The Study of the Relationship between the Production of Corn Ethanol and the Price of the Food Industry

James H. Stephenson

Department of Agriculture and Consumer Sciences

Tarleton State University, Stephenville, TX 76402

Abstract

1. Introduction

The United States of America is the largest producer of corn in the world. According to the U.S. Energy Information Administration, California was the first state to start adding ethanol to gasoline in 2003. By 2005, the Environmental Protection Agency had passed the Energy Policy Act of 2005, which provided regulations to ensure gasoline contained a minimum volume of renewable fuel. Corn Ethanol is by far the largest mass-produced biofuel in the United States. Additionally, it is subsidized by the U.S. government, largely due to the volume of the nation’s corn production. With over 96 million acres of land in the United States being used to produce corn annually, 27% went to the production of ethanol, and 33% went to the production of livestock feed. (United States Department of Agriculture, n.d.)

Corn in the main ingredient in livestock feed, and is also used as a base to bring animals to market weight as quickly as possible. Because of the use of corn in ethanol, there is less of it to meet the demand of livestock farmers, which ultimately raises the cost of the meat market. As the price of corn goes up, it increases the cost of feed. As the cost of feed increases, the cost of raising livestock increases. As the cost of raising livestock increases, the cost of the meat sold from livestock increases.

Tom Webb (2009) summarizes some of the claims made by people in favor of, and in opposition to, the use of biofuels. Those who are for biofuels argue that the increase in alternative fuels helps to reduce pollution in congested cities, and reduce the emissions of greenhouse gases. The scientific community claims that corn-based ethanol also creates more energy than the fossil-fuel energy used in producing ethanol. Those who argue against the use of biofuels claim that while biofuels reduce pollution in the city, they increase the pollution on the Earth as a whole, because of the equipment used for planting, harvesting, and processing. Critics also say that there is no chance ethanol could replace petroleum in the long run. Even using 20% ethanol nationwide would require using 73% of the corn produced annually in the United States. Already, they claim that mandates are to blame for an increase in food prices.

In 2014, gasoline prices nationwide plummeted to the lowest levels in five years. During this time the price of ethanol continued to increase. Due to this imbalance of price ratio it caused the cost of adding ethanol to gasoline to increase drastically. This ratio, which the market was unfamiliar with, caused uncertainty with the future of ethanol. This also causes uncertainties in the supply of the corn market.

2. Objective

The objective of this research is to evaluate the effect the production and price of corn has on ethanol and the meat industry. To be more specific, the research was intended to: (1) Assess the laws and mandates influencing the use of corn to produce a biofuel; (2) Identify the connection between corn and meat industry; (3) Evaluate how the corn ethanol biofuel industry and the food market effect one another as a result of their relation to the production and price of corn.

3. Methods and Procedure

3.1. Literature Review

Many literature and articles exist about the supply and demand of corn and ethanol. In The Economic Impact of the Demand for Corn by Michael Evans, it studies the demand of ethanol and its effect on corn production. Two different models are used in this paper to find the estimations. The multiple regression model used linked the average price of corn to production of corn, amount of corn used for ethanol, and a support price of corn. This determined that corn price is $0.45 higher per bushel when ethanol is produced.

This paper analyzed the impact of the demand for corn.

3.2 Methods

To analyze the data with the highest accuracy possible, a simple and regression model with multiple variables is used. For the simple regression analysis, the independent variable is price of gasoline per gallon and the dependent variable is the supply of U.S. corn. In the multiple regression model includes additional variables. The additional variables are pork, chicken, soybeans, gasoline, and sugar. To keep the same units as corn, soybeans and sugar are listed in millions of bushels. Pork is cents per pound, and chicken is cents per whole chicken.

3.3 Data

Variable Observations Mean Standard Deviation Min Max

Corn 25 9468.04 2082.38 4929 13091

Gasoline 25 29.336 22.731 8.03 85.28

Sugar 25 7780.48 619.92 6691 9032

Soybeans 25 2481.971 508.65 1548 3360

Chicken 25 103.866 20.62 80.17 150.67

Pork 25 244.79 37.68 188.76 311.3

Table 1

All of the data used in this paper is summarized into Table 1. The data in the table is from 1990 to 2010. Accurate information was found using these sources and the time range was used due to account for adequate economic cycles.

Year Corn Gasoline Sugar

Year 1 Corn .8346 1 Gasoline .7198 .7175 1 Sugars .6053 .5054 .2417 1

Soybeans .8043 .7691 .4044 0.7046

Chicken -.1552 -.1885 .0144 -.1111

Pork .9626 .8284 .7436 .5475

Soybeans Chicken Pork

Soybeans 1 Chicken -.1245 1 Pork .7835 -.1040 1

Figure I: correlation between variables

Figure I shows that there is some correlation between variables, but they are not perfectly correlated.

Results

The results used STATA to perform a multiple linear regression and a simple linear regression analysis to estimate the equations.

Simple Linear Regression

Source SS df MS Number of obs =

F( 1, 23) = 25

36.36

Model 63744638.6 1 63744638.6 Prob ; F =

R-squared = 0

.6125

Residual 40327332.4 23 1753362.28 Adj R-squared =

Root MSE = .5957

1324.1

Total 104071971 24 24336332.12 Supply Coef. Std. Err. t P;|t| 95% Conf. Interval

Price 71.69494 11.89056 6.03 0 47.09743 96.29244

_cons 7364.797 437.9623 16.82 0 6458.803 8270.791

Figure II: Corn Supply vs Price of Gasoline

Equation I:

Supply = 7364.8 +71.69 Price

The supply of corn is positively correlated with the price of corn shown in Figure II. P=.0001 and t= 6.03 shows that the results are significant at all levels. The R-squared value is .613 high and supports the positive correlation. Though, it is still low to show a strong relationship which suggest some independent variables were omitted. A multiple regression model is used with other variables found to be important through the research.

Source SS df MS Number of obs =

F( 5, 19) = 25

18.78

Model 86560523.4 5 17312104.7 Prob ; F =

R-squared = 0

.8317

Residual 17511447.6 19 921655.137 Adj R-squared =

Root MSE = .7875

960.03

Total 104071971 24 4336332.12 corn Coef. Std. Err. t P;|t| 95% Conf. Interval

gasoline 34.86259 15.61952 2.23 .038 2.170555 67.55463

sugar .0440322 .4300683 .1 .920 -.856111 .9441755

soybeans 1.751233 .7713512 2.27 .035 .1367768 3.36569

chicken -10.58324 9.642888 -1.1 .286 -30.76603 9.599559

pork 9.975167 13.17029 .76 .458 -17.59056 37.54089

_cons 2413.546 3116.307 .77 .448 -4108.959 8936.051

Figure III: multiple linear regression model

Equation II:

Supply = 2413.55 + 34.86 Price + .044 Sugar + 1.75 Soybeans – 10.58 Chicken + 9.98 Pork

A more significant R-squared is shown in this model than the simple regression model. R-squared increased by 0.2 to .832. This shows an even stronger correlation and the included variables helped eliminate errors that were omitted in the simple regression model.

Source SS df MS Number of obs =

F( 2, 22) = 25

48.17

Model 84725721.6 2 42362860.8 Prob ; F =

R-squared = 0

.8141

Residual 19346249.3 22 879374.969 Adj R-squared =

Root MSE = .7972

937.75

Total 104071971 24 4336332.12 corn Coef. Std. Err. t P;|t| 95% Conf. Interval

gasoline 41.61591 10.43218 3.99 .001 19.98066 63.25093

soybeans 2.277243 .466211 4.88 0 1.310381 3.244106

_cons 2595.143 1024.5487 2.53 0.19 470.3609 4719.926

Figure IV: restricted linear regression model

Equation III:

Corn Supply = 2595.143 + 41.62 Gasoline + 2.28 Soybeans

Equation IV:

F = (R2UR-R2R)/q(1-R2UR)/(n-k-1)

Number of restrictions: q =3, number of independent variables: k = 5, number of observations: n = 25

High significance, 99%, is shown in the restricted model of both soybeans and oil. An f-test value of .663 is found by using equation IV. No significance was found involving chicken prices, sugar and pork prices

Conclusion and Discussion

Many studies have been conducted on the relationship between corn, gasoline and ethanol. Most research has been on the demand side, this research examines the relationship between gasoline prices and the U.S. supply of corn. A positive correlation is established between the two variables to provide significant data that can be used to develop a better understanding of the economic relationship.

Initially the simple regression model is shown that variables are omitted other than the price of gasoline, such as other items used in the making of ethanol and other uses for the grains. The simple regression model shows that price of gasoline has a positive correlated with the supply of corn. A p-value of .0001 and t-value of 6.03 shows that on all levels the results are significant. R-squared had a value of .613 which also shows a positive correlation. Some independent variables were omitted due to the r-square value being slightly low. The above research data provides a positive correlation between the U.S. supply of corn and gasoline prices.

References