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AT .1. . t. .f. . . , . .1: 1... ...y..! 7:... i2}. . l . .V: 1...... .9 ... .1011! .t. t . . z s. ...w|.. \..‘J 7 .. l 3. . 9 . . 3:5... . .15. 1.. .y . . 11”,;le a! 3 LIBRARY Michigan State Ul iIVGI‘SI‘ly This is to certify that the thesis entitled THE IMPACT OF ETHANOL 0N CORN MARKET RELATIONSHIPS AND CORN PRICE BASIS LEVELS presented by Karen Elizabeth Lewis has been accepted towards fulfillment of the requirements for the MASTER OF degree in Agriculture, Food, and SCIENCE Resource Economics W? :4/ V Major Professor’s Signature 03/3 5730 :10 Date MSU is an Afiirmative Action/Equal Opportunity Employer PLACE IN RETURN BOX to remove this Checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 5/08 K:lProj/Acc&Pres/ClRC/DateDue.indd THE IMPACT OF ETHANOL ON CORN MARKET RELATIONSHIPS AND CORN PRICE BASIS LEVELS By Karen Elizabeth Lewis A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Agriculture, Food, and Resource Economics 2010 ABSTRACT THE IMPACT OF ETHANOL ON CORN MARKET RELATIONSHIPS AND CORN PRICE BASIS LEVELS By Karen Elizabeth Lewis Corn market relationships were examined in Michigan, Kansas, Iowa and Indiana to determine if the increase in the percentage of corn used in ethanol production or the increase in the number of ethanol plants affected the annual degree of cointegration of corn prices at different grain markets in these states from September 1998 through June 2008. It was determined that com price relationships at grain markets in the studied states were not affected by the increase in the percentage of corn used in ethanol production or the increase in the number of ethanol plants. Therefore, farmers and commodity traders were correct if they assumed the relationships between corn prices at different grain markets remained the same from September 1998 through June 2008. Corn price basis levels were examined to determine if ethanol plant openings affected corn price basis levels at grain markets located at the site of an ethanol plant opening. On average, grain markets located at the site of an ethanol plant opening in Michigan and Kansas felt the largest corn price basis level increase at the time of an ethanol plant opening while grain markets located at the site of an ethanol plant opening in Iowa and Indiana did not experience substantial corn price basis level increases. Therefore, increased ethanol production increased corn price basis levels in Michigan and Kansas while corn price basis levels in Iowa and Indiana were relatively unaffected. Dedicated to My Family iii AKNOWLEDGEMENTS Foremost, I would like to thank my major professor, Dr. Glynn Tonsor. Dr. Tonsor was available seven days a week to answer the numerous questions I had while creating my thesis. His availability and guidance is deeply appreciated. I would also like to thank my other committee members, Dr. James Hilker and Dr. Soren Anderson. Dr. Hilker and Dr. Anderson also provided critical assistance with concepts relating to my thesis. I would like to thank my Aunt Nancy Streeter-Grant who as an English instructor answered several editing-related questions concerning the writing of my thesis. I would like to thank my immediate family members, my parents Carol Ann and Marty, brothers Charlie and Dan, and sister-in-law Gwyn. I would like to recognize my grandparents Patricia Lewis and the late Donald Lewis and the late Elizabeth and late Judge Halford Streeter. My immediate family members are all very active in the agricultural community and acted as excellent references when discussing ideas regarding my thesis. I would like to thank my Aunt Eve Lewis who as an editor educated me on the business style of writing and also had insightful ideas regarding agriculture. Appreciation extends to the staff of the American Sugarbeet Growers Association, specifically Luther Markwart and Ruthann Geib, who I was an intern for during the summer of 2007 in Washington, DC. They significantly influenced my current thoughts regarding agriculture, economics and politics. Luther Markwart and Ruthann Geib also encouraged me to contribute to the agricultural community, advice which largely contributed to my decision to pursue my Masters Degree in the Department of Agriculture, Food and Resource Economics at Michigan State University. Also, Dr. Kirsty Eisenhart at Western iv Michigan University taught me how to present difficult mathematical concepts to students while I was her teacher assistant at Western Michigan University from the Fall of 2006 through the Spring of 2008. This skill has assisted me with explaining complicated concepts and giving presentations. All of my graduate student friends in the Department of Agriculture, Food and Resource Economics at Michigan State University contributed to making my experience in East Lansing enjoyable. Lastly, I would like to thank all of my friends and family members I did not specifically mention, especially those who reside on the shores of Lake Huron in the summertime who contributed guidance and ideas that assisted me in the completion of my thesis. TABLE OF CONTENTS LIST OF TABLES ................................................................................. vii LIST OF FIGURES ................................................................................. ix CHAPTER 1: GENERAL INTRODUCTION ................................................. 1 REFERENCES .......................................................................................... 4 CHAPTER 2: THE IMPACT OF ETHANOL PRODUCTION ON SPATIAL GRAIN MARKET RELATIONSHIPS .......................................................... 5 2.1 Introduction ..................................................................................... 5 2.2 Materials and methods ....................................................................... 9 2.3 Results and implications ................................................................... 16 2.4 Summary ..................................................................................... 29 APPENDIX 2.]: SUMMARY STATISTICS FOR DATA SET RECEIVED FROM CASH GRAIN BIDS DATA SERVICE ........................................................ 32 APPENDIX 2.2: COINTEGRATION RESULTS FOR KANSAS, IOWA AND INDIANA ............................................................................................ 66 REFERENCES ..................................................................................... 79 CHAPTER 3: THE IMPACT OF ETHANOL PLANT OPENINGS ON CORN PRICE BASIS LEVELS .......................................................................... 81 3.1 Introduction .................................................................................. 81 3.2 Materials and methods ..................................................................... 85 3.3 Results and implications ................................................................... 96 3.4 Summary ................................................................................... 107 APPENDIX 3.1: SUMMARY STATISTICS FOR DATA SET RECEIVED FROM CASH GRAIN BIDS DATA SERVICE ...................................................... 109 APPENDIX 3.2: MODEL I RESULTS FOR KANSAS, IOWA AND INDIANA 143 APPENDIX 3.3: MODEL II RESULTS FOR KANSAS, IOWA AND INDIANA 153 REFERENCES ................................................................................... 163 vi LIST OF TABLES Table 2.a Percent of Com Used in the Production of Ethanol ................................. 5 Table 2b Michigan Ethanol Plants and Production ............................................. 6 Table 2.c Weekly Average Corn Price Statistics (cents/bu) ................................. 13 Table 2.d ADF Test Results ...................................................................... 15 Table 2.c Michigan Grain Markets Multivariate Cointegration Testing Results .......... 17 Table 2.f Michigan Markets Annual Cointegration Tests .................................... 19 Table 2. g Midwestern Region Multivariate Cointegration Testing Results ............... 25 Table 2.h Midwestern U.S. Region Markets Annual Cointegration Tests ................. 26 Table 2.i Michigan Weekly Corn Price Summary Statistics ................................. 32 Table 2.j Kansas Weekly Corn Price Summary Statistics .................................... 34 Table 2.k Iowa Weekly Corn Price Summary Statistics ...................................... 43 Table 2.1 Indiana Weekly Corn Price Summary Statistics .................................... 60 Table 2.m Kansas Grain Markets Multivariate Cointegration Testing Results ............ 66 Table 2.n Kansas Markets Annual Cointegration Tests ....................................... 68 Table 2.0 Iowa Grain Markets Multivariate Cointegration Testing Results ............... 71 Table 2.p Iowa Markets Annual Cointegration Tests ......................................... 72 Table 2.q Indiana Grain Markets Multivariate Cointegration Testing Results ............. 75 Table 2.r Indiana Markets Annual Cointegration Tests ....................................... 76 Table 3.a Michigan Ethanol Plants Opening and Grain Markets Nearby .................. 89 Table 3.b Kansas Ethanol Plant Openings and Grain Markets Nearby ..................... 90 Table 3.c Ethanol Plant Openings in Iowa and Grain Markets Nearby ..................... 91 Table 3.d Indiana Ethanol Plants Opening and Grain Markets Nearby ..................... 92 vii Table 3.c Michigan Equation (2) Estimated Results .......................................... 96 Table 3.f Michigan Equation (4) Estimate Results .......................................... 101 Table 3. g Model III Estimated Results ........................................................ 106 Table 3.h Michigan Monthly Corn Price Basis Level Summary Statistics .............. 109 Table 3.i Kansas Monthly Corn Price Basis Level Summary Statistics .................. 111 Table 3.j Iowa Monthly Corn Price Basis Level Summary Statistics ..................... 120 Table 3.k Indiana Monthly Corn Price Basis Level Summary Statistics ................. 137 Table 3.1 Kansas Equation (6) Estimates ...................................................... 144 Table 3.m Iowa Equation (7) Estimates ....................................................... 147 Table 3.n Indiana Equation (8) Estimates ..................................................... 151 Table 3.0 Kansas Equation (9) Estimate Results ............................................. 154 Table 3.p Iowa Equation (10) Estimate Results .............................................. 157 Table 3.q Indiana Equation (11) Estimate Results .......................................... 161 viii LIST OF FIGURES Figure 3.a Michigan Plants: Months Since Opened Time Impact ......................... 103 Figure 3.b Kansas Plants: Months Since Opened Time Impact ............................ 156 Figure 3.c Iowa Plants: Months Since Opened Time Impact .............................. 160 Figure 3.d Indiana Plants: Months Since Opened Time Impact ........................... 162 ix CHAPTER 1: GENERAL INTRODUCTION The production ofcorn-based-ethanol in the United States has been steadily increasing in the past few decades. The creation of laws at the federal level of government is a central reason for the growth of the ethanol industry. Beginning in 1978 the federal government began passing legislation geared towards increasing domestic energy production in an attempt to create energy independence from foreign countries. Congress passed the Energy Tax Act (ETA) of 1978 which created an exemption on the federal gasoline tax for gasoline that was blended with at least ten percent ethanol. Since 1978, the ETA monetary incentive for ethanol to be blended into gasoline has been adjusted several times. Currently, the Federal Highway Bill of 1998 set the exemption at fifty-one cents for every gallon of pure ethanol blended into gasoline. Besides the ETA, several other laws motivating ethanol production have been passed by Congress such as the Crude Oil Windfall Profit Tax Act of 1980 and the Energy Security Act of 1980. Additionally, the Ethanol Import Tariff Act which establishes a tax on foreign-produced ethanol that is imported into the United States was passed in 1980. In 1988, the Alternative Motor Fuels Act passed which created incentive for vehicles to be produced that were able to run on E85, which is eighty-five percent ethanol blended fuel. The Budget Reconciliation Act of 1990 issued an income tax break to entrepreneurs who pursued the creation of new ethanol plants. The law issued a ten cent per gallon income tax credit on the first fifteen million gallons of ethanol produced at ethanol plants which had production capacity of less than sixty million gallons of ethanol per year. Finally, the Energy Policy Act of 1992 set a standard of l domestic alternative energy fuels accounting for ten percent of the total United States fuel consumption by 2000 and thirty percent by 201 0. Environmental policy laws have also created increased demand for ethanol. The Clean Air Act Amendments in 1990 required the current composition of gasoline to be changed in an attempt to reduce carbon monoxide exhaust emissions. The new composition required gasoline to be reformulated with either ethanol or methyl tertiary butyl ether (MTBE). However, MTBE was found to contaminate ground and surface water; therefore, ethanol production increased greatly after the Clean Air Act Amendments in 1990. Besides the passage of several laws at the federal level, many state and local laws have also created favorable incentives for ethanol production. Together, this stream of legislation created an opportunity for ethanol plant owners to gain profits by producing a larger quantity of ethanol. Accordingly, the number of ethanol plants in the United States rose from fifty to 189 from January 1999 through January 2010 with another eleven ethanol plants under construction or expanding (Renewable Fuels Association). Several aspects of agriculture have been impacted by the rapid increase of the ethanol industry. In an attempt to assess some of the changes that may have occurred as a result of increased ethanol production, this paper will analyze changes in corn production from September 1998 through June 2008. During this time interval, ethanol production increased from 1.4 billion gallons per year to 9 billion gallons per year (Renewable Fuels Association). While ethanol production increased nearly 650% in this time interval, corn production increased by approximately 24% (USDA). This discussion is aimed at discovering the impact increased ethanol production had on corn price relationships at different grain markets in the Midwestern United States and determining the impact ethanol plant openings had on corn price basis levels at grain markets in the Midwestern United States. Chapter 2 will examine the impact increased ethanol production had on the annual degree of cointegration of corn prices at different grain markets in Michigan, Kansas, Indiana, Iowa and the Midwestern Region of the United States in general. It is likely that increased ethanol production affected existing corn price relationships at different grain markets by increasing the annual degree of cointegration of corn prices that existed between different grain markets before the escalation of ethanol production. Ethanol plant openings have created new demand centers for corn which increases competition for corn. Increased competition for corn is expected to increase the annual degree of cointegration of corn prices at different grain markets and ensure that corn prices in different regions differ only by transaction costs (Brester and Goodwin 1993). Chapter 3 will observe the impact ethanol plant openings had on corn price basis levels located at the site of an ethanol plant opening over the average time period after an ethanol plant opened and each month after an ethanol plant opened. Corn price basis levels likely strengthened because ethanol plant openings created a new local demand center for corn. REFERENCES Brester, Gary W. and Barry K. Goodwin. 1993. Vertical and Horizontal Price Linkages and Market Concentration in the US. Wheat Milling Industry. Review of Agricultural Economics 15:507-519. Renewable Fuels Association. 2009. Statistics. http://www.ethanolrfa.org/industry/statistics/. Accessed November 12, 2009. United States Department of Agriculture. 2009. Economics, Statistics, and Market Information System. http://usda.mannlib.comell.edu/MannUsda/viewDocumentInfo.do?documentID=1 047. Accessed November 14, 2009. CHAPTER 2: THE IMPACT OF ETHANOL PRODUCTION ON SPATIAL GRAIN MARKET RELATIONSHIPS 2.1 Introduction In recent years the trend witnessed in the United States concerning com-based- ethanol can be summarized by the rapidly increasing percentage of corn used in the production of ethanol (Table 2.a). The growth of this statistic is largely attributed to the increase in com-based-ethanol production. In 1998 the annual production of ethanol was 1.4 billion gallons and by 2008 annual ethanol production was 9 billion gallons, an increase of 7.6 billion gallons (Renewable Fuels Association). The percentage of corn used in the production of ethanol within the particular states of Michigan, Kansas, Indiana and Iowa also increased from 1998 through 2008 and is also found in Table 2.a. Table 2.a Percent of Corn Used in the Production of Ethanol Year Michigan % Kansas % Iowa % Indiana % U.S. % 1998 0.00% 1.51% 14.15% 4.84% 5.18% 1999 0.00% 1.50% 14.24% 4.92% 5.62% 2000 0.00% 1.53% 14.39% 4.51% 5.93% 2001 0.00% 6.29% 16.24% 4.16% 6.72% 2002 7.71% 11.52% 17.15% 5.83% 8.54% 2003 6.95% 11.13% 19.63% 4.68% 10.02% 2004 7.02% 1 1.49% 22.62% 3.96% 10.40% 2005 6.28% 13.37% 28.24% 4.14% 12.68% 2006 19.42% 22.24% 37.80% 4.36% 16.64% 2007 32.46% ‘ 30.75% 35.79% 16.63% 18.00% 2008 32.03% 36.92% 50.20% 36.94% 26.85% The characteristics of ethanol production in Michigan, Kansas, Iowa and Indiana are reflected in the percentage of com used in the production of ethanol statistic. For example, during the time period of 1998 through 2008 the number of ethanol plants operating in Michigan improved from zero to five. Table 2.b highlights details of ethanol production in the state of Michigan. Table 2.b Michigan Ethanol Plants and Production Annual Ethanol Started Production Location Production Owner (gallons) Caro Nov. 2002 Poet Biorefinery 50 million Albion Aug. 2006 Andersons Albion Ethanol LLC 55 million Lake Odessa Sept. 2006 VeraSun Woodbury LLC* 50 million Riga Feb. 2007 Midwest Grain Processors LLC 57 million Marysville Oct. 2007 Marysville Ethanol LLC 50 million Total Production 262 million *VeraSun declared bankruptcy in the Fall of 2008 The trends of Michigan’s ethanol industry are comparable to the trends found throughout the ethanol industries in Kansas, Iowa and Indiana. From 1998 to 2008 the number of ethanol plants in Kansas increased from three to thirteen, in Iowa the number of ethanol plants ascended from four to thirty-nine and in Indiana the number of ethanol plants increased from one to twelve (Ethanol Producer Magazine). Accordingly, from 1998 to 2008 annual ethanol production in the state of Kansas increased from 17.5 million gallons to 497.5 million gallons, in Iowa ethanol production escalated from 693 million gallons to 3.04 billion gallons and in Indiana ethanol production grew from 102 million gallons to 894 million gallons (Ethanol Producer Magazine). Evidenced by the increase in the percentage of corn used in the production of ethanol, the annual amount of corn harvested in the United States and in the individual states of Michigan, Kansas, Iowa and Indiana did not increase at the same pace as ethanol production. In the United States, corn production increased from 9.76 billion bushels in 1998 to 12.1 billion bushels in 2008, an increase of approximately twenty-four percent (USDA 1). In Michigan corn production increased thirty percent, in Kansas corn production expanded sixteen percent, in Iowa corn production grew twenty-four percent and in Indiana corn production increased fifteen percent (USDA 1). From 1998 to 2008, many economic consequences transpired as the percent of corn used in the production of ethanol increased as a result of the expansion in the number of ethanol plants in the United States. Most predominantly, nominal corn prices increased steadily. In 1998 the national average price for a bushel of corn was $2.21 per bushel (USDA 2). By June 2008, the average price for a bushel of corn was $5.47 (USDA 3). As the price of corn increased, another notable outcome occurred. VeraSun Energy Corporation, owner of twelve ethanol plants, declared bankruptcy in the Fall of 2008. As increased ethanol production created higher corn prices, some critics also blamed ethanol production and ethanol production subsidizes for higher food prices. Because corn is an input for raising several types of livestock, increased corn prices also increased livestock production costs. Somewhat offsetting the negative effect of ethanol production, increased ethanol production also increased the supply of ethanol co- products. The most prominent co-product of ethanol is distillers dried grains with solubles (DDGS) (Baker and Zahniser 2006). DDGS are useful in feeding livestock and many livestock farmers have begun to feed their livestock DDGS instead of corn to lower production costs (Baker and Zahinser 2006). As ethanol production increases, DDGS exports have also been rapidly increasing (Baker and Zahinser 2006). 7 Considering all of the effects of increased ethanol production, from 1998 through 2008, it is possible that the increase in the percent of corn used in the production of ethanol caused changes in market price relationships. Market price relationships regarding increased corn demand in response to ethanol have recently been studied. Harri et al. (2009) discuss changes in the relationships between crude oil and corn prices in risk management strategies for corn producers because of the growing use of corn for ethanol. They found clear evidence that the relationship between corn and oil has strengthened over time as a result of the growing use of corn for ethanol. Additionally, Harri et a1. cite Anderson and Coble (2009) as determining that the strengthening in the relationship between crude oil and corn prices occurred when the corn ethanol production mandates were raised in the Energy Policy Act of 2005. Recent research has determined that market relationships between corn and oil have strengthened as a result of increased ethanol production. However, investigating whether increased ethanol production has altered the existing relationships among corn prices at different grain markets has not been previously studied. If increased ethanol production has altered existing corn price relationships at grain markets, it is important for farmers and grain merchandisers to have this information. When grain merchandisers purchase corn from farmers, knowledge regarding relationships among local grain markets is utilized to make a contract. If increased ethanol production has altered corn price relationships at different grain markets, it is useful for both grain merchandisers and farmers to know how corn price relationships at different grain markets have changed. To determine how the percentage of corn used in the production of ethanol has altered corn price relationships at different grain markets, the concept of cointegration 8 will be used. Cointegration is a technique used to study market price relationships. The ensuing discussion is aimed at first discovering if corn prices at different grain markets throughout the Midwestern United States were cointegrated from 1998 through 2008. Next, whether the percentage of corn used in the production of ethanol altered the annual degree of cointegration of corn prices at different grain markets throughout the Midwestern United States will be determined. In addition, the question of whether the increase in the number of ethanol plants in the Midwestern United States altered the annual degree of cointegration of corn prices at different grain markets throughout the Midwestern United States will be established. Investigating whether different grain markets throughout the Midwestern United States were cointegrated from 1998 through 2008 is important because farmers and commodity traders rely on information regarding corn price relationships at different grain markets to predict corn price movements. From 1998 through 2008, increased ethanol production influenced several sections of the agriculture industry; therefore, it is possible that com price relationships at different grain markets were altered during this time period. Particularly, it is possible that increased ethanol production has strengthened the annual degree of cointegration of corn prices at different grain markets. Increased competition for a commodity helps to ensure grain markets are cointegrated and spatial price discrimination in particular regions does not exist (Brester and Goodwin 1993). 2.2 Materials and Methods Cointegration theory has been utilized to study many economic situations. Economically speaking, two variables are cointegrated if they have a long-term, or 9 equilibrium, price relationship between them (Gujarati and Porter 2009). Cointegration testing can be used to determine long-run price relationships across different markets (Schroeder 1997). For our purposes, we will be investigating cointegration trends of corn prices at different grain markets. Grain markets with cointegrated corn prices maintain a stable spatial price relationship equilibrium which indicates the grain markets are in the same geographic corn procurement market. Conversely, if corn prices at different grain markets are not cointegrated, corn prices diverge from each other; therefore, the grain markets do not operate in a stable spatial price relationship equilibrium which suggests the grain markets are not in the same spatial market. Therefore, if it is discovered that corn prices at different grain markets throughout particular states are cointegrated, the grain markets are operating in a stable, long-run spatial price relationship equilibrium (Pendell and Schroeder 2006). It is worth noting that grain market’s corn price series cointegration has no direct implication on corn price levels. Instead, if corn prices at different grain markets are cointegrated, it is only concluded that there is a long-term, or equilibrium, price relationship found between the corn price series at the different grain markets. Otherwise stated, at any time period, the cointegrated corn price series at different grain markets may deviate from their equilibrium price relationship, but this deviation will be temporary: there are economic forces that drive the corn price series at different grain markets back toward their long-term equilibrium price relationship (Wooldridge 2006). To test if the percentage of corn used in the production of ethanol has affected the annual degree of the cointegration of corn prices at different grain markets, the methodology of Brester and Goodwin (1993) will be followed. They utilized a 10 multivariate cointegration test following the technique created by J ohansen and Juselius (1990) to determine if the increased four-firm (Conagra, Archer Daniels Midland, Peavey/Cargill and Pillsbury/General Mills) concentration in the US. wheat milling industry affected the annual cointegration of wheat prices at different wheat markets. They investigated if the increased four-firm concentration in the US. wheat milling industry created noncompetitive pricing behavior (i.e. spatial price discrimination and thus weakened annual cointegration statistics) . However, Brester and Goodwin (1993, 513) determined that “one cannot conclude that the increase in market concentration in the wheat milling industry caused spatial prices at the four markets considered in their analysis to diverge from a long-run equilibrium” (i.e. the increase in market concentration in the wheat milling industry did not affect the annual cointegration of wheat markets). To begin the cointegration analysis, daily corn price observations were purchased from Cash Grain Bids Data Service. The purchased data included daily corn prices collected from every grain market Cash Grain Bids Data Service had data on within 300 miles of Omaha, Nebraska and within 300 miles of Indianapolis, Indiana. For this study, weekly corn price averages were used and were created from the daily corn price observations recorded by Cash Grain Bids Data Service. Additionally, only weekly corn price averages at grain markets located in Michigan, Kansas, Iowa and Indiana were compiled. The summary statistics for the weekly corn price average series at grain markets in Michigan, Kansas, Iowa and Indiana are found Appendix 1. McNew and Griffith (2005) also used local com price data collected from Cash Grain Bids Data Service in their analysis of measuring the impact of ethanol plants on local grain prices. 11 Michigan, Kansas, Iowa and Indiana were the states chosen to represent the Midwestern United States in this study. These four states geographically are representative of both the Eastern and Western Corn Belt Region. Additionally, from 1998 through 2008 Iowa annually produced the most com in the nation (USDA 1). Michigan, Kansas, Iowa and Indiana account for approximately fifty-two percent of the national annual production of ethanol (Ethanol Producer Magazine). Michigan, Kansas, Iowa and Indiana account for approximately thirty-two percent of the total corn produced in the United States (USDA 1). A state by state approach was utilized to determine if the percentage of corn used in ethanol production affected the annual degree of cointegration of corn prices at different grain markets. Exemplifying the state by state approach, in Michigan the weekly corn price averages recorded at fifty-seven grain markets from September 1998 through June 2008 were complied. Next, two criterions were used to narrow the grain markets to be examined to four grain markets per state. Only four grain markets were examined in each state because of degrees of freedom constraints on annual multivariate cointegration testing. The two criterions were (1) completeness of corn price observations in the weekly average corn price series and (2) geographical dispersion between the locations of the different grain markets chosen. A third criterion would have been utilized regarding volume of trading at each market; however, this information was not accessible. This same process was used to select four grain markets to examine in Kansas, Iowa and Indiana. Table 2.c illustrates which four grain markets were studied in each state along with the characteristics of each weekly average corn price series recorded at each grain market. 12 Table 2.c Weekly Average Corn Price Statistics (cents/bu) Grain Market # of Obs. Mean Std. Dev. Minimum Maximum Blissfield, MI 512 238 85 145 588 Lake Odessa, MI 512 226 86 137 576 Marlette, MI 512 229 83 136 571 Middleton, MI 512 226 84 136 571 Chapman, KS 512 233 89 144 627 Hillsboro, KS 512 235 87 143 580 Lamed, KS 512 241 85 155 576 Osborne, KS 512 230 85 142 555 Algona, 1A 512 218 86 129 567 Audubon, IA 512 218 87 127 603 Cedar Rapids, IA 512 242 81 155 583 Chariton, IA 512 225 80 130 557 Columbus, IN 512 235 86 137 586 Delphi, IN 512 242 86 147 592 Greensburg, IN 512 239 83 143 571 Hamlet, IN 512 237 85 143 589 Criterion one noted completeness of corn price observations in the weekly average corn price series as being one way of selecting the proper grain market to study. However, no grain market contained 100% of their weekly corn price observations. Therefore, missing observations were predicted by regressing the Chicago corn price time series with each individual grain market’s corn price time series. The weekly average Chicago corn price time series from September 1998 through June 2008 was recorded by the Livestock Market Information Center. All grain markets used in the study were missing less than nine percent of their total weekly corn price observations. The first item to investigate when conducting a multivariate cointegration test is to determine if the individual corn market price series are nonstationary and integrated to the same order (Pendell and Schroeder 2006). To test if the individual corn price series 13 were nonstationary, the Augmented Dickey-Fuller (ADF) unit root test was used. The ADF test utilizes the following OLS regression: (1) Ayt = a+pyt_ 1 +21%: 10Ayt_i+et where y is the particular corn price series, A indicates the first difference and j is the lag length that ensures the residual Q; is white noise. The Akaike Information Criteria (AIC) was used to determined proper lag length. The corresponding ADF test statistic is defined as p divided by its standard error. Table 2.d reports the ADF test results for the corn price series used in our study. The AIC lag lengths that were used in the tests also appear on Table 2.d. The test statistics of the individual corn price series at all of the grain markets were greater than the required critical value at the one percent level of significance. Therefore, the null hypothesis that the corn price series contain a unit root was not rejected, implying that the individual corn price series were all nonstationary. Therefore, the corn prices were stochastic and followed a random walk (Gujarati and Porter 2009). The next step in our analysis is to determine whether the first differenced corn price series are stationary. After first differencing the corn price series, all of the test statistics were less than the one percent critical value level of significance. Thus, the null hypothesis that the series contains a unit root was rejected, implying that the first differencing of the individual price series were stationary. Together these results suggest each corn price series was integrated of order one [1(1)] and a multivariate cointegration analysis could be conducted. 14 Table 2.d ADF Test Results Price Series Lag First-Differenced Lag Grain Market Test Statistic Length Test Statistic Length Blissfield, MI 1.755 2 -7.948* 4 Lake Odessa, MI 1.674 3 -7.851* 4 Marlette, MI 1.199 4 -8.066* 4 Middleton, MI 1.1 13 4 -7.771* 4 Chapman, KS 2.756 3 -7.747* 4 Hillsboro, KS 1.187 4 -8.462* 4 Lamed, KS 1.776 1 -22.561* 0 Osborne, KS 1.578 3 -8.396* 4 Algona, IA 1.023 4 -7.535* 4 Audubon, IA 1.835 4 -7.371* 4 Cedar Rapids, IA 1.242 4 -9.63* 3 Chariton, IA 1.773 1 -8.243* 4 Columbus, IN 1.694 2 -8.816* 4 Delphi, IN 1.663 2 -8.531* 4 Greensburg, IN 0.752 4 -9.422* 3 Hamlet, IN 1.368 4 -7.648* 4 * Indicates rejection of the null hypothesis at 1% significance Multivariate cointegration testing was used to determine whether corn prices grain market’s corn price series. series at different grain markets throughout particular states were cointegrated over the time frame of 1998 through 2008. The annual cointegration test statistics were also recorded. The first state explored was Michigan. To determine if the corn price series at the grain markets in Blissfield, Lake Odessa, Marlette and Middleton were cointegrated, maximum likelihood cointegration estimates were taken. Because four markets were used in the cointegration analysis, three independent cointegrating vectors were necessary to determine if one grain market’s corn price series was representative of the all of the Johansen and Juselius (1990) suggest a methodology for both testing for the 15 number of cointegrating vectors and for obtaining maximum likelihood estimates of those vectors (Brester and Goodwin 1993). This methodology involves estimating the following vector autoregressive model: (2) Mt = Xi‘QiTOi AYt — i + VOt Yt—k =Xi(;11T1iAYt—i+vlt where Y represents a matrix of each of the corn price series (y) used for the state of Michigan analysis. There are two test statistics used to test the null hypothesis that there are at most r cointegrating vectors in the system Yt. The following equations represent the maximal eigenvalue test statistic and the trace test statistic: (3) tMAX = —'l‘ln (1 — Ar+ 1) rTRACE = 42}: H 1m (1 —;\i) where T represents the total number of observations in the price series and Ar + 1, , Ap represents the p-r smallest possible correlations of residual VOtwith respect to residual Vlt' 2.3 Results and Implications State Models Michigan was the first state subject to the multivariate cointegration testing. Table 2.c illustrates the results of multivariate cointegration testing on the corn price series at Blissfield, Lake Odessa, Marlette and Middleton grain markets from September 1998 through June 2008. Table 2.e shows both the maximal eigenvalue test statistic and the trace test statistic resulting from the multivariate cointegration procedure. Lag length 16 of two was selected because Akaike’s Final Prediction Error (F PE) was minimized at this amount. Table 2.e Michigan Grain Markets Multivariate Cointegration Testing Results Null Alternative Cointegration 5% Critical Hypothesis Hypothesis Test Stat Value Trace Test Ho: r=0 H1: r>0 157.36* 47.21 Ho: r=1 H1: r>1 82.55“ 29.38 Ho: r=2 H1: r>2 2749* 15.34 Ho: r=3 H1: r>3 2.21 3.84 Max Test Ho: r=0 H1: r=1 74.81* 27.07 Ho: r=1 H1: r=2 55.06* 20.97 Ho: r=2 H1: F3 2527* 14.07 Ho: r=3 H1: r=4 2.21 3.76 *Indicates rejection of the null hypothesis at 5% significance Table 2.e displays three cointegrating vectors for the four corn price series using both the maximal eigenvalue test statistic and the trace test statistic. Therefore, from 1998 through 2008, corn prices at Blissfield, Lake Odessa, Marlette and Middleton grain markets were cointegrated. Thus, there was a long-term, or equilibrium, price relationship found between the corn price series at the different grain markets. Next, we examined if the percentage of corn used in the production of ethanol in Michigan affected the annual cointegration of the corn price series at the different grain markets. To determine this, the Brester and Goodwin procedure was followed. They began their methodology by determining the annual multivariate cointegration test statistics for wheat prices at different grain markets. Accordingly, for our analysis, annual multivariate cointegration tests were used to determine the annual cointegration statistics for the corn price series at Blissfield, Lake Odessa, Marlette and Middleton 17 grain markets. Table 2.f displays the annual cointegration maximal eigenvalue test statistics as well as the proper lag lengths determined by the minimum value of the FPE. The annual test statistic for the null hypothesis r=3 has been excluded from Table 2.f to save space. Additionally, the annual cointegration trace statistics for the Michigan grain markets were recorded but excluded from Table 2.f to save space. 18 Table 2.f Michigan Markets Annual Cointegration Tests FPE Null Maximal 5% Lag Length Hypothesis Eigenvalue Critical Selection Time Period Ho: Test Statistic Value 1 Sept. 1998-1999 r=0 26.78 27.07 r=1 17.77 20.97 r=2 6.57 14.07 2 1999-2000 r=0 34.29”“ 27.07 r=l 19.32 20.97 r=2 16.22 14.07 7 2000-2001 r=0 75.55* 27.07 r=1 13.47 20.97 r=2 11.28 14.07 1 2001-2002 r=0 30.12* 27.07 r=1 21 .54* 20.97 r=2 5.28 14.07 7 2002-2003 F0 51 .14* 27.07 r=1 3735* 20.97 r=2 25.42 14.07 2 2003-2004 r=0 22.02 27.07 r=1 11.50 20.97 F2 4.64 14.07 1 2004-2005 r=0 25.20 27.07 r=1 10.69 20.97 r=2 9.36 14.07 7 2005-2006 r=0 64.51* 27.07 r=1 3035* 20.97 r=2 12.28 14.07 1 2006-2007 r=0 3249* 27.07 r=1 11.08 20.97 r=2 8.13 14.07 1 2007-2008 r=0 24.63 27.07 r=1 19.69 20.97 r=2 7.82 14.07 1 2008-June 2008 r=O 22.74 27.07 r=1 14.46 20.97 r=2 9.29 14.07 *Indicates rejection of the null hypothesis at 5% significance 19 Table 2.f indicates the existence of at least one cointegrating vector in six of the eleven years. The annual cointegration test statistics can be thought of as a measure of the degree of cointegration over time with larger statistics indicating a stronger degree of cointegration (Brester and Goodwin 1993). There are no clear trends quickly revealed in Table 2.f; however, the goal of this model is to determine if the increased Michigan percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of corn prices at the Blissfield, Lake Odessa, Marlette and Middleton grain markets. To discover the answer to the posed question, the maximal eigenvalue test statistics for the years 1998 through 2008 were regressed on the percentage of Michigan’s corn production which was used in the production of ethanol. Similarly, Brester and Goodwin regressed their maximal eigenvalue test statistics on the four-firm concentration ratio of the US. wheat milling industry to determine if the increased four-firm concentration ratio in the US. wheat milling industry affected the annual cointegration of wheat prices at different wheat markets. To run this regression, an ordinary least squares approach would not be sufficient because our regression contains a non normal distribution. A non normal distribution results because the dependent variable in this model is the maximal eigenvalue test statistics. Therefore, Efron’s bootstrapping technique was used to solve the problem of a nonnormal distribution. Brester and Goodwin also utilized Efron’s bootstrapping technique in their analysis. Efron’s bootstrapping technique regurgitates a given sample over and over again and then obtains the sampling distributions of the parameters of interest to fix the problem of non normal distribution (Gujarati and Porter 2009). 20 Using Efron’s bootstrapping technique with 1,000 replications, the result of regressing the annual cointegration maximal eigenvalue test statistic (MAXE) on the Michigan percent of corn used in the production of ethanol (PCEM) is the following: (4) MAXE=43.10 —— 57.81 * PCEM (0.00) (0.22) where the numbers in parentheses are the p-values for the respective parameter estimates. As evidenced by its p-value, the Michigan percent of corn used in the production of ethanol is not significantly different from zero. Therefore, the percentage of Michigan’s corn production used in the production of ethanol is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the percent of Michigan’s corn production used in the production of ethanol in Michigan. The results were the same as the above test (i.e. the Michigan percentage of corn used in the production of ethanol was not significantly different from zero). Therefore, the Michigan percentage of corn used in the production of ethanol is not significantly related to the annual degree of cointegration of grain markets in the state of Michigan. Otherwise stated, one cannot conclude that the increase in the percent of corn used in the production of ethanol has caused corn price relationships at the Blissfield, Lake Odessa, Marlette and Middleton grain markets to diverge from a long- run spatial price relationship equilibrium. Besides testing to establish whether the increased Michigan percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of corn prices at the Blissfield, Lake Odessa, Marlette and Middleton grain 21 markets, also tested was whether the increase in the number of ethanol plants in Michigan altered the annual degree of cointegration of corn prices at the Blissfield, Lake Odessa, Marlette and Middleton grain markets. The following equation uses Efron’s bootstrapping technique with 1,000 replications to regress the annual cointegration maximal eigenvalue test statistic (MAXE) on the using number of ethanol plants in Michigan (EPM): (5) MAXE=42.93 — 3.69 * EPM (0.00) (0.191) where the numbers in parentheses are the p-values for the respective parameter estimates. The coefficient for the number of ethanol plants in Michigan is not significantly different from zero. Therefore, the number of ethanol plants in Michigan is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. When the annual trace test statistics were regressed with the number of ethanol plants in Michigan using Efron’s bootstrapping technique with 1,000 replications the results also suggested that the coefficient for the number of ethanol plants in Michigan was not significantly different from zero. Therefore, one cannot conclude that the increase in the number of ethanol plants in Michigan has caused corn price relationships at the Blissfield, Lake Odessa, Marlette and Middleton grain markets to diverge from a long-run spatial price relationship equilibrium. The same procedure was also completed for Kansas, Iowa and Indiana to determine if the percentage of corn used in the production of ethanol in each state or the number of ethanol plants in each state altered the annual degree of cointegration of corn prices at grain markets in the selected states. To save space, the results and implications 22 are found in Appendix 2. In summary, the results for Kansas, Iowa and Indiana were the same as the results witnessed in Michigan. In Kansas, the grain markets studied were Chapman, Hillsboro, Larned and Osborne. The corn prices at these markets were cointegrated from September 1998 through June 2008. Therefore, the corn prices at Chapman, Hillsboro, Larned and Osborne grain markets had a long-term, or equilibrium, price relationship found between them from September 1998 through June 2008. However, the increase in the percent of corn used in the production of ethanol or the escalation in the number of ethanol plants in Kansas did not conclusively cause corn price relationships at Chapman, Hillsboro, Larned and Osborne to diverge from a long-run price relationship equilibrium. In Iowa, the grain markets studied were Algona, Aubudon, Cedar Rapids and Chariton. The corn prices at these grain markets were cointegrated from September 1998 through June 2008 and contained an equilibrium price relationship. However, we were unable to conclude that the increase in the percent of corn used in the production of ethanol or the escalation in the number of ethanol plants in Iowa caused corn price relationships at Algona, Aubudon, Cedar Rapids and Chariton grain markets to diverge from a long-run price relationship equilibrium. The Indiana grain markets studied were located in Columbus, Delphi, Greensburg and Hamlet. From September 1998 through June 2008 the corn prices at these grain markets were cointegrated. Therefore, the corn prices operated in a long-run price relationship equilibrium. However, there was not enough evidence to conclude that the increase in the percentage of corn used in the production of ethanol or the increase in the number of ethanol plants in Indiana caused corn price relationships at Columbus, Delphi, 23 Greensburg and Hamlet grain markets to diverge from a long-run spatial price relationship equilibrium. National Model Now that the state by state approach to discovering whether the percent of corn used in the production of ethanol influenced the annual degree of cointegration of corn prices throughout certain grain markets is completed, it is of interest to investigate other techniques to test this study’s hypothesis. The following approach utilizes a Midwestern US. Regional model similar to the state specific models. The Midwestern US. Regional model investigates the cointegration of corn prices at four grain markets, one grain market from each of the above investigated states. The grain markets in Marlette, MI; Hillsboro, KS; Chariton, IA; and Greensburg, IN were chosen for the Midwestern US. Regional model. Staying consistent with the previous methodology, the first item to discover is whether the corn prices at the Midwestern US. Regional grain markets were cointegrated from September 1998 through June 2008. Table 2.g details the multivariate cointegration testing of the corn prices in Marlette, Hillsboro, Chariton and Greensburg grain markets. Lag length of three was used in the testing because this was the amount that minimized the FPE. 24 Table 2.g Midwestern Region Multivariate Cointegration Testing Results Null Alternative Cointegration 5% Critical Hypothesis Hypothesis Test Stat Value Trace Test Ho: r=0 H1: r>0 106.43* 47.21 Ho: r=1 H1: r>l 5525* 29.38 Ho: r=2 H1: r>2 19.87* 15.34 Ho: r=3 H1: r>3 1.05 3.84 Max Test Ho: r=0 HIZFI 51.17* 27.07 Ho: r=1 H1:r=2 3539* 20.97 Ho: r=2 H1:r=3 18.82"“ 14.07 Ho: r=3 H1:r=4 1.05 3.76 *Indicates rejection of the null hypothesis at 5% significance Table 2. g displays three cointegrating vectors for the four corn price series using both the maximal eigenvalue test statistic and the trace test statistic. Therefore, the corn prices at Marlette, Hillsboro, Chariton, and Greensburg are cointegrated. Therefore, there is a long-term, or equilibrium, price relationship found between the corn price series at the different grain markets. To determine if the Midwestern U.S. Region percentage of corn used in the production of ethanol affected the annual cointegration of corn prices at Marlette, MI; Hillsboro, KS; Chariton, IN; and Greensburg, IN we first discovered the annual cointegration statistics for the corn prices at the given grain markets. Table 2.h displays the annual cointegration maximal eigenvalue test statistics as well as the proper lag lengths determined by the minimum value of the FPE. The annual test statistic for the null hypothesis r=3 and the corresponding trace statistics have been excluded from Table 2.h to save space. 25 Table 2.h Midwestern U.S. Region Markets Annual Cointegration Tests Lag Maximal Length Null Hypothesis Eigenvalue 5% (using FPE) Time Period Ho: Test Statistic Critical Value 2 Sept. 1998-1999 F0 4911* 27.07 1:1 15.51 20.97 r=2 9.37 14.07 2 1999-2000 r=0 23.65 27.07 1:1 12.58 20.97 r= 5.76 14.07 4 2000-2001 r=0 31.72* 27.07 r=1 14.79 20.97 r=2 11.58 14.07 2 2001-2002 r=0 25.25 27.07 F] 18.36 20.97 r=2 10.51 14.07 1 2002-2003 r=0 26.17 27.07 F] 19.26 20.97 r=2 3.16 14.07 1 2003-2004 F0 309* 27.07 r=1 10.7 20.97 r=2 8.53 14.07 1 2004-2005 F0 3124* 27.07 r=1 8.17 20.97 r=2 4.76 14.07 6 2005-2006 r=0 51.75* 27.07 r=1 29.92* 20.97 r=2 17.19* 14.07 2 2006-2007 r=0 21.98 27.07 r=1 16.70 20.97 r=2 4.59 14.07 2 2007-2008 r=0 22.01 27.07 F] 11.27 20.97 r=2 5.74 14.07 2 2008-June 2008 r=0 56.40* 27.07 r=l 14.95 20.97 r=2 11.57 14.07 26 *Indicates rejection of the null hypothesis at 5% significance Table 2.h indicates the existence of at least one cointegrating vector in six of the eleven years. There are no clear trends illustrated by Table 2.h. To discover if the Midwestern U.S. Region percent of corn used in the production of ethanol affected the annual degree of cointegration, Efron’s bootstrapping technique with 1,000 replications to regress the annual cointegration maximal eigenvalue test statistic (MAXE) on the US. percentage of corn used in the production of ethanol in (US) was employed. The following equation illustrates the results of this procedure: (6) MAXE=26.54 + 61.81 * US (0.00) (0.49) where the numbers in the parentheses are the p-values for the respective parameter estimates. The coefficient for the US. percent of corn used in the production of ethanol is not significantly different from zero. Therefore, the US. percentage of corn used in the production of ethanol is not significantly related to the annual degree of cointegration of grain markets in the Midwestern U.S. Region. Therefore, we are unable to conclude that the increase in the US. percent of com used in the production of ethanol has caused corn price relationships at the Marlette, MI, Hillsboro, KS, Chariton, IA, Greensburg, IN grain markets to diverge from a long-run price relationship equilibrium. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the percent of ethanol used in the production of ethanol. The results were the same as the above test (i.e. the US. percentage of corn used in the production of ethanol was not significantly different from zero). Besides testing to establish whether the increased U.S. percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of com 27 prices at the Marlette, MI, Hillsboro, KS, Chariton, IA, and Greensburg, IN grain markets, also tested was whether the increase in the number of ethanol plants in the US. altered the annual degree of cointegration of corn prices at the Marlette, Hillsboro, Chariton and Greensburg grain markets. Using Efron’s bootstrapping technique with 1,000 replications and regressing the annual cointegration maximal eigenvalue test statistic (MAXE) on the using number of ethanol plants in the US. (EPUS), the following results were found: (7) MAXE = 25.53 + 0.093 * EPUS (0.029) (0.549) where the p-values for the respective parameter estimates are the numbers in parentheses. As shown by its p-value, the number of ethanol plants in the US. is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. When the annual trace test statistics were regressed on the number of ethanol plants in the United States using Efron’s bootstrapping technique with 1,000 replications, the results suggested that the coefficient for the number of ethanol plants in the US. variable was not significantly different from zero. Thus, one cannot conclude that the increase in the number of ethanol plants in the US. has caused corn price relationships at the Marlette, Hillsboro, Chariton and Greensburg grain markets to diverge from a long-run spatial price relationship equilibrium. 28 2.4 Summary The percentage of corn used in the production of ethanol in Michigan, Kansas, Iowa and Indiana did not significantly alter the annual degree of cointegration of corn prices at grain markets in Michigan, Kansas, Iowa and Indiana. Similarly, the increase in the number of ethanol plants in Michigan, Kansas, Iowa and Indiana did not significantly alter the annual degree of cointegration of corn prices at grain markets in Michigan, Kansas, Iowa and Indiana. Moreover, the percentage of corn used in the production of ethanol in the US. (and the number of ethanol plants in the US.) did not significantly alter the annual degree of cointegration of corn prices at grain markets throughout the Midwestern U.S. Region (Michigan, Kansas, Iowa and Indiana). Therefore, one is unable to conclude that the increase in the number of ethanol plants or the escalation in the percent of corn used in the production of ethanol in Michigan, Kansas, Iowa and Indiana caused corn price relationships at the grain markets considered in our study to diverge from a long-term, or equilibrium, price relationship. Additionally, there was not enough evidence to say that the expansion of the number of ethanol plants in the US or the increase in the percent of corn used in the production of ethanol in the US. caused corn price relationships at different grain markets throughout the Midwestern US. to diverge from a long-term price relationship equilibrium. Once again, it is worth noting that if grain market’s corn price series are cointegrated, this has no implication on corn price levels. Instead, if corn prices at different grain markets are cointegrated, it is only concluded that there is a long-term price relationship found between the corn price series at different grain markets. Additionally, if corn price series at the different grain markets are cointegrated, the com 29 price series relationships may deviate from their equilibrium price relationship, but this deviation is temporary because there are economic forces that drive the relationship between corn price series at different grain markets back toward their long-term equilibrium price relationship. These findings have many implications. For instance, despite the increase in the percentage of corn used in the production of ethanol, spatial price relationships at grain markets in the Midwestern U.S. Region and in Michigan, Kansas, Iowa and Indiana have not changed. Therefore, from 1998 through 2008, farmers and commodity traders that utilized knowledge regarding the relationships between corn prices at different grain markets in order to make managerial decisions (e. g. initiating hedging positions or timing of sales) were correct if they assumed the relationships between corn prices at different grain markets remained the same. For example, in Michigan, the relationship between corn prices at Blissfield, Lake Odessa, Marlette and Middleton grain markets did not change, so Michigan farmers selling corn to either grain market were correct to assume the corn price relationships between the four markets remained the same from September 1998 through June 2008. From September 1998 through June 2008, the increase in the percentage of corn used in the production of ethanol escalated, but the relationship between corn prices at different grain markets was not altered. Therefore, from September 1998 through June 2008, it was correct if farmers, hedgers, and merchandisers operated their businesses under the assumption that com price relationships at different grain markets was constant. Despite the fact this analysis only used a subset of grain markets from each state, the grain markets that were analyzed are a good indication of corn price relationships at all the grain markets located throughout Michigan, Kansas, 30 Iowa and Indiana. The existing corn price relationships at grain markets in Michigan, Kansas, Iowa and Indiana did not change when ethanol production expanded. Therefore, the corn price relationships at grain markets in Michigan, Kansas, Iowa and Indiana are the same as they were before the expansion of the ethanol industry. 31 APPENDIX 2.]: SUMMARY STATISTICS FOR DATA SET RECEIVED FROM CASH GRAIN BIDS DATA SERVICE Table 2.i Michigan Weekly Corn Price Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Akron 250 51.17% 267.41 96.51 150.25 564.00 2 Albion 240 53.13% 261.17 106.81 141.00 596.50 3 Auburn 282 44.92% 247.68 103.85 139.00 571.00 4 Blissfield 470 8.20% 241.21 88.16 145.20 588.00 5 Breckenridge 455 11.13% 229.96 88.42 134.00 571 .00 6 Britton 95 81.45% 225.12 65.71 144.00 394.75 7 Brown City 413 19.34% 237.93 89.07 136.00 571.00 8 Buchanan 443 13.48% 224.29 83.94 136.00 581.00 9 Caledonia 486 5.08% 231.59 86.85 137.00 585.00 10 Capac 56 89.06% 412.87 91.60 300.00 570.00 11 Caro 158 69.14% 286.57 120.37 149.00 573.00 12 Clarksville 85 83.40% 167.56 19.10 135.00 204.60 13 Coleman 33 93.55% 457.74 80.98 313.50 565.50 14 Constantine 413 19.34% 217.96 76.06 140.00 600.00 15 Croswell 102 80.08% 342.30 104.48 174.00 564.00 16 Elkton 250 51 .17% 267.42 96.51 150.25 564.00 17 Emmett 80 84.38% 388.83 88.79 293.80 569.50 18 Fremont 362 29.30% 241.28 96.10 143.00 582.00 19 Grand Ledge 383 25.20% 245.82 95.93 147.20 587.00 20 Hamilton 464 9.38% 240.13 88.11 148.00 598.00 21 Hemlock 421 17.77% 210.66 74.33 134.00 574.00 22 Henderson 103 79.88% 347.82 104.72 182.33 565.00 23 Holland 448 12.50% 246.21 87.11 154.00 606.00 24 Howard City 55 89.26% 411.49 91.83 286.33 561.00 25 Hudsonville 366 28.52% 211.19 41.43 152.00 393.67 26 Imlay City 457 10.74% 231.56 84.98 144.00 570.00 27 Jasper 470 8.20% 236.31 87.26 138.20 583.00 28 Jeddo 56 89.06% 406.94 96.02 285.00 570.00 29 Jonesville 219 57.23% 279.31 108.87 144.80 578.00 30 LakeOdessa 486 5.08% 228.35 86.79 137.00 576.00 Note: Highlighting indicates the grain market was chosen for analysis 32 Table 2.i (cont’d). Grain # of % Std. # Market Obs. Missing Mean Dev. Min Max 31 Lennon 102 80.08% 344.58 105.97 174.00 565.00 32 Marlette 469 8.40% 231.05 85.77 136.00 571.00 33 Marshall 430 16.02% 213.77 81.61 133.00 582.00 34 Middleton 469 8.40% 228.35 87.02 136.00 571.00 35 Millington 97 81.05% 215.60 65.50 140.80 386.50 36 Newaygo 469 8.40% 232.31 87.18 136.00 579.00 37 North Star 189 63.09% 196.63 50.76 133.20 386.25 38 - Oakley 141 72.46% 199.78 41.30 141.00 311.75 39 Ottawa Lake 453 11.52% 246.55 89.68 150.60 591.00 40 Palms 56 89.06% 409.65 94.46 291.00 570.00 41 Pigeon 285 44.34% 258.09 94.54 150.25 562.00 42 Ravenna 55 89.26% 41 1.28 91.75 286.33 561 .00 43 Reading 85 83.40% 281.01 122.40 162.60 582.50 44 Richmond 103 79.88% 344.37 106.28 173.50 566.00 45 Riga 56 89.06% 427.69 90.57 314.80 586.60 46 Saginaw 312 39.06% 202.39 33.16 141.00 307.33 47 Saline 119 76.76% 273.77 127.16 138.00 579.00 48 Saranac 204 60.16% 245.07 97.59 160.00 591.00 49 Six Lakes 264 48.44% 253.55 95.24 148.00 551.00 50 Snover 319 37.70% 249.51 92.32 139.00 561.00 51 St. Johns 167 67.38% 173.94 16.58 135.00 204.60 52 Vriesland 52 89.84% 203.36 30.83 147.33 271.50 53 Webberville 481 6.05% 236.04 88.31 145.00 587.00 54 WhitePigeon 322 37.11% 261.18 97.22 144.00 592.00 55 Yale 473 7.62% 233.06 85.77 136.00 571.50 56 Zeeland 461 9.96% 235.36 87.66 147.00 591.00 Note: Highlighting indicates the grain market was chosen for analysis 33 Table 2.j Kansas Weekly Corn Price Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Abbyville 352 31.25% 242.27 89.66 156.40 577.67 2 Abilene 153 70.12% 272.30 125.28 152.00 569.50 3 Agenda 423 17.38% 238.09 92.07 140.60 580.00 4 Albert 281 45.12% 270.84 98.99 163.67 583.00 5 Americus 110 78.52% 171.18 17.02 131.80 207.80 6 Andale 461 9.96% 233.92 87.55 149.00 575.50 7 Anthony 111 78.32% 199.09 15.00 163.60 227.25 8 Argonia 87 83.01% 187.43 16.03 167.50 290.00 9 Arkansas City 361 29.49% 223.34 79.40 150.00 572.00 10 Arlipgton 234 54.30% 294.36 106.11 171.00 587.00 11 Arnold 239 53.32% 291.07 105.06 170.20 585.00 12 Asherville 242 52.73% 265.25 103.95 150.50 566.67 13 Atchison 427 16.60% 217.42 49.12 150.80 411.00 14 Athol 431 15.82% 227.02 90.02 137.25 548.20 15 Barnes 423 17.38% 238.03 92.23 139.80 580.00 16 Bartlett 407 20.51% 232.91 73.89 146.00 561.20 17 Bavaria 308 39.84% 263.07 95.75 158.00 578.33 18 Baxter Springs 231 54.88% 280.16 105.83 164.60 582.50 19 Beattie 478 6.64% 225.35 86.48 135.75 550.20 20 Beloit 442 13.67% 233.23 87.92 139.60 568.67 21 Bennington 133 74.02% 303.88 107.79 145.00 534.50 22 Benton 308 39.84% 265.79 96.64 159.00 582.33 23 Bern 302 41.02% 255.97 99.53 134.00 566.00 24 Bison 400 21.88% 227.42 76.25 151.00 577.00 25 Bogue 253 50.59% 274.44 103.38 151.67 571.50 26 Boyd 260 49.22% 268.17 98.15 163.67 583.00 27 Bremen 342 33.20% 248.45 98.42 137.00 580.00 28 Breton 244 52.34% 260.95 92.40 172.00 574.50 29 Bridgeport 217 57.62% 278.92 110.55 159.00 580.33 30 Brownell 107 79.10% 322.29 84.23 176.25 519.60 34 ' Table 2.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 31 Bucklin 348 32.03% 258.07 99.40 167.00 586.00 32 Buhler 308 39.84% 264.77 97.02 159.00 579.50 33 Burlingame 69 86.52% 404.67 87.75 295.25 576.50 34 Burlington 69 86.52% 404.66 87.76 294.60 576.50 35 Burns 343 33.01% 256.02 94.21 156.00 580.33 36 Burrton 163 68.16% 319.12 107.41 160.50 579.00 37 Cairo 234 54.30% 294.08 105.60 171.00 585.00 38 Caldwell 440 14.06% 230.88 83.26 158.20 570.50 39 Calista 234 54.30% 294.23 105.86 171.00 586.00 40 Canada 218 57.42% 277.63 111.40 156.00 580.33 41 Canton 0 100.00% 0.00 0.00 0.00 0.00 42 Chanute 435 15.04% 229.22 83.91 143.00 587.00 43 Chapman 496 3.13% 233.28 88.65 144.00 579.50 44 Cheney 314 38.67% 265.20 96.23 157.60 572.00 45 Clay Center 299 41.60% 237.86 98.61 139.00 566.50 46 Clayton 69 86.52% 413.93 81.31 312.60 573.00 47 Clifton 416 18.75% 224.77 91.36 140.00 580.00 48 Clyde 430 16.02% 227.85 90.48 140.60 580.00 49 Colby 406 20.70% 221.75 65.06 151.00 473.00 50 Coldwater 68 . 86.72% 429.96 84.74 337.40 594.25 51 Collyer 253 50.59% 276.23 103.75 151.67 576.50 52 Columbus 477 6.84% 242.46 86.57 146.00 580.50 53 Colwich 53 89.65% 345.79 80.41 236.33 555.00 54 Concordia 451 1 1.91% 232.17 86.41 143.60 567.67 55 Conway 304 40.63% 264.83 96.61 159.00 580.33 56 Conway Springs 226 55.86% 197.56 29.11 143.00 283.00 57 Courtland 416 18.75% 215.09 72.01 142.60 567.00 58 Cunningham 187 63.48% 246.73 97.46 161.80 575.60 59 Danville 205 59.96% 267.73 1 11.60 161.00 570.25 60 Delphos 57 88.87% 238.76 27.33 197.50 308.00 Note: Highlighting indicates the grain market was chosen for analysis 35 Table 2.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 61 Dighton 486 5.08% 248.12 87.84 140.00 585.00 62 Dillwyn 242 52.73% 290.68 107.57 173.80 589.00 63 Dodge City 143 72.07% 262.18 59.99 183.00 427.50 64 Dresden 220 57.03% 252.49 69.19 167.60 419.00 65 Durham 107 79.10% 190.92 15.46 150.00 218.75 66 Edgerton 457 10.74% 233.06 89.22 149.00 576.50 67 Effingham 68 86.72% 406.60 90.52 294.80 580.00 68 Ellinwood 348 32.03% 254.44 95.23 162.60 583.00 69 Ellis 40 92.19% 182.16 13.08 152.80 202.20 70 Ellsworth 366 28.52% 248.21 95.83 143.00 560.80 71 Emporia 251 50.98% 221.50 47.86 148.80 381.80 72 Fairview 44 91.41% 428.22 95.65 300.33 566.00 73 Florence 338 33.98% 256.72 94.73 156.00 580.33 74 Ford 342 33.20% 258.35 94.65 161.50 586.00 75 Fredonia 246 51.95% 212.39 82.23 137.60 571.75 76 Galatia 347 32.23% 256.47 93.12 158.00 580.33 77 Galva 308 39.84% 264.03 96.27 158.00 580.33 78 Garden City 430 16.02% 236.50 55.42 167.40 400.00 79 Garden Plain 417 18.55% 246.09 90.97 148.00 572.00 80 Garfield 470 8.20% 243.31 87.73 155.00 576.00 81 Girard 482 5.86% 239.52 84.55 146.00 586.00 82 Glade 65 87.30% 175.39 14.31 142.80 198.00 83 Glen Elder 382 25.39% 242.41 92.02 139.00 565.50 ‘ 84 Goodland 2 99.61% 217.50 43.13 187.00 248.00 85 Gorham 467 8.79% 241.50 86.30 152.50 583.00 86 Grainfield 253 50.59% 276.49 103.97 151.67 576.50 87 Gray 91 82.23% 351.83 115.12 220.20 582.00 88 Great Bend 467 8.79% 238.06 86.10 155.00 583.00 89 Greenleaf 409 20.12% 200.88 48.05 141.60 407.00 90 Greensburg 184 64.06% 219.86 34.17 164.20 325.50 36 Table 2.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 Gridley 147 71.29% 310.27 117.51 157.33 575.00 92 Haddam 260 49.22% 266.68 105.38 143.80 580.00 93 Halstead 452 1 1.72% 238.77 90.86 144.00 579.00 94 Hanover 381 25.59% 193.88 33.04 136.00 301.00 95 Hanston 167 67.38% 308.04 1 10.35 179.00 582.00 96 Hartford 103 79.88% 162.33 17.01 121.75 196.80 97 Haven 447 12.70% 238.55 90.59 149.00 586.33 98 Haviland 417 18.55% 252.29 93.35 150.00 585.50 99 Hepler 52 89.84% 183.38 23.88 125.00 219.00 100 Hiawatha 402 21.48% 193.01 32.71 130.60 302.67 101 Hill City 98 80.86% 376.29 91.72 199.00 571.50 102 Hillsboro 477 6.84% 235.37 86.82 150.00 580.33 103 Hilton 221 56.84% 277.96 110.41 159.00 581.33 104 Hoisington 153 70.12% 315.74 110.62 173.33 582.00 105 Holton 400 21.88% 227.53 92.37 136.00 576.00 106 Home 192 62.50% 219.74 91.58 136.25 558.00 107 Hope 472 7.81% 241.84 89.49 150.00 570.00 108 Hoxie 437 14.65% 236.37 90.17 150.00 570.00 109 Hudson 428 16.41% 228.96 75.96 159.00 588.00 110 Hunter 391 23.63% 242.18 93.16 139.00 569.50 111 Hutchinson 217 57.62% 219.07 36.13 163.00 318.50 112 Inman 217 57.62% 279.51 110.87 159.00 581.33 113 Isabel 428 16.41% 229.38 75.40 160.00 588.00 114 luka 354 30.86% 261.53 99.09 162.40 589.00 115 Jamestown 207 59.57% 210.53 35.51 140.00 303.75 116 Jetmore 167 67.38% 308.08 110.32 179.00 582.00 117 Jewell 25 95.12% 223.91 12.58 198.20 244.33 1 18 Junction City 423 17.38% 212.48 75.63 144.40 564.67 119 Kackley 242 52.73% 265.24 103.95 150.50 566.67 120 Kalvesta 175 65.82% 225.72 36.20 167.60 333.50 Note: Highlighting indicates the grain market was chosen for analysis 37 Table 2.] (cont’d). # of % Std. ‘ # Grain Market Obs. Missing Mean Dev. Min Max 121 Kansas City 67 86.91% 280.50 158.97 148.00 599.00 122 Kensington 420 17.97% 207.30 73.34 136.00 546.75 123 Kingsdown 249 51.37% 288.68 102.64 182.75 586.00 124 Kiowa 149 70.90% 321.06 117.03 163.20 594.00 125 LaCrosse 40 92.19% 195.36 5.07 185.00 207.00 126 Laird 141 72.46% 222.94 40.18 170.20 328.50 127 Lancaster 368 28.13% 239.71 93.07 143.00 578.00 128 Larned 493 3.71% 241.20 85.83 155.00 576.00 129 Lawrence 126 75.39% 332.54 104.58 202.00 576.50 130 Lehigh 308 39.84% 263.23 96.84 156.00 580.33 131 Lenora 232 54.69% 267.77 94.69 166.20 567.25 132 Leonardville 61 88.09% 408.21 89.07 302.00 566.50 133 LeRoy 476 7.03% 229.68 87.72 136.00 575.00 134 Lewis 349 31.84% 209.89 33.23 155.00 322.50 135 Lindsborg 308 39.84% 263.93 96.37 159.00 577.50 136 Linn 259 49.41% 267.25 105.40 143.80 580.00 137 Logan 380 25.78% 200.78 48.13 131.00 403.25 138 Longford 110 78.52% 344.29 110.87 185.60 565.00 139 Ludell 151 70.51% 309.35 114.54 161.33 566.00 140 Macksville 467 8.79% 245 .65 87.51 156.00 576.00 141 Manhattan 436 14.84% 230.43 88.48 136.00 557.50 142 Marion 308 39.84% 263.23 96.84 156.00 580.33 143 Marquette 215 58.01% 191.12 25.98 148.00 287.00 144 Marysville 432 15.63% 233.40 92.96 137.00 580.00 145 McCracken 40 92.19% 195.36 5 .07 185.00 207.00 146 Mche 460 10.16% 231.92 84.60 159.40 585.00 147 McPherson 481 6.05% 235.77 86.59 148.00 581.33 148 Melvern 310 39.45% 256.90 99.16 154.00 576.50 149 Menlo 401 21.68% 228.49 81.87 151.00 574.50 150 Meriden 313 38.87% 234.91 100.32 137.00 571.00 Note: Highlighting indicates the grain market was chosen for analysis 38 Table 2.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 151 Milton 76 85.16% 249.66 62.90 161.50 478.33 152 Miltonvale 61 88.09% 408.20 89.05 302.00 566.50 153 Mingo 402 21.48% 230.34 81.63 154.00 576.50 154 Minneapolis 441 13.87% 222.25 82.96 149.00 568.00 155 Morganville 360 29.69% 214.28 74.14 140.40 566.25 156 Morland 249 51.37% 275.91 103.78 151.67 571.50 157 Morrill 465 9.18% 226.20 87.31 134.25 562.00 158 Moundridge 491 4.10% 235.25 86.67 149.00 580.50 159 Mount Hope 259 49.41% 275.13 103.45 161.00 581.50 160 Mullinville 386 24.61% 233.75 84.16 163.00 590.00 161 Mulvane 386 24.61% 249.18 93.74 131.00 573.00 162 Murdock 307 40.04% 251.96 73.48 157.60 497.00 163 Muscotah 57 88.87% 230.91 32.35 184.80 299.50 164 Narka 184 64.06% 278.50 122.29 141.80 583.00 165 Natoma 40 92.19% 195.33 5.13 184.50 207.00 166 Ness City 142 72.27% 222.71 40.13 170.20 328.50 167 New Cambria 228 55.47% 283.73 110.26 159.00 579.00 168 Newton 343 33.01% 257.28 92.47 159.00 576.33 169 Nickerson 498 2.73% 237.22 86.09 148.00 584.33 170 Norton 419 18.16% 231.79 92.07 143.00 571.00 171 Oakley 450 12.11% 244.89 89.18 154.00 578.00 172 Oberlin 427 16.60% 239.58 90.87 148.00 566.00 173 Offerle 473 7.62% 252.92 87.97 167.00 586.00 174 Osborne 484 5.47% 231.22 87.16 142.00 555.00 175 Ottawa 462 9.77% 227.00 87.98 147.40 576.50 176 Overbrook 457 10.74% 232.71 89.67 144.00 576.50 177 Palmer 168 67.19% 301.71 105.26 165.00 575.00 178 Paola 402 21.48% 197.80 44.25 133.80 405.00 179 Park 188 63.28% 304.22 107.00 151.67 576.50 180 Partridge 308 39.84% 268.65 96.54 165.00 586.33 Note: Highlighting indicates the grain market was chosen for analysis 39 Table 2.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 181 Pawnee Rock 437 14.65% 239.33 88.70 155.00 582.00 182 Peabody 340 33.59% 256.77 94.30 157.00 580.33 183 Penalosa 234 54.30% 294.36 106.11 171.00 587.00 184 Penokee 253 50.59% 274.51 103.56 151.67 571.50 185 Phillipsburg 460 10.16% 228.45 87.22 133.20 548.00 186 Pittsburg 427 16.60% 221.54 74.50 142.25 590.00 187 Pratt 206 59.77% 199.21 23 .76 162.00 290.00 188 Preston 234 54.30% 294.49 106.36 171.00 588.00 189 Protection 288 43.75% 284.02 102.48 166.00 594.25 190 Quinter 261 49.02% 273 .70 103.15 151.67 5 76.50 191 Rago 195 61.91% 278.37 77.60 177.00 497.00 192 Randall 465 9.18% 228.76 86.11 139.00 465.00 193 Ransom 143 72.07% 257.21 127.76 159.60 585.00 194 Republic 300 41.41% 260.57 98.78 147.50 574.00 195 Rexford 253 50.59% 202.69 36.61 150.00 316.50 196 Roxbury 308 39.84% 263.58 96.48 158.00 580.33 197 Rush Center 210 58.98% 218.58 36.78 152.80 320.50 198 Russell 163 68.16% 220.92 34.73 170.75 317.00 199 Sabetha 398 22.27% 229.88 91.12 132.00 556.00 200 Salina 89 82.62% 307.20 121.04 188.00 580.00 201 Scandia 32 93.75% 198.14 4.93 189.00 208.50 202 Scott City 432 15.63% 235.32 56.35 158.40 399.00 203 Scranton 25 95.12% 507.98 52.07 405.00 576.50 204 Sedgwick 251 50.98% 233.45 65.33 160.60 418.50 205 Segin 405 20.90% 228.02 81.57 151.00 574.50 206 Selden 233 54.49% 269.18 95.08 167.50 571.25 207 Seneca 35 93.16% 425 .96 95 .94 300.33 561.40 208 Smith Center 393 23.24% 220.93 88.04 140.00 548.20 209 Solomon 11.0 78.52% 346.27 113.17 185.60 572.00 210 Spearville 210 58.98% 236.65 50.23 182.75 408.50 40 Table 2.j (cont’d). Grain # of % Std. # Market Obs. Missing Mean Dev. Min Max 211 St Marys 376 26.56% 234.53 91.35 139.00 556.00 212 Stafford 278 45.70% 213.20 52.25 158.00 428.50 213 Sterling 484 5.47% 237.72 88.26 149.20 575.00 214 Stockton 439 14.26% 221.00 82.00 142.00 547.75 215 Studley 258 49.61% 272.80 103.16 151.67 571.50 216 Sublette 429 16.21% 239.30 54.78 169.00 400.00 217 Talmage 110 78.52% 344.86 111.52 185.60 567.00 218 Tampa 430 16.02% 231.23 85.51 150.00 575.00 219 Tipton 392 23.44% 242.05 93.06 139.00 569.50 220 Topeka 45 91.21% 239.56 30.13 202.00 316.00 221 Turon 234 54.30% 294.35 106.11 171.00 587.00 222 Utica 81 84.18% 412.88 79.57 313.40 585.00 223 Valley Center 40 92.19% 195.34 4.57 185.75 209.00 224 Wakeeney 250 51.17% 277.22 103.97 151.67 576.50 225 Wakefield 248 51.56% 198.14 78.31 140.00 541.75 226 Waldeck 234 54.30% 294.35 106.11 171.00 587.00 227 Walker 40 92. 19% 198.02 5.14 189.00 208.50 228 Walton 485 5.27% 235.09 86.03 148.00 580.33 229 Wamego 420 17.97% 210.83 74.43 138.75 556.00 230 Washington 261 49.02% 267.33 104.85 144.60 580.00 231 Waterville 368 28.13% 222.68 88.38 135.75 568.00 232 Waverly 69 86.52% 404.66 87.76 294.60 576.50 233 Wellington 385 24.80% 238.84 86.59 160.00 571.00 234 Wellsville 69 86.52% 407.05 86.10 300.25 576.50 235 Westphalia 147 71.29% 310.27 117.51 157.33 575.00 236 White Cloud 108 78.91% 346.22 105.92 180.00 566.00 237 Whitewater 343 33.01% 259.05 94.94 160.00 584.33 238 Whiting 69 86.52% 397.66 89.81 285.80 571.00 239 Wilmore 179 65.04% 219.14 35.30 163.60 322.50 240 Wilroads 238 53.52% 294.25 104.00 182.75 591 .00 41 Table 2.j (cont’d). Grain # of % Std. . # Market Obs. Missing Mean Dev. Min Max 241 Windom 308 39.84% 264.21 96.13 159.00 580.33 242 Winfield 474 7.42% 230.26 83.40 134.00 560.00 243 Wright 457 10.74% 252.48 88.58 167.00 590.00 244 Yates Center 236 53.91% 240.35 88.60 154.50 557.50 245 Zenda 64 87.50% 203.04 8.68 178.75 215.00 246 Zurich 40 92.19% 195.32 5.14 184.50 207.00 42 Table 2.k Iowa Weekly Corn Price Summary Statistics # of % Std. # Grain Market Obs. Missian Mean Dev. Min Max 1 Ackley 383 25.20% 231.92 92.34 131.60 571.00 2 Adair 473 7.62% 225.21 88.22 131.00 571.00 3 Ainsworth 282 44.92% 258.49 93.13 149.75 556.00 4 Akron 283 44.73% 253.60 103.58 134.40 570.00 5 Albert City 346 32.42% 243.78 96.03 136.80 572.00 6 Albia 92 82.03% 234.71 32.36 194.50 308.67 7 Albion 280 45.31% 253.79 98.32 140.60 562.00 8 Alden 474 7.42% 223.06 86.76 133.00 569.00 9 Alexander 403 21.29% 230.75 93.06 126.75 572.00 10 Algona 486 5.08% 220.82 88.56 129.00 567.00 11 Alleman 482 5.86% 225.27 87.10 129.00 568.00 12 Allendorf 309 39.65% 253.49 100.93 143.20 584.00 13 Allison 226 55.86% 260.08 102.78 139.00 553.00 14 Alta 500 2.34% 222.29 88.11 131.20 573.00 15 Alton 463 9.57% 230.93 92.77 136.40 589.00 16 Altoona 378 26.17% 237.39 90.36 134.00 570.00 17 Alvord 230 55.08% 262.91 111.35 146.80 586.00 18 Ames 120 76.56% 240.14 120.81 154.00 569.00 19 Anthon 111 78.32% 165.22 17.61 126.60 205.00 20 Arcadia 161 68.55% 284.92 120.37 133.00 560.50 21 Archer 159 68.95% 295.31 125.61 146.80 587.00 22 Aredale 254 50.39% 250.87 100.51 144.20 553.00 23 Arlington 124 75.78% 314.79 11 1.07 163.25 547.60 24 Armstrong 476 7.03% 217.82 87.73 125.00 562.00 25 Ashton 484 5.47% 225.76 90.82 134.00 584.00 26 Atlantic 279 45.51% 254.30 99.61 135.20 561.00 27 Auburn 133 74.02% 224.00 61.23 136.25 398.25 28 Audubon 471 8.01% 222.51 88.48 127.00 568.00 . 29 Aurelia 495 3.32% 222.35 89.06 130.20 576.00 30 Avoca 162 68.36% 272.03 121.06 130.80 562.00 Note: Highlighting indicates the grain market was chosen for analysis 43 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 31 Avon 354 30.86% 250.91 92.91 145.40 575.00 32 Ayrshire 285 44.34% 249.38 101.78 134.80 565.00 33 Badgr 260 49.22% 259.89 103.39 139.40 568.00 34 Bagley 269 47.46% 192.33 34.91 128.00 298.67 35 Bancroft 193 62.30% 192.80 35.58 131.00 319.00 36 Barnes City 203 60.35% 280.76 105.02 146.00 556.60 37 Barnum 258 49.61% 260.61 103.46 139.40 568.00 38 Battle Creek 149 70.90% 297.35 121.26 133.50 572.00 39 Bayard 441 13.87% 224.79 89.04 132.00 572.00 40 Beaman 488 4.69% 224.07 84.76 131.50 562.00 41 Beaver 396 22.66% 232.52 92.98 132.00 570.00 42 Belmond 237 53.71% 266.17 107.22 150.00 568.00 43 Bettendorf 355 30.66% 255.96 91.78 152.50 569.00 44 Blairsburg 393 23.24% 232.20 94.31 132.00 573 .00 45 Blairstown 191 62.70% 264.21 1 14.05 136.00 559.50 46 Blakesburg 264 48.44% 190.70 24.69 137.20 362.00 47 Blencoe 482 5.86% 224.11 88.78 134.40 572.00 48 Bloomfield 200 60.94% 202.15 35.60 133.00 303.33 49 Bode 260 49.22% 259.90 103.39 139.40 568.00 50 Bondurant 366 28.52% 236.71 91.65 134.00 570.00 51 Boone 471 8.01% 227.40 88.43 133.00 573.00 52 Booneville 437 14.65% 231.38 90.54 129.00 568.00 53 Boxholm 319 37.70% 246.18 97.80 133.40 569.00 54 Boyden 302 41 .02% 262.23 103.05 149.00 590.00 55 Bradford 405 20.90% 228.58 90.87 131.60 571.00 56 Bradgate 210 58.98% 260.64 112.30 139.20 573.00 57 Bristow 91 82.23% 218.55 79.45 141.50 470.00 58 Britt 334 34.77% 239.50 98.99 134.80 573.00 59 Brooklyn 155 69.73% 292.28 114.78 146.60 556.00 60 Brunsville 260 49.22% 221.22 93.28 136.00 590.00 44 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 61 Buckeye 424 17.19% 226.64 91.56 133.20 575.00 62 Buckingham 399 22.07% 234.04 88.95 135.20 558.00 63 Buffalo Center 313 38.87% 243.40 96.77 131.00 562.00 64 Burlington 254 50.39% 277.10 97.31 173.60 565.00 65 Burt 416 18.75% 229.48 91.77 131.00 570.00 66 Callender 190 62.89% 197.18 35.55 133.40 295.67 67 Cambridge 329 35.74% 248.92 95.29 136.60 568.00 68 Carlisle 133 74.02% 231.09 60.44 145.40 397.50 69 Cames 223 56.45% 283.33 105.10 175.00 586.00 70 Carpenter 310 39.45% 244.81 94.54 133.50 555.00 71 Carroll 65 87.30% 306.48 168.72 139.67 568.00 72 Casey 300 41.41% 249.79 98.26 132.40 568.00 73 Cedar Falls 255 50.20% 261.21 99.04 147.25 557.40 74 Cedar Rapids 494 3.52% 244.48 82.91 155.00 583.00 75 Center Point 177 65.43% 214.30 31.22 152.00 306.50 76 Centerville 71 86.13% 225.58 36.90 181.50 299.50 77 Central City 169 66.99% 285.06 112.59 148.60 561.50 78 Chapin 417 18.55% 231.30 92.79 127.00 576.00 79 Chariton 493 3.71% 227.61 81.70 130.00 556.75 80 Charles City 166 67.58% 278.51 121.62 139.25 564.00 ' 81 Chelsea 79 84.57% 378.43 91.97 273.00 559.50 82 Cherokee 473 7.62% 222.78 90.81 129.20 575.00 83 Chillicothe 409 20.12% 207.14 46.35 136.00 396.00 84 Churdan 168 67.19% 271.58 121.69 136.60 572.00 85 Clare 260 49.22% 259.90 103.39 139.40 568.00 86 Clarence 493 3.71% 231.52 82.94 137.50 556.60 87 Clarinda 262 48.83% 257.85 99.47 130.33 555.00 88 Clarion 462 9.77% 222.29 90.66 130.00 573.00 89 Clarksville 251 50.98% 220.76 88.07 129.20 559.00 90 Clayton 442 13.67% 237.97 81.60 133.60 549.25 Note: Highlighting indicates the grain market was chosen for analysis 45 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 Clear Lake 438 14.45% 210.99 83.49 126.00 558.00 92 Cleghom 494 3 .52% 222.87 91.29 127.40 584.00 93 Clemons 241 52.93% 259.39 103.34 139.25 567.00 94 Clermont 124 75.78% 314.37 112.00 160.75 548.00 95 Clinton 441 13.87% 251.08 89.50 148.80 584.00 96 Clutier 236 53.91% 263.89 102.38 151.20 560.00 97 Collins 387 24.41% 237.06 92.52 131.00 568.00 98 C010 490 4.30% 225.59 86.62 133.00 568.00 99 Colwell 152 70.31% 286.64 123.38 139.25 564.00 100 Conrad 447 12.70% 228.40 86.71 133.00 562.00 101 Conroy 504 1.56% 225.81 83.09 134.20 559.25 102 Coon Rapids 455 11.13% 222.06 87.61 132.00 567.00 103 Coming 216 57.81% 183.52 25.51 130.00 263.00 104 Correctionville 292 42.97% 253.71 99.93 138.00 572.00 105 Corvvith 488 4.69% 222.52 88.28 131.00 576.00 106 Corydon 164 67.97% 253 .49 109.66 141.50 547.00 107 Coulter 488 4.69% 222.01 87.96 127.50 572.00 108 Council Bluffs 314 38.67% 202.31 33.36 142.00 310.50 109 Craig 359 29.88% 199.19 48.12 136.60 394.00 110 Crawfordsville 126 75.39% 311.28 108.12 158.50 546.40 111 Cresco 476 7.03% 219.42 84.93 125.00 553.75 112 Creston 497 2.93% 230.30 87.63 129.00 572.00 113 Cylinder 342 33.20% 228.32 100.37 127.80 570.00 114 Dallas Center 406 20.70% 232.48 92.19 129.00 569.00 115 Dana 319 37.70% 248.60 97.22 136.00 572.00 116 Davenport 476 7.03% 245.56 83.29 151.00 569.00 117 Dawson 395 22.85% 232.85 93.42 129.00 571.00 118 Dayton 168 67.19% 271.58 121.69 136.60 572.00 119 Decatur 92 82.03% 229.20 30.97 188.00 301.67 120 Decorah 258 49.61% 252.20 99.53 129.25 542.33 46 Table 2.k (cont’d). # of % Std. . # Grain Market Obs. Missing Mean Dev. Min Max 121 Dedham 361 29.49% 228.11 100.35 130.75 570.00 122 Denison 356 30.47% 232.36 101.22 135.00 574.00 123 Des Moines 354 30.86% 250.99 92.89 140.00 575.00 124 DeSoto 91 82.23% 169.92 20.41 128.60 206.80 125 Dewar 194 62.11% 271.00 113.99 148.20 564.00 126 Dewitt 259 49.41% 269.93 98.55 156.40 564.20 127 Dickens 485 5.27% 221.13 89.38 129.00 574.00 128 Dike 483 5.66% 226.05 84.60 133.80 555.00 129 Dixon 282 44.92% 265.33 94.30 156.40 560.20 130 Dolliver 241 52.93% 268.41 116.73 134.75 662.67 131 Donnellson 111 78.32% 185.35 19.07 143.20 224.00 132 Doon 240 53.13% 257.23 110.06 145.80 583.00 133 Dougherty 319 37.70% 247.46 96.16 136.20 568.00 134 Dow City 48 90.63% 343.71 139.12 194.00 565.00 135 Dows 489 4.49% 222.02 88.38 130.80 573.00 136 Dubuque 199 61.13% 226.34 53.51 142.60 388.67 137 Dumont 337 34.18% 235.87 93.79 140.20 562.00 138 Duncombe 260 49.22% 259.89 103.40 139.40 568.00 139 Dunkerton 497 2.93% 228.18 84.92 136.25 564.00 140 Dunlap 365 28.71% 233.26 95.89 131.00 570.25 141 Dysart 490 4.30% 226.04 83.10 136.00 562.00 142 Eagle Grove 488 4.69% 222.98 87.53 131.00 576.00 143 Earlham 168 67.19% 274.80 120.50 139.40 574.00 144 Early 168 67.19% 270.50 122.38 136.60 572.00 145 Eddyville 474 7.42% 238.41 80.99 147.00 565.75 146 Edgewood 419 18.16% 235.43 85.93 136.00 553.00 147 Elberon 79 84.57% 378.43 91.97 273.00 559.50 148 Eldon 384 25.00% 210.16 45.55 134.00 396.00 149 Eldora 98 80.86% 194.35 26.28 154.00 248.20 150 Eldridge 254 50.39% 273.05 96.03 156.40 565.25 47 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 151 Elgin 124 75.78% 314.39 112.00 160.75 548.00 152 Elk Horn 162 68.36% 271.04 121.00 129.80 560.00 153 Elkader 166 67.58% 279.41 110.78 138.00 533.00 154 Elkhart 492 3.91% 227.47 86.58 131.80 569.00 155 Elliott 162 68.36% 273.43 121.70 131.80 563.00 156 Ellsworth 457 10.74% 220.20 89.52 131.00 569.00 157 Elma 231 54.88% 260.33 106.47 140.00 559.50 158 Emerson 290 43.36% 191.30 31.97 131.00 295.50 159 Emmetsburg 345 32.62% 243.37 96.36 135.80 572.00 160 Estherville 87 83.01% 377.74 100.07 192.00 577.00 161 Everly 216 57.81% 267.48 115.34 135.67 581.00 162 Exira 472 7.81% 222.22 88.45 127.00 568.00 163 Fairbank 414 19.14% 213.44 72.20 137.00 558.00 164 Fairfax 231 54.88% 263.74 100.62 161.00 583.00 165 Fairfield 348 32.03% 241.85 91.61 138.80 550.20 166 Farley 169 66.99% 283.35 111.75 145.40 553.50 167 Famharnville 168 67.19% 271.61 121.66 136.60 572.00 168 Farragut 170 66.80% 285.80 120.08 145.33 576.00 169 Faulkner 50 90.23% 411.97 109.54 294.67 569.00 170 Fayette 193 62.30% 283.22 97.54 137.33 553.00 171 Fenton 317 38.09% 242.91 97.09 131.00 564.00 172 Femald 225 56.05% 199.02 34.57 132.00 299.00 173 Fonda 344 32.81% 244.14 96.22 136.80 572.00 174 Fontanelle 403 21.29% 212.25 86.81 130.00 554.25 175 Fort Atkinson 92 82.03% 223.00 32.17 185.50 297.50 176 Fort Dodge 493 3.71% 223.62 90.28 131.40 573.00 177 Fostoria 265 48.24% 254.68 105.84 134.40 574.00 178 Fredericksburg 352 31.25% 229.56 92.53 127.40 543.00 179 Frederika 259 49.41% 251.77 98.28 133.50 544.00 180 Galva 199 61.13% 266.11 109.97 147.00 580.00 48 Table 2.k (cOnt’d). # of % Std. # Grain Market Obs. Missin Mean Dev. Min Max 181 Garden City 485 5.27% 221.67 86.27 133.00 569.00 182 Garner 395 22.85% 187.55 32.12 128.00 294.75 183 Garrison 77 84.96% 174.34 21.66 135.60 207.20 184 Garwin 297 41.99% 249.48 98.15 133.20 562.00 185 Geneva 85 83.40% 206.39 63.91 138.00 368.00 186 George 310 39.45% 257.01 102.14 148.40 589.00 187 Gilbert 406 20.70% 233 .26 92.26 132.00 568.00 188 Gilman 240 53.13% 253.39 99.41 146.20 557.75 189 Gilmore City 475 7.23% 223.05 89.50 130.00 572.00 190 Gladbrook 284 44.53% 253.95 97.80 141.00 562.00 191 Glidden 464 9.38% 225.70 89.71 135.00 568.00 192 Goldfield 489 4.49% 224.00 87.97 131.00 576.00 193 Gowrie 480 6.25% 224.92 88.81 132.00 573.00 194 Graettinger 256 50.00% 260.97 103.29 135.00 570.00 195 Grafton 340 33.59% 239.24 92.29 133.33 555.00 196 Grant 484 5.47% 220.36 86.83 129.60 563.00 197 Green Mountain 253 50.59% 262.61 99.62 142.00 562.00 198 Greene 355 30.66% 236.89 91.05 140.00 558.67 199 Greenfield 406 20.70% 212.03 86.42 130.00 554.25 200 Greenville 215 58.01% 266.80 113.33 136.00 576.00 201 Grinnell 100 80.47% 258.53 63.88 159.20 394.00 202 Griswold 162 68.36% 273.41 121.63 131.80 563.00 203 Grundy Center 247 51.76% 254.60 96.92 141.50 561.00 204 Gruver 215 58.01% 266.80 113.33 136.00 576.00 205 Guthrie Center 448 12.50% 225.06 89.54 117.50 577.50 206 Halbur 471 8.01% 224.41 89.01 129.00 571.00 207 Hamburg 158‘ 69.14% 270.91 119.32 148.75 571.00 208 Hampton. 244 52.34% 194.04 34.03 127.00 294.50 209 Hancock 162 68.36% 272.99 121.04 131.80 563 .00 210 Hanlontown 491 4.10% 219.63 87.37 124.00 568.00 49 Table 2.k (cont’d). # of 0/0 Std. # Grain Market Obs. Missing Mean Dev. Min Max 211 Hardy 291 43.16% 252.95 101.27 141.00 576.00 212 Harlan 162 68.36% 272.02 121.09 130.80 562.00 213 Harris 272 46.88% 251.85 104.68 145.25 578.00 214 Hartley 503 1.76% 223.40 90.21 127.75 585.00 215 Hartwick 79 84.57% 377.52 91.84 272.00 557.00 216 Hastings 116 77.34% 318.07 119.46 167.67 566.00 217 Haverhill 473 7.62% 226.26 86.08 133.00 566.00 218 Hawarden 452 1 1.72% 223.53 95.34 129.00 586.00 219 Hawkeye 489 4.49% 221.53 81.81 132.00 545.00 220 Henderson 240 53.13% 259.59 108.52 131.80 572.00 221 Hinton 490 4.30% 226.44 89.29 138.00 576.00 222 Holland 119 76.76% 214.66 35.27 158.60 302.00 223 Holstein 254 50.39% 262.05 107.34 137.75 577.00 224 Hopkinton 70 86.33% 292.42 152.52 149.00 566.25 225 Homick 471 8.01% 225.00 89.41 134.40 573.00 226 Hospers 262 48.83% 274.25 102.08 163.00 589.00 227 Hubbard 484 5.47% 220.16 85.41 133.00 567.00 228 Hudson 457 10.74% 225.52 85.53 134.00 553.40 229 Hull 229 55.27% 255.91 108.37 153.00 592.00 230 Humboldt 260 49.22% 259.91 103.43 139.40 573.00 231 Humeston 164 67.97% 251.46 109.43 141.50 545.00 232 Ida Grove 169 66.99% 272.72 121.52 136.60 572.00 233 Independence 169 66.99% 281.75 112.27 145.60 557.50 234 Indianola 491 4.10% 224.76 86.19 131.00 566.00 235 lnwood 177 65.43% 281.01 119.94 146.80 586.00 236 Ionia 319 37.70% 247.30 96.34 135.25 564.00 237 Iowa City 262 48.83% 264.87 94.50 170.20 555.50 238 Iowa Falls 44 91.41% 373.43 162.07 145.20 571.00 239 Ireton 359 29.88% 241.48 101.71 136.40 589.00 240 Irwin 409 20.12% 226.88 93.07 131.00 563.50 50 Table 2.k (cont’d). # 0f o/o Std. # Grain Market Obs. Missing Mean Dev. Min Max 241 Jefferson 472 7.81% 226.21 88.76 132.00 573.00 242 Jesup 474 7.42% 223.25 84.61 133.25 557.00 243 Jewell 450 12.11% 213.89 87.17 129.40 571.00 244 Joice 453 1 1.52% 212.02 84.27 125.00 568.00 245 Kalona 246 51.95% 264.14 97.18 154.80 555.00 246 Kanawha 458 10.55% 221.92 91.08 129.75 573.00 247 Kelley 400 21.88% 233.94 92.18 134.00 568.00 248 Kellogg 249 51.37% 230.36 58.99 146.20 392.50 249 Keokuk 474 7.42% 250.59 83.12 153.00 577.00 250 Keosauqua 131 74.41% 226.61 44.61 155.20 369.00 251 Keota 61 88.09% 384.92 96.57 276.50 559.00 252 Keystone 42 91.80% 280.48 170.13 137.75 566.50 253 Kingsley 320 37.50% 230.41 104.13 128.50 571.00 254 Klemme 426 16.80% 209.18 82.96 130.00 566.00 255 Knierim 260 49.22% 259.91 103.43 139.40 573.00 256 Knoxville 338 33.98% 247.23 89.22 141.60 556.75 257 La Porte City 301 41.21% 258.00 93.23 148.60 562.40 258 Lacona 170 66.80% 277.78 113.33 140.25 556.75 259 Lake City 433 15.43% 223.72 89.84 132.00 572.00 260 Lake Mills 190 62.89% 200.17 35.13 124.00 296.00 261 Lake Park 330 35.55% 238.45 103.39 124.00 574.00 262 Lake View 168 67.19% 270.51 122.41 136.60 572.00 263 Lakota 466 8.98% 220.35 88.25 128.00 564.00 264 Lamoni 484 5.47% 222.90 84.43 125.00 556.75 265 Lamont 124 75.78% 316.35 111.52 165.75 551.00 266 Lanesboro 29 94.34% 472.33 107.70 351.25 568.00 267 Larchwood 241 52.93% 255.77 110.01 143.60 582.00 268 Larrabee 473 7.62% 226.63 92.80 130.20 587.00 269 Latimer 168 67. 19% 269.65 120.95 140.20 568.00 270 Laurel 301 41.21% 216.63 60.70 133.60 392.50 51 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 271 Laurens 478 6.64% 222.06 89.97 127.60 573.00 272 Lawler 319 37.70% 248.13 95.66 139.25 564.00 273 Le Mars 293 42.77% 187.94 46.74 134.00 393.00 274 Ledyard 474 7.42% 218.58 88.30 125.00 564.00 275 Leland 354 30.86% 231.85 94.93 135.60 571.00 276 Lenox 259 49.41% 259.31 106.14 137.75 572.00 277 Lester 31 1 39.26% 242.76 100.85 143.20 582.00 278 Libertyville 171 66.60% 256.50 114.80 135.50 543.00 279 Lidderdale 139 72.85% 237.08 132.69 135.00 568.00 280 Lime Springs 414 19.14% 191.41 43.90 127.00 376.50 281 Lincoln 240 53.13% 257.32 97.04 143.80 561.00 282 Lineville 164 67.97% 253.55 110.13 141.50 547.00 283 Linn Grove 469 8.40% 221.77 91.68 126.60 575.00 284 Liscomb 284 44.53% 254.31 99.52 140.20 567.00 285 Little Rock 501 2.15% 223.16 91.07 129.50 585.00 286 Little Sioux 276 46.09% 256.43 100.97 137.00 572.25 287 Livermore 402 21.48% 230.85 94.19 130.40 576.00 288 Lohrville 260 49.22% 259.90 103.39 139.40 568.00 289 Lone Rock 297 41.99% 252.34 99.49 136.40 575.00 290 Lost Nation 237 53.71% 270.12 93.96 152.40 569.00 291 Luther 369 27.93% 238.97 94.19 136.20 570.00 292 Luverne 261 49.02% 260.12 103.25 139.40 568.00 293 Luzeme 78 84.77% 381.00 91.50 275.00 559.50 294 Lytton 168 67.19% 271.58 121.69 136.60 572.00 295 Madrid 336 34.38% 244.64 96.10 134.00 569.00 296 Malcom 319 37.70% 253.25 92.33 146.80 558.00 297 Mallard 485 5.27% 221.24 88.90 129.00 568.00 298 Manchester 214 58.20% 236.38 86.82 144.20 555.00 299 Manly 482 5.86% 222.88 85.77 126.20 562.00 300 Manning 256 50.00% 245.72 110.14 134.00 566.00 52 Table 2.k (cont’d). # of % Std. # Grain Market Obs. MissLng Mean Dev. Min Max 301 Manson 223 56.45% 270.38 108.48 139.40 573.00 302 Mapleton 293 42.77% 254.10 99.42 138.00 572.00 303 Marathon 501 2.15% 221.49 88.45 127.60 573.00 304 Marble Rock 464 9.38% 220.41 83.85 130.00 557.00 305 Marcus 473 7.62% 225.34 92.08 129.40 584.00 306 Marengo 79 84.57% 378.20 91.44 273.00 557.25 307 Marshalltown 92 82.03% 226.91 31.81 185.60 300.67 308 Martelle 282 44.92% 261.88 94.65 150.40 557.40 309 Mason City 427 16.60% 214.10 83.60 130.00 557.00 310 Massena 455 11.13% 210.25 82.51 130.80 557.75 311 Matlock 296 42.19% 263.27 101.17 149.00 589.00 312 Maurice 451 11.91% 232.46 93.88 135.00 589.00 313 Maxwell 361 29.49% 235.43 91.94 131.00 568.00 314 Maynard 235 54.10% 250.02 107.30 130.80 549.00 315 McCallsburg 406 20.70% 233.14 92.22 132.00 568.00 316 McGregor 182 64.45% 243.92 93.55 150.80 , 554.00 317 Melbourne 487 4.88% 225.21 85.65 134.00 563.00 318 Melvin 248 51.56% 267.78 110.12 143.00 582.00 319 Meriden 429 16.21% 222.24 95.96 127.40 584.00 320 Meservey 235 54.10% 236.19 100.99 132.80 566.00 321 Milford 215 58.01% 266.80 113.33 136.00 576.00 322 Minbum 491 4.10% 223.02 87.38 129.00 569.00 323 Mingo 351 31.45% 245.48 92.17 139.60 565.00 324 Missouri Valley 329 35.74% 243.44 98.65 131.00 574.25 325 Mitchellville 370 27.73% 243.60 92.15 139.40 570.00 326 Modale 486 5.08% 225.26 87.49 133.00 574.25 327 Mondamin 458 10.55% 227.39 88.58 132.00 573.25 328 Monona 162 68.36% 279.33 111.20 137.00 532.00 329 Monroe 109 78.71% 266.40 126.60 154.25 562.00 330 Montezuma 319 37.70% 253.15 92.24 146.80 557.00 53 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 331 Monticello 169 66.99% 284.61 111.90 146.20 557.50 332 Moorland 260 49.22% 259.89 103.39 139.40 568.00 333 Morning Sun 318 37.89% 255.46 91.94 142.00 552.00 334 Moulton 0 100.00% 0.00 0.00 0.00 0.00 335 Mount Union 498 2.73% 231.45 80.70 138.20 557.00 336 Mt Auburn 110 78.52% 178.75 18.00 132.00 206.50 337 Mt Ayr 411 19.73% 217.06 86.64 135.00 561.00 338 Muscatine 227 55.66% 276.87 89.31 175.40 560.00 339 Nashua 491 4.10% 221.88 86.82 128.00 564.00 340 Nemaha 252 50.78% 263.30 108.13 135.33 580.00 341 Neola 162 68.36% 274.04 121.08 132.80 564.00 342 Nevada 406 20.70% 237.44 91.35 137.00 570.00 343 New Hampton 488 4.69% 223.85 86.43 128.00 564.00 344 New Hartford 295 42.38% 238.27 102.65 133.80 558.00 345 New London 236 53.91% 194.40 23.07 137.60 264.00 346 New Providence 477 6.84% 221.03 85.73 131.00 567.00 347 New Sharon 243 52.54% 267.85 101.68 147.00 556.60 348 Newell 278 45.70% 233.40 106.46 133.40 572.00 349 Newton 249 51.37% 264.81 100.91 145.20 560.00 350 Nora Springs 107 79.10% 171.37 18.36 128.60 206.00 351 North Washington 254 50.39% 260.60 102.38 135.25 564.67 352 Northwood 455 11.13% 222.58 85.99 127.00 554.00 353 Oakland 162 68.36% 272.97 121.09 131.80 563.00 354 Oakville 390 23.83% 246.53 88.20 144.00 557.00 355 Ocheyedan 407 20.51% 231.12 98.85 130.00 580.00 356 Odebolt 168 67.19% 272.32 121.58 136.60 572.00 357 Olds 329 35.74% 254.12 89.68 137.00 554.00 358 Olin 240 53.13% 273.62 98.37 152.40 562.00 359 Onawa 487 4.88% 224.24 88.31 130.00 572.00 360 Orange City 455 11.13% 231.96 93.65 135.00 589.00 54 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 361 Osage 477 6.84% 223.42 85.81 128.40 554.00 362 Osceola 111 78.32% 171.77 19.46 125.25 210.00 363 Oskaloosa 472 7.81% 231.48 83.38 137.20 562.75 364 Ossian 108 78.91% 173.92 18.59 131.20 208.80 365 Otho 260 49.22% 259.90 103.44 139.40 573.00 366 Otley 109 78.71% 266.47 126.73 154.25 562.00 367 Ottosen 497 2.93% 220.64 87.38 129.00 571.00 368 Ottumwa 409 20.12% 212.92 71.83 135.00 561.50 369 Owasa 331 35.35% 232.25 98.03 131.00 567.00 370 Oms 486 5.08% 225.81 91.00 134.00 576.00 371 Pacific Junction 328 35.94% 209.19 86.06 131.00 572.00 372 Palmer 260 49.22% 259.91 103.43 139.40 573.00 373 Panama 48 90.63% 343.71 139.12 194.00 565.00 374 Panora 440 14.06% 227.23 90.23 128.00 568.00 375 Parkersburg 299 41.60% 234.55 102.29 132.00 555.00 376 Paton 318 37.89% 246.71 97.68 134.40 570.00 377 Pella 427 16.60% 218.69 77.11 136.00 560.00 378 Persia 162 68.36% 274.04 121.08 132.80 564.00 379 Peterson 474 7.42% 221.46 91.29 126.60 575.00 380 Pickering 216 57.81% 263.68 103.16 146.75 569.00 381 Pierson 390 23.83% 233.36 94.23 130.00 572.00 382 Plainfield 355 30.66% 234.43 91.15 140.20 554.50 383 Pleasant Hill 257 49.80% 210.49 47.29 139.00 383.00 384 Plymouth 403 21.29% 215.60 85.07 130.00 558.00 385 Pocahontas 423 17.38% 227.93 92.98 130.00 573.00 386 Pomeroy 260 49.22% 259.89 103.39 139.40 568.00 387 Portsmouth 172 66.41% 267.16 120.65 131.80 564.00 388 Prairie City 497 2.93% 227.83 84.32 136.20 565.00 389 Protivin 354 30.86% 208.66 74.70 127.00 545.00 390 Radcliffe 157 69.34% 250.77 105.73 139.00 569.00 55 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 391 Rake 354 30.86% 233.40 94.28 135.60 571.00 392 Ralston 472 7.81% 226.19 88.85 131.00 573.00 393 Randall 406 20.70% 205.00 78.78 131.00 570.00 394 Readlyn 92 82.03% 174.12 20.37 131.00 208.80 395 Red Oak 470 8.20% 228.90 89.84 134.00 572.00 396 Redfield 368 28.13% 239.12 93.53 136.00 568.00 397 Reinbeck 386 24.61 % 227.79 86.97 133.00 561.00 398 Rembrandt 111 78.32% 186.05 27.16 148.50 368.00 399 Remsen 476 7.03% 223.70 91.39 135.20 582.00 400 Renwick 291 43.16% 252.43 101.45 141.08 576.00 401 Richland 368 28.13% 243.63 88.17 136.00 550.00 402 Ridgeway 258 49.61% 251.70 100.00 128.25 543.33 403 Rinard 168 67.19% 272.21 121.17 136.60 572.00 404 Ringsted 365 28.71% 221.28 97.89 125.00 563.00 405 Rippey 423 17.38% 229.59 91.70 129.00 570.00 406 Ritter 227 55.66% 275.35 111.39 143.00 582.00 407 Rock Rgpids 459 10.35% 217.72 92.40 128.20 583.00 408 Rock Valley 461 9.96% 220.72 91.75 138.20 586.00 409 Rockford 483 5.66% 218.08 83.00 128.00 557.00 410 Rockwell 449 12.30% 226.74 88.92 130.00 568.00 411 Rockwell City 458 10.55% 225.99 89.54 132.00 573.00 412 Roland 402 21.48% 233.66 92.51 132.00 568.00 413 Rowan 344 32.81% 231.57 100.28 131.00 573.00 414 Royal 323 36.91% 248.56 98.91 133.80 582.00 415 Rudd 458 10.55% 220.23 84.75 130.00 556.60 416 Runnells 476 7.03% 230.55 84.45 135.80 560.00 417 Ruthven 495 3.32% 220.53 88.15 127.80 571.00 418 Rutland 330 35.55% 234.38 96.15 130.40 580.00 419 Ryan 490 4.30% 229.95 81.58 138.25 562.25 420 Sac City 429 16.21% 224.78 91.28 130.00 575.00 56 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Misflpg Mean Dev. Min Max 421 Saint Ansgar 252 50.78% 256.58 102.14 142.50 552.00 422 Sanbom 465 9.18% 227.44 94.98 128.00 585.00 423 Schaller 472 7.81% 221.86 91.02 128.20 573.00 424 Schleswig 89 82.62% 335.71 126.89 165.20 554.00 425 Scranton 464 9.38% 225.18 89.37 129.00 571.00 426 Seymour 124 75.78% 212.90 102.35 132.00 533.00 427 Sheffield 412 19.53% 231.79 92.26 127.00 572.00 428 Shelby 162 68.36% 273.03 121.08 131.80 563.00 429 Sheldon 449 12.30% 233.09 93.86 135.00 589.00 430 Shell Rock 1 10 78.52% 176.42 18.46 130.60 207.40 431 Shellsburg 200 60.94% 273.78 108.06 148.00 562.25 432 Shenandoah 263 48.63% 267.02 101.14 145.80 576.00 433 Sibley 180 64.84% 260.89 129.85 130.00 580.00 434 Sidney 160 68.75% 280.17 121.37 132.00 572.00 435 Sigoumey 137 73.24% 271.59 107.78 153.80 550.25 436 Silver City 114 77.73% 282.59 119.95 182.50 572.00 437 Sioux Center 302 41 .02% 264.16 102.92 156.00 596.00 438 Sioux City 467 8.79% 228.47 90.35 139.00 577.00 439 Sioux Rapids 82 83.98% 164.13 21.11 126.60 201.50 440 Slater 370 27.73% 239.89 93.43 135.00 568.00 441 Sloan 464 9.38% 229.73 89.82 137.00 579.00 442 Somers 168 67.19% 271.58 121.69 136.60 572.00 443 Spencer 215 58.01% 266.80 113.33 136.00 576.00 444 Sperry 480 6.25% 239.15 77.45 149.00 563.00 445 Stacyville 491 4.10% 221.08 84.89 127.60 552.00 446 Stanhope 174 66.02% 253.61 115.30 137.25 572.00 447 Stanton 467 8.79% 225.14 87.80 134.00 564.00 448 Stanwood 237 53.71% 270.12 93.96 152.40 569.00 449 Steamboat Rock 127 75.20% 309.58 122.17 151.40 567.00 450 Stockport 485 5.27% 237.31 81.35 141.00 553.00 57 Table 2.k (cont’d). # of _ % Std. # Grain Market Obs. Missing Mean Dev. ‘ Min Max 451 Storm Lake 64 87.50% 230.77 41.06 185.00 306.50 452 Story City 403 21.29% 235.06 91.78 134.00 568.00 453 Sully 243 52.54% 266.03 101.63 145.00 554.60 454 Sumner 259 49.41% 252.62 98.15 134.50 545.00 455 Sunbury 251 50.98% 269.64 91.44 156.40 570.00 456 Superior 216 57.81% 266.06 113.55 136.00 576.00 457 Sutherland 445 13.09% 230.78 92.35 133.00 586.00 458 Swea City 397 22.46% 220.19 94.33 125.00 564.00 459 Taintor 74 85.55% 384.87 92.02 284.60 554.60 460 Templeton 467 8.79% 226.41 89.08 131.00 573 .00 461 Terril 295 42.38% 251.23 100.09 132.00 571.00 462 Thor 291 43.16% 252.98 101.27 141.00 576.00 463 Thornton 440 14.06% 21 1.69 83.38 127.00 558.00 464 Titonka 470 8.20% 222.04 89.86 126.00 576.00 465 Toeterville 275 46.29% 251.18 99.93 128.25 552.00 466 Tracy 108 78.91% 266.94 127.15 154.25 562.00 467 Traer 275 46.29% 249.03 93.75 146.20 560.00 468 Troy Mills 92 82.03% 231.64 31.95 193.50 306.50 469 Truesdale 82 83 .98% 164.19 20.94 128.00 201.50 470 Union 482 5.86% 222.10 85.96 133 .00 569.00 471 Varina 229 55.27% 195.77 33.09 132.80 295.25 472 Ventura 471 8.01% 222.60 88.06 127.00 568.00 473 Vincent 260 49.22% 259.90 103.39 139.40 568.00 474 Vinton 478 6.64% 230.38 85.14 136.00 562.00 475 Voorhies 198 61.33% 205.51 34.46 135.00 301.33 476 Walcott 251 50.98% 269.64 91 .44 156.40 570.00 477 Wallingford 290 43.36% 248.40 95.62 132.00 579.00 478 Walnut 162 68.36% 272.05 121.07 130.80 562.00 479 Wapello 373 27.15% 237.96 68.53 150.00 549.00 480 Washburn 417 18.55% 231.36 83.36 134.00 560.50 58 Table 2.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 481 Washta 291 43.16% 253.99 99.94 137.75 572.00 482 Watkins 161 68.55% 288.56 114.55 148.50 566.00 483 Waucoma 230 55.08% 258.90 101.38 121.67 545.00 484 Waukee 485 5.27% 224.50 87.58 127.00 569.00 485 Waukon 79 84.57% 306.74 131.94 140.00 531.00 486 Waverly 466 8.98% 221.18 84.12 128.00 547.00 487 Wayland 133 74.02% 240.66 121.71 141.25 564.00 488 Webb 478 6.64% 222.09 89.93 127.60 573.00 489 Webster City 475 7.23% 223.36 87.71 130.00 566.20 490 Wellsburg 94 81.64% 174.88 18.22 143.00 214.00 491 Wesley 476 7.03% 222.38 89.14 128.00 576.00 492 West Bend 495 3.32% 220.59 88.29 128.80 568.00 493 West Burlington 182 64.45% 292.51 1 10.50 174.20 571.00 494 West Union 249 51.37% 261.02 101.59 135.40 553.00 495 Westgate 111 78.32% 174.33 17.87 131.60 215.75 496 Wever 252 50.78% 279.05 96.23 154.67 564.00 497 Whiting 292 42.97% 255.52 99.75 138.00 573.00 498 Whittemore 489 4.49% 221.26 88.64 129.00 568.00 499 Whitten 272 46.88% 250.20 99.90 139.20 562.00 500 Williams 484 5.47% 219.64 86.19 131.00 567.00 501 Williamsburg 291 43.16% 200.26 32.07 137.00 302.00 502 Winfield 499 2.54% 231.31 80.68 138.20 557.00 503 Winterset 416 18.75% 231.27 90.23 131.25 564.00 504 Winthrop 169 66.99% 281.87 112.18 145.60 557.50 505 Woden 459 10.35% 219.61 91.80 125.60 573.00 506 Woodbine 422 17.58% 230.38 90.54 131.60 571.25 507 Woodward 396 22.66% 232.76 93.06 129.00 570.00 508 Woolstock 450 12.11% 224.87 90.96 128.00 573.00 509 Yale 458 10.55% 220.88 87.20 132.00 567.00 510 Yetter 168 67.19% 272.21 121.17 136.60 572.00 512 Zearing 405 20.90% 233.55 92.38 132.00 568.00 59 Table 2.1 Indiana Weekly Corn Price Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Amboy 484 5.47% 243.25 88.50 147.00 599.00 2 Ambia 187 63.48% 244.65 67.47 155.75 401.25 3 Anderson 255 50.20% 268.45 103.52 152.75 598.00 4 Argos 117 77.15% 275.13 138.13 145.50 577.25 5 Attica 224 56.25% 288.12 108.00 158.20 590.50 6 Aurora 474 7.42% 250.20 85.36 151.00 578.00 7 Bluffon 188 63.28% 286.23 117.53 158.00 588.00 8 Boston 266 48.05% 278.22 101.96 152.80 591.00 9 Brazil 469 8.40% 232.71 85.18 141.00 585.00 10 Bremen 446 12.89% 241.47 88.50 145.00 579.00 11 Brook 274 46.48% 227.95 94.04 135.00 590.00 12 Brookston 215 58.01% 282.99 107.35 158.20 581.33 13 Burlington 177 65.43% 312.70 121.17 170.00 614.00 14 Cambria 94 81.64% 224.04 20.34 177.00 276.00 15 Carlisle 434 15.23% 236.09 83.67 150.00 589.00 16 Clay City 259 49.41% 263.54 101.51 147.00 586.00 17 Clymers 61 88.09% 421.54 99.52 306.80 603.40 18 Colfax 94 81.64% 224.08 20.40 177.00 276.00 19 Columbus 491 4.10% 236.79 86.85 137.00 586.00 20 Connersville 405 20.90% 249.38 91.48 146.00 586.00 21 Cortland 308 39.84% 245.28 94.01 139.00 600.00 22 Crawforrdsville 385 24.80% 251.70 93.40 146.00 589.75 23 Dana 475 7.23% 241.62 86.16 147.00 581.00 24 Dacatur 466 8.98% 231.21 96.29 0.00 590.00 25 Delphi 478 6.64% 244.76 87.62 147.20 592.00 26 Dubois 416 18.75% 234.38 74.28 153.00 591.00 27 Dunkirk 486 5.08% 238.83 88.02 145.00 591.00 28 Eaton 181 64.65% 292.98 119.80 154.40 596.00 29 Edinburgh 460 10.16% 243.48 88.68 148.00 592.00 30 Elizabethtown 367 28.32% 232.03 85.91 136.20 596.00 Note: Highlighting indicates the grain market was chosen for analysis 60 Table 2.1 (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 31 Elwood 223 56.45% 277.32 106.86 152.00 598.00 32 Evansville 480 6.25% 256.69 86.35 156.00 590.20 33 Fowler 314 38.67% 262.73 96.37 158.60 578.00 34 Francesville 330 35.55% 241.73 95.65 140.00 599.80 35 Francisco 136 73.44% 333.79 114.04 168.50 590.00 36 Frankfort 472 7.81% 232.82 85.68 147.00 592.00 37 Franklin 108 78.91% 240.16 25.17 183.60 306.25 38 Geneva 295 42.38% 254.44 83.80 149.00 564.00 39 Glenwood 452 1 1.72% 243.14 89.02 146.00 596.00 40 Goodland 304 40.63% 259.97 95.86 154.20 583.00 41 Goshen 386 24.61% 223.59 74.40 145.00 589.00 42 Greensburg 510 0.39% 238.70 83.24 143.20 570.60 43 Greentown 464 9.3 8% 239.69 88.31 143.00 589.00 44 Hagerstown 306 40.23% 271.38 97.86 157.20 597.00 45 Hamlet 496 3.13% 238.21 85.31 143.00 589.00 46 Hammond 397 22.46% 262.93 91.73 165.00 604.00 47 Hebron 88 82.81% 276.40 1 19.09 142.50 550.00 48 Hope 460 10.16% 239.40 88.22 137.00 586.00 49 Hortonville 138 73.05% 206.62 39.72 145.00 303.50 50 Huntingburg 463 9.57% 248.50 84.48 148.00 584.00 51 Idaville 294 42.58% 305.22 123.88 147.20 631.00 52 Jasonville 104 79.69% 183.60 17.21 144.60 224.80 53 Jasper 436 14.84% 252.15 87.37 148.00 584.00 54 Jeffersonville 479 6.45% 248.61 85.30 151.00 576.80 55 Kentland 271 47.07% 267.97 99.93 154.67 573.50 56 Kersey 292 42.97% 198.01 32.37 139.00 306.50 57 Kwanna 233 54.49% 285.15 97.16 150.00 578.00 58 Kingman 280 45.31% 201.51 30.20 136.00 305.67 59 Knightstown 69 86.52% 403.70 89.10 302.60 579.00 60 Knox 285 44.34% 268.24 98.33 158.00 590.50 Note: Highlighting indicates the grain market was chosen for analysis 61 Table 2.1 (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 61 Kokomo 461 9.96% 245.84 89.73 148.00 599.00 62 Kouts 321 37.30% 198.52 30.62 146.00 307.50 63 L Crosse 431 15.82% 230.31 87.06 144.00 591.00 64 La Fontaine 230 55.08% 251.82 91.22 153.67 599.00 65 Ladoga 175 65.82% 309.91 1 10.69 160.60 594.00 66 Lafayette 497 2.93% 250.13 85.71 157.00 598.20 67 Lapaz 449 12.30% 241.26 88.26 145.00 579.00 68 Letts 435 15.04% 239.00 92.70 137.00 586.40 69 Linden 471 8.01% 243.55 91.30 146.00 611.00 70 Logansport 474 7.42% 242.96 88.52 147.00 592.00 71 Loogootee 91 82.23% 395.35 82.24 222.00 576.00 72 Lucerne 111 78.32% 174.82 16.38 132.40 210.80 73 Madison 459 10.35% 246.01 84.65 149.00 571.80 74 Marion 381 25.59% 238.34 90.59 150.00 602.00 75 Markle 267 47.85% 248.60 107.69 137.75 574.60 76 Markleville 140 72.66% 329.02 111.33 158.00 596.00 77 Medaryville 426 16.80% 247.45 90.86 142.00 592.00 78 Mellott 288 43.75% 221.53 58.85 138.00 398.00 79 Mexico 111 78.32% 179.41 17.99 134.00 217.80 80 Milroy 125 75.59% 335.10 108.18 189.00 574.67 81 Monon 378 26.17% 246.83 93.93 133.00 601.75 82 Monroe 110 78.52% 237.08 29.25 181.50 305.33 83 Monroeville 65 87.30% 408.45 92.47 305.00 587.00 84 Monticello 245 52.15% 270.87 102.28 151.75 590.00 85 Montpelier 437 14.65% 233.02 85.08 150.00 596.00 86 Morristown 297 41.99% 235.58 86.90 145.60 589.50 87 Mount Vernon 192 62.50% 316.39 105.83 158.33 585.20 88 Nappanee 463 9.57% 237.13 83.75 141.00 585.00 89 New Carlisle 370 27.73% 246.26 88.98 150.60 591.50 90 New Castle 99 80.66% 372.96 104.58 195.33 598.00 62 Table2.l (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 New Paris 214 58.20% 230.84 62.14 161.00 525.00 92 Newburgh 459 10.35% 257.23 87.85 155.00 589.20 93 Newtown 37 92.77% 212.23 23.15 185.20 273.00 94 Noblesville 143 72.07% 206.21 38.97 150.40 303.50 95 North Vernon 337 34.18% 239.91 89.57 137.00 585.00 96 Oakville 473 7.62% 236.99 88.50 148.40 591.00 97 Owensville 371 27.54% 216.77 42.30 148.00 382.33 98 Pershing 305 40.43% 268.20 97.66 154.40 587.00 99 Pierceton 257 49.80% 190.45 29.48 134.60 326.00 100 Plymouth 99 80.66% 240.74 25.03 195.75 306.50 101 Poneto 72 85.94% 168.86 18.08 130.50 194.40 102 Portage 456 10.94% 244.35 84.15 152.00 577.00 103 Portland 42 91.80% 459.96 97.39 326.50 608.00 104 Princeton 474 7.42% 248.32 86.34 152.20 590.00 105 Ramsey 233 54.49% 236.16 56.43 167.00 419.20 106 Redkey 61 88.09% 213.04 45.07 136.67 310.00 107 Remington 472 7.81% 234.34 84.93 144.00 583.00 108 Rensselaer 217 57.62% 269.59 101.86 155.50 596.00 109 Reynolds 287 43.95% 265.03 99.18 155.00 590.00 110 Richmond 356 30.47% 220.31 47.27 142.00 557.00 1 11 Roachdale 253 50.59% 198.53 22.42 148.00 282.00 1 12 Rochester 381 25.59% 224.98 76.49 145.60 578.25 113 Rockport 171 66.60% 316.38 114.38 158.20 588.20 114 Rolling Prairie 377 26.37% 228.34 90.73 141.80 571.00 115 Romney 177 65.43% 295.43 118.94 154.80 595.00 116 Roselawn 293 42.77% 197.73 32.62 139.00 306.50 117 Rushville 451 11.91% 243.69 84.44 146.40 572.67 118 Russiaville 251 50.98% 205.89 33.37 143.00 311.50 119 Schneider 268 47.66% 204.92 32.52 139.00 307.50 120 Seymour 111 78.32% 175.72 17.97 133.00 208.67 63 Table 2.1 (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 121 Sharpsville 75 85.35% 222.40 21.02 177.00 290.00 122 Shelburn 434 15.23% 247.99 88.88 150.00 589.00 123 Shelbyville 69 86.52% 181.15 20.99 140.00 217.00 124 Sheridan 393 23.24% 216.57 75.03 140.00 591.00 125 Sims 179 65.04% 299.61 118.90 158.00 600.00 126 South Bend 467 8.79% 246.78 86.59 153.00 596.60 127 South Milford 270 47.27% 254.82 109.19 147.00 593.00 128 South Whitley 71 86.13% 181.69 19.04 144.00 218.80 129 Star City 150 70.70% 296.59 119.58 146.00 581.40 130 State Line 205 59.96% 278.93 108.80 159.75 582.00 131 Sullivan 489 4.49% 245.89 85.93 152.00 591.00 132 Summitville 459 10.35% 239.06 89.98 142.00 596.00 133 Swayzee 133 74.02% 275.15 128.54 145.60 599.00 134 Syracuse 105 79.49% 351.27 99.02 181.75 562.20 135 Tefft 296 42.19% 196.87 32.31 138.00 305.50 136 Terre Haute 395 22.85% 237.30 87.31 147.80 593.00 137 Tipton 379 25.98% 248.43 92.25 151.00 592.00 138 Trafalgar 295 42.38% 266.87 101.63 143.00 597.00 139 Union Mills 462 9.77% 233.37 87.30 141.00 591.00 140 Valparaiso 265 48.24% 209.93 50.80 146.00 404.25 141 Vincennes 463 9.57% 243.63 84.59 151.00 586.00 142 Wabash 82 83.98% 180.22 19.15 141.00 217.40 143 Waldron 312 39.06% 236.80 91.59 138.00 586.00 144 Walton 110 78.52% 183.46 16.21 143.00 216.00 145 Wanatah 413 19.34% 204.58 43.74 142.20 398.25 146 Warsaw 438 14.45% 224.02 82.69 140.00 580.00 147 Washington 427 16.60% 231.22 72.36 148.00 580.00 148 Waterloo 463 9.57% 241.47 87.74 149.00 588.00 149 Waveland 362 29.30% 244.01 94.42 134.00 584.75 150 Westfield 91 82.23% 180.95 19.12 139.60 219.80 64 Table 2.1 (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 151 Whitesville 193 62.30% 249.61 62.67 160.00 409.67 152 Williamsport 264 48.44% 270.61 99.80 156.00 583.67 153 Winamac 316 38.28% 202.20 31.17 138.60 306.00 154 Winchester 295 42.3 8% 265.54 99.33 153.80 593.00 155 Windfall 82 83.98% 186.47 18.42 147.25 222.00 156 Wingate 342 33.20% 231.99 86.45 138.00 589.50 157 Winslow 250 51.17% 274.34 106.31 157.80 582.00 158 Wolcott 256 50.00% 271.15 104.17 154.00 584.00 159 Woodbum 340 33.59% 251.45 96.14 147.60 591.00 160 Wyatt 448 12.50% 236.72 89.07 144.00 588.00 161 Yoder 151 70.51% 209.51 95.94 141.40 553.50 65 APPENDIX 2.2: COINTEGRATION RESULTS FOR KANSAS, IOWA AND INDIANA To discover if the Kansas percentage of corn used in the production of ethanol altered the degree of annual cointegration of corn prices at grain markets in Kansas, a procedure similar to the one used for Michigan was utilized. However, before beginning that procedure it was useful to determine whether the corn prices at different grain markets in Kansas were cointegrated from September 1998 through June 2008. Table 2.m details the statistics relating to the Kansas multivariate cointegration testing of corn prices in Chapman, Hillsboro, Larned and Osborne grain markets from September 1998 through June 2008. Lag length of four was used in the testing because the FPE was minimized at this amount. Table 2.m Kansas Grain Markets Multivariate Cointegration Testing Results Null Alternative Cointegration 5% Critical Hypothesis Hypothesis Test Stat Value Trace Test Ho: F0 H1: r>0 121.75* 47.21 Ho: r=1 H1: r>1 58.06* 29.38 Ho: r=2 H1: r>2 23.50* 15.34 Ho: r=3 H1: r>3 3.68 3.84 Max Test Ho: r=0 H1: r=l 63.69* 27.07 Ho: r=1 H12F2 34.56* 20.97 Ho: r=2 H1: r=3 19.82"‘ 14.07 Ho: r=3 H1: r=4 3.68 3.76 *Indicates rejection of the null hypothesis at 5% significance Table 2.m presents three cointegrating vectors for the four corn price series using both the maximal eigenvalue test statistic and the trace test statistic. Therefore, the com 66 prices at Chapman, Hillsboro, Larned and Osborne grain markets are cointegrated. Thus, the corn prices operate in a stable long-run spatial price equilibrium. To determine if the Kansas percentage of corn used in the production of ethanol affected the annual cointegration of corn prices at Chapman, Hillsboro, Larned and Osborne we first must determine the annual cointegration statistics for the corn prices at the given grain markets. Table 2.n displays the annual cointegration maximal eigenvalue test statistics as well as the proper lag lengths determined by the minimum value of the F PE. The annual test statistic for the null hypothesis r=3 and the corresponding trace statistics have been excluded from Table 2.n to save space. 67 Table 2.n Kansas Markets Annual Cointegration Tests FPE Maximal Lag Length Null Hypothesis Eigenvalue 5% Selection Time Period Ho: Test Statistic Critical Value 2 Sept. 1998-1999 r=0 19.59 27.07 r=1 10.84 20.97 r=2 8.14 14.07 1 1999-2000 1:0 34.02* 27.07 r=1 19.96 20.97 r=2 12.48 14.07 2 2000-2001 F0 3585* 27.07 r=1 17.13 20.97 r=2 15.05 14.07 1 2001-2002 F0 3249* 27.07 r=1 21.93 20.97 r=2 18.88 14.07 1 2002-2003 F0 4268* 27.07 r=1 20.69 20.97 r=2 1.70 14.07 7 2003-2004 r=0 54.49* 27.07 r=1 2505* 20.97 r=2 12.77 14.07 7 2004-2005 r=0 51.31 * 27.07 F] 28.2* 20.97 r=2 19.47* 14.07 5 2005-2006 r=0 31.52* 27.07 Fl 18.43 20.97 r=2 7.94 14.07 1 2006-2007 1:0 34.30* 27.07 r=1 18.21 20.97 r=2 9.91 14.07 4 2007-2008 r=0 29.08”“ 27.07 r=1 2594* 20.97 F2 5.69 14.07 3 2008-June 2008 F0 9342* 27.07 r=1 3524* 20.97 r=2 1506* 14.07 *Indicates rejection of the null hypothesis at 5% significance ' 68 Table 2.n indicates the existence of at least one cointegrating vector in ten of the eleven years. To discover if the Kansas percent of corn used in the production of ethanol affected the annual degree of cointegration Efron’s bootstrapping technique with 1,000 replications to regress the annual cointegration maximal eigenvalue test statistic (MAXE) on the Kansas percentage of corn used in the production of ethanol in (KS) was employed. The following equation illustrates the results of this procedure: (8) MAXE=28.67 + 96.74 * KS (0.00) (0.13) where the numbers in the parentheses are the p-values for the respective parameter estimates. The coefficient for the Kansas percentage of corn used in the production of ethanol variable is not significantly related to the annual degree of cointegration of grain markets in the state of Kansas. The increase in the Kansas percent of corn used in the production of ethanol has not conclusively caused spatial corn price relationships at the Chapman, Hillsboro, Larned and Osborne grain markets to diverge from a long-run equilibrium. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the percent of ethanol used in the production of ethanol. The results were the same as the above test (i.e. the Kansas percentage of corn used in the production of ethanol was not significantly different from zero). Besides testing to establish whether the increased percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of corn prices at the Chapman, Hillsboro, Larned and Osborne grain markets, also tested was Whether the increase in the number of ethanol plants in Kansas altered the annual degree 69 of cointegration of corn prices at the Chapman, Hillsboro, Larned and Osborne grain markets. Using Efron’s bootstrapping technique with 1,000 replications and regressing the annual cointegration maximal eigenvalue test statistic (MAXE) on the using number of ethanol plants in Kansas (EPK), the following results were found: (9) MAXE = 20.36 + 3.45 * EPK (0.079) (0.149) where the numbers in parentheses are the p-values for the respective parameter estimates. The coefficient of the number of ethanol plants in Kansas is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. When this process was performed using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the number of ethanol plants in Kansas the estimates from this process indicated that the coefficient for the number of ethanol plants in Kansas variable was not significantly different from zero. Therefore, one cannot conclude that the increase in the number of ethanol plants in the Kansas has caused corn price relationships at the Chapman, Hillsboro, Larned and Osborne grain markets to diverge from a long-run spatial price relationship equilibrium. Next, the same procedure was utilized to determine if the Iowa percentage of corn used in the production of ethanol altered the annual degree of cointegration of corn prices at the Iowa grain markets of Algona, Aubudon, Cedar Rapids and Chariton. Following past methodology, examined first was whether the corn prices at the Iowa grain markets were cointegrated from September 1998 through June 2008. Table 20 details the multivariate cointegration testing of the corn prices in Algona, Audubon, Cedar Rapids 70 and Chariton grain markets. Lag length of six was used in the testing because this was the amount that minimized the FPE. Table 2.0 Iowa Grain Markets Multivariate Cointegration Testing Results Null Alternative Cointegration 5% Critical Hypothesis Hypothesis Test Stat Value Trace Test Ho: F0 H1: r>0 142.45* 47.21 Ho: r=1 H1: r>1 74.75* 29.38 Ho: r=2 H1: r>2 32.67* 15.34 Ho: r=3 H1: r>3 1.36 3.84 Max Test Ho: r=0 H1: r=1 67.70* 27.07 Ho: r=1 H1: r=2 42.08* 20.97 Ho: r=2 H1: r=3 31.32* 14.07 Ho: r=3 H1:r=4 1.36 3.76 *Indicates rejection of the null hypothesis at 5% significance Table 20 displays three cointegrating vectors for the four corn price series using both the maximal eigenvalue test statistic and the trace test statistic. Therefore, the corn prices at Algona, Audubon, Cedar Rapids and Chariton are cointegrated. Thus, the corn prices operate in a stable long-run spatial price equilibrium. To determine if the Iowa percentage of corn used in the production of ethanol affected the annual cointegration of corn prices at Algona, Audubon, Cedar Rapids and Chariton the annual cointegration statistics for the corn prices at the given grain markets must be determined. Table 2.p displays the annual cointegration maximal eigenvalue test statistics as well as the proper lag lengths determined by the minimum value of the FPE. The annual test statistic for the null hypothesis r=3 and the corresponding trace statistics have been excluded from Table 2.p to save space. 71 Table 2.p Iowa Markets Annual Cointegration Tests Lag Maximal Length Null Hypothesis Eigenvalue 5% (using FPE) Time Period Ho: Test Statistic Critical Value 2 Sept. 1998-1999 F0 25.68 27.07 F1 16.70 20.97 F2 12.66 14.07 1 1999-2000 F0 5625* 27.07 F1 2538* 20.97 F2 9.90 14.07 7 2000-2001 F0 41.41 * 27.07 F1 2580* 20.97 r=2 16.19 14.07 1 2001-2002 F0 91 .75* 27.07 F1 3373* 20.97 F2 2073* 14.07 4 2002-2003 F0 3441* 27.07 F1 21 .59* 20.97 F2 1587* 14.07 3 2003-2004 F0 24.58 27.07 F1 12.52 20.97 F2 7.43 14.07 2 2004-2005 F0 22.47 27.07 F1 12.15 20.97 F2 8.42 14.07 4 2005-2006 F0 4946* 27.07 F1 2632* 20.97 F2 8.73 14.07 5 2006-2007 F0 3636* 27.07 F1 15.12 20.97 F2 9.07 14.07 2 2007-2008 F0 19.94 27.07 Fl 10.59 20.97 F2 7.09 14.07 2 2008-June 2008 F0 4563* 27.07 F1 14.15 20.97 F2 6.25 14.07 *Indicates rejection of the null hypothesis at 5% significance 72 Table 2.p indicates the existence of at least one cointegrating vector in seven of the eleven years. To discover if the Iowa percent of corn used in the production of ethanol affected the annual degree of cointegration we employed Efron’s bootstrapping technique with 1,000 replications to regress the annual cointegration maximal eigenvalue test statistic (MAXE) on the Iowa percentage of corn used in the production of ethanol in (IA). The following equation illustrates the results of this procedure: (10) MAXE=47.69 - 28.35 * IA (0.01) (0.66) where the numbers in the parentheses are the p-values for the respective parameter estimates. As illustrated by its corresponding p-value, the coefficient for the Iowa percent of corn used in the production of ethanol is not significantly different from zero. Therefore, the Iowa percentage of corn used in the production of ethanol is not significantly related to the annual degree of cointegration of grain markets in the state of Iowa. Alternatively stated, one cannot conclude that the increase in the Iowa percent of corn used in the production of ethanol has caused spatial corn price relationships at the Algona, Audubon, Cedar Rapids and Chariton grain markets to diverge from a long-run equilibrium. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the percent of ethanol used in the production of ethanol. The results were the same as the above test (i.e. the Iowa percentage of corn used in the production of ethanol was not significantly different from zero.) Besides testing to establish whether the increased percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of com 73 prices at the Algona, Audubon, Cedar Rapids and Chariton grain markets, also tested was whether the increase in the number of ethanol plants in Iowa altered the annual degree of cointegration of corn prices at the A1 gona, Audubon, Cedar Rapids and Chariton grain markets. Using Efron’s bootstrapping technique with 1,000 replications and regressing the annual cointegration maximal eigenvalue test statistic (MAXE) on the using number of ethanol plants in Iowa (EPIA), the following results were found: (11) MAXE = 46.87 - 0.43 * EPIA (0.000) (0.505) where the numbers in parentheses are the p-values for the respective parameter estimates. Therefore, the coefficient for the number of ethanol plants in Iowa is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the number of ethanol plants in Iowa The results found that the number of ethanol plants in Iowa variable was not significantly different from zero. The increase in the number of ethanol plants in the Iowa did not conclusively cause corn price relationships at the Algona, Audubon, Cedar Rapids and Chariton grain markets to diverge from a long-run spatial price relationship equilibrium. Next, the same procedure was used to determine if the Indiana percentage of corn used in the production of ethanol altered the annual degree of cointegration of corn prices at the Indiana grain markets of Columbus, Delphi, Greensburg and Hamlet. Staying consistent with past methodology, whether the corn prices at the Indiana grain markets were cointegrated from September 1998 through June 2008 was determined. Table 2.q details the multivariate cointegration testing of the corn prices in Columbus, Delphi, 74 Greensburg and Hamlet markets. Lag length of four was used in the testing because this was the amount that minimized the FPE. Table 2.q Indiana Grain Markets Multivariate Cointegration Testing Results Null Alternative Cointegration 5% Critical Hypothesis Hypothesis Test Stat Value Trace Test Ho: F0 H1: r>0 206.22* 47.21 Ho: F1 H1: r>l 121.48* 29.38 Ho: F2 H1: r>2 42.57* 15.34 Ho: F3 H1: r>3 1.60 3.84 Max Test Ho: F0 H1:F1 84.74* 27.07 Ho: Fl H1:F2 78.90* 20.97 Ho: F2 H1: F3 4097* 14.07 Ho: F3 H1:F4 1.60 3.76 *Indicates rejection of the null hypothesis at 5% significance Table 2.q displays three cointegrating vectors for the four corn price series using both the maximal eigenvalue test statistic and the trace test statistic. Therefore, the corn price series at Columbus, Delphi, Greensburg and Hamlet contain are cointegrated. Therefore, the corn prices operate in a stable long-run spatial price equilibrium. To determine if the Indiana percentage of corn used in the production of ethanol affected the annual cointegration of corn prices at Columbus, Delphi, Greensburg and Hamlet we must determine the annual cointegration statistics for the corn prices at the given grain markets. Table 2.r displays the annual cointegration maximal eigenvalue test statistics as well as the proper lag lengths determined by the minimum value of the FPE. The annual test statistic for the null hypothesis F3 and the corresponding trace statistics have been excluded from Table 2.r to save space. 75 Table 2.r Indiana Markets Annual Cointegration Tests Lag Maximal Length Null Hypothesis Eigenvalue 5% (using FPE) Time Period Ho: Test Statistic Critical Value 2 Sept. 1998-1999 F0 2773* 27.07 F1 11.79 20.97 F2 6.95 14.07 7 1999-2000 F0 3596* 27.07 F1 2914* 20.97 F2 6.23 14.07 1 2000-2001 F0 3229* 27.07 F1 2622* 20.97 F2 9.74 14.07 6 2001-2002 F0 4287* 27.07 F1 2663* 20.97 F2 5.29 14.07 1 2002-2003 F0 5287* 27.07 F1 16.76 20.97 F2 5.81 14.07 7 2003-2004 F0 6966* 27.07 F1 15.10 20.97 F2 9.07 14.07 1 2004-2005 F0 15.97 27.07 F1 14.94 20.97 F2 5.83 14.07 1 2005-2006 F0 22.38 27.07 F1 11.83 20.97 F2 6.17 14.07 5 2006-2007 F0 3048* 27.07 F1 19.77 20.97 F2 8.88 14.07 6 2007-2008 F0 7297* 27.07 F1 2577* 20.97 F2 10.03 14.07 2 2008-June 2008 F0 2882* 27.07 F1 17.31 20.97 F2 10.79 14.07 *Indicates rejection of the null hypothesis at 5% significance 76 Table 2.r indicates the existence of at least one cointegrating vector in nine of the eleven years. To discover if the Indiana percent of corn used in the production of ethanol affected the annual degree of cointegration we employed Efron’s bootstrapping technique with 1,000 replications to regress the annual cointegration maximal eigenvalue test statistic (MAXE) on the Indiana percentage of corn used in the production of ethanol in (IN). The following equation illustrates the results of this procedure: (12) MAXE=38.37 + 10.51 *IN (0.21) (0.99) where the numbers in the parentheses are the p-values for the respective parameter estimates. As evidenced by its corresponding p-value, the coefficient for the Indiana percent of corn used, in the production of ethanol is not significantly different from zero. Thus, the Indiana percentage of corn used in the production of ethanol is not significantly related to the annual degree of cointegration of grain markets in the state of Indiana. Te increase in the Indiana percent of corn used in the production of ethanol did not conclusively cause spatial corn price relationships at the Columbus, Delphi, Greensburg and Hamlet grain markets to diverge from a long—run equilibrium. This process was also performed by using Efron’s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the percent of ethanol used in the production of ethanol. The results were the same as the above test (i.e. the Indiana percentage of corn used in the production of ethanol was not significantly different from zero). Besides testing to establish whether the increased percentage of corn in used in the production of ethanol had an effect on the annual degree of cointegration of corn prices at the Columbus, Delphi, Greensburg and Hamlet grain markets, also tested was 77 whether the increase in the number of ethanol plants in Indiana altered the annual degree of cointegration of corn prices at the Columbus, Delphi, Greensburg and Hamlet grain markets. Using Efron’s bootstrapping technique with 1,000 replications and regressing the annual cointegration maximal eigenvalue test statistic (MAXE) on the using number of ethanol plants in Indiana (EPIN), the following results were found: (13) MAXE = 38.44 + 0.51 * EPIN (7.10) (0.849) where the numbers in parentheses are the p-values for the respective parameter estimates. The coefficient for the number of ethanol plants in Indiana is not significantly correlated with the annual cointegration maximal eigenvalue test statistic. When this process was performed using Efron‘s bootstrapping technique with 1,000 replications to regress the annual trace test statistics on the number of ethanol plants in Indiana the estimated results found that the coefficient for the number of ethanol plants in Indiana variable was not significantly different from zero. Therefore it was not concluded that the increase in the number of ethanol plants in the Indiana caused corn price relationships at the Columbus, Delphi, Greensburg and Hamlet grain markets to diverge from a long-run spatial price relationship equilibrium. 78 REFERENCES Anderson, J .D., and K. H. Coble. 2009. Impact of Renewable Fuels Standard Ethanol Mandates on the Corn Market. Agribusiness: An International Journal 26,1 :49- 63. Baker, Allan and Steven Zahniser. 2006. Ethanol Reshapes the Corn Market. Amber Waves 4,2, http://www.mynrma.com.au/cps/rde/papp/motoringPoll:motoringPoll/http://www. ers.usda.gov/AmberWaves/MayO7SpecialIssue/PDF/Ethanol.pdf Brester, Gary W. and Barry K. Goodwin. 1993. Vertical and Horizontal Price Linkages and Market Concentration in the US. Wheat Milling Industry. Review of Agricultural Economics 152507-519. Cash Grain Bids Data Service. 2008. Historical Grain Prices. http://www.cashgrainbids.com/. Accessed November, 9, 2008. Ethanol Producer Magazine. 2009. Ethanol Plant List. http://www.ethanolproducer.com/plant-list.jsp. Accessed December 12, 2009. Gujarati, Damodar N. and Dawn C. Porter. 2008. Basic Econometrics. McGraw-Hill Irwin. Harri, Ardian, Nalley Lanier and Darren Hudson. 2009. The Relationship between Oil, Exchange Rates, and Commodity Prices. Journal of Agricultural and Applied Economics 41 ,2:501-510. Johansen, S. and K. Juselius. 1990. Maximum Likelihood Estimation and Inference on Cointegration-With Applications to the Demand for Money. Oxford Bulletin of Economics and Statistics 52: 169-210. Livestock Market Information Center. Corn Price Data, 1998-2008. LMIC, Englewood, CO, 2009. McNew, Kevin and Duane Griffith. 2005. Measuring the Impact of Ethanol Plants on Local Grain Prices. Review of Agricultural Economics 27,2: 164-180. Pendell, Dustin L. and Ted C. Schroeder. 2006. Impact of Mandatory Price Reporting on Fed Cattle Market Integration. Journal of Agricultural and Resource Economics 31,3:568-579. Renewable Fuels Association. 2009. Statistics. http://www.ethanolrfa.org/industry/statistics/. Accessed November 12, 2009. 79 Schroeder, Ted C. 1997. Fed Cattle Spatial Transactions Price Relationships. Journal of Agricultural and Applied Economics 29,2:347-362. United States Department of Agriculture. (1). 2009. Economics, Statistics, and Market Information System. _ http://usda.mannlib.comell.edu/MannUsda/viewDocumentInfo.do?documentID=1 047. Accessed November 14, 2009. United States Department of Agriculture. (2). 2009. National Agricultural Statistics Service. www.mass.usda.gov/Statistics_by_State/New_York/Historical_Data/PricesReceiv ed/ComPrices.xls. Accessed November 18, 2009. United States Department of Agriculture. (3). 2009. National Agricultural Statistics Service. http://www.nass.usda.gov/Charts_and_Maps/graphics/data/pricecn.txt. Accessed November 12, 2009. Wooldridge, Jeffrey M. 2006. Introductory Econometrics. Thomson South-Western. 80 CHAPTER 3: THE IMPACT OF ETHANOL PLANT OPENINGS ON CORN PRICE BASIS LEVELS 3.1 Introduction In recent years there has been a rapid increase in the number of ethanol plants in the United States. In the previous chapter it was determined that cOrn price relationships at different grain markets were not affected by the increase in the percent of corn that went into the production of ethanol from September 1998 through June 2008. However, it is possible that com price levels at particular grain markets were affected when new ethanol plants were built within the same region of an existing grain market. From September 1998 through June 2008 several ethanol plants were built that used corn as their primary input. This likely affected corn price levels at grain markets close to the newly constructed ethanol plants. When the ethanol plants began purchasing corn to produce ethanol, the local demand for corn at grain markets near the ethanol plant increased. Therefore, it is expected that the appearance of new ethanol plants caused increases in corn price levels at nearby grain markets. The idea that newly introduced ethanol plants affect different aspects of agriculture in the region of the ethanol plant opening has previously been studied by Henderson and Gloy (2009). They determined that land price changes were consistent with previous estimates of basis change associated with ethanol plant location. Henderson and Gloy cite McNew and Griffith (2005) as studying the impact ethanol plant openings had on local grain prices. McNew and Griffith estimated the impact of twelve com-based ethanol plants that opened between March 2000 and March 2003 on 81 local corn price levels. They discovered the average impact on corn price basis levels after an ethanol plant opened was positive 12.5 cents at grain markets located at the site of an ethanol plant opening. The 12.5 cent increase was the average corn price basis impact over the entire time period after an ethanol plant opened. Corn price basis levels are defined as the corn price at a local grain market less the futures contract corn price at the Chicago Board of Trade. If corn price basis levels increase at a local grain market, this indicates that the corn price at the local grain market has increased relative to the futures contract corn price at the Chicago Board of Trade. Other studies suggest ethanol plant openings have not increased corn price basis levels. Katchova (2009) used USDA’s ARMS data to discover that farmers located close to ethanol plants did not received significantly higher corn prices compared to farmers who live further away from ethanol plants. Katchova found farmers located in the same zip code of an ethanol plant contracted their corn for an average of 10.9 cents lower than farmers who did not live in the same zip code of an ethanol plant. Basis values are influenced by local supply and demand conditions, transportation costs, and local storage costs for corn. Variation in the location of the final demand center for the contracted corn creates differences in transportation costs which also contribute to the discrepancies in local corn price levels at different grain markets. For example, if the final demand center for the contracted corn is farther away from the grain market where the corn was contracted, this causes transportation costs to increase which leads to declining local corn prices. If a farmer contracts his or her corn to a grain market in Marlette, MI and the corn is to be delivered to an ethanol plant in Marysville, M1, the transportation cost is lower than if the corn is to be delivered to a feedlot in Indiana. 82 Otherwise stated, if the corn has to be shipped a shorter distance, the com’s basis level increases. The local corn price recorded at the Marlette, MI, grain market would be higher if the corn was delivered to Marysville, MI, than if the corn was delivered to the feedlot in Indiana. Consequently, the ethanol plant opening in Marysville created a new demand center in the state of Michigan, which is expected to increase local corn price levels at grain markets located nearby (Marlette) because the ethanol plant opening lowered transportation costs for the closest demand center as well as increased the local demand for corn. Following this logic, McNew and Griffith examined how ethanol plant openings changed local corn price levels at different grain markets. They argued the introduction of a new demand source for grain could induce shifts in trade patterns and adjust the spatial characteristics of grain prices around an ethanol plant opening. To conceptualize the introduction of a new demand center (ethanol plant), McNew and Griffith began their framework by assuming initially that only one demand center for the grain’s final demand destination existed. They referred to this final demand center as “terminal market,” and described it as being located at the end of a line segment measured in distance dE[0,1]. Therefore, “terminal market” was located at d=1. Next, McNew and Griffith assumed the price producers received for their corn that was shipped to “terminal market” was adjusted for transportation costs; therefore,'the price at any location along d could be expressed in terms of the terminal market price, PT, and the per unit cost of shipping, r: (1) P(d)= PT - r (1-d). 83 Using this equation, McNew and Griffith explain that the price for corn continuously increases as one moves closer to the terminal market (as d approaches 1). Similarly, as a result of an ethanol plant opening, it is probable that com price levels increased at the site of an ethanol plant opening. Additionally, the price impact of the ethanol plant openings is most likely spatially varied across the line segment. To empirically determine if com price basis levels increased at the site of an ethanol plant opening, the following section will expand upon modeling techniques similar to the empirical models used by McNew and Griffith. They used a spatial econometric model to examine how corn price basis levels adjusted to changes in national and local corn production, seasonality, transportation rates and the distance grain markets were away from ethanol plant openings. Our modeling of the impact ethanol plant openings had on local corn price basis levels aims to improve the work completed by McNew and Griffith. Our data set examines ethanol plant openings from September 1998 through June 2008 compared to McNew and Griffith who examined ethanol plant openings from March 2000 through March 2003. Examining the impact ethanol plant openings have on corn price basis levels over an eleven year time frame rather than three years will provide a more complete analysis of the impact increased ethanol production had on corn price basis levels. It is worth noting that between September 1998 and June 2008 forty-two ethanol plants opened in Michigan, Kansas, Iowa and Indiana (the states studied in the upcoming analysis) while only twelve ethanol plant openings occurred between March 2000 and March 2003. 84 McNew and Griffith estimated the effect ethanol plant openings had on corn price basis levels over the average of the entire time period after the ethanol plant opened. They did not include any time effect variables in their models. Our analysis will also study the impact ethanol plant openings had on corn price basis levels over the average of the entire time period after the ethanol plant opened. However, our analysis will also add time component variables that estimate the impact ethanol plant openings had on corn price basis levels each month after the ethanol plant opened. By studying the impact ethanol plant openings had on corn price basis levels each month after an ethanol plant opened we will be able to determine how quickly the corn market adjusted to increased demand created by increased ethanol production. Together, a longer time frame and the addition of a time component will enhance the work completed by McNew and Griffith to create a more comprehensive analysis of the impact ethanol plant openings had on corn price basis levels. 3.2 Materials and Methods To determine if corn price basis levels at different grain markets were affected by ethanol plants openings, corn price basis levels at different grain markets in Michigan, Kansas, Iowa and Indiana were identified. As previously mentioned, corn price basis levels are determined by local supply and demand, transportation costs, and local storage costs for corn. Local market conditions, rather than national market circumstances, are reflected by com price basis levels. Therefore, we are able to establish what effect ethanol plant openings had on local grain markets by examining corn price basis levels. 85 Corn price basis levels from September 1998 through June 2008 were purchased from Cash Grain Bids Data Service. McNew and Griffith (2005) also acquired corn price data from Cash Grain Bids Data Service. For this study, daily corn basis levels were collected from every grain market Cash Grain Bids Data Service had data on within 300 miles of Omaha, Nebraska and within 300 miles of Indianapolis, Indiana. Monthly corn basis level averages were created from the daily basis level observations recorded by Cash Grain Bids Data Service. Additionally, only monthly corn price basis level averages at grain markets located in Michigan, Kansas, Iowa and Indiana were compiled. Michigan, Kansas, Iowa and Indiana account for approximately fifty-two percent of the national annual production of ethanol (Ethanol Producer Magazine). These four states also account for approximately thirty-two percent of the total corn produced in the United States (USDA 1). Iowa, from 1998 through 2008, annually produced the most com in the nation (USDA 1). Together, these states provide insight into how increased ethanol production has affected corn price basis levels in the Midwestern United States. The summary statistics for the monthly corn basis levels at grain markets in Michigan, Kansas, Iowa and Indiana are found Appendix 1. Highlighted in Appendix 1 are the grain markets that were used in each state for this study. As shown in Appendix] , not all grain markets contained 100% of their monthly corn price basis level observations. To determine which grain markets to study in each state, a criterion was established that each grain market had to be missing less than five percent of their monthly corn price basis level observations. In Michigan, sixteen grain markets were missing less than five percent of their monthly observations. Kansas contained thirty-three grain markets that were missing less than five percent of their monthly observations. Out of the thirty-three 86 grain markets in Kansas, seven were not missing any monthly basis observations. In Iowa, 121 grain markets were missing less than five percent of their monthly basis observations and fifty—seven of these grain markets were not missing any monthly basis observations. In Indiana, thirty-four markets were missing less than five percent of their monthly basis observations and four of these grain markets contained 100% of their monthly basis observations. Missing monthly observations for the grain markets used in this analysis were predicted by examining the grain market’s weekly corn price observations which were also compiled from the daily corn price observations recorded by Cash Grain Bids Data Service. The weekly corn price observations also included missing observations. Therefore, missing weekly corn price observations were predicted by regressing the Chicago corn price time series with each individual grain market’s corn price time series. The weekly average Chicago corn price time series from September 1998 through June 2008 was recorded by the Livestock Market Information Center. To create the weekly corn price basis levels, once the weekly corn price observations were predicted, the weekly corn price observations less the Chicago corn price observations were calculated. The weekly grain market corn price basis levels were then compiled into monthly corn price basis levels which were used in the ensuing analysis. Ethanol plant information was also required to determine the impact ethanol plant openings had on corn price basis levels at grain markets located nearby. Ethanol plant construction information was compiled from the Renewable Fuels Association and Ethanol Producer Magazine. For this study, all ethanol plant openings that occurred between September 1998 and June 2008 in the states of Michigan, Kansas, Iowa and 87 Indiana were identified. However, ethanol plants that produced less than twenty million gallons of ethanol a year were omitted from analysis. Ethanol plants with annual production capacity of less than twenty million gallons were not thought to impact corn price basis levels the same as ethanol plants with annual production capacity of more than fifty million gallons (which is the production capacity of many ethanol plants in this analysis). Therefore, ethanol plants in Steamboat Rock, IA, Atchison, KS, Garden City, KS, Leoti, KS, and Scandia, KS, were omitted from this analysis because they produced less than twenty million gallons of ethanol a year. In Iowa, ethanol plants in Corning, 1A, Gowrie, IA, and Nevada, IA, all opened in May 2006. This is the only instance where more than one ethanol plant opened in a single month for a given state. To properly examine the monthly effect of ethanol plant openings on corn price basis levels, only the ethanol plant opening at Corning, IA, was examined. Therefore, the ethanol plant openings at Gowrie, IA, and Nevada, IA, were also excluded from analysis. Table 3.a displays ethanol plant data used for analysis in Michigan. Table 3.a illustrates the number of grain markets with recorded basis observations located within 150 miles of an ethanol plant opening. McNew and Griffith also determined the effect ethanol plant openings had on grain market’s corn price basis levels that were located within 150 miles of an ethanol plant opening. They considered grain markets only within 150 miles of an ethanol plant opening in their study because this process allowed them to consider a definite market impact for each ethanol plant opening. For this analysis, a 150 mile radius approach was used for similar reasons. Smaller radius approaches were 88 considered, but were not feasible given the lack of grain observations around several ethanol plants. Table 3.a Michigan Ethanol Plants Opening and Grain Markets Nearby Plant Capacity Number of Grain Michigan (Million Gallons Markets in Plant Location Start-up Date Per Year) 150-m square area Caro November, 2002 50 15 Albion August, 2006 55 16 Lake Odessa* September, 2006 50 16 Riga February, 2007 57 15 Mapysville October, 2007 50 10 *Owned by VeraSun which declared bankruptcy in the Fall of 2008 The ethanol plant in Lake Odessa, MI, was owned by VeraSun Energy Corporation and went bankrupt in the Fall of 2008. This information is irrelevant to our analysis because our data set ends in June of 2008. Table 3.b illustrates the Kansas ethanol plant information used in the ensuing analysis. Table 3.b displays ethanol plant opening dates in the state of Kansas as well as the number of grain markets with recorded basis observations located within 150 miles of an ethanol plant opening. 89 Table 3.b Kansas Ethanol Plant Openings and Grain Markets Nearby Plant Capacity Number of Grain Kansas (Million Gallons Markets in Plant Location Start-up Date Per Year) 150—m square area Russell October 2001 50 23 Colwich December 2002 25 25 Oakley January 2004 45 10 Gamett January 2005 35 20 Phillipsburg January 2006 40 19 Pratt* June 2007 55 22 Garden City October 2007 55 10 Liberal December 2007 1 10 7 Lyons M21 2008 55 26 *Declared bankruptcy in February 2008 The ethanol plant in Pratt, KS went bankrupt in February 2008 and this information is incorporated into the Kansas model which is explained in the upcoming section. Table 3.c displays ethanol plant opening dates in the state of Iowa as well as the number of grain markets with recorded basis observations located within 150 miles of an ethanol plant opening. Table 3.c illustrates the information used for the ensuing analysis. 90 Table 3.c Iowa Ethanol Plant Openings and Grain Markets Nearby Plant Capacity Number of Grain Iowa (Million Gallons Markets in Plant Location Start-up Date Per Year) 150-m square area Sioux Center November 2001 55 69 Galva February 2002 30 98 Coon Rapids August 2002 54 109 Lakota November 2002 100 99 Marcus April 2003 92 86 Hanlontown February 2004 55 97 Ashton March 2004 55 76 West Burlington April 2004 92 35 Iowa Falls November 2004 100 111 Mason City December 2004 80 100 Emmetsburg April 2005 50 105 Denison September 2005 55 97 Fort Dodge* October 2005 110 113 Goldfield December 2005 50 113 Jewell March 2006 60 116 Coming May 2006 60 80 Fairbank June 2006 115 86 Albert City* November 2006 100 1 13 Charles City* April 2007 110 105 Shenandoah June 2007 50 92 Superior November 2007 50 58 St. Ansgar February 2008 100 92 *Owned by VeraSun which declared bankruptcy in the Fall of 2008 Ethanol plants in Fort Dodge, IA, Albert City, IA, and Charles City, IA were all owned by VeraSun Energy Corporation which declared bankruptcy in the Fall of 2008. June 2008 is the end of this model’s data set, so this information is not incorporated in the upcoming model. Table 3.d displays ethanol plant opening dates in the state of Indiana as well as the number of grain markets with recorded basis observations located within 150 miles of 91 an ethanol plant opening. The information contained in Table 3.d is used in the upcoming analysis. Table 3.d Indiana Ethanol Plants Opening and Grain Markets Nearby Plant Capacity Number of Grain Indiana (Million Gallons Markets in Plant Location Start-up Date Per Year) 150-m square area Rensselaer January 2007 40 26 Marion March 2007 40 29 Clymers May 2007 1 10 29 Linden* August 2007 100 31 Portland September 2007 60 28 Alexandria April 2008 65 3O *Owned by VeraSun which declared bankruptcy in the Fall of 2008 The ethanol plant in Linden, IN, was owned by VeraSun Energy Corporation and went bankrupt in the the Fall of 2008. This model’s data set ends in June 2008, so this information is not contained in the approaching model. To determine the effect of an ethanol plant opening on corn price basis levels from September 1998 through June 2008, a state by state approach was utilized. The first state examined was Michigan. The following model was utilized to examine the impact ethanol plant openings had on corn price basis levels in Michigan over the average time period after an ethanol plant opened: . _ 11 15 , _ (2) Blt — aOUSCt + alMCt + 2k “ZkMOk+Zi a3lMK1 2 + 22 = 1 RieEet [56 + BerDISTei + Be2(DISTei) + Be3rtDISTe1 2 +Be4rt(DISTei) l + €11 where Bit is the basis for grain market i in time period t, USCt is US. corn production at time t and MCt is com production in the state of Michigan at time t. Both US. and 92 Michigan corn production are in millions of bushels and are the same values from October through September because corn is harvested in October. The variable MOk is a dummy variable indicating the month of the year which is used to estimate seasonality in basis levels. The variables MKi are dummy variables equal to one if the corn price basis observations are from grain market i. The variable Rie is a dummy variable indentifying whether grain market 1 is within 150 miles of ethanol plant e (in the region of ethanol plant e) where Rieis equal to one if grain market i is within 150 miles of an ethanol plant and equal to zero otherwise. The variable Eet is a dummy variable indicating whether ethanol plant e is open at time t where Eet is equal to one if ethanol plant e is open at time t and equal to zero otherwise. There are five ethanol plant openings in Michigan; therefore, e is indexed from one to five. The variable rt is US. average retail diesel price used to estimate trucking costs. The observations for this variable were compiled from the United States Energy Information Administration. The variable DISTei is the distance grain market i is from ethanol plant e and (DISTei)2 is the distance grain market 1 is from ethanol plant e squared. Equation (2) most likely has spatially correlated errors because many grain markets are located close to each other and operate in overlapping grain production/demand areas. For a given time period, any two grain markets i and j will likely have positive correlation which violates the assumptions of an ordinary least squares regression (McNew and Griffith 2005). To deal with this problem, Anselin (1998) provides a maximum likelihood method for estimating models using cross- 93 sectional spatial data. Following Anselin’s methodology, LeSage and Pace (2009) created a regression model that account for spatial autocorrelation. Similarly, the model we estimated is a spatial error model (SEM) that deals with spatial autocorrelation: (3) y=XB+u u=7th+e €~N(0,621 ) where y is an nxl vector of cross—sectional dependent variables and X represents an nxk matrix of explanatory variables. W is an nxn spatial weight matrix which has zeros along the main diagonal and rows that contain zeros in positions associated with non- contiguous observational units and ones in positions reflecting neighboring units that are first-order contiguous (LeSage and Pace 2009). The spatial weight matrix W quantifies the spatial aspect of the data by signifying which observations are correlated. The spatial weight matrix’s (W) coefficient 71 represents the serial correlation problem in the time- series models (McNew and Griffith 2005). If A is found to be significant, then observations in the same neighboring area exhibit serial correlation. To construct the spatial weight matrix (W) the latitude and longitude of all the grain markets in Michigan were used. Following LeSage and Pace, a function in MATLAB named xy2cont, which is part of Pace and Barry’s Spatial Statistics Toolbox, was utilized. This function uses triangles connecting the x-y coordinates in space to deduce contiguous entries (LeSage and Pace 2009). If two grain markets are contiguous, the spatial weight matrix (W) places a one in the cells of the matrix which links the two grain markets. 94 Many journal articles have cited several ways of dealing with spatial autocorrelation problems in models. Overmars et a1. (2003) discuss various ways of dealing with spatial autocorrelation in land use models. Overmars et al. also explain the usage of models created by LeSage and Anselin to overcome the problem of spatial autocorrelation. Furthermore, Lambert et a1. (2006) discuss using LeSage’s MATLAB code to account for spatial autocorrelation when modeling the effect different attributes had on manufacturing investment flows throughout Indiana between 2000 and 2004. To further examine the impact ethanol plant openings had on corn price basis levels, a second model was built expanding equation (2) that allows the basis effect to adjust as the number of months since an ethanol plant opening increases. Allowing the basis effect to change as the number of months since an ethanol plant opened increases allows Model 11 to estimate a more practical basis effect. Model 1 assumes an average basis effect for the entire time period after the ethanol plant opened. Model 11 allows basis variation to adjust to market conditions as the months since an ethanol plant opening increases. Model 11 will use equation (3) to account for spatial correlation. Specific for the state of Michigan, Model 11 is as follows: (4) Bit = aousct + alMCt + 211,1 aZkMOk+Zi15 a3iMKi 5 2 + Xe = 1 RieEet [5e + Be1DISTei + 832(D15Tei) + BesrtDISTei +pe4rt(orsrei)2 + 883M00pene + eit where the common variables are defined the same as in equation (2), the variable MOopene is the number of months since ethanol plant e opened. The variable MOopene is equal to zero prior to the opening of ethanol plant e. After ethanol plant e opens, the 95 variable MOopene is equal to the number of months since the ethanol plant opened. For example, after the ethanol plant has been opened for one month, the variable MOopene is equal to one. 3.3 Results and Implications Results for Michigan’s Model I, which estimated equation (2) using the maximum likelihood method following equation (3), are found in table 3.c. Table 3.e Michigan Equation (2) Estimated Results Variable Coeff. Variable Coeff. Variable Coeff. USC 7.4E-4** Blissfield 6.9788** Webberville 1.6156** MC -0.1615** Breckenridge -4.0738** Yale -0.5298 January 3.9103 * * Buchanan —1.4109* February 5.3723 * * Caledonia -1.7518** March 2.8123** Hamilton 5.6771** April 6.4076** Holland 11.5830** May 5.6843** Imlay City 0.1388 June 9.5189** Jasper 1.2185* July 8.5343” Lake Odessa -5.0323** August 9.8559“ Marlette -2.9073** September -0.2785 Middleton -5.1 165 * * October -2.4109* * Newago -1.0773 November 1.7605 * * Ottawa Lake 10.5695 * * Lake Variable Caro Albion Odessa Riga Marysville RE(DIST) 0.8883” -2.3097** 0.6638 -0.2863 -0.4446 RE(DIST)2 -0.0053** 0.0140** -0.0018 0.0031 0.0038 REHDIST) -0.0046** 0.0069** -0.0019 7.4E-4 9.7E-5 REr(DIS'I‘) 2 2.7E-5** -4.2E-5* * 0.0E-6 -1.2E-5* -5.0E-6 RE 10.0978** 5.3319** 12.3065** 17.8666** 19.2172** Note: R2=0.6826 and the spatial error coefficient 71:0.7960 was significant at the 1% level. * Indicates significance at the 5% level. ** Indicates significance at the 1% level. 96 Table 3.e shows that A was statistically significant. Therefore, using equation (3) and running the Model I to account for spatial autocorrelation in the error terms, rather than conducting an ordinary least squares regression, was important. The variable USCt is annual corn production in the United States. This variable was significant at the five percent level and positive. To interpret this coefficient, if corn production in the United States were to increase by one billion bushels, corn price basis levels in Michigan would increase by 0.74 cents. This result is unexpected because as corn production in the US. increases, local basis levels are expected to fall because of the increase in national supply. The variable MCt is annual corn production in Michigan, which is significant at the one percent level. If Michigan corn production were to increase by five million bushels, Michigan corn price basis levels would decrease by 0.81 cents. This is consistent with expected results because as corn supply increases, corn price basis levels are expected to decrease. Table 3.e also shows the month dummy variables were significant, except for September. The monthly dummy variables fluctuate which indicates seasonal patterns were existent in corn basis levels at different grain markets in Michigan. The greatest increase in basis levels was felt in the summer months (i.e. basis levels increased on average 9.8559 cents in August). The greatest weakening of corn price basis levels was felt in October, the month most com is harvested. The grain market dummy variables were significant at all grain markets. If a grain market was located in Holland relative to Zeeland corn price basis levels were 11.58 cents higher. 97 2 The coefficients of the variables RieEetDISTei and RieEet(D13Tei) and RieEetrtDISTei and RieEetrt(DlSTei)2 were significant for Caro and Albion ethanol plants. The variable RieEetDISTei is the distance grain market i is from ethanol plant e, multiplied by the dummy variable Rie which indicates whether grain market i is within ISO-miles of ethanol plant e and multiplied by the dummy variable Eet which indicates whether ethanol plant e is open at time t. The variables RieEetrtDISTei and RieEetrt(DISTei)2 are the same as the variables RieEetDISTei and RieEet(DISTei)2 except they include the US. average retail diesel price,rt, which allows the corn price basis levels to adjust depending on trucking costs. In Caro, as the distance grain market 1 was away from ethanol plant e increased, the corn price basis levels increased to 36.62 cents at grain markets located 75 miles from the ethanol plant opening before decreasing to strengthened levels of 13.91 cents 150 miles away from the ethanol plant opening. As the distance grain market i was away from ethanol plant opening e increased, corn price basis levels fluctuated. The coefficient of the variable RieEet was significant at the one percent level in all cases. The variable RieEet represents whether grain market i was within ISO-miles of ethanol plant e and whether ethanol plant e was open at time t. The coefficient was positive for all of the ethanol plants. The coefficient (lie) for variable RieEet represents the corn price basis level impact at the site of the ethanol plant opening because this is where the distance away from the ethanol plant is equal to zero. The average corn price basis level effect across the five ethanol plants in Michigan was positive at 12.97 cents. Therefore, the corn price basis level impact at the site of an ethanol plant opening'in 98 Michigan, experienced, on average, a 12.97 cent increase in corn price basis levels over the average time period after the ethanol plant opened. The results for variations of Model 1 specific for Kansas, Iowa and Indiana are found in Appendix 2. The summary results indicate that grain markets located at the site of an ethanol plant opening in Kansas experienced, on average, a 9.55 cent increase in corn price basis levels over the entire time period after an ethanol plant opened. In Iowa, grain markets located at the site of an ethanol plant opening experienced an average of 2.41 cent increase in corn price basis levels over the average time period after an ethanol plant opened. In Indiana, grain markets located at the site of an ethanol plant opening experienced an average of 2.21 cent decrease in corn price basis levels over the average time period after an ethanol plant opened. The other coefficient estimates for Model I for Kansas, Iowa and Indiana were similar to that of Michigan and are found in Appendix 2. McNew and Griffith found an average increase of corn price basis levels of 12.5 cents at the site of an ethanol plant opening over the average time period after an ethanol plant opened. Model I for the state of Michigan and Kansas had the most similar results to McNew and Griffith who studied a total of twelve ethanol plant openings from March 2000 through March 2003 that occurred in Illinois, Iowa, Montana, South Dakota, and Wisconsin. The improvement in corn price basis levels in Iowa and Indiana were smaller than the increases found by McNew and Griffith. Because the increase in corn basis levels is averaged over the entire time period after an ethanol plant opening it is probable that the increase in corn price basis levels was greater in the McNew and Griffith model because they studied a shorter time period. McNew and Griffith results also are different because they studied the first series of ethanol plant openings in the United States. 99 Model I does not allow the basis effect to change as the number of months since an ethanol plant opened increases. To allow basis variation to adjust to market conditions as the months since an ethanol plant opening increases, Model 11 was estimated. Results for Michigan’s Model 11, which estimates the impact ethanol plant openings had on basis levels each month after an ethanol plant opened, are found in Table 3.f. Michigan’s Model 11 estimates equation (4) and uses equation (3) to correct for spatially correlated CITOI'S. 100 Table 3.f Michigan Equation (4) Estimate Results Variable Coeff. Variable Coeff. Variable Coeff. USC 0.0009** Blissfield 5.8102** Webberville 0.7523** MC -0.1609** Breckenridge -5.3679** Yale -2.1119** January 3.2321** Buchanan 2.8295* February 5.9234** Caledonia -2.7354** March 3.0389“ Hamilton 4.9581“ April 6.5391** Holland 10.7786** May 5.6845“ Imlay City -1.0434** June 9.4370** Jasper 0.2494** July 7.8572** Lake Odessa -5.9524** August 8.4159* * Marlette -3 .9696* * September -1.5830** Middleton -6.3146** October -3.7523** Newago -2.7730** November 0.6105 Ottawa Lake 9.2477** Variable Caro Albion Lake Odessa Riga Marysville REDIST 0.4179** -0.3975 0.3516 0.1075 -1.0599** RE(DIS'I‘)2 -0.0027** 0.0025 -8.6E-4 -0.0016 0.0071** RErDIST -0.023** 9.9E-4 -9.8lE-4 -4.26E-4 0.0024* REr(DIST)2 1.4E-5* * -5.0E-6 -3.0E-6 -3.0E-6 4.0E-6* MOopen -0.3954** -37.3505** 38.6674** 2.1520** -4.3918** RE 19.3810** 45.6282** -1.5457** -1.1860** 8.2394** Note: R2=0.7374 and the spatial error coefficient 2:0.8100 was significant at the 1% level. * Indicates significance at the 5% level. ** Indicates significance at the 1% level. Table 3.f shows that A was statistically significant; therefore, using equation (3) to account for spatial autocorrelation was valuable. The coefficients for the variables USCt, MCt and the monthly dummy variables were all significant, except for the November dummy variable, and similar to estimates found in Model I. The grain market dummy variables were all significant. IF a grain market was located in Holland relative to Zeeland, corn price basis levels were 10.78 cents higher. While most of the coefficients 2 for the variables RieEetDISTei and RieEet(DISTei) and RieEetrtDISTei and 101 RieEetrt(D15Tei)2 were not significant, the values of the coefficients altered corn price basis levels as the distance away from ethanol plant e increased, also similar to Model I results. The coefficients for the variable MOopene were significant at all ethanol plants. The coefficients for RieEet were positive at all of the ethanol plants except the ethanol plants located at Lake Odessa and Riga. The average corn price basis level impact at the site of an ethanol plant opening in Michigan the month an ethanol plant opened experienced an average increase of 14.10 cents in corn price basis levels. In Model I it was estimated that the com price basis level impact at the site of an ethanol plant opening in Michigan experienced an average 12.97 cent increase in corn price basis levels over the average time period after an ethanol plant opened. Model 11 is expected to have a higher corn price basis level average impact than Model I because the basis impact is expected to be the greatest the month an ethanol plant opened and then decrease as the months since an ethanol plant opened increases. The basis impact is expected to diminish over time because market conditions will adjust to the increase in demand created by the ethanol plant opening. Figure 3.a illustrates how the corn price basis level impact at the site of an ethanol plant opening, over the average of the five ethanol plants, changed as the months since an ethanol plant opening increased. Figure 3.a was constructed by using the average coefficient estimates for the variables MOopene which was 0264 102 Figure 3.a Michigan Plants: Months Since Opened Time Impact ,. _. 7 _fi fi. -___.1 -- 4 7. --..... -7 - ,..-- h,_,__.. -‘ . 1 Year Time Impact 16 .-__.___.._._.._._._._..--,-.._-_._..______ . --...._.----.._..__-_.__.-_.,._.-._..-. m-__~.___-__._. .-..«- 14f- 12' I 10 ~: 1 —o—Average of 5 Ethanol Plants 8 ~J{~-———---—- Average basis impact at plant opening 123456789101112 Months Figure 3.a shows that com price basis levels decreased during the first year after an ethanol plant opened. The results for variations of Model 11 specific for Kansas, Iowa and Indiana are found in Appendix 3. The summary results from these models indicate that grain markets located at the site of an ethanol plant opening in Kansas experienced, on average, an increased corn price basis level of 11.02 cents the month the ethanol plant opened. In Iowa, grain markets located at the site of an ethanol plant opening experienced an average of 2.07 cents increase in corn price basis levels the month the ethanol plant opened. In Indiana, grain markets located at the site of an ethanol plant opening experienced an average decrease of 0.29 cents in corn price basis levels the month the ethanol plant opened. In Kansas, Iowa and Indiana corn price basis levels decreased as 103 the number of months since an ethanol plant Opening increased. For an expanded discussion of the other Model 11 estimated coefficients for the Kansas, Iowa and Indiana see Appendix 3. A third model was used to examine the possible causes for variation in the coefficients for the variable RieEet estimated by Model I and Model 11 for the states of Michigan, Kansas, Iowa and Indiana. The coefficients for the variable RieEetestimated by Model I approximate the impact of an ethanol plant opening on corn price basis levels over the average time period after the ethanol plant opened for grain markets located at the site of the ethanol plant opening. The coefficients for RieEetdetermined from Model 11 represent the impact ethanol plant e had on the corn price basis levels at grain markets located at the site of an ethanol plant opening the month the ethanol plant opened. Model 111 will utilize the following equation to explain variation in the coefficients for the Model I and Model 11 variable RieEet3 (5) 112813..3 = 130 +1315e + (323% + 133me + [341(Se +1351Ae +862003or2004e + B720050r2006e + 882007or20088 +89CCRe + ee where 1ReEe are the estimated coefficients from Model I of the variable RieEet- Model 111 was also estimated using the left hand side variable 2ReEe, where the variable 2ReEerepresents the coefficients of the Model 11 variable RieEet- Therefore, there are a total of forty-two observations. The variable Se is the production capacity (size) of ethanol plant e measured in million gallons per year. It is likely that the annual capacity of an ethanol plant contributed to the variation in corn price basis level estimates. If an .104 ethanol plant produces 1 10 million gallons of ethanol a year it is likely the impact on corn price basis levels at the site of the ethanol plant is greater than if an ethanol plant only produces fifty million gallons of ethanol annually. The variables Mle, KSe and IAe are dummy variables that are equal to one if ethanol plant e is located in the corresponding variable state name; otherwise the variables are equal to zero. It is possible that the state location of an ethanol plant contributed to alterations in corn price basis levels. Iowa produces nearly two and a half times more com per year than Indiana (USDA 1). The variable 2003or2004e is a dummy variable equal to one if ethanol plant e was opened in either 2003 or 2004, otherwise the variable is equal to zero. The values of variables 20050120065 and 2007orZOOBe are determined the same as variable20030r2004e. All ethanol plants in this analysis opened after 2001 and it is possible that the basis impact was greatest during a particular year. Finally, the variable CCRe is the county corn ratio. To determine the value of this variable, the number of bushels of corn used in the annual production of ethanol at ethanol plant 6 was divided by the annual county corn production for the county where ethanol plant e is located. A higher statistic indicates a larger portion of the corn produced in the county was used for ethanol production and therefore this likely increases corn price basis levels at nearby grain markets. The county cOrn production estimates were gathered from USDA NASS. Results for Model 111, which explains the variation in the coefficients for the variable Rieliet from both Model I and Model 11 are found in Table 3. g. 105 Table 3.g Model 111 Estimated Results LHS variable IRE 2RE constant 12.9779 10.51 S -0.75 -0.438 82 0.0059 0.0038 Ml 21.4962* 15.719 KS 14.3858 10.703 IA 7.2618 -0.547 varl 3.5946 4.2941 Var2 -0.2124 2.3326 Var3 3.5943 -1.738 CCR 0.0903 0.0073 * Indicates significance at the 5% level. Note: R2=0.2295 when 18913e was the LHS variable and R2=O.1889 when 211.315e was the LHS variable in equation (5). Table 3. g illustrates the reported coefficients for the variables when the left hand side variable for equation (5) was 1ReEe (coefficients from estimated RieEetfrom Model I) and when the left hand side variable for equation (5) was 2ReEe(coefficients from estimated RieEetfrom Model 11). The coefficient for the variable MI was significant at the five percent level when the estimated Model I coefficients for RieEet was the left hand side variable in equation (5). Therefore, if ethanol plant e opened in Michigan relative to opening in Indiana, it caused a increase in corn price basis levels of 21 .50 cents at grain markets located at the site of the ethanol plant opening. No other variables were significant in Model 111. McNew and Griffith also estimated variation in basis levels at the site of an ethanol plant opening similar to our Model 111. They regressed their equivalent coefficients of our Model I RieEet variable against a county corn ratio equivalent to our CCRe variable. Their model a1s6 had a poor R2 (R2=0.209) and found their CCRe 106 ' variable to be insignificant. Neither McNew and Griffith or this study was able to conclude what causes variation in corn price basis levels at the site of an ethanol plant opening. This suggests the variation is due to other factors not considered in eather study. Also, particular states have implemented different ethanol policy incentives that may be effecting the unexplained variation in corn price basis levels at the site of an ethanol plant opening. 3.4 Summary The highest corn price basis level average improvement between the four states was witnessed in Michigan and Kansas. Over the average time period after an ethanol plant opened, Michigan and Kansas corn price basis levels increased an average of 12.97 cents and 9.55 cents respectively at the site of an ethanol plant opening. The month an ethanol plant opened in Michigan and Kansas, average corn price basis levels increased 14.10 cents and 11.02 cents respectively. In Michigan and Kansas as the number of months since an ethanol opening increased, strengthened corn price basis levels began to decline. Results in Michigan and Kansas were similar to the results estimated by McNew and Griffith who discovered corn price basis levels increased 12.5 cents over the average time period after twelve ethanol plants opened in Illinois, Iowa, Montana, South Dakota, and Wisconsin from March 2000 through March 2003. Grain markets located at the site of an ethanol plant opening in Iowa and Indiana experienced an average increase of 2.41 cents and an average decrease of 2.21 cents respectively in corn price basis levels over the average time period after an ethanol plant opened. It is possible that the corn market quickly adjusted to the new conditions that 107 resulted as a consequence of the rise of the ethanol industry in Iowa and Indiana. The results witnessed in Iowa and Indiana are similar to results found by Katchova who found farmers located in the same zip code of an ethanol plant contracted their corn for an average of 10.9 cents cheaper than farmers who did not live in the same zip code of an ethanol plant. Grain markets located at the site of an ethanol plant opening in Iowa and Indiana experienced, on average, a positive 2.07 cent increase and a decrease of 0.29 cents respectively in corn price basis levels the month an ethanol plant opened. In Iowa and Indiana average basis levels decreased steadily as the months since an ethanol plant opening increased. However, in Indiana specifically and also in Iowa, many ethanol plants opened in 2007 and 2008. Additionally, some ethanol plants declared bankruptcy after our time period ended in June 2008. It is likely the corn price basis level effect the months after an ethanol plant opening occurred has changed in these states and studying an expanded time frame would be beneficial. The variation in the impact ethanol plant openings had on grain markets located at the site of an ethanol plant opening over the average time period after an ethanol plant opened and at the time an ethanol plant opened are still not explained. By expanding this study to estimate the impact ethanol plant openings had on grain markets located in other states, the question of variation in the impact ethanol plant openings had on grain markets may be answered. Also, studying a larger time frame could enhance our results because corn prices began to decrease after they peaked during the summer of 2008, which is when our data set ended (USDA 3). 108 APPENDIX 3.1: SUMMARY STATISTICS FOR DATA SET RECEIVED FROM CASH GRAIN BIDS DATA SERVICE ' Table 3.h Michigan Monthly Corn Price Basis Level Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Akron 61 48.74% -33.14 16.58 -57.63 2.95 2 Albion 59 50.42% -24.27 11.19 -52.90 7.61 3 Auburn 81 31.93% -30.17 12.81 -53.25 13.00 4 Blissfield 118 0.84% -18.60 11.35 -49.00 10.27 5 Breckenridge 116 2.52% -30.87 12.70 -52.29 10.93 6 Britton 23 80.67% -33.05 7.88 -55.14 -23.05 7 Brown City 104 12.61% -28.13 13.74 -53.00 7.93 8 Buchanan 110 7.56% -32.12 11.32 -60.28 -4.48 9 Caledonia 118 0.84% -27.46 13.59 -56.50 10.28 10 Capac 15 87.39% -31.82 10.95 -49.53 -10.64 11 Caro 42 64.71% -35.37 11.22 -56.50 -14.70 12 Clarksville 21 82.35% -42.83 7.51 -61.00 -35.00 [3 Coleman 9 92.44% -49.02 4.35 -55.00 -44.00 14 Constantine 102 14.29% -25.34 11.56 -49.59 0.44 15 Croswell 25 78.99% -44.27 8.74 -55.18 -23.81 16 Elkton 61 48.74% -33.14 16.57 -57.63 2.95 17 Emmett 20 83.19% -38.29 13.72 -61.64 -15.50 18 Fremont 92 22.69% -25.71 12.39 -50.18 12.42 19 Grand Ledge 98 17.65% -24.48 1 1.21 -52.05 8.06 20 Hamilton 118 0.84% -20.57 1 1.02 -53.89 7.26 21 Hemlock 105 11.76% -31.96 11.85 -52.21 7.53 22 Henderson 26 78.15% -41.23 8.25 -53.57 -22.10 23 Holland 117 1.68% -14.37 11.69 -45.89 12.59 24 Howard City 14 88.24% -34.14 11.67 ‘-55.00 -19.12 25 Hudsonville 99 16.81% -21 .88 11.44 -49.08 2.43 26 Imlay City 113 5.04% -26.02 13.25 -50.11 14.79 27 Jasper 118 0.84% -23.42 12.03 -55.14 9.93 28 Jeddo 15 87.39% -38.20 6.00 -49.53 -29.82 29 Jonesville 52 56.30% -25.47 23.38 -51.14 78.86 30 LakeOdessa 118 0.84% -30.87 12.45 -58.00 3.39 Note: Highlighting indicates the grain market was chosen for analysis 109 Table3.h (cont’d). Grain # of % Std. # Market Obs. Missing Mean Dev. Min Max 31 Lapeer 105 11.76% -25.55 14.44 -50.11 14.79 32 Lennon 26 78.15% -45.69 8.90 -57.89 -24.10 33 Marlette 118 0.84% -28.71 13.12 -52.00 7.93 34 Marshall 106 10.92% -32.93 10.53 -58.73 -3.32 35 Middleton 118 0.84% -31.38 11.91 -52.81 6.27 36 Millington 24 79.83% -41.16 7.16 -53.21 -28.00 37 Newaygo 118 0.84% -27.79 11.76 -52.00 8.75 38 North Star 49 58.82% -38.84 12.23 -62.47 -6.14 39 Oakley 33 72.27% -34.87 10.69 -53.17 -12.00 40 Ottawa Lake 114 4.20% -15.87 11.37 -46.90 13.10 41 Palms 15 87.39% -35.48 8.15 -49.53 -20.36 42 Pigeon 68 42.86% -32.42 16.07 -57.61 2.95 43 Ravenna 14 88.24% -34.26 11.64 -55.00 -19.12 44 Reading 68 42.86% -18.23 20.82 -48.48 78.86 45 Richmond 26 78.15% -44.46 8.83 -55.25 -23.80 46 Riga 15 87.39% -13.74 10.22 -27.30 8.89 47 Saginaw 80 32.77% -21.64 1 1.38 -42.00 12.90 48 Saline 34 71.43% -28.50 14.07 -53.29 1.50 49 Saranac 48 59.66% -24.67 14.36 -56.24 10.20 50 Six Lakes 69 42.02% -30.71 13.74 -65.00 0.05 51 Snover 78 34.45% -32.65 17.27 -68.50 0.84 52 St. Johns 42 64.71% -38.30 7.75 -60.00 -24.64 53 Vriesland 14 88.24% -28.09 14.59 -59.00 -6.94 54 Webberville 1 16 2.52% -23.23 11.25 -52.43 8.71 55 WhitePigeon 76 36.13% -20.15 12.12 -47.14 6.00 56 Yale 118 0.84% -27.17 15.75 -52.73 15.28 57 Zeeland 115 3.36% -23.84 10.06 -46.71 4.67 Note: Highlighting indicates the grain market was chosen for analysis 110 Table 3.i Kansas Monthly Corn Price Basis Level Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Abbyville 86 27.73% -16.25 11.75 -43.28 10.50 2 Abilene 43 63.87% -21.15 15.03 -48.00 36.00 3 Agenda 99 16.81% -27.50 12.98 -51.20 2.20 4 Albert 71 40.34% -13.51 11.80 -37.58 10.00 5 Americus 26 78.15% -38.55 8.43 -60.00 -27.07 6 Andale 114 4.20% -19.62 13.35 -49.20 9.50 7 Anthony 81 31.93% -11.63 11.87 -46.00 17.33 8 Argonia 23 80.67% -20.26 21.62 -43.00 65.00 9 Arkansas City 88 26.05% -22.49 11.42 -45.33 1.67 10 Arlington 57 52.10% -1 1.05 11.57 -31.58 14.70 11 Arnold 58 51.26% -10.54 13.27 -34.59 14.50 12 Asherville 59 50.42% -29.27 1 1.90 -51.00 -7.33 13 Atchison 1 15 3.36% -17.61 11.29 -43.20 4.93 14 Athol 107 10.08% -35.46 13.90 -76.00 -1.00 15 Barnes 99 16.81% -27.51 13.20 -55.29 2.20 16 Bartlett 105 11.76% -l4.09 12.50 -50.00 13.00 17 Bavaria 76 36.13% -17.70 15.51 -44.05 45.00 18 Baxter Springs 57 52.10% -18.23 11.06 -58.00 10.00 19 Beattie 1 16 2.52% -34.07 13.26 -78.00 -6.20 20 Beloit 104 12.61% -26.98 12.56 -57.11 1.75 21 Bennington 33 72.27% -29.51 19.45 -85.00 0.05 22 Benton 76 36.13% -l4.95 15.75 -43.05 46.00 23 Bern 71 40.34% -31.85 18.47 -63.57 2.11 24 Bison 104 12.61% -17.14 13.81 -51.00 8.67 25 Bogue 63 47.06% -16.53 15.63 -50.00 20.35 26 Boyd 70 41.18% -12.02 14.18 -37.50 47.00 27 Bremen 87 26.89% -28.22 12.70 -63.63 -4.33 28 Breton 60 49.58% -13.24 14.62 47.00 15.71 29 Bridgeport 54 54.62% -21.46 13.07 -43 .05 5.89 30 Brownell 26 78.15% -21.63 12.20 -43.00 -3.00 Note: Highlighting indicates the grain market was chosen for analysis 111 Table 3.1 (cont’d). # of % Std. , # Grain Market Obs. Missifl Mean Dev. Min Max 31 Bucklin 87 26.89% -10.52 12.73 -3-5.33 20.25 32 Buhler 76 36.13% -15.98 15.31 -43.05 45.00 33 Burlingame 17 85.71% -28.99 8.50 -40.50 -10.25 34 Burlington 17 85.71% -28.99 8.50 -40.50 -10.25 35 Bums 85 28.57% -18.14 13.76 -46.05 5.89 36 Burrton 45 62.18% -15.00 13.62 -40.22 9.89 37 Cairo 57 52.10% -11.34 11.79 -32.86 14.70 38 Caldwell 107 10.08% -18.38 10.72 -47.00 5.67 39 Calista 57 52.10% -11.19 11.68 -31.86 14.70 40 Canada 54 54.62% -22.81 13.94 -46.05 5.89 41 Canton 75 36.97% -18.18 14.21 -45.05 5.89 42 Chanute 107 10.08% -20.21 9.49 -48.33 2.1 1 43 Chapman 118 0.84% -24.50 13.61 -51.80 7.80 44 Cheney 78 34.45% -18.32 10.57 -44.00 7.00 45 Clay Center 76 36.13% -29.86 12.48 -55.20 -4.05 46 Clayton 17 85.71% -20.83 18.10 -45.95 14.05 47 Clifton 104 12.61% -30.55 12.83 -60.60 2.00 48 Clyde 102 14.29% -28.7 5 13.70 -60.84 2.20 49 Colby 103 13.45% -21.22 16.60 -56.80 16.33 50 Coldwater 17 85.71% -4.08 11.89 -22.05 17.81 51 Collyer 63 47.06% -14.70 15.33 -45.00 22.35 52 Columbus 116 2.52% -17.24 10.33 -58.00 9.80 53 Colwich 15 87.39% 7.51 11.99 -21.00 21.57 54 Concordia 107 10.08% -26.88 12.40 -55.11 1.75 55 Conway 75 36.97% -16.27 15.27 -43 .05 45.00 56 Conway Springs 62 47.90% -23.31 12.11 -51.67 4.00 57 Courtland 102 14.29% -26.66 10.32 -54.60 -0.33 58 Cunningham 50 57.98% -l3.40 9.80 -35.50 4.70 59 Danville 58 51.26% -25.62 10.99 -53.25 2.14 60 Delphos 85 28.57% -26.16 12.77 -55.11 1.75 Note: Highlighting indicates the grain market was chosen for analysis 112 Table 3.i (cont’d). # of % Std. # Grain Market Obs. Missing - Mean Dev. Min Max 61 Dighton 116 2.52% -10.72 15.75 -44.75 18.33 62 Dillwyn 58 51.26% -8.55 10.79 -28.14 15.00 63 Dodge City 93 21.85% -4.49 13.33 -33.68 17.48 64 Dresden 57 52.10% -15.46 14.40 -48.83 17.45 65 Durham 26 78.15% -18.47 10.12 -37.00 -1.45 66 Edgerton 115 3.36% -28.68 8.00 -48.67 -10.25 67 Effingham 17 85.71% -27.11 8.62 -43.06 -12.32 68 Ellinwood 86 27.73% -15.03 11.38 -37.58 10.00 69 Ellis 11 90.76% -23.52 12.00 -45.73 -8.60 70 Ellsworth 91 23.53% -23.89 14.11 -74.00 12.77 71 Emporia 59 50.42% -23.55 10.00 -43.52 -8.21 72 Fairview 11 90.76% -38.63 8.57 -51.30 -23.43 73 Florence 84 29.41% -17.90 13.64 -46.05 5.89 74 Ford 83 30.25% -4.68 11.59 -30.00 17.18 75 Fredonia 59 50.42% -29.38 9.48 -50.79 -10.50 76 Galatia 10 91.60% -13.48 4.85 -22.25 -5.36 77 Galva 76 36.13% -16.72 15.52 -44.05 45.00 78 Garden City 103 13.45% -3.31 12.86 -38.11 17.36 79 Garden Plain 103 13.45% -20.39 12.25 -48.80 7.00 80 Garfield 116 2.52% -16.81 12.48 -45.67 6.67 81 Girard 1 19 0.00% -17.67 10.52 -43.35 19.50 82 Glade 17 85.71% -33.63 9.26 -52.93 -24.00 83 Glen Elder 94 21.01% -25.92 12.07 -51.00 ‘ 1.67 84 Goodland 2 98.32% -5.00 7.07 -10.00 0.00 85 Gorham 116 2.52% -15.46 14.25 449.67 23.09 86 Grainfield 63 47.06% -14.43 15.38 -45.00 22.35 87 Gray 22 81.51% -6.12 16.24 -34.00 13.27 88 Great Bend 113 5.04% -16.81 12.72 -46.60 9.40 89 Greenleaf 1 16 2.52% -30.40 13.45 -62.40 -0.40 90 Greensburg 78 34.45% -8.01 11.01 -30.56 14.35 Note: Highlighting indicates the grain market was chosen for analysis 113 Table 3.i (cont’d). # of °/o Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 Gridley 38 68.07% -30.33 7.82 -47.50 -12.10 92 Haddam 62 47.90% -27.61 15.14 -57.89 -0.63 93 Halstead l 16 2.52% -19.67 13.35 -51.20 9.89 94 Hanover 92 22.69% -29.66 13.13 -60.36 -3.56 95 Hanston 40 66.39% -8.55 13.95 -34.00 13.27 96 Hartford 24 79.83% -47.78 6.70 -64.28 -38.00 97 Haven 1 13 5.04% -18.83 13.04 -50.80 9.89 98 Haviland 106 10.92% -13.68 12.64 -41.25 9.81 99 Hepler 15 87.39% -22.14 15.39 -49.87 0.67 100 Hiawatha 119 0.00% -31.94 11.61 -62.61 -7.88 101 Hill City 24 79.83% -24.16 17.46 -50.00 9.36 102 Hillsboro 119 0.00% -21.32 13.93 -52.33 5.89 103 Hilton 55 53.78% -21.53 13.35 -43.47 6.89 104 Hoisington 45 62.18% -13.14 12.52 -37.35 9.40 105 Holton 104 12.61 % -31.84 10.94 -62.00 -7.67 106 Home 50 57.98% -31.56 14.16 -60.31 -5.48 107 Hope 116 2.52% -18.10 14.89 -51.00 25.00 108 Hoxie 1 11 6.72% -22.77 16.50 -55.00 17.65 109 Hudson 104 12.61% -13.35 11.91 -43.00 9.00 1 10 Hunter 96 19.33% -24.69 11.99 -47.00 1.67 1 1 1 Hutchinson 76 36.13% -13.03 14.45 -39.05 45.00 112 Inman 54 54.62% -20.85 13.36 -43.05 6.89 113 Isabel 104 12.61% -12.86 11.64 -43.40 9.00 114 luka 85 28.57% -9.60 9.94 -28.14 15.00 115 Jamestown 74 37.82% -25.10 12.76 -56.11 1.67 116 Jetmore 40 66.39% -8.53 13.92 -34.00 13.27 117 Jewell 7 94.12% -8.24 4.25 -14.25 -2.11 118 Junction City 104 12.61% -29.87 13.32 -54.00 3.67 119 Kackley 59 50.42% -29.28 11.90 -51.00 -7.33 120 Kalvesta 77 35.29% -5.56 11.23 -28.94 16.36 Note: Highlighting indicates the grain market was chosen for analysis 114 Table 3.i (cont’d). # of % Std. ' # Grain Market Obs. Missing Mean Dev. Min Max 121 Kansas City 17 85.71% -17.76 7.93 -37.20 -8.00 122 Kensington 106 10.92% -35.73 13.81 -70.08 2.33 123 Kingsdown 60 49.58% -4. 1 9 11.90 -30.00 17.18 124 Kiowa 39 67.23% -16.33 13.95 -49.00 16.28 125 LaCrosse 10 91 .60% -16.29 3.37 -22.46 -9.75 126 Laird 35 70.59% -10.72 14.69 -36.50 14.50 127 Lancaster 97 18.49% -23.81 14.32 -60.67 41.00 128 Larned 118 0.84% -l6.52 12.67 -45.67 6.60 129 Lawrence 31 73.95% -19.87 13.23 -40.50 3.57 130 Lehigh 76 36.13% -17.53 16.00 -46.05 44.00 131 Lenora 60 49.58% -18.55 16.27 —51.38 17.45 132 Leonardville 17 85.71% -31.46 12.47 -50.00 -7.33 133 LeRoy 118 0.84% -28.63 9.62 -52.67 -5.33 134 Lewis 89 25.21% -13.91 12.85 -44.40 10.00 135 Lindsborg 76 36.13% -l6.85 15.15 -43.05 45.00 136 Linn 62 47.90% -27.57 15.22 -58.83 -0.63 137 Logan 100 15.97% -32.38 14.19 -62.00 10.00 138 Longford 27 77.31% -28.07 14.62 -52.05 0.05 139 Ludell 36 69.75% -28.71 14.69 -60.76 0.76 140 Macksville 116 2.52% -14.55 12.63 -45.20 11.00 141 Manhattan 109 8 .40% -30.47 15.94 -62.00 10.00 142 Marion 76 36.13% -17.53 16.00 -46.05 44.00 143 Marquette 109 8.40% -22.66 13.67 -52.00 6.89 144 Magsville 112 5.88% -30.75 12.81 -62.25 -4.33 145 McCracken 10 91 .60% -16.29 3 .37 -22.46 -9.75 146 Mche 111 6.72% -20.55 9.55 -43.33 5.00 ' 147 McPherson 1 19 0.00% -20.52 13.58 -53.00 6.89 148 Melvem 76 36.13% -27.36 8.03 -47.81 -10.25 149 Menlo 105 11.76% -20.35 16.88 -54.00 15.71 150 Meriden 82 31.09% -34.78 11.09 -58.86 -6.14 Note: Highlighting indicates the grain market was chosen for analysis 115 Table 3.i (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 151 Milton 23 80.67% -l7.03 12.35 -34.00 3.57 152 Miltonvale 17 85.71% -31.47 12.48 -50.00 -7.33 153 Mingo 104 12.61% -18.46 16.73 -51.00 16.82 154 Minneapolis 107 10.08% -26.63 13.02 -53.79 14.33 155 Morganville 88 26.05% -29.72 13.09 -55.50 3.00 156 Morland 63 47.06% -16.48 15.65 -50.00 20.35 157 Morrill 115 3.36% -33.29 11.55 -64.37 -9.33 158 Moundridge 1 19 0.00% -20.08 13.44 ~52.20 8.89 159 Mount Hope 66 44.54% -15.30 15.83 -41.05 46.00 160 Mullinville 99 16.81% -11.46 13.22 -38.20 27.75 161 Mulvane 96 19.33% -22.14 11.23 -72.00 1.48 162 Murdock 76 36.13% -16.20 10.03 -34.00 7.00 163 Muscotah 14 88.24% -23.06 4.33 -29.55 -14.30 164 Narka 45 62.18% -34.53 10.41 -53.10 -6.95 165 Natoma 10 91.60% -16.29 3.37 -22.46 -9.75 166 Ness City 35 70.59% -10.69 14.65 -36.50 14.50 167 New Cambria 54 54.62% -21.36 10.93 -41.07 0.48 168 Newton 85 28.57% -l6.98 13.92 -45.77 5.89 169 Nickerson 119 0.00% -18.67 12.89 -49.20 9.89 170 Norton 105 11.76% -26.17 16.50 -60.80 10.15 171 Oakley 105 11.76% -17.14 17.09 -51.00 17.82 172 Oberlin 105 11.76% -25.50 16.95 -60.76 8.20 173 Offerle 116 2.52% -7.13 13.52 -36.00 20.25 174 Osborne 1 19 0.00% -27.14 17.68 -65.00 22.05 175 Ottawa 115 3.36% -30.13 8.30 ~53.33 —10.25 176 Overbrook 1 15 3.36% -29.01 8.85 -52.33 -7.00 177 Palmer 51 57.14% -21.58 13.72 -51.00 4.00 178 Paola 100 15.97% -31.31 10.29 -57.29 -8.08 179 Park 48 59.66% -15.06 17.49 —45.00 22.35 180 Partridge 76 36.13% -12.04 14.01 -37.05 45.00 Note: Highlighting indicates the grain market was chosen for analysis 116 Table 3.i (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 181 Pawnee Rock 111 6.72% -17.39 12.50 -46.60 9.40 182 Peabody 85 28.57% -18.01 13.57 -45.05 5.89 183 Penalosa 57 52.10% -11.05 11.57 -31.58 14.70 184 Penokee 63 47.06% -l6.49 15.58 -50.00 20.35 185 Phillipsburg 118 0.84% -33.02 15.20 -68.10 10.00 186 Pittsburg 104 12.61% -20.45 9.51 -44.00 4.29 187 Pratt 107 10.08% -13.03 12.04 -42.40 15.75 188 Preston 57 52.10% ~10.92 11.46 -31.58 14.70 189 Protection 102 14.29% -5.70 13.04 -35.79 18.85 190 Quinter 102 14.29% -15.48 15.81 -45.08 22.35 191 Rago 49 58.82% ~13.65 8.96 -33.71 3.11 192 Randall 116 2.52% -28.62 12.62 -56.60 1.67 193 Ransom 35 70.59% -21.24 8.92 -43.94 -8.25 194 Republic 71 40.34% -27.12 12.94 -50.83 3.83 195 Rexford 93 21.85% -22.05 17.20 -55.75 11.42 196 Roxbury 76 36. 13% -17.14 15.43 -44.05 45.00 197 Rush Center 54 54.62% -6.66 19.09 -41.00 81.50 198 Russell 40 66.39% -11.25 8.80 -32.87 6.67 199 Sabetha 98 17.65% -33.75 13.35 -62.84 -1.13 200 Salina 24 79.83% -8.10 13.38 -36.00 9.71 201 Scandia 8 93.28% -11.72 3.41 -l7.64 -5.36 202 Scott City 103 13.45% -5.35 13.89 -41.37 18.90 203 Scranton 17 85.71% -28.99 8.50 -40.50 -10.25 204 Sedgwick 62 47.90% -16.40 12.53 -42.00 9.50 205 Seguin 105 1 1.76% -20.42 16.89 -54.00 15.71 206 Selden 60 49.58% -17.04 15.81 -48.83 17.45 207 Seneca 92 22.69% -35.48 10.81 -60.52 -5.33 208 Smith Center 102 14.29% -33.59 16.72 -76.00 0.00 209 Solomon 27 77.31% —25.75 12.88 -48.55 0.05 210 Spearville 68 42.86% -4.35 11.48 -25.77 16.36 Note: Highlighting indicates the grain market was chosen for analysis 117 Table 3.i (cont’d). # of % Std. ~ # Grain Market Obs. Missing Mean Dev. Min Max 211 St Marys 97 18.49% -30.05 13.84 -60.70 -3.39 212 Stafford 71 40.34% -16.70 12.76 -46.00 9.80 213 Sterling 116 2.52% -21.21 13.16 -48.40 9.24 214 Stockton 107 10.08% -28.37 16.68 -69.00 16.71 215 Studley 64 46.22% -16.98 15.89 -50.00 20.35 216 Sublette 103 13.45% -0.06 12.48 -30.84 20.18 217 Talmage 27 77.31% -27.41 14.06 -50.05 0.05 218 Tampa 107 10.08% -17.31 15.43 -49.00 25.00 219 Tipton 96 19.33% -24.68 1 1.98 -47.00 1.67 220 Topeka 42 64.71% -16.08 9.64 -39.00 1.29 221 Turon 57 52.10% -11.06 11.56 -31.58 14.70 222 Utica 20 83.19% -12.80 12.49 -31.00 12.40 223 Valley Center 10 91 .60% -15.89 3.81 -20.83 -10.75 224 Wakeeney 63 47.06% -14.70 15.35 45.00 22.35 225 Wakefield 67 43.70% -38.87 18.80 -82.50 10.50 226 Waldeck 57 52.10% -11.06 11.56 -31.58 14.70 227 Walton 1 18 0.84% -20.95 13.40 -53.33 5.89 228 Wamego 104 12.61% -31.97 12.81 -61.57 -3.70 229 Washington 62 47.90% -27. 18 14.65 -53.95 -0.63 230 Waterville 97 18.49% -33.45 13.38 -62.00 -6.20 231 Waverly 17 85.71% ~28.99 8.50 40.50 -10.25 232 Wellington 92 22.69% -20.16 1 1.17 -45.33 5.73 233 Wellsville 17 85.71% -26.63 10.35 -40.50 -5.25 234 Westphalia 38 68.07% -30.33 7.82 -47.50 , -12.10 235 White Cloud 26 78. 15% -38.47 7.93 -51.73 -21.44 236 Whitewater 85 28.57% -15.07 13.50 -42.05 11.89 237 Whiting 17 85.71% -36.05 8.64 -52.06 '-21.32 238 Wilmore 44 63.03% -8.82 9.37 -33.15 6.43 239 Wilroads 58 51 .26% -5.36 11.89 -25.19 16.36 240 Windom 76 36.13% -l6.54 15.34 -43.05 45.00 Note: Highlighting indicates the grain market was chosen for analysis 118 Table 3.i (cont’d). # of % Std. . # Grain Market Obs. Missing Mean Dev. Min Max 241 Winfield 113 5.04% -22.32 13.57 -61.55 2.86 242 Wright 1 16 2.52% -7.49 13.22 -35.80 20.75 243 Yates Center 58 51.26% —26.28 14.75 -59.79 37.00 244 Zenda 25 78.99% -11.43 8.94 -29.73 -0.11 245 Zurich 10 91 .60% -16.28 3.39 -22.46 -9.67 Note: Highlighting indicates the grain market was chosen for analysis 119 Table 3.j Iowa Monthly Corn Price Basis Level Summary Statistics # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Ackley 92 22.69% -34.57 9.31 -57.37 -14.94 2 Adair 119 0.00% -37.03 9.64 -62.80 -17.83 3 Ainsworth 68 42.86% -32.41 , 12.90 -64.70 -11.87 4 Akron 69 42.02% -36.63 1 1.69 -64.80 -13.73 5 Albert City 82 31.09% 35.49 9.23 -61.15 -18.95 6 Albia 22 81.51% -14.87 4.11 -22.62 6.43 7 Albion 66 44.54% -36.66 12.02 -58.32 -14.54 8 Alden 115 3.36% -33.94 9.09 -57.20 -17.10 9 Alexander 96 19.33% -38.28 9.05 -65.52 -21.00 10 Algona 119 0.00% -38.91 9.10 “-63.20 -20.14 11 Alleman 119 0.00% -35.68 10.55 -61.80 -15.82 12 Allendorf 75 36.97% -31.51 12.05 -59.00 -6.76 13 Allison 57 52.10% -39.92 12.74 -67.44 -17.36 14 Alta 118 0.84% -36.53 9.50 -61.61 -18.23 15 Alton 112 5.88% -30.96 10.49 -59.20 -9.38 16 Altoona 96 19.33% -31.02 11.67 . -59.00 -11.27 17 Alvord 56 52.94% -30.25 13.48 -57.33 -1.80 18 Ames 32 73.11% -32.11 8.30 -51.35 -17.80 19 Anthon 26 78.15% -43.99 6.92 -59.70 -33.31 20 Arcadia 39 67.23% -38.30 10.28 -60.80 -11.72 21 Archer 38 68.07% -33.96 12.26 -55.92 -10.53 22 Aredale 61 I 48.74% -39.34 12.77 -67.05 a13.95 23 Arlington 30 74.79% -48.63 1 1.45 -87.00 -29.00 24 Armstrong 1 17 1.68% -43.59 10.44 -72.25 -25.47 25 Ashton 119 0.00% -31.03 12.17 -64.00 -2.76 26 Atlantic 67 43.70% -36.43 12.32 -60.80 ~14.53 27 Auburn 32 73.11% -36.81 10.89 -60.29 -20.00 28 Audubon 119 0.00% -39.98 9.30 -66.80 -21.39 29 Aurelia 119 0.00% -36.52 9.54 -61.40 -18.00 30 Avoca 40 66.39% -46.65 9.87 -69.24 -22.05 Note: Highlighting indicates the grain market was chosen for analysis 120 Table 3.j (cont’d). #of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 31 Avon 85 28.57% -26.81 12.29 -5321 -6.64 32 Ayrshire 68 42.86% -40.58 9.70 -63.69 -23.14 33 Badger 64 46.22% -36.28 9.58 -63.60 -19.86 34 Bagley 68 42.86% -35.77 9.88 -61.85 -1910 35 Bancroft 46 61.34% -44.55 12.84 -72.25 -26.47 36 BamesCity 51 57.14% -33.52 13.15 -61.00 -7.27 37 Barnum 63 47.06% -36.23 9.65 -63.60 -19.86 38 Battle Creek 37 68.91% -4044 10.28 -63.33 -20.48 39 Bayard 114 4.20% —34.99 9.90 -61.20 -15.28 ' 40 Beaman 119 0.00% -35.39 10.07 -58.32 -15.44 41 Beaver 95 20.17% -37.69 9.17 -63.67 -21.10 42 Belmond 61 48.74% -37.19 9.22 -68.00 -21.11 43 Bettendorf 87 26.89% -16.21 13.06 -53.61 6.68 44 Blairsburg 94 21.01% -36.35 8.29 -58.76 -18.78 45 Blairstown 45 62.18% -36.22 10.23 -58.67 -1900 46 Blakesburg 68 42.86% -28.60 9.09 -60.00 -13.14 47 Blencoe 118 0.84% -36.01 10.60 -62.35 -14.10 48 Bloomfield 48 59.66% -26.93 9.25 -54.60 -10.78 49 Bode 64 46.22% -36.28 9.58 -63.60 -19.86 50 Bondurant 94 21.01% -31.31 11.57 -59.00 -9.14 51 Boone 119 0.00% -34.82 9.46 -60.65 -16.10 52 Booneville 106 10.92% -33.78 10.42 -59.00 -13.39 53 Boxholm 75 36.97% -3773 9.67 -6S.45 -21.10 54 Boyden 71 40.34% -25.62 12.61 -52.65 1.38 55 Bradford 96 19.33% -35.20 9.00 -57.42 -17.00 56 Bradgate 52 56.30% -3519 8.82 -60.29 -20.80 57 Bristow 26 78.15% -43.89 8.21 -57.46 -28.00 58 Britt 79 33.61% -38.46 9.17 -64.44 -22.55 59 Brooklyn 38 68.07% -41.69 8.35 -60.86 -27.45 60 Brunsville 66 44.54% -34.12 10.19 -5300 -3.00 Note: Highlighting indicates the grain market was chosen for analysis 121 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 61 Buckeye 107 10.08% -33.12 9.73 -57.42 -14.89 62 Buckingham 94 21 .01% -33.72 11.51 -59.38 -12.44 63 Buffalo Center 73 38.66% -43.04 12.11 -71.35 -19.13 64 Burlington 60 49.58% -18.89 13.86 -57.26 4.71 65 Burt 101 15.13% -38.36 8.77 -64.70 -22.95 66 Callender 45 62.18% -35.31 9.81 -64.65 -22.10 67 Cambridge 78 34.45% -34.47 11.37 -62.35 -16.73 68 Carlisle 32 73.11% -29.41 14.07 -53.21 -8.30 69 Cames 53 55.46% -28.27 10.39 -54.63 -7.00 70 Camenter 75 36.97% -39.07 12.27 -66.61 -l9.83 71 Carroll 18 84.87% -41.31 10.03 -63.82 -27.95 72 Casey 70 41 .18% -39.05 10.70 -64.29 -20.83 73 Cedar Falls 60 49.58% -34.19 13.10 -65.00 -10.59 74 Cedar Rapids 1 19 0.00% -15.04 10.30 -40.95 4.74 75 Center Point 42 64.71% -17.29 5.31 -27.82 -6.56 76 Centerville 18 84.87% -24.63 3.70 -31.50 -17.40 77 Central City 40 66.39% -37.73 9.94 -56.31 -18.14 78 Chapin 99 16.81% -35.79 8.93 -59.30 -1 7.11 79 Chariton 119 0.00% —32.20 14.32 -72.65 -4.70 80 Charles City 40 66.39% -45.74 9.59 -66.00 -26.19 81 Chelsea 19 84.03% -45.53 8.93 -60.67 -30.32 82 Cherokee 1 13 5.04% -37.70 10.06 -63.93 -17.00 83 Chillicothe 104 12.61% -26.86 11.09 -50.50 -6.84 84 Churdan 42 64.71% -40.41 8.81 -61 .32 -23.33 85 Clare 64 46.22% -36.28 9.58 -63.60 -19.86 86 Clarence 1 19 0.00% -27.12 1 1.61 -62.00 -6.14 87 Clarinda 62 47.90% -40.63 13.81 -69.06 -16.15 88 Clarion 111 6.72% -37.77 9.24 -63.45 -21.14 89 Clarksville 62 47.90% -35.70 10.57 -58.93 -17.00 90 Clayton 109 8.40% -24.60 15.84 -70.00 1.78 Note: Highlighting indicates the grain market was chosen for analysis 122 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 Clear Lake 110 7.56% ~38.79 9.74 ~60.10 ~22.00 92 Cleghom 1 19 0.00% ~36.05 10.87 ~62.40 -13.00 93 Clemons 58 51.26% -36.45 11.54 ~58.91 ~16.59 94 Clermont 30 74.79% ~48.51 9.86 ~68.70 ~28.00 95 Clinton 114 4.20% ~16.42 11.10 ~51.07 5.23 96 Clutier 58 51 .26% -37.68 11.93 ~61.62 ~16.00 97 Collins 97 18.49% -34.46 11.25 ~63.68 ~16.00 98 Colo 119 0.00% -34.56 10.22 ~62.55 ~16.00 99 Columbus Junction 8 93.28% ~32.59 7.34 ~42.41 ~19.60 100 Colwell 39 67.23% -45.88 10.05 ~67.88 -26.19 101 Conrad 114 4.20% ~35.41 10.21 ~58.32 -15.44 102 Conroy 119 0.00% ~32.98 10.69 ~59.00 ~13.14 103 Coon Rapids 115 3.36% ~36.28 11.30 ~63.21 ~8.50 104 Coming 73 38.66% -34.34 10.89 ~60.80 ~9.10 105 Correctionville 70 41 .18% ~35.99 10.76 ~62.65 ~16.86 106 Corwith 1 19 0.00% ~36.77 9.05 ~63.50 ~21 .06 107 Corydon 39 67.23% ~43.00 12.46 ~80.40 ~21 . 1 1 108 Coulter 1 18 0.84% ~37.77 9.37 -66.38 ~19.95 109 Council Bluffs 118 0.84% ~37.77 9.37 ~66.38 ~19.95 110 Craig 89 25.21% ~34.57 10.57 -60.80 ~4.50 l 11 Crawfordsville 32 73.11% ~39.26 13.78 ~71 .25 ~8.78 112 Cresco 116 2.52% ~41 .24 13.00 ~74.00 ~18.50 1 13 Creston 1 19 0.00% ~29.48 11.65 ~55.00 -6.50 1 14 Cylinder 88 26.05% ~41 .96 9.56 ~67.07 ~22.95 115 Dallas Center 100 15.97% ~36.26 10.34 ~61 .22 -17.22 116 Dana 75 36.97% ~35.66 9.84 ~61.71 ~19.00 117 Davenport 118 0.84% ~15.51 12.05 ~53.76 6.74 118 Dawson 95 20.17% ~37.62 9.15 ~63.67 ~21.10 119 Dayton 42 64.71% ~40.41 8.81 ~61.32 ~23.33 120 Decatur 22 81.51% ~20.31 5.00 ~28.65 -9.75 Note: Highlighting indicates the grain market was chosen for analysis 123 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 121 Decorah 61 48.74% 4348 15.30 ~76.00 ~17.71 122 Dedham 87 26.89% ~39.16 8.10 ~62.60 ~20.92 123 Denison 89 25.21% ~35.09 9.68 ~56.95 ~5.15 124 Des Moines 87 26.89% ~25.84 11.42 ~48.75 664 125 DeSoto 22 81.51% ~39.17 5.74 ~55.25 ~31.33 126 Dewar 47 60.50% ~36.48 10.26 ~58.75 ~9.00 127 Dewitt 61 48.74% ~25.20 12.77 ~54.00 ~3.23 128 Dickens 119 0.00% -38.62 9.52 ~63.73 ~19.29 129 Dike 119 0.00% -38.62 9.52 ~63.73 ~19.29 130 Dixon 67 43.70% ~25.25 13.47 ~58.00 ~5.00 131 Dolliver 56 52.94% ~40.99 10.74 ~64.50 ~20.90 132 Donnellson 26 78.15% ~23.72 7.38 ~44.79 ~12.50 133 Doon 58 51.26% ~32.41 13.15 ~58.33 ~1.75 134 Dougherty 75 36.97% -36.87 9.97 ~60.53 ~21 .70 135 Dow City 18 84.87% ~35.04 12.23 ~50.20 ~12.27 136 Dows 116 2.52% ~37.21 8.50 ~60.61 ~20.94 137 Dubuque 61 48.74% ~26.23 17.36 ~67.68 2.55 138 Dumont 85 28.57% -37.56 9.67 ~57.73 ~20.90 139 Duncombe 64 46.22% ~36.28 9.58 ~63.60 ~19.86 140 Dunkerton l 19 0.00% ~30.80 10.29 ~57.70 ~6.05* 141 Dunlap 88 26.05% ~37.52 11.60 ~64.38 ~12.33 142 Dysart 118 0.84% -33.24 11.13 ~59.90 ~13.14 143 EagLe Grove 119 0.00% ~36.04 8.78 ' ~62.00 ~20.06 144 Earlham 42 64.71% -37.18 9.25 ~58.53 - ~19.00 ‘145 Early 42 64.71% 41.38 9.39 ~63.00 ~23.33 146 Eddyville 116 2.52% ~19.32 11.96 ~53.00 2.26 147 Edgewood 107 10.08% ~31.26 12.11 ~63.05 ~10.21 148 Elberon 19 84.03% ~45.53 8.93 -60.67 ~30.32 149 Eldon 105 11.76% ~26.53 9.53 ~50.00 ~8.55 150 Eldora 24 79.83% ~32.37 7.90 ~49.13 ~20.00 Note: Highlighting indicates the grain market was chosen for analysis 124 Table 3.j (cont’d). . #of % Std. # Grain Market Obs. Missing Mean Dev. Min Max . 151 Eldridge 61 48.74% ~25.75 13.53 ~55.00 ~5.00 152 Elgin 30 74.79% ~48.52 9.85 ~68.70 ~28.00 153 Elk Horn 40 66.39% ~47.67 9.93 ~70.24 ~22.95 154 Elkader 41 65.55% ~46.91 14.28 ~87.05 ~25.95 155 Elkhart 119 0.00% ~32.49 11.40 ~59.21 ~12.00 Elliott 39 67.23% ~45.25 10.05 -68.24 ~21.11 Ellsworth 111 6.72% ~36.52 8.53 ~59.20 ~18.90 Elma 55 53.78% ~42.54 11.67 ~67.40 ~22.00 Emerson 104 12.61% ~36.84 10.06 ~61.80 ~18.90 Emmetsburg 82 31.09% ~36.11 9.64 ~62.37 ~19.24 Estherville 22 81.51% ~34.33 5.81 ~48.36 ~24.44 Everly 51 57.14% ~37.72 12.02 ~65.95 ~17.52 Exira 1 19 0.00% ~40.02 9.29 ~66.80 ~21.39 Fairbank 104 12.61% ~30.95 1 1.05 ~60.79 ~9.60 Fairfax 56 52.94% ~16.01 12.42 ~40.63 3.50 Fairfield 83 30.25% ~31.33 12.35 ~68.00 ~9.32 Farley 40 66.39% ~39.62 1 1.14 ~65.00 ~20.90 Famhamville 42 64.71% ~40.37 8.77 ~61.30 ~23.33 Farragut 40 66.39% ~37.55 9.54 ~58.50 ~11.25 Faulkner 13 89.08% ~36.56 7.70 ~50.2l ~26.71 Fayette 48 59.66% ~32.63 20.09 ~70.58 3.15 Fenton 75 36.97% ~42.13 10.81 ~70.25 ~24.47 Femald 59 50.42% ~31 .45 7.38 ~50.50 ~18.11 Fonda 82 31.09% ~35.51 9.29 ~62.71 ~18.95 Fontanelle 105 11.76% ~40.54 11.21 ~68.62 ~16.82 Fort Atkinson 67 43.70% ~43.75 15.85 ~74.58 ~16.33 . (177 FortDodge 115 3.36% ~36.97 8.56 ~61.75 ~21.82 r18 Fostoria 63 47.06% ~38.88 10.75 ~63.73 ~19.86 P79 Fredericksburg 83 30.25% ~42.91 12.60 ~76.81 ~19.37 _1fi80 Frederika 61 48.74% ~43.90 13.72 ~76.00 ~19.53 Note: Highlighting indicates the grain market was chosen for analysis 125 Table 3. j (cont’d). ’ # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 181 Galva 51 57.14% ~33.45 10.24 ~58.11 ~16.35 182 Garden City 117 1.68% ~34.14 9.17 ~57.20 ~16.86 183 Garner 106 10.92% ~38.32 9.91 ~64.20 ~21.44 184 Garrison 18 84.87% ~34.30 5.35 ~43.54 ~24.37 185 Garwin 70 41.18% ~35.97 11.75 ~57.90 ~14.50 186 Geneva 24 79.83% ~42.04 8.38 ~56.38 ~26.00 187 George 75 36.97% ~28.22 12.45 ~56.29 ~2.00 188 Gilbert 100 15.97% ~35.75 9.56 ~62.00 ~18.61 189 Gilman 58 51.26% ~36.08 11.30 ~59.00 ~13.50 190 Gilmore City 116 2.52% ~37.37 8.49 ~62.20 ~20.40 191 Gladbrook 67 43.70% ~35.81 12.01 ~57.90 ~14.50 192 Glidden 114 4.20% ~37.14 9.52 ~63.50 ~18.39 193 Goldfield 1 19 0.00% ~35 .91 8.63 ~62.00 ~20.06 194 Gowrie 1 19 0.00% ~36.29 8.96 ~62.00 ~19.10 195 Graettinger 61 48.74% ~37.96 10.80 ~62.50 ~20.10 196 Grafton 81 31.93% ~38.47 12.43 ~66.16 ~15.00 197 Grant 116 2.52% ~38.85 11.43 ~69.90 ~17.10 198 Green Mountain 61 48.74% ~36.03 12.31 ~57.88 ~14.50 199 Greene 86 27.73% ~34.11 10.77 ~59.23 ~14.15 200 Greenfield 105 11.76% ~40.52 11.18 ~68.65 ~16.50 201 Greenville 51 57.14% ~38.97 10.90 ~61.07 ~18.20 202 Grinnell 38 68.07% ~34.90 12.63 ~63.00 ~13.50 203 Griswold 40 66.39% ~45 .3 1 9.85 ~68.24 ~21.11 204 Grundy Center 60 49.58% ~36.02 12.23 ~58.05 ~14.12 205 Gruver 51 57.14% ~38.97 10.90 ~61.07 ~18.20 206 Guthrie Center 116 2.52% ~34.21 11.19 ~65.57 ~10.40 207 Halbur 119 0.00% ~37.99 9.64 ~64.40 ~16.28 208 Hamburg 41 65.55% -25.78 24.06 ~53.25 30.00 209 Hampton 97 18.49% ~37.33 8.78 ' -58.37 ~21.00 210 Hancock 40 66.39% ~45.63 9.88 ~68.24 ~21.05 Note: Highlighting indicates the grain market was chosen for analysis 126 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 211 Hanlontown 119 0.00% ~39.99 10.14 ~65.60 ~18.19 212 Hardy 68 42.86% ~35.82 9.69 ~61.55 ~20.06 213 Harlan 39 67.23% ~46.59 10.04 ~69.24 -22.35 214 Harris 65 45.38% ~32.23 8.41 ~59.37 ~12.77 215 Hartley 119 0.00% ~35.53 10.76 ~65.40 ~12.15 216 Hartwick 19 84.03% ~46.50 9.07 ~61.67 ~31.32 217 Hastings 28 76.47% ~42.06 9.27 ~57.50 ~19.00 218 Haverhill 119 0.00% ~34.60 9.95 ~57.05 ~14.50 219 Hawarden 1 14 4.20% ~35.34 13.03 ~67.40 -0.15 220 Hawkeye 119 0.00% ~38.33 12.66 ~75.00 ~15.18 221 Henderson 57 52.10% ~40.57 10.88 ~68.05 ~18.27 222 Hinton 119 0.00% ~33.30 10.10 ~57.62 ~10.50 223 Holland 29 75.63% ~25.43 6.80 ~37.45 ~14.12 224 Holstein 60 49.58% ~34.79 10.50 ~63.75 ~16.05 225 Hopkinton 19 84.03% ~42.53 9.82 ~57.85 -24.75 226 Homick 1 16 2.52% ~36.42 10.30 ~62.40 ~15.67 227 Hospers 62 47.90% ~25.53 10.27 ~52.60 ~4.00 228 Hubbard 117 1.68% ~35.62 9.25 ~59.19 -18.86 229 Hudson 1 13 5.04% ~35.72 10.73 ~65.00 ~12.95 230 Hull 58 51.26% ~26.65 12.30 ~51.29 ~2.00 231 Humboldt 64 ‘ 46.22% ~36.21 9.50 ~63.60 ~19.86 232 Humeston 39 67.23% ~45.13 12.78 ~82.40 ~21.16 233 Ida Grove 42 64.71% ~39.59 9.16 ~61.58 ~20.17 234 Independence 40 66.39% ~41 .09 10.21 ~60.44 ~20.14 235 Indianola 119 0.00% ~35.34 10.66 ~59.45 ~13.58 236 lnwood 45 62.18% ~30.10 14.65 ~57.33 ~1.80 237 Ionia 75 36.97% -'37.11 11.70 ~67.31 ~17.12 238 Iowa City 63 47.06% ~32.44 14.35 ~81 .00 ~8.00 239 Iowa Falls 38 68.07% ~34.68 9.92 ~53.50 ~16.71 240 Ireton 86 27.73% ~30.65 11.80 ~55.23 ~0.85 Note: Highlighting indicates the grain market was chosen for analysis 127 (Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 241 Irwin 98 17.65% ~39.26 10.41 ~63.60 ~18.93 242 Jefferson 119 0.00% ~35.91 9.14 ~62.00 ~18.10 243 Jesup 113 5.04% -33.28 10.95 ~59.00 ~9.35 244 Jewell 109 8.40% ~37.34 9.05 ~61 .40 ~19.56 245 Joice 1 10 7.56% ~37.57 9.84 ~62.60 ~20.00 246 Kalona 60 49.58% ~30.53 14.49 ~66.31 ~6.36 247 Kanawha 1 1 1 6.72% ~38.27 9.44 ~65.20 ~22.55 248 Kelley 100 15.97% ~34.89 9.92 ~62.00 ~16.61 249 Kellogg 66 44.54% ~31.81 13.00 ~58.50 ~9.57 250 Keokuk 116 2.52% ~10.23 11.36 ~46.76 10.50 251 Keosauqua 40 66.39% ~22.25 9.92 ~46.67 ~5.53 252 Keota 16 86.55% ~42.25 12.94 ~62.06 ~23.88 253 Keystone 1 1 90.76% ~43.02 11.84 ~59.3l ~26.56 254 Kingsley 77 35.29% ~41.06 9.54 ~65.00 ~19.14 255 Klemme 119 0.00% ~36.68 10.29 ~63.13 ~14.05 256 Knierim 64 46.22% ~36.21 9.50 ~63.60 ~19.86 257 Knoxville 81 31.93% ~33.60 15.08 ~72.50 ~10.00 258 La Porte City 71 40.34% ~29.65 12.79 ~56.00 ~6.95 259 Lacona 40 66.39% 45.83 11.30 ~72.50 ~23.85 260 Lake City 108 9.24% ~36.88 8.75 ~61.32 ~20.28 261 Lake Mills 46 61.34% ~36.46 11.70 ~60.20 ~22.7l 262 Lake Park 80 32.77% ~39.3O 11.27 ~66.00 ~14.00 263 Lake View 42 64.71% ~41.38 9.39 ~63.00 ~23.33 264 Lakota 98 17.65% ~35.56 10.56 ~63.40 ~11.40 265 Lamoni 116 2.52% ~37.17 12.96 ~72.50 ~10.40 266 Lamont 30 74.79% 46.48 9.32 ~65.90 ~28.95 267 Lanesboro 8 93.28% ~37.01 7.09 ~48.00 ~27.95 268 Larchwood 58 51.26% ~33.59 13.29 ~59.33 ~2.80 269 Larrabee 113 5.04% ~33.82 11.10 ~62.40 ~11.76 270 Latimer 42 64.71% ~42.42 8.37 ~58.84 ~26.33 Note: Highlighting indicates the grain market was chosen for analysis 128 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 271 Laurel 73 38.66% -33.74 9.42 ~56.27 ~13.50 272 Laurens 1 13 5 .04% ~3 8.43 8.85 ~65 .00 ~21.95 273 Lawler 75 36.97% ~36.21 11.07 ~64.50 ~17.06 274 Le Mars 72 39.50% ~42.02 8.46 ~61.00 ~22.25 275 Ledyard 1 17 1.68% ~43.05 9.81 ~70.25 ~24.47 276 Leland 86 27.73% ~39.12 8.77 ~60.74 ~21.14 277 Lenox 61 48.74% ~36.11 11.69 ~63.06 ~12.00 278 Lester 76 36.13% ~33.02 12.68 ~56.45 ~2.29 279 Libertyville 42 64.71% ~37.27 13.97 -73.12 ~18.00 280 Lidderdale 34 71.43% ~3 7.66 5.90 ~52.50 ~27.90 281 Lime Springs 100 15.97% ~41.34 12.37 ~76.75 ~19.38 282 Lincoln 59 50.42% ~35.52 12.76 ~58.05 ~13.12 283 Lineville 39 67.23% ~42.85 12.35 ~80.40 ~20.63 284 Linn Grove 112 5.88% -39.05 10.07 ~68.00 ~19.00 285 Liscomb 67 43.70% -35.40 11.25 ~58.32 ~14.44 286 Little Rock 119 0.00% ~35.60 12.16 ~65.00 ~6.10 287 Little Sioux 66 44.54% ~34.85 13.01 ~62.38 ~10.28 288 Livermore 94 21.01% -36.43 8.88 ~61.55 V ~20.06 289 Lohrville 64 46.22% ~36.28 9.58 ~63.60 ~19.86 290 Lone Rock 71 40.34% ~37.42 10.32 ~67.70 ~22.00 291 Lost Nation 58 51 .26% ~27.87 13.48 ~61.36 ~4.00 292 Luther 89 25.21% ~35.35 10.12 ~63.35 ~17.80 293 Luverne 64 46.22% ~36.14 9.59 ~63.6O ~19.86 294 Luzeme 19 84.03% ~44.05 9.31 ~58.67 ~28.32 295 Lytton 42 64.71% ~40.41 8.81 -61.32 ~23.33 296 Madrid 83 30.25% ~35.38 10.36 ~63.63 ~17.80 297 Malcom 75 36.97% ~31.79 12.86 ~58.50 ~10.72 298 Mallard . 1 19 0.00% ~38.59 9.00 ~63.47 ~20.14 299 Manchester 51 57.14% ~31.15 14.82 ~67.53 ~8.69 300 Manly 116 2.52% ~35.81 10.59 ~60.74 ~13.50 Note: Highlighting indicates the grain market was chosen for analysis 129 Table 3.j (cont’d). # of . % Std. # Grain Market Obs. Missing Mean Dev. Min Max 301 Manning 63 47.06% ~39.18 8.01 ~58.85 ~19.11 302 Manson 55 53.78% ~37.21 8.92 ~59.55 ~21.00 303 Mapleton 71 40.34% ~35.41 10.78 ~60.55 ~16.86 304 Marathon 1 19 0.00% ~37.43 9.25 ~65.00 ~18.95 305 Marble Rock 115 3.36% ~35.79 9.94 ~59.00 ~18.05 306 Marcus 1 13 5.04% ~35.12 10.80 ~62.40 ~13.00 307 Marengo 19 84.03% ~45.82 9.28 ~60.67 ~30.32 308 Marshalltown 104 12.61% ~34.72 10.16 ~57.91 ~14.50 309 Martelle 67 43.70% ~28.74 13.98 ~63.00 ~7.23 310 Mason City 109 8.40% ~37.26 10.14 ~59.75 ~19.29 311 Massena 1 10 7.56% ~39.64 11.95 ~68.62 ~16.67 312 Matlock 70 41 .18% ~26.90 10.70 ~52.60 ~4.00 313 Maurice 112 5.88% ~31 .96 11.55 ~61.50 ~4.00 314 Maxwell 94 21.01% ~33.55 10.61 ~59.75 ~9.14 315 Maynard 56 52.94% ~41.39 10.40 ~67.85 ~24.50 316 McCallsburg 100 15.97% ~35.88 9.51 ~62.22 ~18.61 317 McGregor 47 60.50% ~30.61 16.64 -69.89 1.41 318 Melbourne 119 0.00% ~35.39 9.99 ~58.47 ~16.22 319 Melvin 59 50.42% ~30.28 13.77 ~57.35 ~3.20 320 Meriden 102 14.29% -37.39 10.34 ~62.40 ~15.56 321 Meservey 57 52.10% ~37.19 11.05 ~63.13 ~20.47 322 Milford 51 57.14% ~38.97 10.90 ~61.07 ~18.20 323 Minbum 119 0.00% ~37.10 9.80 ~61.80 ~18.22 324 Mingo 83 30.25% ~32.76 12.20 ~58.74 ~13.94 325 Missouri Valley 79 33.61% ~34.07 12.17 ~62.33 ~9.33 326 Mitchellville 89 25.21% ~30.98 11.76 ~59.21 ~12.00 327 Modale 119 0.00% ~34.54 11.26 ~62.33 ~9.33 328 Mondamin 112 5.88% ~35.02 11.35 ~62.86 ~10.28 329 Monona 41 65.55% ~48.13 14.38 ~88.05 ~26.47 330 Monroe 29 75.63% ~41.53 10.54 ~59.00 ~18.44 Note: Highlighting indicates the grain market was chosen for analysis 130 Table 3.j (cont’d). # of °/o Std. # Grain Market Obs. Missing Mean Dev. Min Max 331 Montezuma 75 36.97% ~31.91 12.90 ~59.20 ~10.72 332 Monticello 40 66.39% ~38.23 10.45 ~60.41 ~19.90 333 Moorland 64 46.22% ~36.28 9.58 ~63.60 ~19.86 334 Morning Sun 88 26.05% ~25.7l 13.02 ~67.47 ~3.64 335 Mount Union 1 19 0.00% ~26.57 10.82 ~65.44 ~8.33 336 Mt Auburn 26 78.15% -30.43 6.35 -42.67 ~19.40 337 Mt Ayr 105 11.76% ~35.05 1 1.69 ~61.00 ~9.47 338 Muscatine 57 52.10% ~17.55 15.49 ~58.67 5.95 339 Nashua 119 0.00% ~37.51 10.58 ~67.31 -17.12 340 Nemaha 60 49.58% ~33.37 10.39 ~63.65 ~14.06 341 Neola 40 66.39% ~44.63 9.89 ~67.24 ~19.90 342 Nevada 100 15.97% ~31 .59 9.91 ~57.00 ~13.61 343 New Hampton 1 19 0.00% ~36.22 10.65 ~63.29 ~17.12 344 New Hartford 70 41.18% ~35.96 8.03 ~63.29 ~23.13 345 New London 62 47.90% ~25.05 8.72 ~46.67 ~8.46 346 New Providence 116 2.52% ~35.65 9.44 ~59.20 ~18.14 347 New Sharon 57 52.10% ~32.67 12.84 ~61.00 ~7.14 348 Newell 99 16.81% -34.18 10.05 ~61.32 2.20 349 Newton 60 49.58% ~33.4l 13.26 ~56.52 ~8.18 350 Nora Springs 26 78.15% ~36.97 6.37 ~51 .15 ~25.25 351 North Washington 61 48.74% ~38.13 12.41 ~67.31 ~17.12 352 Northwood 1 13 5.04% ~39.29 10.70 ~68.21 ~20.32 353 Oakland 39 67.23% ~45.58 10.06 ~68.24 ~21.33 354 Oakville 104 12.61% ~22.76 11.90 ~63.79 ~2.3O 355 Ocheyedan 105 l 1.76% ~36.44 12.16 ~68.6O ~9.77 356 Odebolt 42 64.71% ~39.57 9.21 ~61.58 ~20.l7 357 Olds 85 28.57% ~27.22 12.45 ~67.58 ~7.38 358 Olin 59 50.42% ~28.36 13.86 ~59.00 ~4.00 359 Onawa 118 0.84% ~35.51 10.56 ~62.16 ~14.33 360 Orange City 112 5.88% ~31.98 11.57 ~62.00 ~4.00 Note: Highlighting indicates the grain market was chosen for analysis 131 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 361 Osage 115 3.36% ~37.51 10.26 ~63.20 ~16.71 362 Osceola 26 78.15% ~37.32 8.32 ~59.10 ~24.75 363 Oskaloosa 1 13 5.04% ~25.66 1 1.40 -58.00 ~1.43 364 Ossian 26 78.15% ~35.08 8.32 ~52.10 ~20.15 365 Otho 64 46.22% ~36.20 9.50 ~63.60 ~19.86 366 Otley 29 75.63% ~41.52 10.53 ~59.00 ~18.44 367 Ottosen 1 19 0.00% ~38.46 8.86 ~63.40 ~19.67 368 Ottumwa 104 12.61% ~31.28 11.73 ~68.00 ~11.63 369 Owasa 80 32.77% ~37.50 9.17 ~59.21 ~18.95 370 Oyens 1 19 0.00% ~34.22 1 1.06 ~60.20 823 371 Pacific Junction 95 20.17% ~38.74 10.16 ~68.40 ~18.00 372 Palmer 64 46.22% ~36.21 9.50 ~63.60 ~19.86 373 Panama 18 84.87% ~35.04 12.23 ~50.20 ~12.27 374 Panora 106 10.92% ~3 7.44 9.82 ~62.40 ~19.06 375 Parkersburg 72 39.50% ~38.84 8.37 ~66.29 ~17.33 376 Paton 75 36.97% ~37.50 9.60 ~63.65 -21.10 . 377 Pella 106 10.92% ~29.09 12.74 ~59.00 ~8.16 378 Persia 40 66.39% ~44.64 9.88 ~67.24 ~20.05 379 Peterson 1 13 5.04% ~38.98 10.05 ~68.00 ~19.68 380 Pickering 54 54.62% ~34.17 9.9.1 ~51.15 ~13.50 381 Pierson 99 16.81% ~37.58 9.90 ~62.50 ~16.95 382 Plainfield 86 27.73% ~36.97 10.25 ~62.22 ~20.75 383 Pleasant Hill 89 25.21% ~27.69 11.90 ~53.27 ~7.16 384 Plymouth 102 14.29% ~36.94 10.13 ~59.80 ~19.24 385 Pocahontas 108 9.24% ~37.60 8.30 ~62.20 ~21.00 386 Pomeroy 64 46.22% ~36.28 9.58 -63.60 ~19.86 387 Portsmouth 41 65.55% ~45.02 10.00 ~68.20 ~20.05 388 Prairie City 119 0.00% ~31.39 10.72 ~55.61 ~11.67 389 Protivin 93 21 .85% ~40.13 12.91 ~76.00 ~17.27 390 Radcliffe 40 66.39% ~36.17 10.18 ~56.58 ~19.05 Note: Highlighting indicates the grain market was chosen for analysis 132 Table 3.j (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 391 Rake 86 27.73% ~37.50 9.41 ~60.74 ~18.36 392 Ralston 119 0.00% ~36.12 9.17 ~62.00 ~18.10 393 Randall 101 15.13% ~35.90 9.41 ~60.00 ~17.70 394 Readlyn 21 82.35% ~35.30 5.86 ~47.83 ~25.53 395 Red Oak 116 2.52% ~32.05 10.61 ~57.20 ~11.10 396 Redfield 89 25.21% ~35.95 9.67 ~61.55 ~18.71 397 Reinbeck 92 22.69% ~34.70 10.76 ~58.05 ~13.12 398 Rembrandt 28 76.47% ~38.4l 9.43 ~61.00 ~21.00 399 Remsen l 14 4.20% ~36.47 8.87 ~60.50 ~16.20 400 Renwick 68 42.86% ~36.37 9.41 ~61.55 ~21.06 401 Richland 97 18.49% ~29.04 12.70 ~69.47 ~9.20 402 Ridgeway 61 48.74% ~43.96 15.16 ~75.00 ~18.47 403 Rinard 42 64.71% ~39.86 8.24 ~61.32 ~23.33 404 Ringsted 92 22.69% ~45 .81 9.78 ~71 .25 ~29.90 405 Rippey 100 15.97% ~37.21 9.25 ~63.71 ~19.88 406 Ritter 55 53.78% ~29.97 13.99 ~56.67 ~3.20 407 Rock Rapids 1 1 1 6.72% ~37.70 12.64 ~64.80 ~2.70 408 Rock Valley 11 1 6.72% ~34.47 12.14 ~62.60 ~0.71 409 Rockford 116 2.52% ~36.64 9.94 ~59.00 ~18.91 410 Rockwell 1 15 3.36% ~37.50 8.95 ~60.43 ~21.70 411 Rockwell City 117 1.68% ~36.62 8.58 ~63.60 ~19.86 412 Roland 100 15.97% ~35.92 9.55 ~62.00 ~18.61 413 Rowan 87 26.89% ~38.90 8.13 ~59.19 ~26.05 414 Royal 75 36.97% ~35.50 9.65 ~63.28 ~20.52 415 Rudd 115 3.36% ~36.44 10.20 ~61.00 ~18.77 416 Runnells 116 2.52% ~29.67 12.17 ~57.00 ~6.11 417 Ruthven 118 0.84% ~38.80 9.55 ~64.20 ~20.10 418 Rutland 81 31.93% ~37.74 8.02 ~59.22 ~21.00 419 Ryan 1 19 0.00% ~29.84 12.26 ~61.00 ~9.12 420 Sac City 108 9.24% ~36.02 9.23 ~63.20 ~17.47 Note: Highlighting indicates the grain market was chosen for analysis 133 Table 3.j (cont’d). . . # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 421 Saint Ansgar 61 48.74% ~39.49 12.12 ~65.00 ~16.28 422 Sanbom 1 13 5.04% ~33.49 12.59 ~65.00 ~2.00 423 Schaller 1 13 5.04% -38.82 9.83 ~63.93 ~20.00 424 Schleswig 22 81.51% ~37.76 10.53 ~59.89 ~14.42 425 Scranton 1 18 0.84% ~37.89 9.40 ~64.00 ~19.10 426 Seymour 30 74.79% ~37.17 19.91 ~91.00 ~13.20 427 Sheffield 98 17.65% ~35.78 1 1.46 ~65.85 -8.14 428 Shelby 40 66.39% ~45.64 9.88 ~68.24 ~20.95 429 Sheldon I 13 5.04% ~31.76 l 1.57 ~62.00 ~4.00 430 Shell Rock 26 78.15% ~32.57 6.32 ~44.64 ~21.00 431 Shellsburg 50 57.98% ~35.03 12.60 ~61.00 ~12.67 432 Shenandoah 65 45.3 8% ~30.50 12.18 ~58.46 ~8.25 433 Sibley 43 63.87% ~34.14 10.25 ~53.30 ~9.77 434 Sidney 39 67.23% ~43.97 10.18 ~68.00 ~18.36 435 Sigoumey 39 67.23% ~30.11 16.84 ~71.00 ~9.94 436 Silver City 29 75.63% ~29.74 7.96 ~48.90 ~20.65 437 Sioux Center 71 40.34% ~23.76 11.45 ~49.33 1.38 438 Sioux City 114 4.20% ~33.12 10.38 ~59.76 ~11.60 439 Sioux Rapids 21 82.35% ~44.50 5.31 ~57.56 ~37.12 440 Slater 89 25.21% ~34.75 10.69 ~62.9O ~16.4l 441 Sloan 119 0.00% ~32.91 10.33 ~59.40 ~10.90 442 Somers 42 64.71% ~40.41 8.81 ~61.32 ~23.33 443 Spencer 51 57.14% ~38.97 10.90 ~61.07 ~18.20 444 Sperry 119 0.00% ~18.29 13.40 ~63.33 4.06 445 Stacyville 119 0.00% ~38.51 10.81 ~64.00 ~17.90 446 Stanhope 48 59.66% ~37.53 10.24 ~60.67 ~22.50 447 Stanton 116 2.52% ~35.91 11.31 ~66.14 ~13.75 448 Stanwood 58 51.26% ~27.87 13.48 ~61.36 ~4.00 449 Steamboat Rock 33 72.27% ~36.77 8.52 ~49.76 ~20.52 450 Stockport 1 19 0.00% ~22.84 12.30 ~70.30 ~2.75 Note: Highlighting indicates the grain market was chosen for analysis 134 Table 3.j (cont’d). # of 0/0 Std. # Grain Market Obs. Missing Mean Dev. Min Max 451 Storm Lake 15 87.39% ~21.95 4.40 ~28.35 ~14.12 452 Story City 100 15.97% ~34.35 9.66 ~60.00 ~16.61 453 Sully 57 52.10% ~34.48 12.61 ~63.00 ~9.14 454 Sumner 61 48.74% ~43.05 13.88 ~74.25 ~18.53 455 Sunbury 60 49.58% ~25.34 13.25 ~57.64 ~5.00 456 Superior 51 57.14% ~39.17 11.30 ~65.68 ~18.20 457 Sutherland 109 8.40% ~33.40 10.91 ~66.20 ~7.00 458 Swea City 101 15.13% ~44.09 10.23 ~70.70 ~24.47 459 Taintor 18 84.87% ~42.61 10.05 ~63.00 ~28.68 460 Templeton 1 19 0.00% ~36.23 9.17 ~62.40 ~18.10 461 Terril 70 41.18% ~38.72 9.87 ~65.50 ~23.10 462 Thor 68 42.86% ~35.85 9.66 ~61.55 ~20.06 463 Thornton 1 1 1 6.72% ~39.00 9.76 ~61.20 ~22.00 464 Titonka 1 19 0.00% -3 8.70 9.98 ~68.00 ~21.06 465 Toeterville 65 45.3 8% ~40.55 12.52 ~64.43 ~18.84 466 Tracy 29 75.63% ~41.60 10.57 ~59.00 ~18.44 467 Traer 66 44.54% ~34.36 12.25 ~58.35 ~12.12 468 Troy Mills 22 81.51% ~18.08 5.17 ~28.23 ~9.72 469 Truesdale 21 82.35% ~44.44 5.33 ~58.56 ~37.12 470 Union 117 1.68% ~34.04 9.59 ~57.20 ~15.20 471 Varina 53 55.46% ~38.06 10.72 ~65.21 ~21.95 472 Ventura 116 2.52% -3 8.66 9.83 ~63.60 -21.05 473 Vincent 64 46.22% ~36.28 9.58 ~63.60 ~19.86 474 Vinton 118 0.84% ~30.40 10.85 ~55.65 852 475 Voorhies 50 57.98% ~27.24 7.41 ~46.00 ~13.33 476 Walcott 60 49.58% ~25.34 13.25 ~57.64 ~5.00 477 Wallingford 69 42.02% ~38.64 9.92 ~65.50 ~23.00 478 Walnut 39 67.23% ~46.56 10.02 ~69.24 ~22.35 479 Wapello 89 25.21% ~15.83 13.94 ~74.00 4.95 480 Washbum 108 9.24% ~29.75 10.73 ~56.00 ~10.00 Note: Highlighting indicates the grain market was chosen for analysis 135 Table. 3.j (cont’d). # of % Std. - # Grain Market Obs. Missing Mean Dev. Min Max 481 Washta 70 41.18% ~36.06 10.73 ~62.65 ~16.86 482 Watkins 39 67.23% ~39.42 9.40 ~56.41 ~18.13 483 Waucoma 56 52.94% -46.22 15.65 ~73.82 ~17.35 484 Waukee 119 0.00% ~36.16 10.39 ~60.50 ~15.76 485 Waukon 22 81 .51% ~50.00 19.84 ~89.05 ~17.63 486 Waverly 116 2.52% ~39.26 11.68 ~73.00 ~18.33 487 Wayland 79 33.61% ~24.59 11.75 ~58.58 ~2.24 488 Webb 113 5.04% ~38.46 8.85 ~65.00 ~21.95 489 Webster City 116 2.52% ~36.96 8.72 ~61.40 ~19.14 490 Wellsburg 24 79.83% ~40.07 9.84 ~56.29 ~18.00 491 Wesley 1 18 0.84% ~37.66 9.85 ~66.60 ~20.06 492 West Bend 1 19 0.00% ~38.64 8.96 ~63.47 ~20.36 493 West Burlington 43 63.87% ~22.39 10.92 ~50.76 ~1.95 494 West Union 60 49.58% ~37.02 14.57 ~69.85 ~9.58 495 Westgate 26 78. 15% ~34.88 5.82 ~47.22 ~25.00 496 Wever 61 48.74%: ~19.78 14.50 ~57.00 3.74 497 Whiting 70 41 .18% ~34.2l 11.31 ~62.90 ~15.62 498 Whittemore 1 19 0.00% ~38.64 8.98 -63.57 ~20.36 499 Whitten 64 46.22% ~36.76 11.50 ~58.91 ~16.59 500 Williams 117 1.68% ~36.17 9.05 ~59.20 ~19.15 501 Williamsburg 75 36.97% ~27.13 8.30 -48.00 ~10.50 502 Winfield 1 19 0.00% ~26.62 10.93 ~65.44 ~8.33 503 Winterset 97 18.49% ~35.88 1 1.12 ~62.30 ~16.67 504 Winthrop 40 66.39% ~40.97 10.31 ~60.4l ~18.71 505 Woden 1 1 1 6.72% ~40.40 9.49 ~66.45 ~23.29 506 Woodbine 100 15.97% -35.40 1 1.51 ~63.38 ~11.44 507 Woodward 95 20.17% ~37.50 9.78 ~64.65 ~18.10 508 Woolstock 1 12 5.88% ~37.80 8.39 ~63.40 ~21.40 509 Yale 115 g 3.36% ~37.72 9.49 ~63.05 ~15.50 510 Yetter 42 64.71% ~39.86 8.24 ~61.32 ~23.33 511 Zearing 100 15.97% ~35.67 9.30 ~62.00 ~1 8.61 Note: Highlighting indicates the grain market was chosen for analysis 136 Table 3.k Indiana Monthly Corn Price Basis Level Summary Statistics # of 0/o Std. # Grain Market Obs. Missing Mean Dev. Min Max 1 Amboy 116 2.52% -15.57 9.27 ~36.65 12.24 2 Ambia 45 62.18% ~23.67 8.68 ~38.87 ~6.40 3 Anderson 63 47.06% -17.18 1 1.45 ~40.55 8.78 4 Argos 36 69.75% ~32.78 11.30 ~51.36 2.00 5 Attica 54 54.62% ~18.17 8.22 ~35.94 ~3.59 6 Aurora 116 2.52% ~9.96 12.24 ~45.47 11.24 7 Bluffon 47 60.50% ~23.21 9.26 ~39.44 ~1.50 8 Boston 63 47.06% ~15.61 12.12 ~44.78 14.38 9 Brazil 116 2.52% ~23.73 11.65 ~52.95 7.58 10 Bremen 1 13 5.04% ~20.79 9.99 ~45.00 7.06 1 1 Brook 92 22.69% ~22.02 9.40 ~42.00 ~0.58 12 Brookston 52 56.30% ~20.05 8.96 ~36.89 ~l.57 13 Burlington 42 64.71% ~5.85 8.59 ~23.95 14.15 14 Cambria 23 80.67% ~15.18 6.13 ~25.18 ~1.92 15 Carlisle 105 11.76% ~13.90 10.50 ~42.82 14.48 16 Clay City 63 47.06% ~24.46 13.63 ~52.57 7.58 17 Clymers 15 87.39% ~15.05 11.28 ~37.05 3.13 18 Colfax 23 80.67% ~15.18 - 6.12 ~25.18 ~1.92 19 Columbus 1 19 0.00% ~22.17 12.52 ~48.48 14.70 20 Connersville 104 12.61% ~17.91 11.87 ~45.00 15.90 21 Cortland 83 30.25% ~18.76 15.74 ~47.93 21.81 22 Crawforrdsville 97 18.49% ~18.57 10.54 ~43 .79 8.00 23 Dana 116 2.52% ~16.72 11.56 ~41.91 40.00 24 Dacatur 1 14 4.20% ~19.75 10.50 ~44.80 10.67 25 Delphi 119 0.00% ~15.41 9.75 ~37.55 10.31 26 Dubois 102 14.29% ~8.16 10.18 ~30.35 22.38 27 Dunkirk 116 2.52% ~19.57 9.68 ~40.71 7.90 28 Eaton 43 63 .87% ~21.77 8.15 ~40.32 ~2.79 29 Edinburgh 114 4.20% ~16.32 10.12 ~39.62 14.95 30 Elizabethtown 88 26.05% ~22.37 15.23 ~49.31 24.00 Note: Highlighting indicates the grain market was chosen for analysis 137 Table 3.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 31 Elwood 63 47.06% ~18.48 10.80 ~44.00 5.47 32 Evansville 116 2.52% ~2.82 10.81 ~34.25 15.40 33 Fowler 74 37.82% ~20.59 8.52 ~40.14 1.29 34 Francesville 83 30.25% ~18.78 11.83 ~44.55 11.20 35 Francisco 32 73.11% ~17.11 9.22 ~37.11 6.81 36 Frankfort 117 1.68% ~23.45 8.31 ~44.30 ~1.92 37 Franklin 27 77.31% ~11.20 13.43 ~32.50 14.70 38 Geneva 70 41.18% ~19.66 15.65 ~48.55 16.43 39 Glenwood 1 13 5.04% ~16.13 11.45 ~40.89 16.86 40 Goodland 74 37.82% ~21.62 9.97 ~39.35 0.69 41 Goshen 102 14.29% ~20.40 10.90 ~41.83 10.22 42 Greensburg 1 19 0.00% ~18.74 13.24 ~53.19 17.69 43 Greentown 116 2.52% ~21.19 9.54 ~41.32 6.48 44 Hagerstown 73 38.66% ~15.25 12.25 ~41.62 11.25 45 Hamlet 119 0.00% ~20.21 10.50 ~41.47 6.81 46 Hammond 103 13.45% ~5.32 8.13 ~25.04 14.69 47 Hebron 26 78.15% ~32.71 18.62 ~71.00 ~4.88 48 Hope 118 0.84% ~21.60 12.74 ~49.13 14.70 49 Hortonville 32 73.11% ~24.79 11.45 ~46.05 3.14 50 Huntingburg 117 1.68% ~11.01 12.39 ~39.22 18.95 51 Idaville 63 47.06% ~22.70 11.03 ~46.17 2.67 52 Indianapolis 60 49.58% ~12.02 13.77 ~35.21 21.18 53 Jasonville 26 78.15% ~25.13 7.08 ~42.11 ~16.60 54 Jasper 109 8.40% ~10.67 12.60 ~40.00 18.95 55 Jeffersonville 115 3.36% * ~10.93 12.24 ~49.00 12.19 56 Kentland 64 46.22% ~25.12 10.71 ~45.70 1.75 57 Kersey 75 36.97% ~25.20 9.04 ~45.50 ~0.58 58 Kwanna 56 52.94% ~25.58 13.47 ~50.33 ~0.32 59 Kingman 72 39.50% ~23.83 8.56 ~41.33 ~3.72 60 Knightstown 17 85.71% ~30.55 9.14 ~41.10 ~10.10 Note: Highlighting indicates the grain market was chosen for analysis 138 Table 3.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 61 Knox 67 43.70% ~22.36 11.79 ~42.72 3.81 62 Kokomo 116 2.52% ~15.55 9.03 ~35.50 11.00 63 Kouts 83 30.25% ~25.76 8.07 -44.06 ~6.67 64 L Crosse 109 8.40% ~24.42 10.00 -43.65 7.33 65 La Fontaine 61 48.74% ~15.04 11.88 -38.75 9.60 66 Ladoga 45 62.18% ~17.04 11.46 ~41.90 9.54 67 Lafayette 59 50.42% ~14.57 9.62 ~41.37 6.67 68 Lapaz 1 13 5.04% ~20.74 10.10 ~45.00 8.00 69 Letts 112 5.88% ~22.16 13.30 ~50.65 18.50 70 Linden 114 4.20% ~16.23 9.45 ~38.06 9.57 71 Logansport 116 2.52% ~16.07 9.41 ~36.88 11.29 72 Loogootee 22 81 .51% ~17.21 16.49 ~45.14 10.33 73 Luceme 26 78.15% ~35 .02 5.72 ~51.90 ~26.20 74 Madison 115 3.36% ~13.45 12.93 ~50.00 8.35 75 Marion 97 18.49% ~15.45 10.24 ~34.00 15.40 76 Markle 64 46.22% ~31.53 7.81 ~46.07 ~12.21 77 Markleville 39 67.23% ~19.65 11.91 ~42.30 10.71 78 Medaryville 106 10.92% ~19.43 10.32 ~44.00 6.80 79 Mellott 75 36.97% ~26.38 9.32 ~43.00 ~0.5‘0 80 Mexico 26 78.15% ~30.22 7.41 ~51.32 ~18.00 81 Milroy 30 74.79% ~29.62 11.20 ~52.00 ~10.19 82 Monon 101 15.13% ~19.29 11.18 ~41.50 13.00 83 Monroe 27 77.31% ~13.24 13.09 ~32.93 8.26 84 Monroeville 17 85.71% ~23.23 9.45 ~32.88 ~3.95 85 Monticello 62 47.90% ~21.20 10.96 ~43.1 7 4.67 86 Montpelier 107 10.08% ~15.95 9.83 ~34.94 14.50 87 Morristown 71 40.34% ~16.84 1 1.45 ~41.25 15.95 88 Mount Vernon 47 60.50% ~5.14 12.56 ~36.89 14.79 89 Nappanee 1 16 2.52% ~22.72 1 1.03 ~48.00 11.90 90 New Carlisle 94 21 .01% ~22.13 11.11 ~43.46 7.31 Note: Highlighting indicates the grain market was chosen for analysis 139 Table 3.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 91 New Castle 26 78.15% ~23.36 8.80 ~36.00 -3.08 92 New Paris 52 56.30% ~22.99 17.91 ~75.00 14.42 93 Newburgh 1 15 3.36% ~3.88 10.82 ~34.40 15.00 94 Newtown 9 92.44% ~27.66 6.19 ~35.21 ~17.30 95 Noblesville 34 71.43% ~25.33 11.35 ~45.48 3.14 96 North Vernon 92 22.69% ~21.14 15.82 ~61.80 15.05 97 Oakville 1 14 4.20% ~20.78 10.09 ~44.71 6.00 98 Owensville 98 17.65% ~14.67 10.90 ~42.88 10.86 99 Pershing 73 38.66% ~18.65 13.76 ~44.62 10.21 100 Pierceton 62 47.90% ~29.87 9.77 ~48.20 ~3.67 101 Plymouth 24 79.83% ~12.85 8.67 ~24.06 2.68 102 Poneto 19 84.03% ~36.15 11.78 ~51.57 ~16.20 103 Portage 116 2.52% ~17.42 10.68 ~44.00 10.65 104 Portland 11 90.76% ~11.87 6.77 ~21.79 3.00 105 Princeton 116 2.52% ~11.60 11.68 ~46.33 15.86 106 Ramsey 73 38.66% ~4.77 12.13 ~36.27 30.00 107 Redkey 19 84.03% ~41.19 15.85 ~65.00 ~20.00 108 Remington 1 18 0.84% ~22.14 8.98 ~40.00 2.54 109 Rensselaer 53 55.46% ~15.94 10.80 ~40.00 8.17 110 Reynolds 71 40.34% ~16.53 10.96 ~40.48 8.68 1 11 Richmond 97 18.49% ~16.20 14.49 ~44.00 14.25 112 Roachdale 68 42.86% ~17.35 8.63 ~38.38 3.00 1 13 Rochester 95 20.17% ~20.73 10.72 ~42.84 6.05 114 Rockport 41 65.55% ~7.17 11.51 ~34.57 13.76 115 Rolling Prairie 96 19.33% ~34.75 10.00 ~54.56 ~9.38 116 Romney 42 64.71% ~23.16 7.41 ~39.00 ~5.15 117 Roselawn 75 36.97% ~25.25 9.23 ~45.50 ~0.58 118 Rushville 109 8.40% ~18.23 13.97 ~54.00 17.50 119 Russiaville 65 45.38% ~19.25 8.92 ~40.50 6.24 120 Schneider 71 40.34% ~21.72 10.1 1 ~46.00 4.00 Note: Highlighting indicates the grain market was chosen for analysis 140 Table 3.k (cont’d). # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 121 Seymour 26 78.15% ~33.97 9.56 ~54.67 i ~17.00 122 Sharpsville 19 84.03% ~12.90 7.75 ~26.27 4.58 123 Shelbum 107 10.08% ~14.14 10.37 ~42.88 14.33 124 Shelbyville 18 84.87% ~27.36 8.27 ~46.53 ~14.25 125 Sheridan 102 14.29% ~25.49 10.25 ~54.82 3.00 126 Sims 42 64.71% ~18.12 7.65 ~35.80 2.05 127 South Bend 114 4.20% ~13.47 9.99 ~37.05 14.81 128 South Milford 64 46.22% ~26.43 7.92 ~46.33 ~6.89 129 South Whitley 18 84.87% ~27.36 5.85 ~41.44 ~18.60 130 Star City 38 68.07% ~30.20 9.12 ~48.56 ~10.81 131 State Line 51 57.14% ~17.96 8.48 ~34.33 ~1.36 132 Sullivan 116 2.52% ~12.54 10.28 ~38.76 16.90 133 Summitville 111 6.72% ~22.00 10.99 ~43.67 5.86 134 Swayzee 38 68.07% ~16.65 11.93 ~37.85 11.81 135 Syracuse 25 78.99% ~34.41 1 1.70 ~56.18 ~5.68 136 Tefft 75 36.97% ~26.04 9.09 ~46.50 ~0.58 . 137 Terre Haute 97 18.49% ~16.03 10.45 ~43.94 12.52 138 Tipton 92 22.69% ~18.20 11.81 ~41.30 10.85 139 Trafalgar 70 41.18% ~18.61 14.94 ~51.55 15.30 140 Union Mills 115 3.36% ~23.27 9.07 ~43.50 ~1.65 141 Valparaiso 63 47.06% ~26.26 9.58 ~45.90 ~2.40 142 Vincennes 96 19.33% -1 1.59 11.62 ~41.38 15.82 143 Wabash 20 83.19% ~28.62 7.29 ~46.05 ~17.00 144 Waldron 87 26.89% ~24.6l 12.84 ~48.20 10.63 145 Walton 26 78.15% ~26.31 5.42 ~42.55 ~18.50 146 Wanatah 100 15 .97% ~26.94 9.08 ~49.41 ~3.32 147 Warsaw 106 10.92% ~24.37 10.86 ~49.38 8.90 148 Washington 106 10.92% ~12.39 13.00 ~41.00 20.70 149 Waterloo 106 10.92% ~19.48 9.95 ~40.10 8.15 150 Waveland 92 22.69% ~29.69 10.18 ~54.79 ~4.61 Note: Highlighting indicates the grain market was chosen for analysis 141 Table 3.k (cont’d). . # of % Std. # Grain Market Obs. Missing Mean Dev. Min Max 151 Westfield 22 81 .51% ~28.71 7.50 ~47.05 ~16.25 152 Whitesville 48 59.66% ~15.98 11.97 ~39.08 8.00 153 Williamsport 64 46.22% ~18.46 10.93 ~39.15 9.15 154 Winamac 79 33.61 % ~21.09 9.28 ~48.05 2.44 155 Winchester 102 14.29% ~22.96 10.76 ~41.20 8.24 156 Windfall 19 84.03% ~24.15 5.28 ~37.15 ~17.12 157 Wingate 88 26.05% ~26.20 9.19 ~43.67 ~0.50 158 Winslow 70 41.18% ~15.14 10.21 ~37.35 6.33 159 Wolcott 61 48.74% ~23.64 9.77 ~43.26 4.26 160 Woodbum 81 31.93% ~21.62 9.73 ~43.94 7.33 161 Wyatt 114 4.20% ~23.22 9.79 ~48.00 0.50 162 Yoder 37 68.91% ~31.05 ‘ 10.11 ~62.85 ~16.00 Note: Highlighting indicates the grain market was chosen for analysis 142 APPENDIX 3.2: MODEL I RESULTS FOR KANSAS, IOWA AND INDIANA The following equation was estimated to determine spatial variation in corn price basis levels for Kansas: (6) Bit = aoUSCt + a1 MCt + 2&1 a2kM0k+2§32 a3iMKi 9 2 + Xe = 1 RieEet [Se + (391 DIS'I‘ei + 892(DIS'I‘ei) + Be3rtDISTei +Be4rt(DISTei)2] + a. where the variables are defined the same as they were defined previously in equation (2). The variables are specific for the state of Kansas and equation (6) includes nine ethanol plant openings. Kansas is the only state where an ethanol plant went bankrupt during our time frame. The ethanol plant located in Pratt went bankrupt in February 2008. To accommodate this information, the variable Bet was equal to zero before the ethanol plant was opened, equal to one while the ethanol plant was open and equal to zero after the ethanol plant went bankrupt in February 2008. Equation (6) was estimated with the inclusion of Pratt and Table 3.1 outlines the results. To confirm that the bankruptcy of Pratt did not disrupt our model, equation (6) was also estimated without the inclusion of Pratt and the results remained the same. Equation (6) was estimated using equation (3) to account for spatial correlation. 143 Table 3.1 Kansas Equation (6) Estimates Var. Coeff. Var. Coeff. Var. Coeff. USC 0.0018** Andale ~6.2186** Hope ~6.9206** KC ~0.0945** Atchison 4.9927** Larned ~5.6646** Jan. 2.1807** Beattie ~18.6637** LeRoy ~9.7599** Feb. ~l .7129* * Chapman ~12.7895** Macksville ~3.8444** March 01834 Columbus 31694” McPherson ~1 1.5067" April ~0.9842** Dighton ~2.4004** Morrill -13.4885** May 2.8241 * * Edgerton ~9.8527** Moundridge ~10.9891** June 2.4862** Garfield ~5.8277** Nickerson ~10.1544** July 3.1965* * Girard 0.6005** Offerle 3.0608" Aug. ~5.6723** Gorham ~3.7782** Osborne ~17.2522** Sept. ~0.7737* Great Bend ~5.5813** Ottawa ~12.2953** Oct. ~0.6162** Greenleaf ~17.1758** Overbrook ~9.7146** Nov. 6.5097** Halstead -6.7874** Phillipsburg ~21.9146** Haven ~6.9678** Randall ~19.5112** Hiawatha ~12.5590** Sterling ~11.6416** Hillsboro ~1 1.0974** Winfield ~l .0444** Variable Russell Colwich Oakley Gamett Phillipsburg RE(DIST) 0.3154** 0.2155** ~0.0040 ~0.1176 ~0.5126** RE(DIST)2 ~0.0018** ~0.0015** ~6.4E~4 ~0.0011* . 0.0034“ REr(DIST) ~0.0013** ~0.0012** ~0.0015** ~6.9E~4** 0.0015" REr(DIST)2 6.0E~6** 8.0E-6** 9.0E~6** 1.0E~5** ~9.0E~6** RE 10.4715** ~0.2618** 16.9145** 9.3896“ 6.1046** Garden Variable Pratt City Liberal Lyons RE(DIST) 1.3830“ 0.4674* ~1.6664** ~1.1928** RE(DIST)2 ~0.0071* ~0.0030 0.0067** 0.0154** REr(DIST) ~0.0042** ~3.8E-4 0.0012* ‘ 0.0028** REr(DIST)2 215—5* 3.0E-6 ~5.0E~6 -3.4E~5** RE 4.0657** ~16.1202** 71 .6646** ~16.3000** Note: R2=0.7480 and the spatial error coefficient A=0.5890 was significant at the 1% level. * Indicates significance at the 5% level. ** Indicates significance at the 1% level. Table 3.1 shows that 71 was statistically significant at the five percent level. Therefore, using equation (3) and running the model to account for spatial autocorrelation in the 144 error terms was important. The variable USCt was significant at the one percent level and positive. To interpret this coefficient, if corn production in the United States were to increase by one billion bushels, basis levels in Kansas would be expected to increase by 1.8 cents. The variable KCt is was also significant at the one percent level. If Kansas corn production were to increase by five million bushels, Kansas corn price basis levels would be expected to decrease by 0.47 cents. Table 3.c also shows the month dummy variables were significant, except for March. The month dummy variables vary only slightly which indicates seasonal patterns were somewhat existent in corn basis levels at different grain markets in Kansas. The grain market durrrmy variables were significant in all cases. If a grain market was located in Beattie relative to Wright, the corn price basis level was 18.66 cents lower. 2 The coefficients for the variables RieEetDISTei and RieEet(DISTei) were significant at all ethanol plants except Oakley, Gamett and Garden City. The coefficients of the variables RieEetrtDlSTei and RieEetrt(DISTei)2 were significant at all ethanol plants regions except Garden City and Liberal. As the distance a grain market is away from an ethanol plant opening increases, corn price basis levels fluctuated. The coefficient of the variable RieEet was significant at the one percent level in all cases. The coefficient (Se) was positive for five of the nine ethanol plants. The average corn price basis level effect across all nine ethanol plants in Kansas was positive at 9.55 cents. Therefore, the corn price basis level impact at the site of an ethanol plant opening in Kansas was an average increase of 9.55 cents in corn price basis levels over the average time period after the ethanol plant opened. 145 tit. The following equation was estimated to examine spatial variation in corn price basis levels for Iowa: (7) Bit = (10 USCt + 011 MCt + 2&1 “2kM0k+21120 a3iMKi 27- - 8 ST - DIST - 2 DIST - +2e = 1 RleEetI e + BelDI €1+BEZ( er) + Be3rt er 2 +Be4rtIDISTei) l + 8it where the variables are defined the same as equation (2). The Iowa specific variables include the opening of twenty-two ethanol plants. Equation (7) was estimated using equation (3) to account for spatial correlation. Table 3.m outlines the estimates of the variables in equation (7). 146 Table 3.m Iowa Equation (7) Estimates Variable Coeff. Variable Coeff. Variable Coeff. USC ~0.0033** Conroy 9.3562** Hubbard 5.3032** IAC ~0.0059** Coon Rapids 3.3562** Hudson 13.7545” January 1.7919* * Corwith 3.0068* * Indianola 64000“ February 5 .0485 * * Coulter 1.8457* * Jefferson 3.6024* * March 1.5884" Council Bluffs 18.3835“ Jesup 10.0008“ April 4.1312** Cresco 8.0281 ** Keokuk 34.5184** May 0.3006** Creston 12.0720** Klemme 3.1348" June 2.6392** Davenport 28.0785** Lamoni 7.6793" July ~2.6764* * Dickens ~1.1715* * Larrabee 3 .0487* * August ~9.3276** Dike 2.6042** Laurens 07652” September ~3 .2968* * Dows 2.5047* * Ledyard ~l .43 79* * October ~3.2968** Dunkerton 12.3250** Little Rock ~1.9452** November 3.5402** Dysart 10.4733** Mallard 0.2155** Adair 2.2489** Eagle Grove 3.2238** Manly 4.7262" Alden 6.5371 ** Eddyville 25.2848” Marathon 1.1384** Algona 0.7753** Elkhart 8.4886“ Marble Rock 4.4203“ Alleman 5.6895** Exira ~0.8485** Marcus 1.1750“ Alta 1.8460** Fort doge 3.4714** Melbourne 6.0515" Armstrong ~3.3682** Garden City 6.9038** Minbum 3.3856” Ashton 3.2483“ Gilmore City 2.2301 ** Modale 3.6478" Audubon ~0.3750** Glidden 2.9405” Mt. Union 20.1262** Aurelia 1.0545* * Goldfield 3.3805* * Nashua 3.9309** Bayard 5.3423“ Gowrie 30766” New Hamp. 7.2509** Beaman 6.1078** Grant 2.2188** New Prov. 5.6070** Blencoe 3.2834** Guthrie Center 6.7765" Northwood 2.3629** Boone 5.2826* * Halbur l.4324* * Onawa 3.5729" Cedar Rapids 31 .6566** Hanlontown ~0.1859** Osage 4.0020** Chariton 9.9738“ Hartley 0.2832** Okaloosa 17.5735” Cherokee ~0.3370** Haverhill 6.5120** Ottosen 0.8281 ** Clarence 19.623 5 * * Hawarden ~0.3649* * Oyens 0.8665 * * Clinton 7.2300** Hawkeye 10.2533** Peterson ~1.5686** Colo 5.9627** Hinton 1.7795** Prairie City 10.1142** Conrad 7.4809“ Homick 2.5970** Ralston 3.1127** Red Oak 9.0525** 147 F . -.~2‘;r‘1fl-! I _ A -' AAxA-A A Table 3.m (cont’d). Variable Coeff. Variable Coeff. Variable Coeff. Remsen ~0.241 1** Sheldon 4.4230**‘ Vinton ' 11.7227** Rockford 3.1674** Sioux City 5.3868** Waukee 5.9068** Rockwell 3.5082” Sloan 3.5265** Waverly 5.5108** Rockwell City 3.9251 ** Sperry 26.7341 ** Webb 0.1057“ Rudd 3.6035** Stacyville 2.7045** Webster City 2.5091 ** Runnells 12.5866** Stanton 5.1148** Wesley 3.1229** Ruthven ~0.3977** Stockport 23.73 86** West Bend 0.4813“ Ryan 16.9481 * * Templeton 2.6163** Whittemore 0.4783“ Sanbom 3.1272** Titonka 2.1325** Williams 3.8555" Schaller 0.2879** Union 6.9176** Winfield 19.8733" Scranton 2.3132* * Ventura 1.7490* * Yale 2.9842“ Sioux Coon Variable Center Galva Rapids Lakota RE(DIST) 00955“ 0.0761 ** 0.1165** ~0.0175 RE(DIST)2 4.3E~4* ~3.6E-4 ~6.2E~4** 3.4E~4* REr(DIST) 5.5E~4** ~3.6E-4* ~9.3E~4** 4.8E~4** REr(DIST)2 ~3.0E~6** 2.0E-6 6.0E-6** ~3.0E~6** RE 4.5484“ 2.7821** 6.9937** ~3.9111** W. Variable Marcus Hanlotown Ashton Burlington RE(DIST) ~0.0060 ~0.5773** ~0.2297** 0.7599** RE(DIST)2 1.6E-4 0.0034** 0.0012** -0.0052** REr(DIST) -1.4E~4 0.0025** 4.9E~4** . ~0.0024** REr(DIST)2 1.0E-6 ~1.5E~5** ~3.0E~6** 1.6E~5** RE 2.1430** 4.7034** 9.4875** ~0.1834** Mason Variable Iowa Falls City Emmetsbug Denison RE(DIST) 0.0948” 0.7519** O.7756** ~0.0225 RE(DIST)2 ~1.1E~5 ~0.0056** ~0.0045** 6.1E~4** REr(DIST) ~4.3E~4** ~0.0037** ~0.0034** 2.9E~4** REr(DIST)2 1.0E-6 2.513-5M 2113-5M .3055“ RE ~8.1324** 4.3692** ~6.0361** ~2.3431** 148 Table 3.m (cont’d). Variable Ft. Dodge Goldfield Jewell Corning RE(DIST) ~0.3336** 04093" 0.0724" ~0.1823** RE(DIST)2 0.0020" 0.0019" ~1.2E~4 0.0010** REr(DIST) 8.8E~4** 0.0013M ~6.9E~4** 4013-4" REr(DIST)2 -6.0E~6** ~6.0E~6** 3013-5" 305-5" RE -12472" 9.2749** ~1.9383’” 7.3227" Variable Fairbank Albert City Charles Shanedoah RE(DIST) -0.3257** ~0.8711** ~0.2162** 0.4504" 12130315192 0.0024" 0.0050** 0.0015M -0.0031** REr(DIST) 0.0011** 0.0033" 5.6E~4** 00020" t REr(DIST)2 ~8.0E~6** ~2.1E~5** ~5.0E~6** 1.3E~5** RE 1.6332** 7.2957M 7.5637" 10.7626" E Variable Swerior Stangstar ‘ RE(DIST) 0.3918M 0.8906" RE(DIST)2 00019" -0.0051** REr(DIST) 00012" -0.0020** REr(DIST)2 6.0E-6** 1,213-5“ RE 8.6053” ~10.5765** Note: R2=0.7644 and the spatial error coefficient 2=0.4270 was significant at the 1% *ell:cllicates significance at the 5% level. ** Indicates significance at the 1% level. Table 3.m shows that It was statistically significant at the one percent level. The variable USC. was significant at the one percent level and positive. If corn production in the United States were to increase by one billion bushels, basis levels in Iowa would be expected to decrease by 3.3 cents. The variable lACt is was also significant at the one percent level. If Iowa corn production were to increase by five million bushels, Iowa corn price basis levels would be expected to decrease by 0.3 cents. Table 3.c also shows the month dummy variables were all significant at the one percent level. The values of the coefficients do not greatly alter the corn price basis levels. The grain market dummy 149 variables were all significant. As the distance a grain market is away from an ethanol plant opening increases, corn price basis levels changed. The coefficients of the variable RieEet were significant at the one percent level in all cases. The grain markets located at the site of an ethanol plant opening in Iowa experienced, on average, a 2.41 cent increase in corn price basis levels over the average time period after an ethanol plant opened. The following equation was estimated to determine spatial variation in corn price basis levels for Indiana: _ _ 11 33 , . (8) 8112 — a0USCt + alMCt + 2k a2kMOk+zi (X31MKl 2 + 23 = 1 RieEet I5e 'l' BelDlSTei + Be2(DISTei) + Be3rtDISTei 2 +Be4rt(DISTei) l + err where the variables are defined the same as equation (2) and specific for the state of Indiana. Equation (8) estimates the impact six ethanol plant openings had on corn price basis levels. Equation (8) was estimated using equation (3) to account for spatial correlation. Table 3.n outlines the estimates of the variables in equation (8). 150 Table 3.n Indiana Equation (8) Estimates Var. Coeff. Var. Coeff. Var. Coeff. USC 5.0E~4** Amboy 11.1868” Hope 5.78l2** KC ~0.0374** Aurora 18.1923“ Huntingburg 17.0896** Jan. 2.9132** Brazil 3.5746** Jeffersonville 17.0190** Feb. ~0.7996 Bremen 6.8273** Kokomo 11.8766** March 30778“ Columbus 4.4424** Lapaz 6.9589” April 1.2035“ Decatur 6.2425** Linden 9.9660" May 4.1226** Delphi 10.5540** Logansport 10.9383" June 1.1545** Dana 10.6844** Madison 15.6453" July ~0.8850* Dunkirk 6.9633** Nappanee 4.8331 ** Aug. ~10.3857** Edinburg 10.7958** Newburgh 23.6662" Sept. ~9.4145** Evansville 24.1532** Oakville 5.8891" Oct. ~4.5018** Frankfort 3.1750** Portage 11.01 77** Nov. 6.1134* * Glenwood 1 1.0917** Princeton 16.0547** Greensburg 7.6998** Remington 4.8889” Greentown 57683” South Bend 14.0330** Hamlet 6.3115** Sullivan 14.6232** Union Mills 4.2654** Variable Rensselear Marion Clymers Linden Portland RE(DIST) ~0.8443** ~0.0718 ~0.1038 0.5084* 0.8247* RE(DIST)2 0.0056“ 0.0013 ~6.4E~4 ~0.0027 ~0.0050* REr(DIST) 0.0027** 4.7E-4 5.7E-4 ~0.0018** ~0.0017 REr(DIST)2 ~1.8E~5** ~7.0E~6 1.0E-6 9.0E-6 1.0E-5 RE ~1.4253** ~1.7957** 11.1396** 4.7404** ~20.4049** Variable Alexandria RE(DIST) 0.0780 RE(DIST)2 0.0014 REr(DIST) 1.4E~4 REr(DIST)2 ~6.0E~6 RE ~5.4513** Note: R2=0.5398 and the spatial error coefficient 71:0.4290 was significant at the 13% level. * Indicates significance at the 5% level. ** Indicates significance at the 1% level. 151 Table 3.n shows that .1 was statistically significant at the thirteen percent level. The variable USCt was significant at the one percent level and positive. The variable INCt is was also significant at the one percent level. Table 3.e also shows the monthly dummy variables were all significant except for February. The grain market dummy variables were all significant. The coefficient of the variable RieEet was significant at the one percent level in all cases. The average basis level effect was negative 2.21 cents. Therefore, the grain markets located next to an ethanol plant opening in Indiana experienced, on average, a 2.21cent increase in corn price basis levels over the average time period after an ethanol plant opened. 152 APPENDIX 3.3: MODEL II RESULTS FOR KANSAS, IOWA AND INDIANA The following equation estimates Model 11 for the state of Kansas: 9 . _ 11 32 _ , ( ) Blt — aoUSCt + alMCt + 2k a2kM0k+Zi (X31MK1 9 2 + 28 = 1 RieEet [be + (38101511,, + Be2(DISTei) + Be3rtDISTei 2 +Be4rt(DlSTei) + Be3MOopene + eit where the variables are the same as equation (4) and explicit for the state of Kansas. Results for equation (9), which estimates the impact ethanol plant openings had on basis levels each month after the ethanol plant was opened, are found in Table 3.0. 153 FL_—‘—_f‘ Table 3.0 Kansas Equation (9) Estimate Results Var. Coeff. Var. Coeff. Var. Coeff. USC 0.0015** Andale -7.5605** Hope 960%” KC ~0.0798** Atchison 0.5288“ Larned ~8.7014** Jan. 1.3545** Beattie ~21.1114** LeRoy ~14.7955** Feb. ~2.4884** Chapman ~15.3057** Macksville ~6.7022** March ~0.7621* Columbus ~1.3692* * McPherson ~13.9460** April ~1.7154** Dighton ~5.1582** Morrill ~17.4482** May 2.3313“ Edgerton ~14.1457** Moundridge ~13.6597** June 1.9029** Garfield ~8.7295** Nickerson ~11.9727** July 2.5167** Girard ~4.5149** Offerle 0.4930** Aug. ~6.3490* * Gorham ~7.1998** Osborne ~20.1717** Sept. ~1.7194** Great Bend ~8.8053** Ottawa ~16.9611** Oct. ~1.1733** Greenleaf ~19.0193** Overbrook ~14.8505** Nov. 6.0447** Halstead ~10.6015** Phillipsburg ~24.2803** Haven ~10.2388** Randall ~20.9066** Hiawatha ~16.6859** Sterling ~13.5046** Hillsboro ~13.9046** Winfield ~6.1972** Variable Russell Colwich Oakley Gamett Phillipsburg RE(DIST) 0.1261** 0.1061** ~0.0179 ~0.1662** ~0.4867** RE(DIST)2 ~7.0E~4** ~9.4E-4** ~4.0E~4 ~5.2E~4 0.0033“ REr(DIST) ~4.9E~4** ~8.9E~4** ~0.0016** 1.7E~4 0.0016** REr(DIST)2 1.0E-6 7.0E~6** 9.0E~6** 6.0E~6** ~1.0E~5** MOopen 0.4239** ~0.9024** ~0.2097 0.7274** 03712“ RE 8.3711** 3.8490“ 23.9771 ** 10.8869** 4.8572** Table 3.0 (cont’d). Garden Variable Pratt City Liberal Lyons RE(DIST) 1.9346M -0.0717 -1.2527** ~2.3328** RE(DIST)2 -0.0099** 6.1E-4 0.0042* 0.0214** REr(DIST) ~0.0062** 0.0012 1.3135 0.0053M REr(DIST)2 3.15-5" ~7.0E~6 2.0E-6 ~4.7E~5** MOopen 05246” 2.5642** ~6.4480** 0.333 RE 2.2919M ~18.5547** 73.6873" ~10.2182** Note: R2=0.7792 and the spatial error coefficient 2=0.5840 was significant at the 1% *ellricllicates significance at the 5% level. ** Indicates significance at the 1% level. Table 3.0 shows that 2 and the coefficient for the variable KCt and USCt , were statistically significant at the one percent level. Table 3.0 also shows the month dummy variables and the grain market dummy variables were all significant. The coefficient for the variable MOopene was significant at all ethanol plants except for Lyons and Oakley. The coefficients of the variable RieEet were significant at the one percent level in all cases. The average corn price basis level effect across the nine ethanol plant in Kansas was positive 11.02 cents. Therefore, the corn price basis level impact at the site of an ethanol plant opening in Kansas experienced an average increase of 11.02 cents in corn price basis levels the month the ethanol plant opened. Figure 3.b illustrates the basis impact at the ethanol plant site opening and how this impact changed as the number of month since an ethanol plant opening increased. Figure 3.b was constructed by using the average coefficient estimates for the variables MOopene which was ~0.44. Figure 3.a shows that strengthened corn price basis levels decreased as the months since an ethanol plant opened increased in Kansas. 155 Figure 3.b Kansas Plants: Months Since Opened Time Impact ‘1". l‘ ‘5 8. .E 0% '18 .o to co 8 > < at plant opening ' ,. +Average of 9 Ethanol Plants f 1 11 12 Months The following equation estimates Model 11 for Iowa: (10) Bit = aoUSCt + alMCt + 211,1 aZkM0k+Ei120 a3iMKi 2 + 23,2: 1 RieEet [6e + BelDISTei + 862(D15Tei) + pegrtnrsre, 2 +Be4rt(DISTei) + Be3MOopene + eit where the variables are the same as equation (4) and specific for the state of Iowa. Results for equation (10) are found in Table 3.p. 156 Table 3.p Iowa Equation (10) Estimate Results Variable Coeff. Variable Coeff. Variable Coeff. USC ~6.2E-4** Conroy 8.2873** Hubbard 5.6570** IAC ~0.0212** Coon Rapids 4.1942** Hudson 13.3133** January 2.1974* * Corwith 3 .9749* * Indianola 7.0200* * February 6.4128* * Coulter 3 .4982* * Jefferson 4.8797* * March 2.5340** Council Bluffs 19.4709** Jesup 9.6563** April 4.5338” Cresco 4.9046** Keokuk 32.6713** May 0.3694* * Creston 10.0471 * * Klemme 4.6077* * June 2.7208** Davenport 26.3970** Lamoni 5.3640** July ~3.2157* * Dickens 0.4763 * * Larrabee 3 .9998** August ~2.6907* * Dike 3.8721 * * Laurens 1.8907* * September ~10.3705** Dows 3.8453** Ledyard ~0.7257** October ~4.4159** Dunkerton 12.0478** Little Rock ~0.1694** November 3.5544" Dysart 10.2234* * Mallard 1.5284** Adair 3.4855** Eagle Grove 4.3999** Manly 6.0559** Alden 7.6501 ** Eddyville 23.7132** Marathon 2.1184** Algona 1.7979** Elkhart 9.7070** Marble Rock 55410" Alleman 6.8220** Exira 0.2345** Marcus 1.9112** Alta 2.4547** Fort doge 4.8965** Melbourne 7.1414** Armstrong ~1.6905** Garden City 7.0641 ** Minburn 4.73 53** Ashton 3.9446** Gilmore City 35847“ Modale 4.4211** Audubon 0.7922** Glidden 4.1154** Mt. Union 16.8962** Aurelia 1.7484* * Goldfield 4.4769* * Nashua 5.4400* * Bayard 6.2939** Gowrie 4.5052** New Hamp. 6.2305” Beaman 7.0767** Grant 2.0095** New Prov. 6.3443** Blencoe 4.6747** Guthrie Center 7.8751** Northwood 3.2720** Boone 6.9138** Halbur 2.7776** Onawa 48275“ Cedar Rapids 27.7706** Hanlontown 0.5571 ** Osage 5.2941 ** Chariton 10.3945 * * Hartley l.7304* * Okaloosa 16.3785* * Cherokee 0.4662** Haverhill 7.7598** Ottosen 2.0262** Clarence 15.7169* * Hawarden 1.2598* * Oyens 2.4656* * Clinton 5.3547** Hawkeye 6.5954** Peterson ~0.5005** Colo 7.2177** Hinton 3.2694** Prairie City 11.1878** Conrad 8.3548** Homick 3.3552** Ralston 4.2359“ Red Oak 8.6296** 157 Table 3.p (cont’d). Variable Coeff. Variable Coeff. Variable Coeff. Remsen 1.0766* * Sheldon 5 3244* * Vinton 13.7408* * Rockford 4.8908** Sioux City 7.1306** Waukee 6.2164** Rockwell 5.3176* * Sloan 5.5760* * Waverly 5.0798** Rockwell City 5.1863** Sperry 25.1120** Webb 1.2018** Rudd 5.2945" Stacyville 4.0209** Webster City 4.0866** Runnells 13.5190* * Stanton 4.6390* * Wesley 3.9424* * Ruthven 1.2131* * Stockport 20.1333** West Bend 1.7766** Ryan 13.5471 * * Templeton 4.3493 * * Whittemore 1.8178** Sanbom 3.6733** Titonka 2.6962** Williams 5.1029** Schaller 0.9260** Union 7.9017** Winfield 17.0123** Scranton 3 .4234* * Ventura 3 .4601 * * Yale 4.1590M Sioux Coon Variable Center Galva Rapids Lakota RE(DIST) ~0.2627** 0.1865** 0.2986** 0.0109 RE(DIST)2 0.0016** ~0.0011** ~0.0022** 1.6E-4 REr(DIST) 0.0014** ~0.0012** ~0.0020** ~1.1E~4 REr(DIST)2 ~9.0E~6** 8.0E~6** 1513-5M 0.0E-6 MOopen 0.8366** ~0.3735** 1.6536** ~0.6540** RE 4.6197** ~0.1923** 3.2354“ ~3.8904** W. Variable Marcus Hanlotown Ashton Burlington RE(DIST) 0.0190 0.2542 ** ~0.0316 ~0.0205 RE(DIST)2 ~9.2E-5 ~0.0018** ~3.7E-5 4.0E~5* REr(DIST) ~3.8E-4* ~0.0020** ~7.7E~4** 5.8E~4** REr(DIST)2 2.0E~6* 1.4E~5** 6.0E~6** ~4.0E~6** MOopen ~2.1297** ~5.0774** 4.9359** 4.0132** RE 2.7383“ 3.1612** 10.0462** -3.5362** ' Mason Variable Iowa Falls City Emmetsburg Denison RE(DIST) 0.1440 ** ~0.3877 ** 0.0296 -0.8506** RE(DIST)2 ~0.0011** 0.0025** ~7.7E~4** 0.0057** REr(DIST) ~0.0011** 0.0020** ~4.6E~4** 0.0036** REr(DIST)2 6.0E~6** 1.3E~5** 5.0E~6** ~2.4E~5** MOopen ~20.3151** 18.3531“ ~3.0787** ~2.7891** RE 9.4713 ** ~3.1925 ** 5.7144** 3.0240" 158 Table 3.p (cont’d). Variable Ft. Dodge Goldfield Jewell Corning RE(DIST) -0.2394** ~0.2874 ** ~0.2782 ** 0.1300** RE(DIST)2 0.0024** 2.9134 0.0018** .9754“ REr(DIST) 5413-4" 7.2E-4 ** 6.2E~4** ~4.8E~4** REr(DIST)2 ~7.0E~6** 0.0E-6 ~4.0E~6** 3.0E~6** MOopen 8.3017** ~5.8074** 0.2968" ~4.7628** RE -7.2141** 15.8989** 2.5313** 2.1707** Variable Fairbank Albert City Charles Shanedoah RE(DIST) ~0.3600 ** -02201 ** ~0.0365** 0.1006** RE(DIST)2 0.0025** 0.0011** ~1.1E~4 -5.1E~4** REr(DIST) 0.0011" 8.9E~4** -4.01~:-4** ~5.7E~4** REr(DIST)2 ~8.0E~6** ~6.0E~6** 3.0E-6** 3.0E-6** MOopen 9.5526 ** ~4.6975** 9.6384** 91911" RE -0.5905** 5.7203 ** 2.1075 ** 2.2770** Variable Superior Stangstar RE(DIST) -0.5245** 0.3253** RE(DIST)2 0.0037" -0.0012** RBrler) 0.0014** 4113-4" REr(DIST)2 ~1.1E~5** 1.0E-6 MOopen 0.2626** -43474 RE 8.8755** ~3.3894** Note: R2 =0.8254 and the spatial error coefficient 2:0.5820 was significant at the 1% *ellr‘icllicates significance at the 5% level. ** Indicates significance at the 1% level. Table 3.p shows that 2 was statistically significant. The coefficients for the variables USCt and IACt were both significant. Table 3.p also shows the coefficients for the month dummy variables and the grain market dummy variables were all significant. The coefficient of the variable RieEet was significant at the one percent level in all cases. The average corn price basis level effect across the twenty-two ethanol plants in Iowa was positive 2.71 cents. Therefore, the corn price basis level impact at the site of an ethanol plant opening in Iowa experienced an average increase of 2.71 cents in corn price 159 basis levels the month an ethanol plant opened. Figure 3.c displays the basis impact at the ethanol plant site opening and how this impact changed as the month since an ethanol plant opening increased. Figure 3.c was constructed by using the average coefficient estimates for the variables MOopene. Figure 3.c shows that com price basis levels increased decreased as the number of months since an ethanol plant opening increased. Figure 3.c Iowa Plants: Months Since Opened Time Impact h; at ‘plant opening 3‘ 1* i 4 "Ni "94 r: n _m .- n: n . —O—Average of 22 Ethanol Plants 1" vi , L". a . 33 .o o 8 > < 1 1 12 Months The following equation estimates Model 2 for Indiana: ,11 _ __ 11 33 _ , ( ) Blt — a0USCt + alMCt + 2k aZkMOk-l-Zi a3lMK1 2 + 22 = 1 RieEet [6e + Belorsre, + Be2(DISTei) + BegrtDlSTei where the variables are the same as equation (4) and explicit for the state of Indiana. Results for equation (11) are found in Table 3.q. 160 Table 3.q Indiana Equation (11) Estimate Results Var. ' Coeff. Var. Coeff. Var. Coeff. USC 6.3E~4** Amboy 10.8764** Hope 5.4475“ KC ~0.0384** Aurora 17.83 84** Huntingburg 16.8311“ Jan. 3.2424” Brazil 3.2855** Jeffersonville 16.7257** Feb. ~0.7662 Bremen 6.5016** Kokomo 1 1.4792** March 2.9406** Columbus 4.1137** Lapaz 6.6318“ April 1.2564* * Decatur 6.2297** Linden 95894” May 3.9554** Delphi 10.2167** Logansport 10.6205" June 0.6396 Dana 10.3812** Madison 15.2984** July ~1.1619** Dunkirk 6.6449** Nappanee 4.5061 ** Aug. ~10.6799** Edinburg 10.4811** Newburgh 23.7775** Sept. ~9.6774** Evansville 24.2647** Oakville 54847” Oct. ~4.6450** Frankfort 2.8278** Portage 10.6263“ Nov. 5.8905** Glenwood 10.7163** Princeton 15.7951 ** Greensburg 7.3386** Remington 4.5469“ Greentown 5.4347** South Bend 13.6613** Hamlet 5.9866** Sullivan 14.3146** Union Mills 3.9175** Variable Rensselear Marion Clymers Linden Portland RE(DIST) ~0.6802** ~0.1004 ~0.2339 0.3567 0.1411 RE(DIST)2 0.0047** ~2.1E-4 0.0013 ~0.0023 ~5.7E-4 REr(DIST) 0.0021 * * 6.2E-4 9.3E-4 ~0.0015* 1.4E-4 REr(DIST)2 ~1.5E~5** ~2.0E~6 ~5.0E-6 8.0E-6 ~3.0E~6 MOopen ~1.9964** 3.1336** l.3121** ~6.1820** 21767" RE ~0.1421** ~1.8456** 6.1057** 8.5057** ~10.8316** Variable Alexandria ’ RE(DIST) 0.9068** RE(DIST)2 -0.0037 REriDIST) ~0.0022** REr(DIST)2 8.0E-6 MOopen 14822“ RE ~3.5635** Note: R2 =0.5446 and the spatial error coefficient 2=0.4290 was significant at the 13% level. * Indicates significance at the 5% level. ** Indicates significance at the 1% level. 161 Table 3.q shows that 11 was significant at the thirteen percent level. The coefficients for the variables USCt, lNCt and the grain market dummy variables were significant. The coefficients of the variable RieEet were significant at the one percent level in all cases. The average basis level impact over the six ethanol plants was a negative .29 cents. Therefore, the corn price basis level impact at the site of an ethanol plant opening in Indiana experienced an average decrease of .29 cents in corn price basis levels the month the ethanol plant opened. Figure 3.d was constructed by using the average coefficient estimates for the variables MOopene. Figure 3.d displays that com price basis levels decreased as the number of months since an ethanol plant opening increased in Indiana. Figure 3.d Indiana Plants: Months Since Opened Time Impact o uh ‘ 2 a Let 191549114111 1.? w": . 3.3,, a; .3-4' asis -0—Average of 6 Ethanol Plants . ea? m . b '55 tv' ‘01.), h ., fi' Q. 162 REFERENCES Anselin, L. 1998. Spatial Econometrics: Methods and Models. 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Corn Price Data, 1998-2008. LMIC, Englewood, CO, 2009. MATLAB. 2009. Version 7.8.0.347. MATLAB & SIMULINK Student Version. The MathWorks. McNew, Kevin and Duane Griffith. 2005. Measuring the Impact of Ethanol Plants on Local Grain Prices. Review of Agricultural Economics 27,2:164-180. Overmars, K.P., G.H J. Koning and A. Veldkamp. 2003. Spatial Autocorrelation in Multi-Scale Land Use Models. Ecological Modelling 164:257-270. Pace and Barry’s Spatial Statistics Toolbox. 2009. Spatial Statistics Software and Articles. http://www.spatial-statistics.com/. Accessed November 1, 2009. Renewable Fuels Association. 2010. Statistics. http://www.ethanolrfa.org/industry/statistics/. Accessed November 12, 2009. 163 United States Department of Agriculture. (1). 2009. Economics, Statistics, and Market Information System. . http://usda.mannlib.comell.edu/MannUsda/viewDocumentInfo.do?documentID=1 047. Accessed November 14, 2009. United States Department of Agriculture. (2). 2010. National Agricultural Statistics Service. http://www.nass.usda.gov/Charts_and_Maps/Crops_County/Data/index.asp Accessed January 19, 2010. United States Department of Agriculture. (3). 2009. National Agricultural Statistics Service. http://www.nass.usda.gov/Charts_and_Maps/graphics/data/pricecn.txt. Accessed November 12, 2009. United States Energy Information Administration. 2009. Weekly Retail On-Highway Diesel Prices. http://tonto.eia.doe.gov/oog/info/wohdp/diesel.asp. Accessed December 5, 2009. 164 VERSITV LIBRARIES ll 11 ll llllllll mlllllllllllllllllllllllllll 31293 03 063 5472