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

ii

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

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

Figure 1.1: Historical Ethanol Production

1980

1982

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2000

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0

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Historical Ethanol ProductionM

illio

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f Gal

lons

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

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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.

6

Figure 2.1: U.S. Corn Production

1990

1991

1992

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1995

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U.S. Corn ProductionM

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hels

Source: USDA National Agricultural Statistics Service

Figure 2.2: Percentage of U.S. Corn Production Used for Ethanol and Exports

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Percentage of U.S. Corn Production Used for Ethanol and Exports

EthanolExports

Source: USDA Economic Research Service

7

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 co-

products 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 bio-

materials.

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-to-

ethanol 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

Source: Center for Agricultural and Rural Development, Iowa State University

19

Figure 3.1: Corn Basis Map

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

20

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

-60

-50

-40

-30

-20

-10

0

Corn Basis

IllinoisIowaIndianaKansasMinnesotaNebraskaSouth DakotaWisconsinFull SampleCen

ts p

er b

ushe

l

Figure 3.2: Average Annual Corn Basis

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

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) + ∑i=1

7

β i 6 * (State) + ∑i=1

3

β i 7 * (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,

23

potential spatial dependency must be accounted for in the model. The following sections

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 w ijs =wij /∑

jw ij. This row standardization allows

25

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)

with

εt = λWεt + vt. (3.7)

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 I⊗ N)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 I⊗ N) + σ2

v (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 φI⊗ N + (B’B)-1) + ET (B’B)⊗ -1] = σ2

v Σu, (3.12)

Where φ= σ2μ/ σ2

v, KT = JT/T, ET = IT – KT, and Σu = [KT (T φI⊗ N + (B’B)-1) + ET (B’B)⊗ -1] thus:

Σu-1 = KT (T φI⊗ N + (B’B)-1)-1 + ET (B’B)⊗ (3.13)

and:

27

|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=−NT2

ln 2 π σ v2−1

2ln [|T ϕ I N+( B' B )−1

∨¿+ T−12

ln|B' B|− 12σ v

2 u' Σu−1u

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

29

Figure 3.4: Counties with Ethanol Plants

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 Std. Dev. Min Max

Basis (cents) -28.14 14.52 -92 16Local Corn Production (million bushels) 227.28 109.11 7 544Ethanol (MGY) 136.32 155.49 0 1026Midwest Diesel Price (cents) 211.46 80.86 115.40 434.20National Corn Production (billion bushels) 10.65 1.26 8.97 13.04Production Ratio 2.14 0.99 0.07 4.34Local Stocks (million bushels) 155.03 92.38 2.51 543.99National Stocks (billion bushels) 5.10 2.79 0.96 10.28Stocks Ratio 3.71 2.35 0.08 13.70Interest Rate 6.12 1.95 3.25 9.50

CHAPTER 4

31

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). The

32

null hypothesis is no spatial modeling and the tests are chi-square distributed with one degree of

freedom. The tests are:

LM error=

( e' Weσ̂ 2 )¿2

T(4.1)

LM lag=( e' Wy

σ̂ 2 )¿2

nJ (4.2)

where

T=Tr [ (W '+W ) W ] (4.3)

J=1

n∗σ̂2 [ (WX β̂ )' M (WX β̂ )+T σ̂ 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

33

tests were conducted from the final time observation in the data set, but were found to also hold

in other time periods.)

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 = σ μ2= 0,

the test statistic:

LM j=NT

2 (T−1 )G2+ N2 T

bH 2 (4.6)

is used, where using the OLS residuals, u, G=u' (J T⊗ I N ) 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 σ μ2 = 0) is

LM 2=√ N2 Tb

H (4.7)

34

which returns a value of 93.63. The statistic is asymptotically distributed χ12 thus this null

hypothesis is also rejected.

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

Model A Model B Model C Model D Model E Model FIntercept 20.32 ** 4.18 5.99 0.93 20.78 ** 21.64 **Prod Ratio -1.29 * -1.11 -1.18 * -1.22 * -1.29 *Stocks Ratio -2.20 *Diesel -0.10 ** -0.11 ** -0.12 ** -0.11 ** -0.09 **Ethanol 0.0085 ** 0.0084 ** 0.0083 ** 0.0083 ** 0.0084 ** 0.0085 **Interest -2.16 ** -2.19 ** -2.40 ** -2.23 ** -2.27 **Futures -0.01Illinois -6.46 ** 6.20 ** -6.88 ** -6.60 ** -6.65 ** -6.46 **Iowa -13.12 ** 12.89 ** -11.03 ** -13.02 ** -12.45 ** -13.12 **Kansas -7.48 * 3.98 -6.88 -7.39 * -7.36 * -7.49 *Minnesota -19.72 ** -6.42 ** -17.21 ** -19.55 ** -18.90 ** -19.73 **Nebraska -8.36 * 4.53 -6.20 -8.16 ** -7.60 * -8.37 **South Dakota -22.10 ** -8.83 ** -19.55 ** -21.85 ** -21.27 ** -22.11 **Wisconsin -24.35 ** -11.00 ** -22.98 ** -24.24 ** -24.04 ** -24.35 **Fall -2.39 2.39 3.13 -3.24 -2.68Spring 2.25 5.52 6.37 -0.11 2.18Summer -1.38 2.97 2.17 -4.61 * -1.74σ v

2 11.66 ** 11.65 ** 11.64 ** 11.84 ** 11.65 ** 11.66 **

σ μ2 17.91 ** 17.83 ** 16.76 ** 17.71 ** 17.43 ** 17.92 **

λ 0.95 ** 0.95 ** 0.96 ** 0.95 ** 0.95 ** 0.98 **Likelihood -18447.8 -18464.7 -18682.2 -18511.2 -18533.4 -18446.5Parameters 15 15 14 14 12 16AIC 36926 36959 37392 37050 37091 36925BIC 36898 36932 37367 37025 37069 36896

*Notes significance at the five percent level**Notes significance at the one percent level

36

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 T-valuesIntercept 20.3183 ** 4.6724Production Ratio -1.2884 * -2.30243Diesel Price -0.1049 ** -9.80638Ethanol Production 0.0085 ** 7.65322Interest Rate -2.1638 ** -4.85065Illinois -6.4601 ** -2.86512Iowa -13.1158 ** -5.06022Kansas -7.4836 * -2.24027Minnesota -19.7159 ** -7.11868Nebraska -8.3617 * -2.53333South Dakota -22.0950 ** -6.9716Wisconsin -24.3472 ** -5.32627Fall -2.3943 -1.05198Spring 2.2472 1.05091Summer -1.3769 -0.93145σ v

2 11.6639 ** 54.58875σ μ

2 17.9099 ** 7.43203λ 0.9489 ** 22.1769*Notes significance at the five percent level**Notes significance at the one percent level

38

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 σ2

μ 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 W-

matrix 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

50-Miles Contiguous

N=5 N=7 N=10

Intercept 20.32

** 11.15

** 20.28

** 13.74 * 24.83

Prod Ratio -1.29 * -2.20 ** -3.18 ** -3.30 ** -2.96 **Diesel -0.10 ** -0.08 ** -0.10 ** -0.08 ** -0.07Ethanol 0.00

85** 0.01

10** 0.01

35** 0.011

3** 0.012

0**

Interest -2.16 ** -1.86 ** -2.69 ** -3.17 ** -5.90 **Illinois -6.46 ** -3.75 -4.48 * -

0.0004

-4.07 *

Iowa -13.1

2

** -11.0

2

** -9.44 ** -4.70 ** -2.51

Kansas -7.48 * -5.87 -6.56 ** -0.02 ** 3.02Minnesota -

19.72

** -17.8

1

** -17.0

0

** -12.17 ** -8.59 **

Nebraska -8.36 * -10.2

7

** -10.3

7

** -4.15 * -1.61

South Dakota -22.1

0

** -22.0

7

** -21.6

5

** -16.76 ** -12.61

**

Wisconsin -24.3

5

** -18.4

2

** -19.2

7

** -15.99 ** -15.86

**

Fall -2.39 1.58 2.01 3.07 0.03Spring 2.25 2.12 6.16 ** 6.85 * 10.45Summer -1.38 -0.52 3.95 5.59 16.51

41

σ v2 11.6

6** 13.5

1** 12.8

8** 12.82 ** 13.36 **

σ μ2 17.9

1** 26.4

3** 26.1

1** 27.36 ** 25.92 **

λ 0.95 ** 0.97 ** 0.96 ** 0.96 ** 0.99 **Likelihood Value

-1844

7

-1790

2

-1780

8

-17579

-1762

7*Notes significance at the five percent level**Notes significance at the one percent levelT-values have been omitted from this table

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

42

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

Table 4.4 Compare Monthly and Quarterly Data

Illinois Iowa KansasModel Q Model M Model Q Model M Model Q Model M

Intercept

7.3887(1.02)

5.6286(1.31)

16.7189

(1.10)

6.4620(2.14)

* -8.6216(-1.53)

-13.584(-2.46)

**

Prod. Ratio

2.5985(0.99)

2.5905(1.17)

-11.121(-4.22)

** -9.3507(-4.40)

** -8.7416(-4.98)

** -9.8474(-8.19)

**

Diesel -0.118(-6.53)

** -0.1097

(-11.81)

**

-0.1511(-3.34)

** -0.0864

(-14.37)

** -0.1034

(-11.21)

** -0.1018(-16.47)

**

Ethanol Prod.

0.0053(1.46)

0.0059(2.07)

* 0.0068(4.53)

** 0.0078(7.55)

** 0.0290(3.37)

** 0.0269(4.68)

**

Interest Rate

-1.6445(-2.21)

* -1.5913(-4.07)

* -2.3777(-1.27)

-2.4114(-9.70)

** -1.0418(-2.87)

** -0.9656(-3.94)

**

Winter 3.1845(0.78)

X 9.5181(0.92)

X 1.5210(0.79)

X

Spring 5.3328(1.30)

X 16.7766

(1.64)

X 3.7244(1.89)

X

Summer

1.7289(0.40)

X 5.2033(0.53)

X 2.1851(1.10)

X

January X 0.0198(0.36)

x 1.7902(1.00)

X 4.0990(2.20)

*

February

X 5.3393(1.86)

x 6.1760(3.57)

X 6.8035(3.61)

**

March X 1.5001 x 2.4707 X 1.8101

43

(0.61) (1.43) (1.04)April X 6.5565

(2.27)* x 6.8406

(3.68)X 6.4703

(3.39)**

May X 2.5564(0.88)

x 2.0747(1.07)

X 3.4880(1.83)

June X 4.7021(1.55)

x 5.2190(2.95)

X 9.0699(4.79)

**

July X 3.2697(1.06)

x 0.0046(0.31)

X 5.1208(2.71)

**

August X 6.3509(2.18)

* x 4.2921(2.30)

X 8.2566(4.37)

**

September

X -6.3120(-2.07)

* x -5.3528(-2.84)

X 1.0314(0.66)

October X -4.1511(-1.42)

x -3.9794(-2.00)

X 1.8171(1.04)

November

X 4.9603(1.62)

x 4.2610(2.32)

X 6.0732(3.22)

σ v2 11.81

(25.74)** 19.19

(44.97)**

11.94(28.61

)

** 18.07(49.96

)

** 19.89(18.22

)

** 27.35(31.83)

**

σ μ2 18.56

(3.85)** 19.92

(3.87)**

16.39(3.99)

** 23.61(4.12)

** 32.49(2.13)

* 37.55(2.26)

*

λ 0.94(10.91)

** 0.94(9.10)

**

0.97(8.94)

** 0.97(14.14

)

** 0.98(11.17

)

** 0.98(11.82)

**

*Notes significance at the five percent level**Notes significance at the one percent levelT-values reported in parentheses

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

44

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

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

45

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.

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.

46

- 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.)

Table 4.5: Compare Full Sample and Sub-Sample Estimates

Fall 1999 - Summer 2003

(Time 1)

Fall 2003 - Summer 2009

(Time 2)

Fall 1999 - Summer 2009

(Full Time)

Sub-sample (N=22)

Intercept -38.1568(-6.10)

** 23.34869(2.80)

** -13.6663(-2.64)

**

Production Ratio

0.21435(0.20)

-13.4581(-5.34)

** -3.97666(-2.59)

**

Diesel -0.01474(-0.38)

-0.10721(-17.33)

** -0.06996(-10.31)

**

Ethanol 0.13347(11.51)

** 0.02065(6.98)

** 0.00751(2.47)

*

47

Illinois 15.36167(4.32)

** 11.90544(2.73)

** 11.66646(2.38)

*

South Dakota -5.22206(-1.75)

-22.607(-4.39)

** -7.41843(-2.10)

*

Wisconsin 9.37036(2.00)

* -14.7958(-1.98)

* 1.43494(0.28)

Winter 4.08061(3.36)

** 2.64308(2.42)

* 2.22307(1.58)

Spring 4.42004(3.62)

** 4.31058(3.95)

** 4.46042(3.16)

**

Summer -0.99808(-0.81)

1.96208(1.78)

3.37015*(2.39)

*

σ v2 17.24287

(12.85)** 42.16872

(15.77)** 23.62253

(20.89)**

σ μ2 16.57584

(3.00)** 29.13658

(2.18)* 11.30683

(2.90)**

λ 0.56(2.30)

* 0.30(1.25)

0.86(5.48)

**

Table 4.5 Continued

Fall 1999 - Summer 2003

(Time 1)

Fall 2003 - Summer 2009

(Time 2)

Fall 1999 - Summer 2009

(Full Time)

Full Sample

(N =153)

Intercept -15.8176(-2.02)

* 13.93429(4.60)

** 5.994(1.08)

Production Ratio

-1.59076(-2.74)

** -2.50443(-3.55)

** -1.10714(-1.92)

Diesel 0.02141(0.43)

-0.12038(-17.19)

** -0.12284(-7.95)

**

Ethanol 0.00779(2.38)

* 0.01126(7.68)

** 0.00833(7.43)

**

48

Illinois -5.61763(-2.56)

* -5.07186(-2.24)

* -6.87933(-2.77)

**

Iowa -14.2573(-5.94)

** -14.4399(-6.40)

** -11.0317(-3.49)

**

Kansas -7.04125(-2.31)

* -8.46234(-3.01)

** -6.87786(-1.42)

Minnesota -21.7964(-8.49)

** -20.6492(-8.59)

** -17.2101(-5.07)

**

Nebraska -8.54345(-2.87)

** -12.5121(-4.70)

** -6.19979(-1.29)

South Dakota -25.5823(-8.50)

** -23.0244(-7.93)

** -19.5501(-5.20)

**

Wisconsin -21.5224(-4.94)

** -25.4419(-5.96)

** -22.9762(-4.62)

**

Fall 3.626(2.17)

* 1.41794(0.92)

3.12733(0.95)

Spring 5.30165(5.30)

** 4.25605(2.95)

** 6.36547(1.78)

Summer 0.00749(0.25)

2.51643(1.72)

2.16525(0.85)

σ v2 5.4353

(33.80)** 14.22327

(41.80)** 11.64203

(54.61)**

σ μ2 18.24325

(7.45)** 24.77653

(7.33)** 16.75687

(7.51)**

λ 0.93(21.05)

** 0.87(12.02)

** 0.96(29.23)

**

* Notes significance at the five percent level**Notes significance at the one percent levelT-values reported in parentheses

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

BasisTime 1 Time 2 Full

Time

49

Sub-Sample 6.67 1.03 0.38Full 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

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

50

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 long-

term 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)

Table 4.7 Compare Full Sample and Sub-Sample Estimates with Interest

Fall 1999 - Summer 2003

Fall 2003 - Summer 2009

Fall 1999 - Summer

2009 (Full)

Sub-sample (N=22)

Intercept -37.01(-9.56)

** 20.39(2.70)

** -1.45(-0.37)

Production Ratio

-1.77(-1.86)

-11.39(-4.85)

** -2.95(-2.56)

**

Diesel 0.21 ** -0.094 ** -0.061 **

51

(8.11) (-14.30) (-10.99)Ethanol 0.0388

(4.16)** 0.0182

(6.10)** 0.0064

(2.40)**

Interest -3.48(-21.04)

** -0.99(-4.30)

** -2.64(-13.88)

**

Illinois 15.26(5.27)

** 11.31(2.96)

** 11.33(3.97)

**

South Dakota -7.43(-2.91)

** -19.44(-4.14)

** -7.11(-2.69)

**

Wisconsin 5.56(1.33)

-11.33(-1.68)

3.00(0.76)

Winter 2.95(4.27)

** 2.88(2.87)

** 2.72(2.75)

**

Spring 2.82(4.05)

** 3.91(3.88)

** 3.53(3.55)

**

Summer -3.67(-5.19)

** 1.34(1.32)

0.68(0.67)

σ v2 9.02

(12.85)** 58.54

(15.58)** 24.53

(20.73)**

σ μ2 13.28

(3.19)** 22.26

(2.15)* 10.01

(3.08)**

λ 0.38(1.31)

** 0.06(0.24)

0.61(2.42)

**

Table 4.7 Continued

Fall 1999 - Summer 2003

Fall 2003 - Summer 2009

Fall 1999 - Summer

2009 (Full)Full Sample

(N =153)Intercept -16.15

(-2.58)* 14.96

(4.71)** 20.32

(4.67)**

Production Ratio

-1.88(-3.26)

** -2.55(-3.59)

** -1.29(-2.30)

*

52

Diesel 0.240(5.43)

** -0.118(-16.45)

** -0.105(-9.80)

**

Ethanol 0.0075(2.29)

* 0.0113(7.63)

** 0.0085(7.65)

**

Interest -4.19(-17.53)

** -0.26(-1.01)

-2.16(-4.85)

**

Illinois -4.74(-2.37)

* -5.00(-2.19)

* -6.46(-2.87)

**

Iowa -14.67(-7.10)

** -14.46(-6.30)

** -13.12(-5.06)

**

Kansas -6.96(-2.64)

** -8.46(-2.96)

** -7.48(-2.24)

*

Minnesota -23.06(-10.27)

** -20.67(-8.43)

** -19.72(-7.12)

**

Nebraska -9.83(-3.93)

** -12.62(-4.67)

** -8.36(-2.53)

*

South Dakota -27.33(-10.10)

** -23.05(-7.77)

** -22.10(-6.97)

**

Wisconsin -21.34(-5.38)

** -25.35(-5.90)

** -24.35(-5.33)

**

Fall 1.95(1.36)

1.39(1.05)

-2.39(-1.05)

Spring 2.52(1.83)

4.11(3.04)

** 2.25(1.05)

Summer -3.30(-2.40)

* 2.41(1.80)

-1.38(-0.93)

σ v2 5.11

(33.75)** 14.27

(41.80)** 11.66

(54.59)**

σ μ2 20.45

(7.60)** 25.09

(7.34)** 17.91

(7.43)**

λ 0.90(7.60)

** 0.86(11.80)

** 0.95(22.18)

**

* Notes significance at the five percent level**Notes significance at the one percent levelT-values reported in parentheses

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

53

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)Time 1 Time 2 Full

Time

Sub-Sample 1.94 0.91 0.32Full Sample 0.38 0.56 0.43

54

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

55

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.

56

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

57

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.

58

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

Biomass-based Diesel

Undifferentiated Advanced Biofuel

Total RFS

2008 9 92009 10.5 0.6 0.5 0.1 11.12010 12 0.95 0.1 0.65 0.2 12.952011 12.6 1.35 0.25 0.8 0.3 13.952012 13.2 2 0.5 1 0.5 15.22013 13.8 2.75 1 1.75 16.552014 14.4 3.75 1.75 2 18.152015 15 5.5 3 2.5 20.52016 15 7.25 4.25 3 22.252017 15 9 5.5 3.5 242018 15 11 7 4 262019 15 13 8.5 4.5 282020 15 15 10.5 4.5 302021 15 18 13.5 4.5 332022 15 21 16 5 36Source: Renewable Fuels Association

APPENDIX B

59

Table B.1: Ethanol Plants Included in the Sample

Firm NameYear Opened

County State

Nameplate Capacity (MGY)

Abengoa Bioenergy Corp. 2002 Sedgwick KS 25Abengoa Bioenergy Corp. 2007 Buffalo NE 88Abengoa Bioenergy Corp. 1993 York NE 55Absolute Energy, LLC* 2008 Mitchell IA 110ACE Ethanol, LLC 2002 Chippewa WI 41Adkins Energy, LLC* 2002 Stephenson IL 40Advanced Bioenergy, LLC 2007 Fillmore NE 100AGP* 1995 Adams NE 52Agri-Energy, LLC* 1999 Rock MN 21Al-Corn Clean Fuel* 1996 Dodge MN 42AltraBiofuels Indiana, LLC 2008 Putnam IN 92Amaizing Energy, LLC* 2005 Crawford IA 55Archer Daniels Midland 1981 Linn IA 420Archer Daniels Midland 1982 Clinton IA 237Archer Daniels Midland 2009 Platte NE 300Archer Daniels Midland 1978 Macon IL 290Archer Daniels Midland 2002 Lyon MN 40Archer Daniels Midland 1980 Peoria IL 100Arkalon Energy, LLC 2007 Seward KS 110Aventine Renewable Energy, LLC 1995 Hamilton NE 50Aventine Renewable Energy, LLC 1981 Tazewell IL 157Badger State Ethanol, LLC* 2002 Green WI 48Big River Resources Galva, LLC 2009 Henry IL 100Big River Resources, LLC* 2004 Des Moines IA 100Big River United Energy 2008 Dubuque IA 110BioFuel Energy - Buffalo Lake Energy, LLC 2008 Martin MN 115BioFuel Energy - Pioneer Trail Energy, LLC 2007 Hall NE 115Bonanza Energy, LLC 2007 Finney KS 55Bridgeport Ethanol 2008 Morrill NE 54Bushmills Ethanol, Inc.* 2005 Kandiyohi MN 50Cardinal Ethanol 2008 Randolph IN 100Cargill, Inc. 1995 Washington NE 85Cargill, Inc. NA Wapello IA 35Castle Rock Renewable Fuels, LLC 2007 Juneau WI 50Center Ethanol Company 2008 St. Clair IL 54Central Indiana Ethanol, LLC 2007 Grant IN 40Central MN Ethanol Coop* 1999 Morrison MN 21.5Chief Ethanol 1985 Adams NE 62

60

Chippewa Valley Ethanol Co.* 1996 Swift MN 45Corn Plus, LLP* 1994 Faribault MN 44Corn, LP* 2005 Wright IA 60Cornhusker Energy Lexington, LLC 2005 Dawson NE 40Dakota Ethanol, LLC* 2001 Lake SD 50DENCO, LLC 1999 Stevens MN 24Didion Ethanol 2008 Columbia WI 40E Energy Adams, LLC 2007 Gage NE 50E3 Biofuels 2007 Saunders NE 25East Kansas Agri-Energy, LLC* 2005 Anderson KS 35ESE Alcohol Inc. 1991 Wichita KS 1.5Gateway Ethanol 2007 Pratt KS 55Glacial Lakes Energy, LLC - Mina 2008 Edmunds SD 107Glacial Lakes Energy, LLC* 2001 Codington SD 100Global Ethanol/Midwest Grain Processors 2002 Kossuth IA 98Golden Grain Energy, LLC* 2004 Cerro Gordo IA 115Grain Processing Corp. NA Muscatine IA 20Granite Falls Energy, LLC* 2005 Yellow Medicine MN 52Green Plains Renewable Energy 2008 Wells IN 110Green Plains Renewable Energy 2004 Merrick NE 100Green Plains Renewable Energy 2009 Valley NE 50Green Plains Renewable Energy 2007 Page IA 55Green Plains Renewable Energy 2008 Dickinson IA 55Guardian Energy 2009 Waseca MN 110Hawkeye Renewables, LLC 2006 Bremer IA 110Hawkeye Renewables, LLC 2003 Hardin IA 90Hawkeye Renewables, LLC 2008 Guthrie IA 110Hawkeye Renewables, LLC 2008 Butler IA 110Heartland Corn Products* 1995 Sibley MN 100Heartland Grain Fuels, LP 2008 Brown SD 50Heartland Grain Fuels, LP 1998 Beadle SD 32Heron Lake BioEnergy, LLC 2007 Jackson MN 50Highwater Ethanol LLC 2009 Redwood MN 55Homeland Energy 2009 Chickasaw IA 100Husker Ag, LLC* 2003 Pierce NE 75Illinois River Energy, LLC 2006 Ogle IL 100Iroquois Bio-Energy Company, LLC 2007 Jasper IN 40KAAPA Ethanol, LLC* 2003 Kearney NE 40Kansas Ethanol, LLC 2008 Rice KS 55Lincolnland Agri-Energy, LLC* 2004 Crawford IL 48Lincolnway Energy, LLC* 2006 Story IA 55Little Sioux Corn Processors, LP* 2003 Cherokee IA 92Louis Dreyfus Commodities 2009 Greene IA 100

61

Louis Dreyfus Commodities 2007 Madison NE 45Marquis Energy, LLC 2008 Putnam IL 100Mid America Agri Products/Horizon 2007 Furnas NE 44Mid America Agri Products/Wheatland

2007 Perkins NE 44

Midwest Renewable Energy, LLC 1999 Lincoln NE 25Minnesota Energy* 1997 Renville MN 18NEDAK Ethanol 2008 Holt NE 44Nesika Energy, LLC 2008 Republic KS 10New Energy Corp. 1984 St. Joesph IN 102North Country Ethanol, LLC* 1994 Roberts SD 20NuGen Energy 2008 Turner SD 110One Earth Energy 2009 Ford IL 100Otter Tail Ag Enterprises 2008 Otter Tail MN 57.5Patriot Renewable Fuels, LLC 2008 Henry IL 100Penford Products 2008 Linn IA 45Pine Lake Corn Processors, LLC 2005 Hardin IA 31Platinum Ethanol, LLC* 2008 Ida IA 110Plymouth Ethanol, LLC* 2009 Plymouth IA 50POET Biorefining - Alexandria 2008 Madison IN 68POET Biorefining - Ashton 2004 Osceola IA 56POET Biorefining - Big Stone 2002 Grant SD 79POET Biorefining - Bingham Lake 1997 Cottonwood MN 35POET Biorefining - Chancellor 2003 Turner SD 110POET Biorefining - Coon Rapids 2002 Carroll IA 54POET Biorefining - Corning 2006 Adams IA 65POET Biorefining - Emmetsburg 2005 Palo Alto IA 55POET Biorefining - Glenville 1999 Freeborn MN 42POET Biorefining - Gowrie 2006 Webster IA 69POET Biorefining - Hanlontown 2004 Worth IA 56POET Biorefining - Hudson 2004 Lincoln SD 56POET Biorefining - Jewell 2006 Hamilton IA 69POET Biorefining - Lake Crystal 2005 Blue Earth MN 56POET Biorefining - Mitchell 2006 Davison SD 68POET Biorefining - North Manchester 2008 Wabash IN 68POET Biorefining - Portland 2007 Jay IN 68POET Biorefining - Preston 1999 Fillmore MN 46POET Biorefining - Scotland 1988 Bon Homme SD 11POET Biorefining- Groton 2003 Brown SD 53Prairie Horizon Agri-Energy, LLC 2006 Phillips KS 40Quad-County Corn Processors* 2002 Ida IA 30Redfield Energy, LLC * 2006 Spink SD 50Reeve Agri-Energy 1982 Finney KS 12

62

Renew Energy 2007 Jefferson WI 130Riverland Biofuels 2007 Fulton IL 37Siouxland Energy & Livestock Coop* 2003 Sioux IA 60Siouxland Ethanol, LLC 2007 Dakota NE 50Southwest Iowa Renewable Energy, LLC *

2009 Pottawattmie IA 110

The Andersons Clymers Ethanol, LLC 2007 Cass IN 110Trenton Agri Products, LLC 2004 Hitchcock NE 40United Ethanol 2007 Rock WI 52United WI Grain Producers, LLC* 2005 Columbia WI 49Utica Energy, LLC 2003 Winnebago WI 48Valero Renewable Fuels 2006 Buena Vista IA 110Valero Renewable Fuels 2007 Boone NE 110Valero Renewable Fuels 2003 Brookings SD 120Valero Renewable Fuels 2007 Floyd IA 110Valero Renewable Fuels 2005 Webster IA 110Valero Renewable Fuels 2008 O'Brien IA 110Valero Renewable Fuels 2007 Montgomery IN 110Valero Renewable Fuels 2008 Martin MN 110Western Plains Energy, LLC* 2004 Logan KS 45Western Wisconsin Renewable Energy, LLC*

2006 Dunn WI 40

* Denotes locally owned plant

Source: Renewable Fuels Association

APPENDIX C

Table C.1 contains the list of grain elevators which contributing cash corn prices to sample.

Table C.1: Grain Elevators

State County City CompanyIllinois Adams Quincy ADMIllinois Bureau Ohio Northern Grain MarketingIllinois Cass Beardstown ADM

63

Illinois Champaign Champaign The AndersonsIllinois Christian Stonington Stonington Coop GrainIllinois Clark Casey Huisinga GrainIllinois Dewitt Weldon Weldon CoopIllinois Douglas Arcola Okaw CoopIllinois Grundy Minooka Consolidated GrainIllinois Hancock Nauvoo Colusa ElevatorIllinois Henry Galva Gateway CoopIllinois Iroquios Watseka Watseka InterstateIllinois Jasper Rushville Western Grain MarketingIllinois Kane Elburn Elburn CoopIllinois Kankakee Manteno Farmers ElevatorIllinois Knox Galesburg GrainStoreIllinois La Salle Streator Missal Farmers GrainIllinois Livingston Strawn Trainor GrainIllinois Logan Latham Farmers GrainIllinois Marshall Lacon ADMIllinois Mason Easton Farmers Elevator Biggs and EasIllinois McDonough Bushnell West Central FSIllinois McLean Towanda Towanda GrainIllinois Montgomery Raymond Sorrells ElevatorIllinois Morgan Jacksonville Pisgah CoopIllinois Moultrie Bethany Bethany GrainIllinois Ogle Polo Bocker Grain IncIllinois Peoria Elmwood Ag Land FSIllinois Piatt Monticello TopFlight GrainIllinois Sangamon Williamsville Culver-Fancy Prairie CoopIllinois Shelby Cowden Tate and LyleIllinois Tazewell Minier Minier CoopIllinois Whiteside Albany BungeIllinois Woodford Minonk Ruff BrothersIndiana Bartholomew Columbus Premier AgIndiana Carroll Delphi The AndersonsIndiana Decatur Greensburg Lowes PelletsIndiana Fayette Glenwood PeaveyIndiana Jasper Remington Co-AllianceIndiana Kosciusko Warsaw Zolman FarmsIndiana Madison Summitville Harvest Land CoopIndiana Miami Amboy Kokomo GrainIndiana Porter Portage CargillIndiana St. Joesph South Bend New Energy CorpIndiana Starke Hamlet Starke County CoopIndiana Sullivan Sullivan ADM

64

Indiana Tippecanoe Lafayette Tate and LyleIowa Adair Adair West Central CoopIowa Black Hawk Dunkerton Dunkerton CoopIowa Buchanan Jesup East Central Iowa CoopIowa Buena Vista Alta First CoopIowa Cass Massena 21st Century CoopIowa Cedar Clarence River Valley CoopIowa Chickasaw New Hampton Five Star Farmers CoopIowa Clayton Clayton Consolidated Grain and BargeIowa Delaware Ryan Ryan CoopIowa Emmet Armstrong Stateline CoopIowa Floyd Rockford Farmers CoopIowa Franklin Coulter AgVantage FSIowa Grundy Beaman Mid-Iowa CoopIowa Guthrie Guthrie Center Rose Acre Farms Feed MillIowa Hamilton Williams Prairie Land CoopIowa Hancock Kanawha North Central CoopIowa Hardin Union Prairie Land CoopIowa Harrison Modale United Western CoopIowa Henry Mount Union Prairie Ag CoopIowa Humbolt Ottosen Ottosen ElevatorIowa Iowa Conroy Heartland CoopIowa Jasper Prairie City Heartland CoopIowa Linn Cedar Rapids ADM GrowmarkIowa Lyon Little Rock Farmers Coop SocietyIowa Mahaska Oskaloosa Quad County GrainIowa Mitchell Stacyville Northern Country CoopIowa Monona Blencoe Western Iowa CoopIowa Montgomery Grant Hoye Feed and GrainIowa O'Brien Hartley Ag PartnersIowa Osceola Ashton United Farmers CoopIowa Palo Alto West Bend Max Yield CoopIowa Polk Runnells Runnells GrainIowa Scott Davenport Cenex Harvest StatesIowa Sioux Alton Midwest Farmers CoopIowa Tama Dysart Tama-Benton CoopIowa Union Creston DeBruce GrainIowa Van Buren Stockport Roquette Stockport ElevatorIowa Wapello Eddyville CargillIowa Webster Gowrie West Central CoopIowa Winnebago Thompson Farmers CoopIowa Worth Hanlontown Five Star CoopIowa Wright Goldfield Gold Eagle Coop

65

Kansas Barton Great Bend Great Bend CoopKansas Coffey Le Roy LeRoy CoopKansas Dickinson Chapman Alida Pearl CoopKansas Franklin Ottawa Ottawa CoopKansas Gray Ingalls Irsik and DollKansas Kearny Lakin Cropland CoopKansas Marion Hillsboro Coop GrainKansas Marshall Beattie Beattie CoopKansas McPhereson Moundridge Mid Kansas CoopKansas Meade Plains Plains EquityKansas Norton Norton Ag Valley CoopKansas Pawnee Larned Pawnee County CoopKansas Reno Nickerson Farmers CoopKansas Russell Gorham United AgKansas Sedgwick Andale Andale Farmers CoopKansas Sheridan Seguin Frontier AgKansas Smith Athol Athol CoopMinnesota Big Stone Barry Beardsley FarmersMinnesota Blue Earth Lake Crystal Crystal Valley CoopMinnesota Brown Sleepy Eye River Region CoopMinnesota Chippewa Clara City Farmers ElevatorMinnesota Dodge Dodge Center Greenway CoopMinnesota Faribault Delavan Watonwan Farm ServiceMinnesota Goodhue Dennison Central Valley CoopMinnesota Jackson Heron Lake New VisionMinnesota Lac qui Parle Bellingham Bellingham FarmersMinnesota Lyon Marshall ADM EthanolMinnesota McLeod Hutchinson Hutch CoopMinnesota Mower Adams Northern Country CoopMinnesota Nicollet Lafayette United Farmers CoopMinnesota Olmsteade Stewartville All American CoopMinnesota Pipestone Jasper Eastern Farmers CoopMinnesota Redwood Redwood Falls Meadowland Farmers CoopMinnesota Renville Renville Coop Country FarmersMinnesota Rock Hills New VisionMinnesota Swift Holloway Western Consolidated CoopMinnesota Yellow Medicine Clarkfield Prairie GrainNebraska Butler Bellwood Frontier CoopNebraska Cass Greenwood Midwest Farmers CoopNebraska Clay Ong Aurora CoopNebraska Dawson Gothenburg Farmland Service CoopNebraska Fillmore Shickley Shickley GrainNebraska Frontier Maywood Ag Valley Coop

66

Nebraska Greeley Spalding Country Partners CoopNebraska Jefferson Diller Firth CoopNebraska Lancaster Firth Firth CoopNebraska Nemaha Brownville Bartlett GrainNebraska Phelps Funk Cooperative ProducersNebraska Pierce Osmond Battle Creek Farmers CoopNebraska Platte Humphrey Central Valley AgNebraska Red Willow McCook Frenchman Valley CoopNebraska Thayer Bruning Bruning GrainNebraska Valley North Loup Country Partners CoopSouth Dakota Brookings Brookings AgFirst Farmers CoopSouth Dakota Brown Aberdeen South Dakota Wheat GrowersSouth Dakota Grant Milbank Western Consolidated CoopSouth Dakota Hanson Emery CargillSouth Dakota Hutchinson Dimock Central Farmers CooperativeSouth Dakota Lake Madison Madison FarmersSouth Dakota Spink Northville North Central FarmersSouth Dakota Union Elk Point Southeast Farmers CoopWisconsin Columbia Cambria Landmark CoopWisconsin Dane Cottage Grove Landmark CoopWisconsin Pepin Durand Countryside Coop

APPENDIX D

Table D.1 – Summary Statistics by State

Illinois Mean St. Dev Min MaxBasis -21.70 11.50 -68 6

Local Corn Production (million bushels) 295.95 81.88 114 544Ethanol (MGY) 166.26 154.62 0 637

Production Ratio 2.77 0.66 1.21 4.34Local Stocks (million bushels) 194.46 89.98 33.37 543.99

Stocks Ratio 4.49 1.93 1.38 12.46

Indiana Mean St. Dev Min MaxBasis -16.73 11.94 -54 14

Local Corn Production (million bushels) 208.94 73.52 83 403Ethanol (MGY) 66.60 95.40 0 431

Production Ratio 1.97 0.66 0.93 3.45

67

Local Stocks (million bushels) 133.72 69.32 26.45 402.80Stocks Ratio 3.16 1.66 0.99 9.92

Iowa Mean St. Dev Min MaxBasis -32.20 12.01 -71 8

Local Corn Production (million bushels) 289.33 78.38 106 472Ethanol (MGY) 184.40 201.09 0 1026

Production Ratio 2.72 0.68 1.11 4.32Local Stocks (million bushels) 203.50 81.51 40.93 471.72

Stocks Ratio 4.99 2.53 1.28 13.70

Kansas Mean St. Dev Min MaxBasis -20.06 16.12 -78 16

Local Corn Production (million bushels) 39.69 25.97 7 111Ethanol (MGY) 16.33 28.18 0 177

Production Ratio 0.37 0.24 0.07 1.17Local Stocks (million bushels) 27.46 20.29 2.51 110.72

Stocks Ratio 0.68 0.58 0.08 4.15

Minnesota Mean St. Dev Min MaxBasis -38.96 12.42 -86 1

Local Corn Production (million bushels) 253.04 62.13 128 397Ethanol (MGY) 169.66 98.55 21 621

Production Ratio 2.39 0.56 1.34 3.72Local Stocks (million bushels) 176.16 71.87 45.06 396.52

Stocks Ratio 4.16 1.76 1.55 10.84

Nebraska Mean St. Dev Min MaxBasis -28.42 11.06 -58 13

Local Corn Production (million bushels) 168.69 58.18 34 331Ethanol (MGY) 96.48 121.19 0 574

Production Ratio 1.58 0.51 0.38 2.60Local Stocks (million bushels) 115.53 53.31 14.63 330.58

Stocks Ratio 2.86 1.64 0.44 8.58

South Dakota Mean St. Dev Min MaxBasis -39.32 12.13 -69 -5

68

Local Corn Production (million bushels) 131.11 50.80 51 275Ethanol (MGY) 146.22 115.65 0 436

Production Ratio 1.23 0.44 0.49 2.27Local Stocks (million bushels) 90.09 45.54 17.74 274.53

Stocks Ratio 2.18 1.23 0.57 7.74

Wisconsin Mean St. Dev Min MaxBasis -48.73 15.94 -92 -26

Local Corn Production (million bushels) 120.10 10.55 100 137Ethanol (MGY) 42.07 30.68 0 81

Production Ratio 1.14 0.13 0.97 1.40Local Stocks (million bushels) 88.80 24.93 46.33 136.89

Stocks Ratio 2.31 1.38 1.09 7.63

Table D.2 – Summary Statistics by Year

Fall 1999 - Summer 2000 Mean St. Dev Min MaxBasis (cents) -34.34 11.35 -64 -8

Local Corn Production (million bushels) 200.16 88.40 8 349Ethanol (MGY) 68.83 100.08 0 402

Midwest Diesel Price 139.31 8.53 125.57 148.87National Corn Production (billion bushels) 9.43 0.00 9.43 9.43

Production Ratio 2.12 0.94 0.09 3.71Local Stocks (million bushels) 142.47 76.34 4.01 349.48

National Stocks (billion bushels) 4.74 2.35 1.72 8.04Stocks Ratio 3.58 2.17 0.10 11.00

Fall 2000 - Summer 2001 Mean St. Dev Min MaxBasis (cents) -29.86 11.53 -63 3

Local Corn Production (million bushels) 202.19 89.36 10 354Ethanol (MGY) 69.29 100.07 0 402

Midwest Diesel Price 148.28 4.98 143.80 156.53National Corn Production (billion bushels) 9.92 0.00 9.92 9.92

Production Ratio 2.04 0.90 0.10 3.57

69

Local Stocks (million bushels) 144.70 76.79 4.40 354.34National Stocks (billion bushels) 5.10 2.46 1.90 8.52

Stocks Ratio 3.32 1.92 0.12 9.84

Fall 2001 - Summer 2002 Mean St. Dev Min MaxBasis (cents) -22.41 10.88 -51 10

Local Corn Production (million bushels) 196.34 86.39 9 361Ethanol (MGY) 72.89 100.01 0 402

Midwest Diesel Price 125.83 6.54 115.40 133.33National Corn Production (billion bushels) 9.50 0.00 9.50 9.50

Production Ratio 2.07 0.91 0.10 3.80Local Stocks (million bushels) 136.40 74.06 4.83 361.23

National Stocks (billion bushels) 4.81 2.49 1.60 8.26Stocks Ratio 3.41 1.99 0.12 9.92

Fall 2002 - Summer 2003 Mean St. Dev Min MaxBasis (cents) -13.05 11.26 -40 15

Local Corn Production (million bushels) 202.36 100.46 8 387Ethanol (MGY) 84.67 100.77 0 402

Midwest Diesel Price 147.89 6.49 143.73 159.10National Corn Production (billion bushels) 8.97 0.00 8.97 8.97

Production Ratio 2.26 1.12 0.09 4.32Local Stocks (million bushels) 130.81 84.98 4.21 387.10

National Stocks (billion bushels) 4.21 2.44 1.09 7.63Stocks Ratio 3.88 2.59 0.11 13.49

Fall 2003 - Summer 2004 Mean St. Dev Min MaxBasis (cents) -17.09 9.52 -36 15

Local Corn Production (million bushels) 217.25 103.64 7 411Ethanol (MGY) 102.20 105.33 0 402

Midwest Diesel Price 161.99 12.14 147.07 179.43National Corn Production (billion bushels) 10.09 0.00 10.09 10.09

Production Ratio 2.15 1.03 0.07 4.08Local Stocks (million bushels) 137.63 89.13 2.51 411.41

National Stocks (billion bushels) 4.29 2.61 0.96 7.95Stocks Ratio 4.11 2.64 0.09 12.88

70

Fall 2004 - Summer 2005 Mean St. Dev Min MaxBasis (cents) -29.99 12.50 -66 2

Local Corn Production (million bushels) 252.13 113.54 9 459Ethanol (MGY) 120.20 115.09 0 411

Midwest Diesel Price 220.27 19.29 202.20 251.30National Corn Production (billion bushels) 11.81 0.00 11.81 11.81

Production Ratio 2.14 0.96 0.08 3.89Local Stocks (million bushels) 180.39 96.15 4.79 459.26

National Stocks (billion bushels) 5.66 2.74 2.11 9.45Stocks Ratio 3.76 2.25 0.09 11.16

Fall 2005 - Summer 2006 Mean St. Dev Min MaxBasis (cents) -37.28 13.03 -73 0

Local Corn Production (million bushels) 238.14 109.01 10 436Ethanol (MGY) 145.72 137.24 0 652

Midwest Diesel Price 270.84 16.63 245.23 289.97National Corn Production (billion bushels) 11.11 0.00 11.11 11.11

Production Ratio 2.14 0.98 0.09 3.93Local Stocks (million bushels) 167.06 93.93 5.26 436.19

National Stocks (billion bushels) 5.78 2.93 1.97 9.81Stocks Ratio 3.44 2.09 0.10 10.51

Fall 2006 - Summer 2007 Mean St. Dev Min MaxBasis (cents) -29.11 13.35 -70 16

Local Corn Production (million bushels) 231.79 111.89 7 436Ethanol (MGY) 176.93 157.77 0 706

Midwest Diesel Price 267.88 16.71 250.73 289.70National Corn Production (billion bushels) 10.53 0.00 10.53 10.53

Production Ratio 2.20 1.06 0.07 4.14Local Stocks (million bushels) 153.25 95.59 3.79 435.86

National Stocks (billion bushels) 4.96 2.85 1.30 8.93Stocks Ratio 3.88 2.60 0.08 13.70

Fall 2007 - Summer 2008 Mean St. Dev Min Max

71

Basis (cents) -39.87 13.09 -92 -6Local Corn Production (million bushels) 274.42 129.68 11 544

Ethanol (MGY) 232.06 179.01 0 816Midwest Diesel Price 383.93 47.99 324.60 434.20

National Corn Production (billion bushels) 13.04 0.00 13.04 13.04Production Ratio 2.10 0.99 0.09 4.17

Local Stocks (million bushels) 183.88 109.41 4.56 543.99National Stocks (billion bushels) 5.70 3.23 1.62 10.28

Stocks Ratio 4.08 2.78 0.11 13.70

Fall 2008 - Summer 2009 Mean St. Dev Min MaxBasis (cents) -28.40 13.70 -69 11

Local Corn Production (million bushels) 257.97 118.55 12 524Ethanol (MGY) 290.36 223.50 0 1026

Midwest Diesel Price 248.42 30.22 214.97 293.40National Corn Production (billion bushels) 12.09 0.00 12.09 12.09

Production Ratio 2.13 0.98 0.10 4.34Local Stocks (million bushels) 173.69 102.20 5.62 524.21

National Stocks (billion bushels) 5.75 3.13 1.67 10.08Stocks Ratio 3.67 2.20 0.12 11.48

72

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