.24
-3.24
-0.11
-4.61
11.84
17.71
0.95
-18511.2
14
37050
37025
**
**
*
**
**
**
**
*
**
**
**
Model E
20.78 **
-1.22 *
-0.11 **
0.0084 **
-2.23 **
-6.65
-12.45
-7.36
-18.90
-7.60
-21.27
-24.04
**
**
*
**
*
**
**
11.65 **
17.43 **
0.95 **
-18533.4
12
37091
37069
Model F
21.64 **
-1.29 *
-0.09
0.0085
-2.27
-0.01
-6.46
-13.12
-7.49
-19.73
-8.37
-22.11
-24.35
-2.68
2.18
-1.74
11.66
17.92
0.98
-18446.5
16
36925
36896
**
**
**
**
**
*
**
**
**
**
**
**
**
In order to pick the most appropriate model in a panel setting, Frees (2004) recommends
Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC). Both statistics
allow for comparison between both nested and non-nested models, making them fitting for this
application. They are calculated:
AIC = -2 * ln(maximized likelihood) + 2 * (number of model parameters)
and
BIC = -2 * ln(maximized likelihood) + ln(number of model parameters).
For models with the same number of parameters, the AIC is equivalent to maximizing the log
likelihood. The preferred model is indicated by the smallest AIC or BIC statistic. The difference
between the two is that the BIC gives greater weight to the number of parameters, and using both
measures gives greater confidence in the results.
Model F returns the lowest AIC and BIC, very closely followed by Model A. The
difference between the models is the inclusion of the futures price in Model F, which is found to
be statistically insignificant. Due to the closeness of the AIC and BIC, for the remainder of the
study Model A will be regarded as the base model.
Most importantly, all models indicate that ethanol production is statistically significant
and estimate a coefficient value which is similar in magnitude. Overall, the models appear to be
relatively similar, with the exception being Model B. When the stocks ratio is used rather than
the production ratio Illinois, Iowa, Kansas, and Nebraska have positive values. A more detailed
explanation of variable interpretation will follow in Section 4.3.
37
4.3 PARAMETER ESTIMATES
As noted in Section 4.2, Model A serves as the base model for the remainder of this
study. Table 4.2 reports the coefficients and t-values for this model. All parameters, except the
seasonal dummy variables, are statistically significant at the five percent level.
Table 4.2 Model Estimates
Variable
Coefficient
20.3183
Intercept
-1.2884
Production Ratio
-0.1049
Diesel Price
0.0085
Ethanol Production
-2.1638
Interest Rate
-6.4601
Illinois
-13.1158
Iowa
-7.4836
Kansas
-19.7159
Minnesota
-8.3617
Nebraska
-22.0950
South Dakota
-24.3472
Wisconsin
-2.3943
Fall
2.2472
Spring
-1.3769
Summer
11.6639
𝛔𝟐𝐯
17.9099
𝛔𝟐𝛍
0.9489
λ
*Notes significance at the five percent level
**Notes significance at the one percent level
38
**
*
**
**
**
**
**
*
**
*
**
**
**
**
**
T-values
4.6724
-2.30243
-9.80638
7.65322
-4.85065
-2.86512
-5.06022
-2.24027
-7.11868
-2.53333
-6.9716
-5.32627
-1.05198
1.05091
-0.93145
54.58875
7.43203
22.1769
The parameters of the model, β, can be interpreted as partial derivatives, similar to the
least-squares interpretation (Lesage & Pace, 2009). Thus, δyi/δxir = βr for all i, r and δyi/δxjr = 0,
for all j≠i and for all variables r.
The production ratio measures local corn production as a percentage of national corn
production. As expected, the coefficient has a negative sign. This implies an increase in local
supply, relative to national supply, will cause the basis to weaken. The coefficient implies a one
percent increase in local corn production relative to national production results in a decreased
basis of -1.29 cents.
The coefficient of diesel price is also negative. Transportation costs greatly influence
basis and the results indicate that as transport costs rise, the basis weakens. The coefficient of
-0.1 implies that if the price of diesel rises by 10 cents it is expected that the basis will widen by
one cent.
The ethanol coefficient in positive and indicates an ethanol production positively affects
basis. An ethanol plant is predicted to strengthen basis by .0085 cents per million gallons of
ethanol production. This means a 50 MGY plant will increase basis by 0.425 cents.
The interest rate is negative as expected. It is a proxy for the opportunity cost of storage,
and as the opportunity cost of holding grain increases it is expected that more will be sold on the
cash market, causing the basis to weaken. A one percent increase in the interest rate will cause
the basis to widen by 2.12 cents.
All of the states had statistically significant coefficients when compared to the base state
of Indiana. The basis is Indiana is fairly strong relative to the other states in the sample (see
Figures 3.1 and 3.2) so it is not surprising that the dummy coefficients for the other states are
39
negative. It should also be noted, none of the seasonal dummy variables are statistically
significant.
The model also estimates λ, σ2v and σ2μ to be statistically significant. The spatial
autocorrelation coefficient is denoted as λ, σ2μ is the variance of the random-effects vector and
σ2v is the variance of the error vector. Their significance implies they are needed in the model,
further verifying the model’s validity.
4.3.1 Weights Matrix
An important feature of spatial models, and one that is frequently ignored in the
literature, is the construction of the NxN spatial weights matrix. There are numerous ways to
build a weights matrix and for this particular model McNew and Griffith (2005) suggest that any
locations within 50 miles will be correlated.
Nevertheless, the field of spatial econometrics provides no set rules for picking a Wmatrix structure so it is best to try a variety of specifications to test the robustness of the results.
This study creates and tests five different weights matrices in the model. Using the latitude and
longitude of the county centroid for all counties with a dependent variable observation (N=153),
the following spatial weight matrices are constructed:
-
50-mile Buffer – For county i, the neighborhood set includes all counties with
centroids within 50 miles of the centroid of county i. In this specification, counties
have between one and ten neighbors.
-
Contiguous Counties – For county i, the neighborhood set includes all counties
contiguous to county i.
40
-
Nearest Neighbors (NN) - For county i, the neighborhood set includes the m counties
nearest to county i. The specifications examined are m = 5, 7, and 10.
The model was run with these different W-matrix specifications and the results are shown in
Table 4.3.
Table 4.3 Spatial Weight Variations
Intercept
Prod Ratio
Diesel
Ethanol
Interest
Illinois
Iowa
Kansas
Minnesota
Nebraska
South Dakota
Wisconsin
Fall
Spring
Summer
𝛔𝟐𝐯
𝛔𝟐𝛍
λ
Likelihood Value
50-Miles
Contiguous
N=5
N=7
20.32 **
11.15 **
20.28 **
13.74 *
-1.29 *
-2.20 **
-3.18 **
-3.30 **
-0.10 **
-0.08 **
-0.10 **
-0.08 **
0.0085 ** 0.0110 ** 0.0135 **
0.0113 **
-2.16 **
-1.86 **
-2.69 **
-3.17 **
-6.46 **
-3.75
-4.48 *
-0.0004
-13.12 **
-11.02 **
-9.44 **
-4.70 **
-7.48 *
-5.87
-6.56 **
-0.02 **
-19.72 **
-17.81 **
-17.00 **
-12.17 **
-8.36 *
-10.27 **
-10.37 **
-4.15 *
-22.10 **
-22.07 **
-21.65 **
-16.76 **
-24.35 **
-18.42 **
-19.27 **
-15.99 **
-2.39
1.58
2.01
3.07
2.25
2.12
6.16 **
6.85 *
-1.38
-0.52
3.95
5.59
11.66 **
13.51 **
12.88 **
12.82 **
17.91 **
26.43 **
26.11 **
27.36 **
0.95 **
0.97 **
0.96 **
0.96 **
-18447
-17902
-17808
-17579
*Notes significance at the five percent level
**Notes significance at the one percent level
T-values have been omitted from this table
41
N=10
24.83
-2.96 **
-0.07
0.0120 **
-5.90 **
-4.07 *
-2.51
3.02
-8.59 **
-1.61
-12.61 **
-15.86 **
0.03
10.45
16.51
13.36 **
25.92 **
0.99 **
-17627
Lasage and Pace (2009) note there is not a formal measure to compare models with
different spatial weight matrices because they are not nested models. Still, they recommend
comparing the log-likelihood function values. This measure indicates the best model is when the
W-matrix is specified with seven neighbors.
When comparing different W-matrix specifications the main variable of interest, ethanol
production, is always statistically significant. Interestingly, the 50-mile neighbor relationship
specified by the literature returns the lowest estimate for ethanol production’s impact on basis.
The seven-neighbor model estimates the impact of ethanol production to be 0.0113 per million
gallons, which equates to a 0.565 cent increase in basis for a 50 MGY plant. The five-neighbor
matrix returns the largest ethanol production impact and predicts a .675 cent increase for a 50
MGY plant.
4.4 TIME COMPARISON
For computational ease the data in the study was aggregated in to quarterly time periods
as discussed in Section 3.3. As this step reduces variation, it is necessary to verify the results do
not significantly change as a consequence. To ensure varying time units do not yield different
results, separate models were run using both quarterly and monthly time periods for the states of
Illinois, Iowa, and Kansas. These states were selected because of their high number of county
observations in the data.
Table 4.4 compares the observations over the full sample time period of October 1999
through September 2009 using quarterly observations in Model Q and monthly observations in
42
Table 4.4 Compare Monthly and Quarterly Data
Illinois
January
Model Q
7.3887
(1.02)
2.5985
(0.99)
-0.118 **
(-6.53)
0.0053
(1.46)
-1.6445 *
(-2.21)
3.1845
(0.78)
5.3328
(1.30)
1.7289
(0.40)
X
February
X
March
X
April
X
May
X
June
X
July
X
August
X
September
X
October
X
November
X
Intercept
Prod.
Ratio
Diesel
Ethanol
Prod.
Interest
Rate
Winter
Spring
Summer
𝛔𝟐𝐯
𝛔𝟐𝛍
λ
11.81
(25.74)
18.56
(3.85)
0.94
(10.91)
**
**
**
Iowa
Model M
5.6286
(1.31)
2.5905
(1.17)
-0.1097 **
(-11.81)
0.0059 *
(2.07)
-1.5913 *
(-4.07)
X
X
X
0.0198
(0.36)
5.3393
(1.86)
1.5001
(0.61)
6.5565
(2.27)
2.5564
(0.88)
4.7021
(1.55)
3.2697
(1.06)
6.3509
(2.18)
-6.3120
(-2.07)
-4.1511
(-1.42)
4.9603
(1.62)
19.19
(44.97)
19.92
(3.87)
0.94
(9.10)
Model Q
16.7189
(1.10)
-11.121 **
(-4.22)
-0.1511 **
(-3.34)
0.0068 **
(4.53)
-2.3777
(-1.27)
9.5181
(0.92)
16.7766
(1.64)
5.2033
(0.53)
x
x
x
*
x
x
x
x
*
x
*
x
x
x
**
**
**
11.94
(28.61)
16.39
(3.99)
0.97
(8.94)
*Notes significance at the five percent level
**Notes significance at the one percent level
T-values reported in parentheses
43
**
**
**
Kansas
Model M
6.4620 *
(2.14)
-9.3507 **
(-4.40)
-0.0864 **
(-14.37)
0.0078 **
(7.55)
-2.4114 **
(-9.70)
X
X
X
1.7902
(1.00)
6.1760
(3.57)
2.4707
(1.43)
6.8406
(3.68)
2.0747
(1.07)
5.2190
(2.95)
0.0046
(0.31)
4.2921
(2.30)
-5.3528
(-2.84)
-3.9794
(-2.00)
4.2610
(2.32)
18.07
(49.96)
23.61
(4.12)
0.97
(14.14)
Model Q
-8.6216
(-1.53)
-8.7416 **
(-4.98)
-0.1034 **
(-11.21)
0.0290 **
(3.37)
-1.0418 **
(-2.87)
1.5210
(0.79)
3.7244
(1.89)
2.1851
(1.10)
X
X
X
X
X
X
X
X
X
X
X
**
**
**
19.89
(18.22)
32.49
(2.13)
0.98
(11.17)
**
*
**
Model M
-13.584 **
(-2.46)
-9.8474 **
(-8.19)
-0.1018 **
(-16.47)
0.0269 **
(4.68)
-0.9656 **
(-3.94)
X
X
X
4.0990
(2.20)
6.8035
(3.61)
1.8101
(1.04)
6.4703
(3.39)
3.4880
(1.83)
9.0699
(4.79)
5.1208
(2.71)
8.2566
(4.37)
1.0314
(0.66)
1.8171
(1.04)
6.0732
(3.22)
27.35
(31.83)
37.55
(2.26)
0.98
(11.82)
*
**
**
**
**
**
**
*
**
Model M. A visual inspection of that data shows that generally the variables are similar in sign
and magnitude across models. A few notable differences include: ethanol production is not
statistically significant in the Illinois quarterly model whereas it is in the monthly model; the
interest rate is not statistically significant in the Iowa quarterly model whereas it is in the
monthly model; some of the monthly dummy variables are statistically significant whereas none
of the seasonal dummy variables are significant at the five percent level.
4.5 COMPARISON TO LITERATURE
The impact of ethanol on corn basis found in this study is somewhat surprising when
compared to results in previous work. Here it is estimated a 50 MGY ethanol plant within 50
miles of a county centroid has a 0.425 cent impact on local basis, whereas others suggest the
impact may be greater. This section aims to not only further validate this study’s results, but also
gain insight into these inconsistent findings. To achieve this end, the model employed here is
closely compared to the McNew and Griffith (2005) study.
The McNew and Griffith study examined the regions surrounding 12 different ethanol
plants from March 2000 to March 2003. They find that in the 150-mile square region
surrounding the plant the average impact is a 5.9 cent increase in basis. Besides the different
estimation of the ethanol production impact, there are some important differences to note
between the studies:
-
The time period used in the McNew and Griffith study is a sub-sample of the time
period used in this study. It should be noted the Midwestern average annual corn
basis was continually increasing between 2000 and 2003, whereas over the full time
44
period of 1999 to 2009 the average annual basis is decreasing. (See Figure 3.2 for a
visual representation.)
-
To estimate their model, McNew and Griffith use state-level corn production,
national-level corn production, monthly dummy variables, a dummy variable for
ethanol production, and a sophisticated transportation variable. Differences in this
study’s analysis are the use of a corn production ratio, a more crude proxy for
transportation, and interest rate as a proxy for storage costs.
-
The McNew and Griffith study uses locations that were within approximately 75
miles of a new ethanol plant. They specifically look at regions with new ethanol
plants whereas the data set used in this study contains counties with pre-existing
plants, counties which gain plants during the 10-year period, and some counties
which never have a plant.
To test the model used in this study a sub-sample of the data was extracted from the data
set. This sub-sample only includes counties from Illinois, Iowa, South Dakota, and Wisconsin
because these are the states used by McNew and Griffith (they also have one observation in
Missouri). Also, in keeping with McNew and Griffiths selection criteria, the sub-sample only
includes counties where an ethanol plant opened between Spring 2000 and Spring 2003.
Near the end of their report, McNew and Griffith state they were unable to identify
whether the price impacts would persist over time due to data constraints. Over half of the plants
in their sample had been open for less than six months. They predicted over time the price
impact of a plant will diminish as market conditions adjust to the new demand center (p. 176).
Thus, they conclude their estimates are likely measures of short-term impacts and not indicative
of an ethanol plant’s long-term price impact.
45
Table 4.5 contains the estimates of six different models used to compare the results of
this study to the results of McNew and Griffith. To mimic the time period used by McNew and
Griffith the data is separated into Time 1 (Fall 1999 – Summer 2003) and Time 2 (Fall 2003 –
Summer 2009). Thus the models run are:
-
Sub-sample in Time 1 - This model is the direct comparison to McNew and Griffith.
-
Sub-sample in Time 2 – This model investigates whether the impact found by
McNew and Griffith persists over time.
-
Sub-sample over the full time period.
-
Full sample in Time 1 – This model examines whether the impacts found by McNew
and Griffith hold when the model includes counties with and without an ethanol plant.
-
Full sample in Time 2
-
Full sample over the full time period.
(Note: Table 4.5 expands over two pages.)
46
Table 4.5: Compare Full Sample and Sub-Sample Estimates
Intercept
Production
Ratio
Diesel
Ethanol
Illinois
South Dakota
Subsample
(N=22)
Wisconsin
Winter
Spring
Summer
σ2v
σ2μ
λ
Fall 1999 Summer 2003
(Time 1)
Fall 2003 Summer 2009
(Time 2)
-38.1568
(-6.10)
0.21435
(0.20)
-0.01474
(-0.38)
0.13347
(11.51)
15.36167
(4.32)
-5.22206
(-1.75)
9.37036
(2.00)
4.08061
(3.36)
4.42004
(3.62)
-0.99808
(-0.81)
17.24287
(12.85)
16.57584
(3.00)
0.56
(2.30)
23.34869
(2.80)
-13.4581
(-5.34)
-0.10721
(-17.33)
0.02065
(6.98)
11.90544
(2.73)
-22.607
(-4.39)
-14.7958
(-1.98)
2.64308
(2.42)
4.31058
(3.95)
1.96208
(1.78)
42.16872
(15.77)
29.13658
(2.18)
0.30
(1.25)
**
**
**
*
**
**
**
**
*
47
**
**
**
**
**
**
*
*
**
**
*
Fall 1999 Summer 2009
(Full Time)
-13.6663
(-2.64)
-3.97666
(-2.59)
-0.06996
(-10.31)
0.00751
(2.47)
11.66646
(2.38)
-7.41843
(-2.10)
1.43494
(0.28)
2.22307
(1.58)
4.46042
(3.16)
3.37015*
(2.39)
23.62253
(20.89)
11.30683
(2.90)
0.86
(5.48)
**
**
**
*
*
*
**
*
**
**
**
Table 4.5 Continued
Fall 1999 Summer 2003
(Time 1)
Intercept
Full
Sample
(N =153)
Fall 2003 Summer 2009
(Time 2)
-15.8176 *
13.93429
(-2.02)
(4.60)
Production
-1.59076 **
-2.50443
Ratio
(-2.74)
(-3.55)
Diesel
0.02141
-0.12038
(0.43)
(-17.19)
Ethanol
0.00779 *
0.01126
(2.38)
(7.68)
Illinois
-5.61763 *
-5.07186
(-2.56)
(-2.24)
Iowa
-14.2573 **
-14.4399
(-5.94)
(-6.40)
Kansas
-7.04125 *
-8.46234
(-2.31)
(-3.01)
Minnesota
-21.7964 **
-20.6492
(-8.49)
(-8.59)
Nebraska
-8.54345 **
-12.5121
(-2.87)
(-4.70)
South Dakota
-25.5823 **
-23.0244
(-8.50)
(-7.93)
Wisconsin
-21.5224 **
-25.4419
(-4.94)
(-5.96)
Fall
3.626 *
1.41794
(2.17)
(0.92)
Spring
5.30165 **
4.25605
(5.30)
(2.95)
Summer
0.00749
2.51643
(0.25)
(1.72)
2
5.4353 **
14.22327
σv
(33.80)
(41.80)
2
18.24325 **
24.77653
σμ
(7.45)
(7.33)
0.93 **
0.87
λ
(21.05)
(12.02)
* Notes significance at the five percent level
**Notes significance at the one percent level
T-values reported in parentheses
48
**
**
**
**
*
**
**
**
**
**
**
**
**
**
**
Fall 1999 Summer 2009
(Full Time)
5.994
(1.08)
-1.10714
(-1.92)
-0.12284
(-7.95)
0.00833
(7.43)
-6.87933
(-2.77)
-11.0317
(-3.49)
-6.87786
(-1.42)
-17.2101
(-5.07)
-6.19979
(-1.29)
-19.5501
(-5.20)
-22.9762
(-4.62)
3.12733
(0.95)
6.36547
(1.78)
2.16525
(0.85)
11.64203
(54.61)
16.75687
(7.51)
0.96
(29.23)
**
**
**
**
**
**
**
**
**
**
In all six models the results of ethanol production, the main variable of interest, come
back statistically significant. It should also be noted that McNew and Griffith did not use a
measure of storage or storage costs so it was not included here. Table 4.6 shows the impacts a
50 MGY ethanol production plant will have on basis, as estimated by each of the models.
Table 4.6: Ethanol Impacts
Ethanol
Impact on
Basis
Time 1
Time 2
Full
Time
Sub-Sample
6.67
1.03
0.38
Full Sample
0.39
0.56
0.42
When looking at the sub-sample in Time 1, which is designed to mimic the sample of
McNew and Griffith, a basis improvement of 6.67 cents per bushel is found. This is in the range
of the 5.9 cent impact found by McNew and Griffith, because they cite improvements ranging
between 1.5 cents and 12 cents for individual plants. Additionally, the 50 MGY size used in
these calculations is larger than the average plant size used in the McNew and Griffith sample,
yet they indicate the plant size is relatively unimportant.
It is found that when all locations are examined in Time 1, the price impact of an ethanol
plant is considerably less. This could be for a variety of reasons. First, McNew and Griffith may
over attribute some of the increase in basis to ethanol production. Over the time period, both
ethanol production and basis are continually increasing so the lack of locations without ethanol
plants may lead to over estimating the impacts.
A second and more probable explanation is that the lack of price impact from local
ethanol production in the full sample does not mean there is no impact; rather it may mean the
price impacts from ethanol production are being spread well beyond the 50-mile region defined
49
by the model. This means even counties without an ethanol plant within 50 miles are still
receiving positive price impacts from ethanol production. Thus, when including counties in the
sample without plants, the direct impact of local ethanol production is diluted as impacts are still
being felt by counties further away.
Another interesting finding in Table 4.6 is that over time the price impact of ethanol
plants seems to decrease. Examining the sub-sample, in Time 1 the impact is 6.67 cents, but in
Time 2 the impact of ethanol production is 1.03 cents. As suggested by McNew and Griffith
(2005), it appears that the price impact diminishes over time as market conditions evolve to meet
new centers of demand. The extended sample period allows for the examination of more longterm price impacts, rather than capturing the short-term price adjustments. These long-term
impacts are expected to be smaller, possibly contributing to the difference between this study and
previous literature.
As previously noted, McNew and Griffith do not account for storage opportunity costs
whereas this study takes them into account. The six models were re-run, this time including the
interest rate and the results are in Table 4.7. (Note: Table 4.7 expands over two pages)
50
Table 4.7 Compare Full Sample and Sub-Sample Estimates with Interest
Fall 1999 Summer 2003
Intercept
Production
Ratio
Diesel
Ethanol
Interest
Illinois
South Dakota
Sub-sample
(N=22)
Wisconsin
Winter
Spring
Summer
σ2v
σ2μ
λ
-37.01
(-9.56)
-1.77
(-1.86)
0.21
(8.11)
0.0388
(4.16)
-3.48
(-21.04)
15.26
(5.27)
-7.43
(-2.91)
5.56
(1.33)
2.95
(4.27)
2.82
(4.05)
-3.67
(-5.19)
9.02
(12.85)
13.28
(3.19)
0.38
(1.31)
51
**
**
**
**
**
**
**
**
**
**
**
**
Fall 2003 Summer 2009
20.39
(2.70)
-11.39
(-4.85)
-0.094
(-14.30)
0.0182
(6.10)
-0.99
(-4.30)
11.31
(2.96)
-19.44
(-4.14)
-11.33
(-1.68)
2.88
(2.87)
3.91
(3.88)
1.34
(1.32)
58.54
(15.58)
22.26
(2.15)
0.06
(0.24)
**
**
**
**
**
**
**
**
**
**
*
Fall 1999 Summer
2009 (Full)
-1.45
(-0.37)
-2.95 **
(-2.56)
-0.061 **
(-10.99)
0.0064 **
(2.40)
-2.64 **
(-13.88)
11.33 **
(3.97)
-7.11 **
(-2.69)
3.00
(0.76)
2.72 **
(2.75)
3.53 **
(3.55)
0.68
(0.67)
24.53 **
(20.73)
10.01 **
(3.08)
0.61 **
(2.42)
Table 4.7 Continued
Fall 1999 Summer 2003
Intercept
Fall 2003 Summer 2009
-16.15 *
14.96
(-2.58)
(4.71)
Production
-1.88 **
-2.55
Ratio
(-3.26)
(-3.59)
Diesel
0.240 **
-0.118
(5.43)
(-16.45)
Ethanol
0.0075 *
0.0113
(2.29)
(7.63)
Interest
-4.19 **
-0.26
(-17.53)
(-1.01)
Illinois
-4.74 *
-5.00
(-2.37)
(-2.19)
Iowa
-14.67 **
-14.46
(-7.10)
(-6.30)
Kansas
-6.96 **
-8.46
(-2.64)
(-2.96)
Minnesota
-23.06 **
-20.67
(-10.27)
(-8.43)
Full Sample
(N =153)
Nebraska
-9.83 **
-12.62
(-3.93)
(-4.67)
South Dakota
-27.33 **
-23.05
(-10.10)
(-7.77)
Wisconsin
-21.34 **
-25.35
(-5.38)
(-5.90)
Fall
1.95
1.39
(1.36)
(1.05)
Spring
2.52
4.11
(1.83)
(3.04)
Summer
-3.30 *
2.41
(-2.40)
(1.80)
2
5.11 **
14.27
σv
(33.75)
(41.80)
20.45 **
25.09
σ2μ
(7.60)
(7.34)
0.90 **
0.86
λ
(7.60)
(11.80)
* Notes significance at the five percent level
**Notes significance at the one percent level
T-values reported in parentheses
52
**
**
**
**
*
**
**
**
**
**
**
**
**
**
**
Fall 1999 Summer
2009 (Full)
20.32 **
(4.67)
-1.29 *
(-2.30)
-0.105 **
(-9.80)
0.0085 **
(7.65)
-2.16 **
(-4.85)
-6.46 **
(-2.87)
-13.12 **
(-5.06)
-7.48 *
(-2.24)
-19.72 **
(-7.12)
-8.36 *
(-2.53)
-22.10 **
(-6.97)
-24.35 **
(-5.33)
-2.39
(-1.05)
2.25
(1.05)
-1.38
(-0.93)
11.66 **
(54.59)
17.91 **
(7.43)
0.95 **
(22.18)
Again the price impacts of a 50 MGY ethanol plant are calculated and can be found in
Table 4.8. When accounting for storage opportunity costs the impact of ethanol production in
the sub-sample during Time 1 decreases from 6.67 cents to 1.94 cents. This may indicate an
unaccounted for factor played an important role in predicting basis, and its absence resulted in an
over-estimation of the price impacts. The difference between estimations in the sub-sample and
the full sample remains and it is believed that this variation is still a result of the factors
described above.
Table 4.8 Ethanol Impacts with Interest
Ethanol
Impact (with
Interest)
Sub-Sample
Full Sample
Time 1
Time 2
Full
Time
1.94
0.38
0.91
0.56
0.32
0.43
53
CHAPTER 5
CONCLUSION
5.1 SUMMARY
The ethanol industry has experienced rapid growth over the past decade. This growth
primarily occurred in the starch-based ethanol sector, resulting in much greater demand for corn.
While it is certain there is an impact on the national grain sector, the magnitude of this impact on
local corn basis, especially in the long run, has been sparsely studied.
This thesis aimed to measure the impact ethanol plants have on local corn basis. The
specific objectives were to:
1. develop and estimate a spatial panel model of corn basis;
2. assess the impacts of ethanol plants on the local corn basis; and
3. determine if these impacts are consistent with the short-run impacts found in previous
studies.
Given the above objectives, a description of the ethanol and corn markets, along with a
discussion of the linkages between the two was provided. A review of economic literature on the
topic reveals ethanol plants have a documented impact on market conditions. Specifically, some
research suggests ethanol plants have the ability to strengthen basis, though other research fails
to reach these conclusions. These inconsistencies, coupled with the fact that no research looks
from the beginning of the ethanol growth period to present, rationalizes this study’s purpose of
determining how ethanol plants affect corn basis.
With a focus on predicting corn basis, a model was built to account for supply and
demand factors which influence the relationship between the local and national prices. These
54
include the amount of ethanol produced within 50 miles, a ratio of local and national corn
production, diesel price as a proxy for transportation, interest rate as proxy for storage
opportunity cost, and state and seasonal dummy variables.
To measure the impact of ethanol on corn basis, data from 153 Midwestern counties over
40 quarters was used. Tests were conducted which supported the use of a random effects model
and the hypothesis of spatial dependency. The spatial nature of the data can lead to biased or
inefficient estimates when using OLS, thus this study built a spatial error components model to
estimate the impact ethanol has on corn basis.
The econometric results indicated local ethanol production has a statistically significant
and positive impact on local corn basis. The results predict on average the entrance of a 50
MGY ethanol production plant in the Midwest will increase local basis between 0.40 and 0.65
cents, dependent upon the specification of the weights matrix. Still, these results are much
smaller than those predicted by previous work.
5.2 CONCLUSIONS
The following conclusions are drawn in response to the objectives and are based on the
empirical results from Chapter 4, as well as arguments developed in previous chapters.
In response to Objective 1, a spatial error component model was built to account for the
spatial and panel nature of the data. The Hausman Test verified the use an error component
model and Lagrange Multiplier tests supported the use of a spatial error model.
In regards to Objective 2, the impact of having an ethanol production plant within 50
miles was measured to be a 0.40 cent basis improvement.
55
In response to Objective 3, it was determined that the long-term price impact of ethanol
production is considerably less than the impact found in the short-run. The data was able to
closely replicate short-term finding of previous studies, but over time the impacts were found to
decrease. The findings also suggest the price impacts of ethanol production reach further than
the 50 mile radius assigned by this study.
5.3 SUGGESTIONS FOR FURTHER RESEARCH
Additional research in this area will be of benefit to allow for a deeper understanding of
the long-run impacts of ethanol on corn basis. Three extensions or modifications of the current
study that will allow for a great understanding of the topic are to improve measures of
transportation costs, investigate the reach of ethanol price impacts, and provide region specific
estimates.
Transportation costs are one of the driving forces in determining basis and in this study
they were modeled using the proxy of Midwest monthly average diesel price to capture the
variation in cost over time. However, transportation costs also vary over space so some measure
of distance to a terminal grain market may be useful. The depth of the McNew and Griffith
(2005) data set allowed them to account for the specific distance grain travels, but this analysis
only partially accounts for difference over space by using state-level dummy variables.
Including specific transportation distance to terminal market variables may allow for a more
complete analysis of specific price impacts.
Additionally, the findings of this study suggest ethanol’s price impact extends beyond the
50 mile county buffer as defined in the model. It would be interesting in future work to include
56
an ethanol production and distance interaction term to estimate the reach of ethanol production
price impacts. This type of analysis was conducted in the McNew and Griffith (2005) study, but
it would be useful to see this analysis updated with more long-term estimates.
Another interesting extension of the current research would be to narrow the scope of the
research to investigate the impacts of ethanol in particular regions. McNew and Griffith were
able to show a wide range of impacts depending on the plant, so it would be useful to see if that
is also true on a long-range scale. It is likely that the impacts of ethanol production on corn basis
are greater in corn deficit areas.
Finally, to truly understand how ethanol production affects basis more work will need to
be done as the industry matures. During the past decade the grain market had to first adapt to the
increased demand for corn by the ethanol industry. Then, the industry had to adjust to the large
upswing, then downswing in corn prices. Now, without changes in EPA blending policies, it is
likely that the growth rate of the ethanol industry has nearly plateaued as the Renewable Fuel
Standard Program caps corn-based ethanol production at 15 billion gallons.
It cannot be denied that the use of corn in ethanol production has drastically altered the
grain market. Today over 30 percent of the nation’s corn goes into ethanol production and
increased demand has been shown to be partially responsible for the price run-up in 2007-08.
However this study finds that at the most local level, between Fall 1999 and Summer 2009, the
mere presence of ethanol production within 50 miles is not likely to induce a large long-term
shift in corn basis.
57
APPENDIX A
The Renewable Fuel Standard Program (RFS2) was updated from the original RFS
program developed by the Energy Independence and Security Act of 2007. It increased the
previous mandates to specify production of renewable fuels to equal at least 36 billion gallons by
2022. It breaks the volume requirements into four categories:
1) renewable biofuel – ethanol derived from corn-starch,
2) advanced biofuel – essentially anything but corn-starch ethanol,
3) cellulosic biofuel – fuel produced from cellulose, hemicelluloses, or lignin,
4) biomass-based diesel – diesel from fats and oils, not co-processed with petroleum.
Table A.1: Renewable Fuel Standard Program Mandates – Billion Gallons
Year Renewable
Biofuel
Advanced
Biofuel
Cellulosic
Biofuel
9
2008
10.5
0.6
2009
12
0.95
2010
12.6
1.35
2011
13.2
2
2012
13.8
2.75
2013
14.4
3.75
2014
15
5.5
2015
15
7.25
2016
15
9
2017
15
11
2018
15
13
2019
15
15
2020
15
18
2021
15
21
2022
Source: Renewable Fuels Association
Biomassbased
Diesel
0.1
0.25
0.5
1
1.75
3
4.25
5.5
7
8.5
10.5
13.5
16
58
0.5
0.65
0.8
1
Undifferentiated Total RFS
Advanced
Biofuel
0.1
0.2
0.3
0.5
1.75
2
2.5
3
3.5
4
4.5
4.5
4.5
5
9
11.1
12.95
13.95
15.2
16.55
18.15
20.5
22.25
24
26
28
30
33
36
APPENDIX B
Table B.1: Ethanol Plants Included in the Sample
Firm Name
Abengoa Bioenergy Corp.
Abengoa Bioenergy Corp.
Abengoa Bioenergy Corp.
Absolute Energy, LLC*
ACE Ethanol, LLC
Adkins Energy, LLC*
Advanced Bioenergy, LLC
AGP*
Agri-Energy, LLC*
Al-Corn Clean Fuel*
AltraBiofuels Indiana, LLC
Amaizing Energy, LLC*
Archer Daniels Midland
Archer Daniels Midland
Archer Daniels Midland
Archer Daniels Midland
Archer Daniels Midland
Archer Daniels Midland
Arkalon Energy, LLC
Aventine Renewable Energy, LLC
Aventine Renewable Energy, LLC
Badger State Ethanol, LLC*
Big River Resources Galva, LLC
Big River Resources, LLC*
Big River United Energy
BioFuel Energy - Buffalo Lake Energy, LLC
BioFuel Energy - Pioneer Trail Energy, LLC
Bonanza Energy, LLC
Bridgeport Ethanol
Bushmills Ethanol, Inc.*
Cardinal Ethanol
Cargill, Inc.
Cargill, Inc.
Castle Rock Renewable Fuels, LLC
Center Ethanol Company
Year
Opened
2002
2007
1993
2008
2002
2002
2007
1995
1999
1996
2008
2005
1981
1982
2009
1978
2002
1980
2007
1995
1981
2002
2009
2004
2008
2008
2007
2007
2008
2005
2008
1995
NA
2007
2008
59
County
Sedgwick
Buffalo
York
Mitchell
Chippewa
Stephenson
Fillmore
Adams
Rock
Dodge
Putnam
Crawford
Linn
Clinton
Platte
Macon
Lyon
Peoria
Seward
Hamilton
Tazewell
Green
Henry
Des Moines
Dubuque
Martin
Hall
Finney
Morrill
Kandiyohi
Randolph
Washington
Wapello
Juneau
St. Clair
State Nameplate
Capacity (MGY)
KS
25
NE
88
NE
55
IA
110
WI
41
IL
40
NE
100
NE
52
MN
21
MN
42
IN
92
IA
55
IA
420
IA
237
NE
300
IL
290
MN
40
IL
100
KS
110
NE
50
IL
157
WI
48
IL
100
IA
100
IA
110
MN
115
NE
115
KS
55
NE
54
MN
50
IN
100
NE
85
IA
35
WI
50
IL
54
Central Indiana Ethanol, LLC
Central MN Ethanol Coop*
Chief Ethanol
Chippewa Valley Ethanol Co.*
Corn Plus, LLP*
Corn, LP*
Cornhusker Energy Lexington, LLC
Dakota Ethanol, LLC*
DENCO, LLC
Didion Ethanol
E Energy Adams, LLC
E3 Biofuels
East Kansas Agri-Energy, LLC*
ESE Alcohol Inc.
Gateway Ethanol
Glacial Lakes Energy, LLC - Mina
Glacial Lakes Energy, LLC*
Global Ethanol/Midwest Grain Processors
Golden Grain Energy, LLC*
Grain Processing Corp.
Granite Falls Energy, LLC*
Green Plains Renewable Energy
Green Plains Renewable Energy
Green Plains Renewable Energy
Green Plains Renewable Energy
Green Plains Renewable Energy
Guardian Energy
Hawkeye Renewables, LLC
Hawkeye Renewables, LLC
Hawkeye Renewables, LLC
Hawkeye Renewables, LLC
Heartland Corn Products*
Heartland Grain Fuels, LP
Heartland Grain Fuels, LP
Heron Lake BioEnergy, LLC
Highwater Ethanol LLC
Homeland Energy
Husker Ag, LLC*
Illinois River Energy, LLC
Iroquois Bio-Energy Company, LLC
2007
1999
1985
1996
1994
2005
2005
2001
1999
2008
2007
2007
2005
1991
2007
2008
2001
2002
2004
NA
2005
2008
2004
2009
2007
2008
2009
2006
2003
2008
2008
1995
2008
1998
2007
2009
2009
2003
2006
2007
60
Grant
Morrison
Adams
Swift
Faribault
Wright
Dawson
Lake
Stevens
Columbia
Gage
Saunders
Anderson
Wichita
Pratt
Edmunds
Codington
Kossuth
Cerro Gordo
Muscatine
Yellow Medicine
Wells
Merrick
Valley
Page
Dickinson
Waseca
Bremer
Hardin
Guthrie
Butler
Sibley
Brown
Beadle
Jackson
Redwood
Chickasaw
Pierce
Ogle
Jasper
IN
MN
NE
MN
MN
IA
NE
SD
MN
WI
NE
NE
KS
KS
KS
SD
SD
IA
IA
IA
MN
IN
NE
NE
IA
IA
MN
IA
IA
IA
IA
MN
SD
SD
MN
MN
IA
NE
IL
IN
40
21.5
62
45
44
60
40
50
24
40
50
25
35
1.5
55
107
100
98
115
20
52
110
100
50
55
55
110
110
90
110
110
100
50
32
50
55
100
75
100
40
KAAPA Ethanol, LLC*
Kansas Ethanol, LLC
Lincolnland Agri-Energy, LLC*
Lincolnway Energy, LLC*
Little Sioux Corn Processors, LP*
Louis Dreyfus Commodities
Louis Dreyfus Commodities
Marquis Energy, LLC
Mid America Agri Products/Horizon
Mid America Agri Products/Wheatland
Midwest Renewable Energy, LLC
Minnesota Energy*
NEDAK Ethanol
Nesika Energy, LLC
New Energy Corp.
North Country Ethanol, LLC*
NuGen Energy
One Earth Energy
Otter Tail Ag Enterprises
Patriot Renewable Fuels, LLC
Penford Products
Pine Lake Corn Processors, LLC
Platinum Ethanol, LLC*
Plymouth Ethanol, LLC*
POET Biorefining - Alexandria
POET Biorefining - Ashton
POET Biorefining - Big Stone
POET Biorefining - Bingham Lake
POET Biorefining - Chancellor
POET Biorefining - Coon Rapids
POET Biorefining - Corning
POET Biorefining - Emmetsburg
POET Biorefining - Glenville
POET Biorefining - Gowrie
POET Biorefining - Hanlontown
POET Biorefining - Hudson
POET Biorefining - Jewell
POET Biorefining - Lake Crystal
POET Biorefining - Mitchell
POET Biorefining - North Manchester
2003
2008
2004
2006
2003
2009
2007
2008
2007
2007
1999
1997
2008
2008
1984
1994
2008
2009
2008
2008
2008
2005
2008
2009
2008
2004
2002
1997
2003
2002
2006
2005
1999
2006
2004
2004
2006
2005
2006
2008
61
Kearney
Rice
Crawford
Story
Cherokee
Greene
Madison
Putnam
Furnas
Perkins
Lincoln
Renville
Holt
Republic
St. Joesph
Roberts
Turner
Ford
Otter Tail
Henry
Linn
Hardin
Ida
Plymouth
Madison
Osceola
Grant
Cottonwood
Turner
Carroll
Adams
Palo Alto
Freeborn
Webster
Worth
Lincoln
Hamilton
Blue Earth
Davison
Wabash
NE
KS
IL
IA
IA
IA
NE
IL
NE
NE
NE
MN
NE
KS
IN
SD
SD
IL
MN
IL
IA
IA
IA
IA
IN
IA
SD
MN
SD
IA
IA
IA
MN
IA
IA
SD
IA
MN
SD
IN
40
55
48
55
92
100
45
100
44
44
25
18
44
10
102
20
110
100
57.5
100
45
31
110
50
68
56
79
35
110
54
65
55
42
69
56
56
69
56
68
68
POET Biorefining - Portland
POET Biorefining - Preston
POET Biorefining - Scotland
POET Biorefining- Groton
Prairie Horizon Agri-Energy, LLC
Quad-County Corn Processors*
Redfield Energy, LLC *
Reeve Agri-Energy
Renew Energy
Riverland Biofuels
Siouxland Energy & Livestock Coop*
Siouxland Ethanol, LLC
Southwest Iowa Renewable Energy, LLC *
The Andersons Clymers Ethanol, LLC
Trenton Agri Products, LLC
United Ethanol
United WI Grain Producers, LLC*
Utica Energy, LLC
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Valero Renewable Fuels
Western Plains Energy, LLC*
Western Wisconsin Renewable Energy,
LLC*
* Denotes locally owned plant
2007
1999
1988
2003
2006
2002
2006
1982
2007
2007
2003
2007
2009
2007
2004
2007
2005
2003
2006
2007
2003
2007
2005
2008
2007
2008
2004
2006
Source: Renewable Fuels Association
62
Jay
Fillmore
Bon Homme
Brown
Phillips
Ida
Spink
Finney
Jefferson
Fulton
Sioux
Dakota
Pottawattmie
Cass
Hitchcock
Rock
Columbia
Winnebago
Buena Vista
Boone
Brookings
Floyd
Webster
O'Brien
Montgomery
Martin
Logan
Dunn
IN
MN
SD
SD
KS
IA
SD
KS
WI
IL
IA
NE
IA
IN
NE
WI
WI
WI
IA
NE
SD
IA
IA
IA
IN
MN
KS
WI
68
46
11
53
40
30
50
12
130
37
60
50
110
110
40
52
49
48
110
110
120
110
110
110
110
110
45
40
APPENDIX C
Table C.1 contains the list of grain elevators which contributing cash corn prices to sample.
Table C.1: Grain Elevators
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Adams
Bureau
Cass
Champaign
Christian
Clark
Dewitt
Douglas
Grundy
Hancock
Henry
Iroquios
Jasper
Kane
Kankakee
Knox
La Salle
Livingston
Logan
Marshall
Mason
McDonough
McLean
Montgomery
Morgan
Moultrie
Ogle
Peoria
Piatt
Sangamon
Shelby
Tazewell
Whiteside
Woodford
City
Quincy
Ohio
Beardstown
Champaign
Stonington
Casey
Weldon
Arcola
Minooka
Nauvoo
Galva
Watseka
Rushville
Elburn
Manteno
Galesburg
Streator
Strawn
Latham
Lacon
Easton
Bushnell
Towanda
Raymond
Jacksonville
Bethany
Polo
Elmwood
Monticello
Williamsville
Cowden
Minier
Albany
Minonk
63
Company
ADM
Northern Grain Marketing
ADM
The Andersons
Stonington Coop Grain
Huisinga Grain
Weldon Coop
Okaw Coop
Consolidated Grain
Colusa Elevator
Gateway Coop
Watseka Interstate
Western Grain Marketing
Elburn Coop
Farmers Elevator
GrainStore
Missal Farmers Grain
Trainor Grain
Farmers Grain
ADM
Farmers Elevator Biggs and Eas
West Central FS
Towanda Grain
Sorrells Elevator
Pisgah Coop
Bethany Grain
Bocker Grain Inc
Ag Land FS
TopFlight Grain
Culver-Fancy Prairie Coop
Tate and Lyle
Minier Coop
Bunge
Ruff Brothers
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Bartholomew
Carroll
Decatur
Fayette
Jasper
Kosciusko
Madison
Miami
Porter
St. Joesph
Starke
Sullivan
Tippecanoe
Adair
Black Hawk
Buchanan
Buena Vista
Cass
Cedar
Chickasaw
Clayton
Delaware
Emmet
Floyd
Franklin
Grundy
Guthrie
Hamilton
Hancock
Hardin
Harrison
Henry
Humbolt
Iowa
Jasper
Linn
Lyon
Mahaska
Mitchell
Monona
Columbus
Delphi
Greensburg
Glenwood
Remington
Warsaw
Summitville
Amboy
Portage
South Bend
Hamlet
Sullivan
Lafayette
Adair
Dunkerton
Jesup
Alta
Massena
Clarence
New Hampton
Clayton
Ryan
Armstrong
Rockford
Coulter
Beaman
Guthrie Center
Williams
Kanawha
Union
Modale
Mount Union
Ottosen
Conroy
Prairie City
Cedar Rapids
Little Rock
Oskaloosa
Stacyville
Blencoe
64
Premier Ag
The Andersons
Lowes Pellets
Peavey
Co-Alliance
Zolman Farms
Harvest Land Coop
Kokomo Grain
Cargill
New Energy Corp
Starke County Coop
ADM
Tate and Lyle
West Central Coop
Dunkerton Coop
East Central Iowa Coop
First Coop
21st Century Coop
River Valley Coop
Five Star Farmers Coop
Consolidated Grain and Barge
Ryan Coop
Stateline Coop
Farmers Coop
AgVantage FS
Mid-Iowa Coop
Rose Acre Farms Feed Mill
Prairie Land Coop
North Central Coop
Prairie Land Coop
United Western Coop
Prairie Ag Coop
Ottosen Elevator
Heartland Coop
Heartland Coop
ADM Growmark
Farmers Coop Society
Quad County Grain
Northern Country Coop
Western Iowa Coop
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Montgomery
O'Brien
Osceola
Palo Alto
Polk
Scott
Sioux
Tama
Union
Van Buren
Wapello
Webster
Winnebago
Worth
Wright
Barton
Coffey
Dickinson
Franklin
Gray
Kearny
Marion
Marshall
McPhereson
Meade
Norton
Pawnee
Reno
Russell
Sedgwick
Sheridan
Smith
Big Stone
Blue Earth
Brown
Chippewa
Dodge
Faribault
Goodhue
Jackson
Grant
Hartley
Ashton
West Bend
Runnells
Davenport
Alton
Dysart
Creston
Stockport
Eddyville
Gowrie
Thompson
Hanlontown
Goldfield
Great Bend
Le Roy
Chapman
Ottawa
Ingalls
Lakin
Hillsboro
Beattie
Moundridge
Plains
Norton
Larned
Nickerson
Gorham
Andale
Seguin
Athol
Barry
Lake Crystal
Sleepy Eye
Clara City
Dodge Center
Delavan
Dennison
Heron Lake
65
Hoye Feed and Grain
Ag Partners
United Farmers Coop
Max Yield Coop
Runnells Grain
Cenex Harvest States
Midwest Farmers Coop
Tama-Benton Coop
DeBruce Grain
Roquette Stockport Elevator
Cargill
West Central Coop
Farmers Coop
Five Star Coop
Gold Eagle Coop
Great Bend Coop
LeRoy Coop
Alida Pearl Coop
Ottawa Coop
Irsik and Doll
Cropland Coop
Coop Grain
Beattie Coop
Mid Kansas Coop
Plains Equity
Ag Valley Coop
Pawnee County Coop
Farmers Coop
United Ag
Andale Farmers Coop
Frontier Ag
Athol Coop
Beardsley Farmers
Crystal Valley Coop
River Region Coop
Farmers Elevator
Greenway Coop
Watonwan Farm Service
Central Valley Coop
New Vision
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Wisconsin
Wisconsin
Wisconsin
Lac qui Parle
Lyon
McLeod
Mower
Nicollet
Olmsteade
Pipestone
Redwood
Renville
Rock
Swift
Yellow Medicine
Butler
Cass
Clay
Dawson
Fillmore
Frontier
Greeley
Jefferson
Lancaster
Nemaha
Phelps
Pierce
Platte
Red Willow
Thayer
Valley
Brookings
Brown
Grant
Hanson
Hutchinson
Lake
Spink
Union
Columbia
Dane
Pepin
Bellingham
Marshall
Hutchinson
Adams
Lafayette
Stewartville
Jasper
Redwood Falls
Renville
Hills
Holloway
Clarkfield
Bellwood
Greenwood
Ong
Gothenburg
Shickley
Maywood
Spalding
Diller
Firth
Brownville
Funk
Osmond
Humphrey
McCook
Bruning
North Loup
Brookings
Aberdeen
Milbank
Emery
Dimock
Madison
Northville
Elk Point
Cambria
Cottage Grove
Durand
66
Bellingham Farmers
ADM Ethanol
Hutch Coop
Northern Country Coop
United Farmers Coop
All American Coop
Eastern Farmers Coop
Meadowland Farmers Coop
Coop Country Farmers
New Vision
Western Consolidated Coop
Prairie Grain
Frontier Coop
Midwest Farmers Coop
Aurora Coop
Farmland Service Coop
Shickley Grain
Ag Valley Coop
Country Partners Coop
Firth Coop
Firth Coop
Bartlett Grain
Cooperative Producers
Battle Creek Farmers Coop
Central Valley Ag
Frenchman Valley Coop
Bruning Grain
Country Partners Coop
AgFirst Farmers Coop
South Dakota Wheat Growers
Western Consolidated Coop
Cargill
Central Farmers Cooperative
Madison Farmers
North Central Farmers
Southeast Farmers Coop
Landmark Coop
Landmark Coop
Countryside Coop
APPENDIX D
Table D.1 – Summary Statistics by State
Illinois
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
Indiana
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
Iowa
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
Kansas
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
67
Mean
St. Dev
Min
Max
-21.70
11.50
-68
6
295.95
81.88
114
544
166.26
154.62
0
637
2.77
0.66
1.21
4.34
194.46
89.98
33.37
543.99
4.49
1.93
1.38
12.46
Mean
St. Dev
Min
Max
-16.73
11.94
-54
14
208.94
73.52
83
403
66.60
95.40
0
431
1.97
0.66
0.93
3.45
133.72
69.32
26.45
402.80
3.16
1.66
0.99
9.92
Mean
St. Dev
Min
Max
-32.20
12.01
-71
8
289.33
78.38
106
472
184.40
201.09
0
1026
2.72
0.68
1.11
4.32
203.50
81.51
40.93
471.72
4.99
2.53
1.28
13.70
Mean
St. Dev
Min
Max
-20.06
16.12
-78
16
39.69
25.97
7
111
16.33
28.18
0
177
0.37
0.24
0.07
1.17
27.46
20.29
2.51
110.72
0.68
0.58
0.08
4.15
Minnesota
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
Nebraska
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
South Dakota
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
Wisconsin
Basis
Local Corn Production (million bushels)
Ethanol (MGY)
Production Ratio
Local Stocks (million bushels)
Stocks Ratio
68
Mean
St. Dev
Min
Max
-38.96
12.42
-86
1
253.04
62.13
128
397
169.66
98.55
21
621
2.39
0.56
1.34
3.72
176.16
71.87
45.06
396.52
4.16
1.76
1.55
10.84
Mean
St. Dev
Min
Max
-28.42
11.06
-58
13
168.69
58.18
34
331
96.48
121.19
0
574
1.58
0.51
0.38
2.60
115.53
53.31
14.63
330.58
2.86
1.64
0.44
8.58
Mean
St. Dev
Min
Max
-39.32
12.13
-69
-5
131.11
50.80
51
275
146.22
115.65
0
436
1.23
0.44
0.49
2.27
90.09
45.54
17.74
274.53
2.18
1.23
0.57
7.74
Mean
St. Dev
Min
Max
-48.73
15.94
-92
-26
120.10
10.55
100
137
42.07
30.68
0
81
1.14
0.13
0.97
1.40
88.80
24.93
46.33
136.89
2.31
1.38
1.09
7.63
Table D.2 – Summary Statistics by Year
Fall 1999 - Summer 2000
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2000 - Summer 2001
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2001 - Summer 2002
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
69
Mean
St. Dev Min
Max
-34.34
11.35
-64
-8
200.16
88.40
8
349
68.83
100.08
0
402
139.31
8.53
125.57
148.87
9.43
0.00
9.43
9.43
2.12
0.94
0.09
3.71
142.47
76.34
4.01
349.48
4.74
2.35
1.72
8.04
3.58
2.17
0.10
11.00
Mean
St. Dev Min
Max
-29.86
11.53
-63
3
202.19
89.36
10
354
69.29
100.07
0
402
148.28
4.98
143.80
156.53
9.92
0.00
9.92
9.92
2.04
0.90
0.10
3.57
144.70
76.79
4.40
354.34
5.10
2.46
1.90
8.52
3.32
1.92
0.12
9.84
Mean
St. Dev Min
Max
-22.41
10.88
-51
10
196.34
86.39
9
361
72.89
100.01
0
402
125.83
6.54
115.40
133.33
9.50
0.00
9.50
9.50
2.07
0.91
0.10
3.80
136.40
74.06
4.83
361.23
4.81
2.49
1.60
8.26
3.41
1.99
0.12
9.92
Fall 2002 - Summer 2003
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2003 - Summer 2004
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2004 - Summer 2005
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
70
Mean
St. Dev Min
Max
-13.05
11.26
-40
15
202.36
100.46
8
387
84.67
100.77
0
402
147.89
6.49
143.73
159.10
8.97
0.00
8.97
8.97
2.26
1.12
0.09
4.32
130.81
84.98
4.21
387.10
4.21
2.44
1.09
7.63
3.88
2.59
0.11
13.49
Mean
St. Dev Min
Max
-17.09
9.52
-36
15
217.25
103.64
7
411
102.20
105.33
0
402
161.99
12.14
147.07
179.43
10.09
0.00
10.09
10.09
2.15
1.03
0.07
4.08
137.63
89.13
2.51
411.41
4.29
2.61
0.96
7.95
4.11
2.64
0.09
12.88
Mean
St. Dev Min
Max
-29.99
12.50
-66
2
252.13
113.54
9
459
120.20
115.09
0
411
220.27
19.29
202.20
251.30
11.81
0.00
11.81
11.81
2.14
0.96
0.08
3.89
180.39
96.15
4.79
459.26
5.66
2.74
2.11
9.45
3.76
2.25
0.09
11.16
Fall 2005 - Summer 2006
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2006 - Summer 2007
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
Fall 2007 - Summer 2008
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
71
Mean
St. Dev Min
Max
-37.28
13.03
-73
0
238.14
109.01
10
436
145.72
137.24
0
652
270.84
16.63
245.23
289.97
11.11
0.00
11.11
11.11
2.14
0.98
0.09
3.93
167.06
93.93
5.26
436.19
5.78
2.93
1.97
9.81
3.44
2.09
0.10
10.51
Mean
St. Dev Min
Max
-29.11
13.35
-70
16
231.79
111.89
7
436
176.93
157.77
0
706
267.88
16.71
250.73
289.70
10.53
0.00
10.53
10.53
2.20
1.06
0.07
4.14
153.25
95.59
3.79
435.86
4.96
2.85
1.30
8.93
3.88
2.60
0.08
13.70
Mean
St. Dev Min
Max
-39.87
13.09
-92
-6
274.42
129.68
11
544
232.06
179.01
0
816
383.93
47.99
324.60
434.20
13.04
0.00
13.04
13.04
2.10
0.99
0.09
4.17
183.88
109.41
4.56
543.99
5.70
3.23
1.62
10.28
4.08
2.78
0.11
13.70
Fall 2008 - Summer 2009
Basis (cents)
Local Corn Production (million bushels)
Ethanol (MGY)
Midwest Diesel Price
National Corn Production (billion bushels)
Production Ratio
Local Stocks (million bushels)
National Stocks (billion bushels)
Stocks Ratio
72
Mean
St. Dev Min
Max
-28.40
13.70
-69
11
257.97
118.55
12
524
290.36
223.50
0
1026
248.42
30.22
214.97
293.40
12.09
0.00
12.09
12.09
2.13
0.98
0.10
4.34
173.69
102.20
5.62
524.21
5.75
3.13
1.67
10.08
3.67
2.20
0.12
11.48
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