The Demand and Welfare Analysis of Vegetable Oils, Biofuel, Sugar Cane, and Ethanol in Europe, Brazil and the U.S

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Texas Tech University, Turker Dogruer, March 2016

The demand and welfare analysis of Vegetable oils, biofuel, Sugar cane, and ethanol in Europe, Brazil and the U.S.

Turker Dogruer

A Thesis

Agricultural and Applied Economics

Submitted to the Graduate Faculty

Of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

Masters

Approved

Dr. Darren Hudson (chair)

Dr. Eduardo Segarra

Dr. Murova Olga

March 24, 2016

Texas Tech University, Turker Dogruer, March 2016

Table of Contents CHAPTER 1 ...... 1 INTRODUCTION ...... 1 1.1 Problem Statement ...... 1 1.2 Objectives...... 2 CHAPTER 2 ...... 4 LITERATURE REVIEW ...... 4 2.1 Biofuel Production and Food Prices ...... 4 2.2 Demand Estimation Models and Welfare Analysis ...... 9 2.3.1 United States ...... 14 2.3.2 European Union ...... 17 2.3.3 The Brazilian Biofuel Industry ...... 19 CHAPTER 3 ...... 26 DATA AND ESTIMATION ISSUES ...... 26 3.1 Summary Statistics ...... 28 CHAPTER 4 ...... 31 CONCEPTS AND METHODS ...... 31 4.1 Conceptual Framework ...... 32 4.2 Empirical Framework ...... 35 CHAPTER 5 ...... 39 RESULTS ...... 39 4.1 Estimation Results ...... 39 4.1.2 Welfare Analysis ...... 50 4.2 Conclusion ...... 52 5.1 Appendix ...... 55

Texas Tech University, Turker Dogruer, March 2016

Acknowledgments

I dedicate this thesis to my parents Fikriye and Tekin Dogruer who supported me with all they could during my research.

I also would like to express my deepest appreciation to my committee chair professor

Darren Hudson for his invaluable support during, not only this research, but also the journey in the Agricultural and Applied Economics department.

I would like to thank my committee members Dr. Edwardo Segarra, and Dr. Olga Murova, for their constructive and informative instruction, and more importantly their encouragement and supports during completing my thesis.

My deepest appreciation goes to my friends who during this research pathed the way to a less stressful and more productive work. Their encouragements and support help me endure the hardship I encountered during the completion of this study. These friends are: Abbas

Aboohamidi, Erdem Tokgoz, Fatih Koca, and Emre Cekin.

Last but not least, my greatest gratitude goes to my wife, Funda Dogruer, who despite her pregnancy and her school load work supported me with her presence, encouragement, observations, comments, patience, and love made this long journey not only easier but more pleasant. I would also like to express my appreciation to my unborn son, Ali, who would age with this paper, and who with his coming surprise illuminated my life.

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Texas Tech University, Turker Dogruer, March 2016

CHAPTER 1

INTRODUCTION

1.1 Problem Statement

The topic of biofuel (biodiesel and ethanol) production from vegetable oils, sugarcane, animal fats, and some crop seeds has drawn the attention of researchers, policy makers, marketers, farmers as well as consumer advocacy groups. But examining the biofuel and food sectors, including vegetable oils and sugar can be challenging. Because biofuel is associated with both energy market as well as food market, each responding to different supply and demand characteristic. Although biofuels offer an alternative to fossil fuels in terms of CO2 reduction, their competition with products that are traditionally used for human and animal consumption raises concerns in regard to food and feed prices, and therefore to food security, especially in poor countries (Steer and

Hanson,2015). Thus, biofuel production can represent an important factor on consumer welfare and vegetable oil market. In fact, the expansion of biofuel production in United States, Brazil, and

European Union countries triggered a cascade of events in the agriculture sector: expansion of the land use by the crops used in biofuel production (corn, sugarcane, oilseed, and so on), increase in the prices of these crops, and increase in the prices of other food items.

Various studies dealing with the issue of fuel versus food have been produced with the aim to quantify the effect of biofuel supply expansion on food prices. For instance, Collins (2008) attributes the increase in the farm level and retail food prices too many factors, including biofuels and specifically corn based ethanol. According to Collins (2008), biofuel is becoming a significant factor in high food prices. The rise in the crude oil prices by mid-2000 increased the use of corn

Texas Tech University, Turker Dogruer, March 2016 in ethanol production reaching levels as high as 40% of corn in United States. In fact, 98% of the

US ethanol production use corn as an input and the percentage of corn used in the production of ethanol to the total usage increased from 35% in 2010 to 40% in 2013.

In a parallel way, According to USDA, the production of ethanol in Brazil uses sugar cane and Brazil is the second largest ethanol producer behind United States. Moreover, 52% of the increase in soybean use is explained by biodiesel production. Creating upward pressure on the prices of edible soybean oil. In addition, Baier et al. (2009) predicts that the increase in ethanol production accounts for 30% of the rise in corn price. Runge and Senauer (2007) also stated that expansion in ethanol production increases corn demand, prices, and producer profit. They argue that the demand for biofuel has caused the basic food prices in the developing countries to substantially increase. The impact of the expansion of ethanol production has not been limited to sugar, corn and soybeans. The recent focus on biofuel production has affected various vegetable oils such as sunflower oil and canola oil as well, but the intensity of the impact is smaller compared to sugar and corn (Jorge and Sarquis, 2012).

The effects of biofuel production have had different reactions and consequences in various countries, and many other factors contribute to the changes of food prices, besides biofuels, as mentioned by Baier et al. (2009). For example, the increase in US biofuel production is responsible about 60 percent of increase in crop prices, while Brazil accounts for 14 percent, and European

Union’s biofuel production accounted for 15 percent only (Baier et al; 2009).

1.2 Objectives

The overall objective of this study is to estimate the effect of the biofuel supply expansion on the prices of certain food items and their consumption. In addition, we estimate the welfare effects

Texas Tech University, Turker Dogruer, March 2016 under different scenarios of policies supporting the biofuel production (mandate, tax credit, and etc.) this study aims to:

*To analyze vegetable oils demand in the United States and European countries, and sugar demand in the U.S and Brazil, at the consumer level,

* To examine the effect of biodiesel production on vegetable oil market price and biofuel production on sugar market price in specific countries, and

* To investigate the welfare effect of biofuel production expansion on consumer consumption of edible products derived from biofuel.

In this analysis, we consider three major players in the field of biofuels and its supporting policies: United States, Brazil, and, the European Union. In the United States and European Union, we estimate a system of demand equations for five vegetable oils and their corresponding price equations. For Brazil, we estimate a system of equations consisting of a demand equation for sugar and its corresponding price equation. The welfare effect of implementing policies that support biofuel production expansion is measured using the second order Taylor approximation of the compensating variation.

The hypothesis is that the price of vegetable oils/sugar depends on the quantity produced of biofuel. Our expectations are that the quantity demanded of vegetable oils depends on the quantity produced of biofuel. And the price of each vegetable oils depends on quantity of biofuel as well. That is, the price of vegetable oils affects the quantity of vegetable oil directly, and as a result, quantity of biofuel affects the quantity of other vegetable oils indirectly. The expectation is that the prices of vegetable oils will rise with the increase of biofuel production.

Texas Tech University, Turker Dogruer, March 2016

CHAPTER 2

LITERATURE REVIEW

This chapter is composed of three sections. The first section reviews the studies that highlight the relationship between food prices and biofuel production and prices. The second section presents a brief literature review of the demand studies and their use in welfare estimation.

The third section provides an overview of the ethanol industry in United States and Brazil and biodiesel industry in Europe.

2.1 Biofuel Production and Food Prices

Vegetable oils are a primary source of oil fat in human diets. These vegetable oils are produced from the fruits or seeds of plants that are relatively easy to cultivate and the oil extraction process also produces by-products that can be used as meals for animal feed. Vegetable oils can be used for cooking, for fuel production, and for several other industrial uses. However, the recent focus on renewable energy has drawn attention to vegetable oils as potential feedstock in biofuel, especially in biodiesel. Many countries, including the United States, and the European

Union (EU) countries, are exploring biodiesel production from vegetable oils, but the diversion of vegetable oils to fuel use might have implications for consumers in these countries and elsewhere. Similarly, in Brazil, sugarcane is heavily used for the production of ethanol.

According to the Brazilian Sugarcane Industry Association (UNICA, 2014), Brazil produced more than 650,000 tons of sugarcane in the 2013/2014 harvest season of which 38,000 tons of sugar and 7.4 million of US gallons of ethanol were produced.

The topic of biofuel (biodiesel and ethanol) production from vegetable oils, sugarcane,

animal fats, and some crop seeds has drawn the attention of researchers, policy makers,

Texas Tech University, Turker Dogruer, March 2016 marketers, farmers as well as consumer advocacy groups. At the research level, the literature of the usage of vegetable oils and crop seeds (e.g. corn in USA, sugarcane in Brazil) as an input for the production of biodiesel and ethanol can be divided into two categories. The first category of literature deals with the effect of the biofuel production surge on the farm and food prices.

For instance, Collins (2008) attributes the increase in the farm level and retail food prices to many factors, including biofuels and specifically corn based ethanol. According to Collins

(2008), biofuel is becoming a significant factor in high food prices. The increase in the crude oil prices by mid-2000 caused the use of corn in ethanol production to increase by 40 percent in the US. The production of ethanol in the US uses 98 percent corn as an input and as a result percentage of corn use in the production of ethanol to the total usage rose from 35% in 2010 to

40% 2013. The production of ethanol in Brazil uses sugar cane to make Brazil second largest ethanol production after US which mirrors the use of corn for ethanol in the US. -In a parallel way, the production of ethanol in Brazil uses sugar cane to make Brazil the second largest ethanol production behind United States. Collins (2008), states that 52% of the rise in soybean use is accounted for biodiesel production. This phenomenon causes a rise in the prices edible soybean oil. Further, Collins (2008) believes that the increase in ethanol production is responsible for 30 percent of the increase in corn prices.

Wright (2014) states that the price jump in food market since 2005 is explained through increased biofuel demand. This study argues that the increase in biofuel stock led to a sharp rise in grain stock, such as the price of substitute and complement grains, by increasing demand, which was caused by the shortage of corn supply. The study also mentions that the biofuel mandates and policies in the agricultural sector generated substantial wealth transfer from consumers to producers. Using data on price and production, the author claims that production

Texas Tech University, Turker Dogruer, March 2016

movement of the three grains (wheat, corn and, rice) in the grain market could not be explained

by Marshallian model. However, the price movement of these three grains could be explained

through Marshalian model. According to Wright (2007), the biofuel expansion would have

effect on the grain market.

Runge and Senauer (2007) suggested that, expansion in ethanol production increases corn

demand, prices, and producer profit. They argue that the demand for biofuel has caused the

basic food prices in the developing countries to substantially increase. First, the authors argue

that government tries to keep the biofuel industry active by providing subsidies and other

supports. These policies lead only to artificial mobility for biofuel industry. Second, if oil prices

fall, then the biofuel will not be profitable, causing the price in the U.S to fall, which in turn

hurts the farmer’s return. Third, the authors provide two scenario of decrease and increase in

the price of oil to show the effect on ethanol produced. For example, if the price of oil reduces

to $30 a barrel, biofuel production is not going to be profitable anymore, which reduces profit

for the farmers. The study points out that the biofuel industry is not operated based on the

market forces but rather by the policies and the interest of large companies.

Along the same line of thought, Banse et al. (2008) show that enhanced demand for biofuel crops under the EU biofuel directive has a strong impact on agriculture at the global and EU levels.

They suggest that without the mandatory blending and subsidies, the target of the EU biofuel use would not be reached. Gerasimchuk and Yam Koh (2013) claim that the EU biofuel policy is responsible for the increase in the industrial use of palm oil by 365% over 2006-2012 period. This pressure on the palm oil will put pressure on the prices consumers pay for this product and other consumer products derived from palm oil. Similarly, Pimentel et al. (2009) argue that growing crops for fuel takes away land, water, and other resources from the production of food for human

Texas Tech University, Turker Dogruer, March 2016 consumption. They estimate that using corn for ethanol production increases the price of US beef, chicken, pork, eggs, breads, cereals, and milk more than 10% to 30%.

Mitchell (2008) states that the production of biofuel from food grains and oilseeds is an important factor that contributes to the rapid increase in food prices and specifically vegetable oil prices in EU and US. Similarly, the IMF estimates that the increased demand for biofuels accounts for 70 percent of the increase in maize prices and 40 percent of the increase in soybean prices

(Lipsky, 2008). Gilbert (2010) affirms that the demand for grains and oilseeds as biofuel feedstock has been recognized as the main reason of the price rise; however, there is little to no direct evidence for this contention. In this sense, Gilbert (2010) suggested that the correlation between the oil price and food prices, in terms of both levels and changes, accounted for expected relationship and not of a direct causal link. These results do not offer support for restrictions on the use of food commodities as biofuel feedstock.

Baier et al. (2009) investigate the effect of biofuel production on commodity and global food prices. In their study, they explore producing countries in a specific period to examine the supply and demand elasticities and the size of indirect effect of biofuel production on prices. They find that the increase in biofuel production over the period of 2007 through 2009 has had significant impact on corn, sugar, barely, and soybean prices, but not so much global food prices.

They show that biofuel production in the world caused the corn, soybean, and sugar prices to increase by 27, 21 and 12 percent, respectively. Baier et al. (2009) also point out that U.S and

Brazil are the two countries that mostly contributed to the increase of these prices with the US responsible for more than 20 percent for corn and 15 percent soybeans while the European Union only responsible for 3 percent, Brazil alone is responsible for the entire rise in the price of sugar.

Sexton et al. (2008) use a global multi-market partial equilibrium model for corn, soybean,

Texas Tech University, Turker Dogruer, March 2016 biofuel and gasoline to analyze the welfare effect of the U.S. biofuel production on food and gasoline markets. The authors indicate that in the hypothetical case of an elastic food market and elastic gasoline market, biofuel supply will positively impact gasoline consumers’ welfare and have only a small negative impact of food consumers’ welfare. These impacts will be inversed in the hypothetical case of an elastic food demand and an inelastic gasoline demand. In sum, the authors conclude that biofuels contributed in reducing the price of gasoline but shortened the food supply and contributed in increasing food prices.

In another study Coyle (2007) pointed out that the increase in crude oil prices with rising oil demand boosted the usage of alternative fuels such as biofuel. Accordingly, Coyle (2007) stated that global biofuel production tripled between 2000 and 2007. However, the study also mentioned that increase in biofuel demand leaded to higher food and feed prices. As a result, Coyle (2007) suggested that biofuel is not permanent solution to high-energy prices. His findings indicate that the future of global fuel depends on their profitability and technology. In fact, recent developments in technology enhanced the efficiency of biofuel production. For example, In the U.S., replacing all current gasoline consumption with ethanol would require more land in corn production as compared to current agricultural production. At this point, technology will be key to boosting the role of biofuels. Coyle (2007) also mentioned that profitability depends on interrelated factors such as price of vegetable oils and animal fats.

Sexton et al. (2008) pointed out that increase in biofuel production is linked to food production. Increasing biofuel production reduced gasoline price; however leaded to food shortage and raise food prices. Accordingly, increasing biofuel production significantly affects the seriousness of world food crisis. They also assert that rapid increase in biofuel production and consumption leads to the shortage of agricultural commodities. To estimate welfare effect in this

Texas Tech University, Turker Dogruer, March 2016 study, authors point out three scenarios, which are called as high, mid and low by looking consumer surplus for biofuel. High scenario is indicated by elastic means price sensitive and respectively. For example, using data from 2007, in which 18.3 percent of U.S. corn production was used for ethanol, we find that ethanol raised corn prices at least 18 percent and perhaps as much as 39 percent, depending on elasticity assumptions. Our analysis demonstrates that biofuels reduce the price of gasoline to the benefit of gasoline consumers and confirms other reports that biofuels hurt food consumers.

2.2 Demand Estimation Models and Welfare Analysis

Several demand estimation models are used across research studies for food commodities such as dairy products and edible oil. In order to estimate demand and prediction of the variation in consumer preferences, many factors, including expenditure, price, and price of related goods, and income will be used. The Linear Expenditure System (LES; Stone, 1954) derived from the

Stone utility function as a general linear formulation of demand with algebraically imposed theoretical restrictions of additively, homogeneity and symmetry in one popular method. The LES model is best applied to estimate demand for goods with independent marginal utilities such as large categories of expenditure (food, clothing, housing, and durables). According to Sarntisart and Warr, (2011), the LES is the model subject to consumer budget and maximum utility of consumer. In addition, this model enables researchers to estimate consumer demand elasticities.

In this study, they estimate a demand system, using 19 non-linear equations, based on the household consumption unit. Results are taken from the LES model indicated that there is a significant differences in expenditure behavior, especially among households with higher income as compared to those with lower income. Demand elasticities for food commodities is lower than non-food commodities. Moreover, the relation among food commodities is observed as weak

Texas Tech University, Turker Dogruer, March 2016 complementarities. Almost each commodity has complements because foods play significant role in consumption. An increase in price of products will leads to reduced income, thus demand for other commodities will also be reduced. Own price elasticity displayed showed that there is a significant difference among commodities in terms of high-income or low-income, and rural or urban households. Additionally, non-foods like education; cloths are more responsive to change in price than other primary needs like foods.

Another major model is log-log model (double log). The double-log demand equation is obtained by taking natural logarithm of both side of the demand function. Double-log demand is convenient to measure price elasticity of demand by taking into account given parameters.

Among many demand estimation model Full-AIDS and LA-AIDS models have a particularly long history, and they have been highly developed and often applied in consumer demand systems modeling. Those models are described and formulated by Schmit and colleagues for various elasticities of demand. These models describe how an approach can be applied to goods

(such as vegetable oils, beverages, dairy products) to estimate income and price elasticities of demand. Each model enables the researcher to estimate income and price elasticity by using different methods (Deaton and Muellbauer, 1980). According to Alemu and Schalkwyk (2004), the AIDS model appears as very popular in demand analysis, especially in the field of

Agricultural economics. The AIDS model derives the demand function from the expenditure function. In fact, the AIDS model is a pure derivation from particular expenditure function with

Shephard’s Lemma (Shephard, 1953)

Mattson, Sun and Koo (2004) analyzed the oilseed, meal and oil domestic demand for the

US using AIDS model. The objective of this estimation was to understand relation between income and consumption of these commodities. The finding of the research indicated that soybean

Texas Tech University, Turker Dogruer, March 2016 consumption elasticity is higher than other oilseed consumption, whereas the peanut oil consumption elasticity is the less one. Moreover, rapeseed and sunflower seed have high expenditure elasticities. This result implies that the consumption of rapeseed and sunflower seed responsive to change in income. The result of studies regarding consumption of oilseeds (soybean, corn, sunflower seed and rapeseed), consumption of meal (soybean meal, cornmeal, sunflower seed meal, rapeseed meal) and the consumption of oils ( soybean oil, corn oil, sunflower oil, rapeseed oil) indicate that the own price elasticities are negative, and the expenditure elasticities are positive. The soybean demand is more elastic than corn, sunflower seed, peanut seed, and rapeseed.

Meal products are more sensitive to changes in price, with higher price elasticities than oilseeds and oils. U.S consumption of oilseed, meals and oils have a positive correlation with income. Results shows that as GDP increases 1 percent expenditure on oilseed increase 0.5 percent, meal increases by 0.7 percent, and oil increases by 0.8 percentages, respectively.

As cited in Davis et al. (2010) study, the Hicksian (Compensated) and Marshallian

(Uncompensated) demand elasticities for dairy products can be derived from the censored AIDS model. In a recent study by Davis et al. (2010), regarding household demand in the U.S for dairy products, certain interesting findings were highlighted, which provide a perspective towards understanding how fluctuation in prices and household expenditures, as well as a number of demographic characteristics, affect household demand for 12 dairy products (including yogurt) and margarine. They found that at α=0.10 level, the own price elasticities were negative and statistically significant for the 13 products. They used a censored demand system and Nielsen

Homescan data for purchase of items in the 13 categories and estimated two types of elasticities: price and expenditure. It was found that 10 of the 13 products have negative own-price elasticities

Texas Tech University, Turker Dogruer, March 2016 with absolute values greater than 1 and strong substitution relationships exist among most dairy product categories. Moreover, expenditure elasticities were statistically significant and positive.

“Seven of the 13 product categories have expenditure elasticities that are 1 or greater.” (Davis,

2010)

In another study conducted by Davis (2012), they also use censored demand and fluid

milk data from Nielsen 2007 Nomescan. The main purpose of this study was to determine the

impact of demographic variables, retail prices and total milk expenditure on flavored and non-

flavored milk purchases. They stated that 112 of the 189 demographic coefficients are

statistically significant at the one percent level, two at the five percent level, and 11 at the 10

percent level. Moreover, it was observed that all own-price coefficients were significant,

whereas 34 of 45 cross-price coefficients are significant. Two sets of demand elasticities

(Marshalian, Hicksian) were derived from censored demand models. For all own-price

elasticities of Marshalian and Hicksian are negative and statistically significant.

Alemu and Schalkwyk (2004) proposed the study that examines about the demand for meat in South Africa. They used LA-AIDS model in their study in order to predict the demand relations for meet groups (beef, chicken, pork). In an explanation for their use of the LA-AIDS model, they mentioned the difference between the AIDS model and LA-AIDS model, which is there specification of the price index. The demand factors in which budget share, price and expenditure variables, and dummy variables are analyzed to estimate LA-AIDS model. They found compensated own price elasticity to be inelastic and statistically significant at the 5% level. The own price elasticities of the pork is the most elastic with (-0.31), mutton (-0.28) chicken (-0.19), and beef (-0.16), respectively. Likewise, cross price elasticities are statistically significant at the

5% level. Regarding cross price elasticities, pork and beef are substituted e= (0.38) is higher than

Texas Tech University, Turker Dogruer, March 2016 pork, whereas the second substitute consumption is the mutton is substitute to chicken because price of chicken (0.17). In contrast, the price of pork is less than beef it has no effect on consumption of beef. Uncompensated own price elasticities are also calculated and found to be statistically significant at the 5% level. In general, result in this study indicated that beef and mutton are seen as luxury considering to pork and chicken.

In terms of welfare estimation, there have been some applications in the case of biofuels

in the United States. For instance, Martinez and Sheldon (2007) estimate the impact of imposing

trade barriers on U.S. imports of ethanol from Brazil. The authors estimate an export supply

and an import demand for ethanol and use the derived elasticities to estimate the deadweight

loss implied by the trade distortions. They conclude that the elimination of trade distortions

between Brazil and U.S. in the ethanol market will benefit both countries. While Cooper and

Drabik (2012) use a general equilibrium model to analyze the welfare impact of biofuel blend

mandate and consumption subsidy in the presence of pre-existing labor and fuel tax. The results

of removing the fuel tax credit imply a positive effect on the welfare that the authors estimate

to be $9 billion, while the welfare cost of the blend mandate is $8.3 billion.

Jingbo et al. (2010) consider the welfare implications of US policies to support biofuels:

a carbon tax subsidizing biofuels and taxing fuel; an import tariff an oil; and an export tax on

corn. The implementation of a carbon tax policy along with a tax on fuel and a subsidy for

ethanol has the largest welfare gain from the terms of trade, especially in the oil market.

Similarly, Lasco (2010) examines the effect of biofuel policies on welfare and social surplus.

The author considers policies, such as the ethanol tax credit for corn ethanol, the ethanol import

tariff, and the renewable fuel standard mandate, and simulates their effect using different market

scenarios. In Langpap and Wu (2010), a general equilibrium is used to study the interactions

Texas Tech University, Turker Dogruer, March 2016

between crop, food, and energy markets and evaluate the price and welfare effect of biofuel

mandates and subsidies. The authors find that higher food prices and lower consumer welfare

are associated with the implementation of biofuel mandates. Sexton and Zilberman (2008)

decompose the distribution of the benefits of US ethanol production tax credit between gasoline

consumers and corn and soy consumers. For the formers, an ethanol production tax credit

constitutes a benefit; while for the latter, the tax credit is a cost.

2.3 Overview of the Biofuel Industry in US, EU, and Brazil

In the last few years, the biofuel industry has played significant role in the United States

and European countries, keeping up with the recent increase in world oil prices and

environmental needs. Reducing fossil energy usage by increasing renewable fuels is considered

as an important way to protect environment. Many policy makers from the EU and U.S have

devoted considerable attention to this issue and set up their own policy legislations. Al-Riffai

et al. (2010) stated that biofuel mandates in U.S and EU have had an impact on global

agricultural market and global biofuel market. Therefore, the policy makers seek to reach

maximum renewable energy consumption by considering cost efficiency and environmental

efficiency.

2.3.1 United States

Inter-American Development Bank (2010) indicated that incentives and mandates for the

US biofuel industry started in the early 1970s with each state having its own regulators. For example, Minnesota increased its biofuel production before the federal government with the amount of 20% in 2013. The first major policy was introduced under the Energy Tax Act of 1978 and the act imposed low tax for the low fuel economy automobiles. The act also introduced tax exemptions and subsidies for blending ethanol in gasoline. Second, the Energy Policy act of 1992

Texas Tech University, Turker Dogruer, March 2016 aimed at improving the overall energy efficiency and clean energy use in United States. Most importantly, The Energy Policy Act of 2005 played a significant role in the United States biofuel industry because mandates on biofuel consumption were initiated with the Act. The act focuses on biofuel energy production in the U.S. between 2005 and 2007. Two years later, the Energy

Independence and Security Act (EISA) of 2007 expanded competition targets from 9 million gallons to 36 million gallons by 2022. In order to do this, EISA’s main goal was to focus on transportation biofuel usage in United States.

The biofuel industry in U.S. is regulated through federal policy, which includes requirements regarding renewable fuel usage. The Congressional Research Service, CRS, (2013); mentioned that federal policy makers in US generate their biofuel standards through Renewable

Fuel Standard (RFS). In other words, RFS set standards for U.S. biofuel production and control processes and implementation of the production through their regulations. The CRS also indicated that RFS increased the biofuel-mandated volume gradually from 4 million gallons to 7.5 million between 2006 and 2012. The expanded version of the RFS expected that biofuel usage in 2007 would rise to 36 million gallons in 2022. Table 2.1 shows the expansion of the renewable fuel in billions of gallons from 2006 to 2014. All columns in the table increased gradually since the start in 2006

Table 2.1 EISA 2007 Expansion of the Renewable Fuel Standard (billions of gallons)

Year RFS1 Total Cap on Total Noncorn Biofuel Renewable Corn Starch Starch Mandate Fuels derived In EPAct Ethanol Of 2005 2006 4.0 ------2007 4.7 ------2008 5.4 9.0 9.0 0.00 2009 6.1 11.10 10.5 0.60

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2010 6.8 12.95 12.0 0.95 2011 7.4 13.95 12.6 1.35 2012 7.5 15.20 13.2 2.00 2013 7.6 (EST) 16.55 13.8 2.75 2014 7.7(EST) 18.15 14.4 3.75 Source: RFS1 is from EP Act (P.L. 109-58), Section 1501; RFS2 is from EISA (P.L. 110-140),

Section 202.

Most of the biofuel (ethanol) produced in the US comes from corn crops. Because of the increase in biofuel production (14 million gallons nearly), corn crop usage share increased from

6% to 40% between 2000 and 2012 (Schnepf, 2013). The increased role and expansion of the biofuel industry led to an increase in crop prices, thus corn prices trended upward as the results of the increase in mandated demand for ethanol.

The main source of the biodiesel production is soybean oil with the 364 million pounds out of total 786 million pounds feed stock used to produce biodiesel in the United States. Other feedstocks that are used to produce biodiesel are shown table 2.1 According to the monthly biodiesel production report of the Energy Information Administration (EIA, 2014), overall United

States biodiesel production reached 101 million gallons by the end of 2014 from 93 million in

April of the same year.

Table 2.2 U.S. Inputs to biodiesel production

Year Canola Oil Corn Oil Soybean Oil Palm Oil

2012 790 646 4,042 W

2013 646 1,068 5,507 632

2014 (5 Month) 220 357 1,621 W

Source: U.S. Energy Information Administration (EIA)

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Also figure 2.1 provides historical biofuel production figures including ethanol and biodiesel in

Figure 2.1 US Biofuel Productions Since 1995

Source: The Energy Information Agency (EIA) Department of Energy from EISA P.L

Figure 2.1 indicates the historical biofuel production of biomass-based diesel RFS, unspecified advanced biofuel RFS, cellulosic biofuel RFS, corn –starch ethanol RFS, actual biodiesel production, and actual ethanol production from 1995 to 2013. Starting from 2013 figure

2.1 shows the mandated use up to 2020. Corn starch RFS is the highest among the others followed by cellulosic biofuel RFS on unspecified advanced biofuel RFS, and biomass-based diesel RFS

2.3.2 European Union

As compared to the U.S, European Union is the most significant biodiesel producer in the world.

In fact, several legislation and formal directives play a crucial role in the promotion of biofuel production. For example, reducing greenhouse gas emissions, boosting the decarburization of

Texas Tech University, Turker Dogruer, March 2016 transport fuels, diversifying fuel supply sources, and developing long-term replacements for fossil oil are motivator for the EU to promote production and use of biofuels. Accordingly, to date, biodiesel is the most important biofuel using 80% of the production rapeseed oil. The EU produced an estimated 768 million gallons of biofuel in 2004 as compared to U.S with biofuel production of

3.4 billion gallons (mostly ethanol). Specifically, Germany produces over half of their biodiesel need.

Historically, fuel consumption used in road transportation in Western Europe rapidly grew by 50% between 1985 and 2004. In fact, by 2000, the EU-15 have imported 75% of their petroleum needs with import grow to meet demand into future. Accordingly, Western Europe used over 270 million metric tons as compared to US’s fuel consumption 177.6 billion gallons in 2004. On the other hand, petroleum still accounts for about 98% of the European countries transport fuels. In this sense, biofuels consumption shares only about 2%. According to a 2001 forecast, the EU expects that vehicle fuel use in the EU will reach 325 million metric tons (MMT) by 2020.

For EU biofuel production, the feedstock supplies are key to the success of the EU’s biofuel strategies because they are the main cost component in their production process. As mentioned before, the core feedstock for EU biodiesel production is rapeseed oil. Accordingly, EU biodiesel production used about 4.1 MMT of rapeseed. In this sense, the EU harvested oilseeds on about 7.5 million hectares (60% rapeseed, 29% sun flowere, and 4% soybeans). Therefore, the production of biofuel is significantly related to the volume of harvested oilseeds.

The EU has significant regulations and policies that control biofuel productions and use, including the EU’s Common Agricultural Policy (CAP), Blair House Restrictions, CAP Land Use

Rules, Sugar Sector Reform, European Commission (EC) Directives, and Biofuels Use Directive.

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In fact, the main common goal of these regulations and policies is to control taxation, quality, promotion, and production of biofuels.

Figure 2.2 Use of Vegetable Oils and Animal Fats as Biodiesel Feedstock in EU-27

Source: IISD-GSI analysis of Oil World data.

2.3.3 The Brazilian Biofuel Industry

According to Goldemberg (2008), most of the approximately 3% of the biofuel consumed in the world comes from the sugar cane in Brazil and corn in the U.S. and Brazil’s ethanol production was squeezed by the 1970s oil crisis. After the crisis, the Brazilian government determined that some of the sugar cane production be directed to ethanol as a substitute for gasoline and more importantly the reduction of oil imports. Table 2.4 shows world ethanol production from 1990 to

2010, and Table 2.3 shows the consumption of ethanol for this period.

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Table 2.3

World Ethanol Consumption (from Sugar Cane/Thousand Tones)

Region 1990 1995 2000 2006 2010 Growth Growth Rate Rate 1990/2000 2000/2010 Brazil 12,676 13,575 10,769 14,083 13,779 -0.15 2.3

OECD 3,311 3,968 5,923 8,160 9,851 5.4 4.7

U.S 1,974 2,269 4,211 6,300 7,857 7.1 5.8

World 18,509 19,873 19,269 25,192 26,768 0.4 3.0

Source: FAOSTAT (1990, 1995, 2010)

Table 2.4

World Ethanol Production (from Sugar Cane/Thousand Tones)

Region 1990 1995 2000 2006 2010 Growth Growth Rate Rate 1990/2000 2000/2010 Brazil 12,028 12,700 10,900 14,268 14,017 -0.9 2.3

OECD 3,487 3,789 5,129 7,214 8,790 3.6 5.0

U.S 2,216 2,540 3,999 5,905 7,359 5.5 5.7

World 18,391 19,418 19,284 25,192 26,768 0.4 3.0

Source: FAOSTAT (1990, 1995, 2010)

The two tables above indicate both the production and consumption of three different regions and the globe for ethanol from 1990 to 2010. Brazil had negative growth rates both in production and consumption of ethanol during the period of 1990 to 2000, where the U.S and other regions in the table show a positive growth rate. However, the trend from 2000 to 2010 changed for Brazil with a positive growth in both areas of production and consumption like other two regions. This

Texas Tech University, Turker Dogruer, March 2016 can be attributed to the policies that were implemented after the year 2000. It is worth mentioning that the ethanol production in table (2.5) comes from sugarcane.

Table 2.5 and Figure 3 indicate overall world production for ethanol.

Table 2.5

World Fuel Ethanol Production by Country or Region (Million Gallons)

Country 2007 2008 2009 2010 2011 2012 2013

USA 6521.00 9309.00 10938.00 13298.00 13948.00 13300.00 13300.00

Brazil 5019.20 6472.20 6578.00 6921.54 5573.24 5577.00 6267.00

Europe 570.30 733.60 1040.00 1208.58 1167.64 1179.00 1371.00

China 486.00 501.90 542.00 541.55 554.76 555.00 696.00

Canada 211.30 237.70 291.00 356.63 462.30 449.00 523.00

Rest of World 315.30 389.40 914.00 984.61 698.15 752.00 1272.00

WORLD 13123.10 17643.80 20303.00 23310.91 22404.09 21812.00 23429.00

Source: World Bank

As table 2.5 indicates for the two countries (Brazil and US) and also the European region, the production of ethanol increased from 2007 to 2011, but saw a slower growth from 2012 to 2013.

Brazilian production, on the other hand, shows more variability compared to the US production.

Brazilian production growth was slower compare to the US for the same period; and, for 2011 and

2012 the production decreased relative to previous years. However, the production increased slightly in later years to the 2008 level. Table 3 shows, the European region had similar growth rate as the Brazilian growth for 2007 and 2008, but a higher and a growth after 2008. European

Texas Tech University, Turker Dogruer, March 2016 production shows 41 percent growth from 2008 to 2009 and 61 percent in2009- 2010. However, production shows a small decline in 2011 and 2012.

The Brazilian government launched its first ethanol program (proAlcool) in 1975. At the time, the military government saw the increasing amount of oil import as a financial challenge to the already fragile financial system and its energy security- 80 percent of fuel used in Brazil’s transportation was imported (Meyer et. al., 2012). Because the implementation of the proAlcool was initially aimed to replace the oil import under this program, Petro Bras, the state-owned oil company, would buy a set amount of ethanol that was promised by the government from the ethanol producers in Brazil. Moreover, to create the economic incentives needed, the Brazil government offered two billion US dollars to the companies in the agro-industrial sector in Brazil.

In return, the companies would produce ethanol and pay low interest rate for the loans they received from the government. This caused the production to jump, and by 2007, ethanol production in Brazil reached 18 billion liters (Meyer et al., 2012).

The Brazilian government also encouraged vehicle users to buy cars with engines that are made for gasoline mixed with ethanol (5 to 10 % ethanol), but when the gasoline prices decreased with the end of the crisis, drivers who already had shifted to such engines found themselves paying higher prices for ethanol. With fallen oil prices and remaining import problem in Brazil, the government decided to improve the efficiency of ethanol production to place itself where eventually can reduce its subsidies to ethanol producers by making ethanol production less costly.

In this process, the Brazilian government coupled the Brazilian agriculture cooperation with the

Copersucar to help reach its goal of more efficient ethanol production. By 2002, ethanol production reached 12.6 million cubic meters from 0.6 in 1975. By 2009, the Brazilian government reached

Texas Tech University, Turker Dogruer, March 2016 its goal of reducing subsidies to ethanol producer. Figure 2.4 shows sugarcane allocation for ethanol and sugar production in Brazil from 1994 to 2010

Figure 2.4

Brazil’s ethanol and sugar production collected from sugar cane. -1994-2010

Source; FAOSTAT, ERS/USDA (Form 1994 to 2010)

As it can be seen from the figure 2.4 the sugarcane production in Brazil for both ethanol and sugar production increased in the time period between 1994 and 2010. The increasing yield, the decreased cost of ethanol production, and the flex-fuel engines that came with the collaboration of foreign owned vehicle producers, caused a dramatic change in consumption habit of the Brazilian drivers, and thus created a stronger market structure and environment for ethanol.

In general, as Meyer et al. (2012) describe, the history of Brazilian ethanol industry can be broken down to 5 phases: the first phase is the pre 1975 time; the Brazilian ethanol program in

Texas Tech University, Turker Dogruer, March 2016

1970s was founded on three bases: 1- the location and the strength of Brazilian sugar industry; 2- long history of producing ethanol during crisis and a method of insuring against sugar price fluctuation in the world market; 3- the form of the Brazilian military government and its policies implemented in the industrial and energy sectors in the Brazilian economy. The second phase is the 1975-1979 time period: the rescue of the sugar industry - the proAlcool program that was introduced by President Geisel was an attempt to both reduce Brazil’s dependence on foreign oil and transform the sugar industry to a competitive ethanol producer in the world. This, in turn, helped save the sugar industry in Brazil. The third phase is the 1979-1985 time frame: the growth of the ethanol production process continued with more expansion and innovation in Brazil.

The fourth phase is the 1985-2003 time period: Brazil faces uncertainty and crisis as the military government transformed to a civilian government in 1985. The government change caused

Brazil to encounter a long period of difficulties such as debt crisis and hyperinflation. This crisis along with decreased oil prices made it difficult for the ethanol industry to survive. The fifth phase is from 2003 to present: the introduction of Flex-fuel vehicles, following the difficult period from

1985 to 2003, the flex-fuel vehicles brought a new life to the ethanol program in Brazil. The

Volkswagen golf car was the first flex-fuel car that was introduced in Brazil and it was followed by automobiles from General Motors and fiat. This introduction made it more affordable for consumers to choose their desired mixed gasoline

Meyer et al. (2012) conclude that Brazil in 2010 produced nearly 27 billion liters of ethanol, which is 50 times more than was produced when the proAlcool program was launched in 1975.

Additionally, ethanol is responsible for roughly 20 percent of the major roads transportation fuel- mix in Brazil with gasoline accounting for 27 percent and diesel the balance.

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Meyer et al. (2013) point out that the effects of the Brazilian government policies in 1985 toward ethanol production, which caused the sugarcane planted area in Brazil to grow by 7.56 percent per year for the past three decades. For this growth, the southwest region is accounted for

55 percent of this area in 2010 leading to an appreciation of land prices. Another implication that is pointed out by the authors is the contribution of sugar ethanol industry to supply co-products for cattle operations is small and can be a concern for the growing livestock sector.

Brazil generated 717 million tons of sugarcane that yield 36.1 million tons of sugar and 27 billion liters of ethanol in the year of 2010 (Meyer et al., 2013). With this production, Brazil is the largest sugar producer in the globe and second as ethanol producer after the US Large amount of the ethanol produced in Brazil is consumed domestically in two forms ethanol fuel and mixed with gasoline. Table 2.5 indicates the price of gasoline and ethanol in Brazil regionally.

Table 2.5 Regional Ethanol and Gasoline Price in Brazil (Dollars per liter)

Regions/Fuel 2001 2002 2003 2004 2005 2006 2007 2008 2009 Type

Southeast

Ethanol 0.40 0.33 0.41 0.37 0.50 0.68 0.68 0.72 0.69

Gasoline 0.73 0.58 0.66 0.69 0.93 1.14 1.26 1.33 1.22

Northeast

Ethanol 0.49 0.39 0.50 0.49 0.69 0.87 0.88 0.96 0.88

Gasoline 0.75 0.60 0.68 0.73 0.99 1.23 1.35 1.43 1.31

Center-West

Ethanol 0.46 0.38 0.47 0.47 0.64 0.84 0.80 0.89 0.82

Gasoline 0.75 0.60 0.69 0.75 1.00 1.22 1.35 1.41 1.32

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South

Ethanol 0.45 0.37 0.46 0.45 0.62 0.82 0.79 0.83 0.79

Gasoline 0.75 0.61 0.70 0.74 1.01 1.21 1.30 1.38 1.27

North

Ethanol 0.55 0.45 0.57 0.56 0.76 0.99 0.99 1.05 0.96

Gasoline 0.81 0.63 0.72 0.77 1.05 1.24 1.36 1.47 1.37

National Avrg Price

Ethanol 0.47 0.38 0.48 0.47 0.64 0.84 0.83 0.89 0.83

Gasoline 0.76 0.60 0.69 0.74 1.00 1.21 1.33 1.41 1.30

Source: USDA, Economic Research Service using data from ANP (2009)

Although the Brazilian government deregulated the price control of ethanol, in 1997, it still favors the ethanol price through tax treatment. According to the Brazilian government for ethanol to be competitive with gasoline the price of ethanol should be set to be two-third less than the price of gasoline (Valdes, 2011). Above table indicates the price of ethanol in different regions in Brazil compare to gasoline.

CHAPTER 3

DATA AND ESTIMATION ISSUES

The data used to estimate the model previously described were comprised of annual consumption of vegetable oils and biofuel production for the period between 1969 to 2014. The data contains per gallon dollar sales volume sales (domestic consumption) for different type of vegetable oils (corn, rapeseed, sunflower seed, soybeans oils) and biofuel (i.e., biodiesel and ethanol) in the United States and Europe. The data were retrieved from several research and governmental organizations including Bloomberg, US Department of Energy Alternative Fuels

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Data Center, USDA Economic Research Service, and the Renewable Fuels Association (RFA).

Bloomberg provided the data regarding vegetable oil consumption in Europe and the US and biodiesel and ethanol prices between 1997 and 2014 in Europe. Furthermore, biodiesel price data for years between 1997 and 2014 in United States were retrieved from US Department of Energy

Alternative Fuels Data Center. Ethanol price data in United States were taken from USDA

Economic Research Service. Vegetable oil price data in Europe and United States was retrieved from Bloomberg data service and USDA, respectively. Ethanol production for US was obtained from the Renewable Fuels Association (RFA) database while ethanol production data for Europe was provided by Bloomberg database system.

Tables 3.1 and 3.2 and Tables 3.3 and 3.4 present summary statistics of the variables in this study for Europe and the US, respectively.

The data for vegetable oils consumptions and production of biodiesel are given as 1000 metric tons. Bioethanol is provided as million gallons for each region. Ethanol price for Europe and United States are indicated with the dollar value for cubic meter and per gallon, respectively.

Biodiesel price is represented with the dollar value for liter and gallon for Europe and United

States. Additionally, vegetable oils price data is given by dollar value per ton in Europe and cent per pound in United States.

As detailed in tables, early 1970s and 1980s biodiesel production is relatively lower than

1990s and 2000s, and thereby the data had huge fluctuations in the amount of production and consumption. Based on previous research, biofuel production in 1960s and 1970s was growing slowly, and it is very difficult to find the data regarding the consumption and pricing of biofuel in

Texas Tech University, Turker Dogruer, March 2016 this period. In this sense, the standard deviations for each study variables were greatly affected by this rapid change by years.

3.1 Summary Statistics

Table 3.1 Summary statistics of oil consumption and price in Europe

Oil Consumption (1000 Metric Tons) Mean Std.Dev Minimum Maximum Corn(1000 Metric Tons) 76.2 34.55 10 184 Rapeseed (1000 Metric Tons) 3432.52 3061.68 495 9938 Soybean (1000 Metric Tons) 1997.48 478.4 1161 3413 Sunflowerseed (1000 Metric Tons) 2200.52 100.49 54 3701 Oil Price ($/Ton) CornOil Price ($/Ton) 384.06 324.45 54 1246 Rapeseed Oil Price ($/Ton) 492.6 383.31 87 1410 Soybean Oil Price ($/Ton) 467.6 380.78 62 1410 Sunflower Oil Price ($/Ton) 578.9 368.83 238 1639

Table 3.2 Summary statistics of biodiesel and ethanol production in Europe.

Mean Std.Dev Minimum Maximum Biodiesel Production (1000 Metric Tons) 3641.61 2932.7 319 8376 Ethanol Production (Million Gallons) 152.74 253.3 1 949 Ethanol Price (Gallon) 2.27 0.53 2 3 Biodiesel Price (Gallon) 0.39 0.07 0.26 0.52

Table 3.3 Summary statistics of oil consumption and price in United States

Oil Consumption (1000 Metric Tons) Mean Std.Dev Minimum Maximum Corn(1000 Metric Tons) 335.87 82.44 203 500

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Rapeseed (1000 Metric Tons) 536.04 606.16 3 2220 Soybean (1000 Metric Tons) 5755.22 1874.72 2854 8476 Sunflower seed (1000 Metric Tons) 102.41 78.36 0 272 Oil Price ($/Ton) CornOil Price ($/Gallon) 30.82 13.2 16 74 Rapeseed Oil Price ($/Gallon) 34.41 14.56 17 66 Soybean Oil Price ($/Gallon) 27.82 12.16 14 56 Sunflower Oil Price ($/Gallon) 35.21 20.78 16 91

Table 3.4 Summary statistics of biodiesel and ethanol production in U.S.

Mean Std.Dev Minimum Maximum Biodiesel Production (1000 Metric Tons) 1378201.8 1589519.36 1.5 474331 Ethanol Production (Million Gallons) 343.66 4172.76 10 14319 Ethanol Price (Gallon) 1.55 0.66 1 3 Biodiesel Price (Gallon) 1.84 1.22 1 4

The data for the consumption of corn seed, sunflower seed, rapeseed, and soybean seed for both European countries and the United States were obtained from earth policy data center.

The price for soybean seed in EU also was obtained from the same source. Table 3.5 indicates the summary statistics for these data. Ethanol price in Brazil during the 1987 through 2014 period shows a smooth trend with mean of 0.165 and the median of 0.163 of U.S dollar per gallon. Sugar price shows the same steady trend for this period and that could be attributed the Brazilian government sugar price control policy as it is mentioned in the literature. Sugarcane and sugar production in Brazil increased as a result of the Brazilian government protection policies.

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Table 3.5 the consumption of seeds and their relative prices in the U.S. and EU

Variables Mean Median Standard Min Max Deviation Corn 5.49 5.48 0.906 3.64 6.93 (U.S) Sunflower 290.4 1.53 428.72 1.09 979 Seed (U.S) Rape Seed 320.67 121.5 351.27 1.05 986 (U.S) Soybean Seed 45.12 47.89 6.61 31.17 53.47 (U.S) Sunflower 6.87 6.79 0.91 5.05 8.58 Seed (EU) Soybean Seed 15.66 15.69 1.68 13.25 20.09 (EU) Rape Seed 14.08 11.29 6.14 7.56 25.82 (EU) Sugarcane pro 3.19 3.12 0.27 2.68 3.71 (U.S)

Soybean Seed 295.96 240.75 116.85 179.6 561.3 Price (EU)

The data presented in table 3.5 range from 1987 to 2014. The prices are in US Dollars and the unit for the quantity of consumption is based on dollar per bushel. The data for the European countries and the U.S is obtained from the USDA data base. As table 6 indicates the Corn seed mean and median value do not differ during this period, meaning that the prices do not show a major fluctuation in this period. The same is true about the soybean seed prices during the same period. However, the major fluctuation in the prices of Sunflower seed in the United States prices, but not in Europe. Sugarcane production shows a steady trend in the same period in the United

States.

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In addition to the data mentioned above, the produced quantity of ethanol along with the prices, the quantity of sugarcane, and prices, and the GDP were retrieved from Bloomberg data base for

Brazil. Table 3.6 shows the summary statistics for both the quantity and the price ethanol, and also the GDP for which years. The prices for all variables are in the US dollars and the quantities are in million gallons.

Table 3.6 quantity produced and prices for Ethanol and Sugarcane along with GDP for Brazil

Variables Mean Median Standard Min Max Deviation Quantity of 4156.42 3610 1376.08 2773 7270 Ethanol produced Price of Ethanol 0.165 0.163 0.038 0.10 0.23

Quantity of 9426.17 9350 1802.47 6400 12000 Sugar produces

Price of Sugar 22.59 21.72 8.26 11.28 38.79

GDP 393.69 431.95 314.56 1.08 882.2

Sugarcane 25.50 20.40 10.94 7.8 38.6 Production

CHAPTER 4

CONCEPTS AND METHODS

This chapter presents the methodology used to conduct the current analysis. In this context, this analysis proceeds following three steps. First, a system of demand equations and reduced form inverse demand equations are estimated. In the second step, the results of the first step are used to simulate the effect of biofuel supply expansion, due to policies (mandates, tax credit…), on the prices of vegetable oils and sugar in United States, European Union, and Brazil. Finally, the new

Texas Tech University, Turker Dogruer, March 2016 prices are used to estimate the welfare effects of these policies. In the first section of the chapter, the conceptual framework is presented. The second section presents the empirical model. Finally, the chapter concludes by discussing the data sources and estimation issues.

4.1 Conceptual Framework

The starting point of the model is a consumer demand function facing the industries in question

(vegetable oil industry, sugar industry) and given by:

Qi = f (pi; p j; Z;w); (4.1)

where pj is the price of item i, and pi is a vector of prices of related goods, w is income and Z is a vector of demand shifters.

Economic theory tells us that the demand for that input, usually called derived demand, is determined or derived from consumer demand for the final good. For instance, the demand for corn, used as an input by the vegetable oil industry and the biofuel industry, is derived from the demand of corn and the demand of ethanol. That is, the demand of corn depends on the price of corn, the price of corn, the price of bio-ethanol, and the production function in each industry.

In each industry, firms choose the quantity of corn to use in order to maximize the profit given by

πj = piqj-TCj(qj), j = 1, 2, (4.2) where j is the index for the industry (Let j = 1 for vegetable oil and j = 2 for biofuel), pj is the price of the output in industry j, qj is the level of output in industry j, and TCj is the total cost in industry j. Assuming firms in each industry produce their output according to the production function

푐표푟푛 qj= (푞푗 ,Wj, pi), (4.3)

Texas Tech University, Turker Dogruer, March 2016

푐표푟푛 where 푞푗 is the quatity of corn used industry j and Wj is a vector of other input factor in industry j. The profit maximization problem becomes

푐표푟푛 푐표푟푛 max πj=pifj(푞푗 ,Wj),-TCj(fj(푞푗 ,Wj)). (4.4)

The optimality condition implies the equity of the marginal revenue product and the marginal resource cost

푐표푟푛 푐표푟푛 휕푓푗(푞푗 ,푊푗) 휕푇퐶푗(푓푗(푞푗 ,푊푗)) pi 푐표푟푛 = 푐표푟푛 . (4.5) 휕푞푗 휕푞푗

Assuming a perfectly competitive input market, the optimality condition becomes

푐표푟푛 휕푓푗(푞푗 ,푊푗) corn pi 푐표푟푛 =p (4.6) 휕푞푗

for ease of presentation, I will use corn as a specific example; however, the analysis could be generalized to any item.

In this study, I prefer to call ethanol the ethanol produced from corn or sugarcane or bio-ethanol.

The derived input demand for corn is given by:

푐표푟푛 corn 푞푗 =gj (pg, p , Zj), j = 1, 2 (4.7) where pcorn is the price the firms in the two industries pay to acquire corn. Assuming a functional form for the production function (e.g. Cobb Douglas), where Zj is a vector of input demand shifters. The aggregate demand for corn by the two industries is then

corn corn corn q =g1(p1,p ,Z1)+g2(p2,p ,Z2). (4.8)

Letting p1 = pcorn and p2 = pethanol, this aggregate corn demand could be written as

Texas Tech University, Turker Dogruer, March 2016

corn q =g(pcorn,pethanol,Z1,Z2). (4.9)

On the other hand, the demand for ethanol could be written in an inverse format as

Pethanol = h (Qethanol, D), (4.10) where D is a vector of variables that shift the inverse demand. Through successive substitutions, one can derive the following reduced form equation that relates the price of corn to the production of ethanol1. That is,

pcorn = m(Qethanol, X), (4.11) where X contains other relevant variables. This relation is very important as it can be used estimate the effect of the expansion of ethanol production on the prices of related food items, specially vegetable oils and sugar.

The demand for corn is also related to the production of ethanol through the following expression

Qcorn=f (pcorn Z) = g (m (Qethanol, X), Z. (4.12)

Using this expression, the effect of the expansion of the ethanol production using corn on the demand for vegetable oil is given by

휕푄 휕푔 휕 푐표푟푛 = . (4.13) 휕푄푒푡ℎ푎푛표푙 휕푚 휕푄푒푡ℎ푎표푙

Having established the relationship between the vegetable oil consumption and the production of ethanol (or biofuel in general), the effect of government policies can be simulated. (Such as mandates, subsidies, and tax credit.) on the consumption of food items such as vegetable oils and sugar. This exercise allows one to evaluate vegetable oils and sugar consumer’s welfare. To do so,

1 According De Gorter and Just (2010), the corn price is very sensitive to a change in the price of ethanol. In turn, the price of ethanol is related to the quantity of ethanol produced and hence there is a link between the price of corn oil and the quantity of corn ethanol produced.

Texas Tech University, Turker Dogruer, March 2016 the estimated demand parameters and simulated price changes are used to compute the compensating variation (CV) given by

1 1 1 0 CV=e (p , V (p , W)) - e (p , V (p , W)), (4.14)

1 1 1 where e (p , V (p , W)) is the expenditure needed to reach the utility level V (p , W) at the new prices, and e (p1, V (p0, W)) is the expenditure needed to reach the old utility, V (p0, W) facing new prices. In other words, CV represents the amount of money consumers would need to receive to be restored to the old level of utility but facing the new prices.

4.2 Empirical Framework

In this thesis, I analyze two groups of food items in three types of countries are analyzed here:

Vegetable oils in United States and European Union countries and sugar in Brazil. The models presented in the conceptual framework section are estimated separately in each country/group of countries. For the United States, I use the Almost Ideal Demand System (AIDS) model of Deaton and Muellbauer (1980) is used to model the demand system for five vegetable oils. Specifically:

퐸푥푝푈푆 푤푈푆 = 훼푈푆 + ∑5 훾푈푆 푙푛푝푈푆 + 훽 ln ( ) + 휖푈푆, 푖, 푗 = 1,2, … ,5 (4.2.1) 푖 푖 푗=1 푖푗 푗 푖 푃푈푆 푖

푃푈푆푞푈푆 where 푤푈푆 is the budget share of vegetable oil I given by 푤푈푆= 푖 푖 , 퐸푥푝푢푠 is the total 푖 푖 퐸푥푝푢푠

2, 푈푆 푈푆 expenditure on vegetable oils 푞푖 is the consumption of vegetable oil I, 푙푛푝푗 is the natural logarithm of the price of vegetable oil i, PUS is the price index given by 푙푛푃푈푆 =

1 훼푈푆 + ∑5 훼푈푆 푙푛푝푈푆 + ∑5 ∑5 훾푈푆 푙푛푝푈푆푙푛푝푈푆 . (4.2.2) 0 푖=1 푖 푖 2 푖=1 푗=1 푖푗 푗 푗

2 Here, I assume that the consumption of these five vegetable oils is assumed to be separable from the consumption of the rest of the food items.

Texas Tech University, Turker Dogruer, March 2016 where 훼푈푆, 훽푈푆, 푎푛푑 훾푈푆are vectors of parameters to be estimated. In addition, the parameters must satisfy the theoretical restriction of adding up3, homogeneity, and symmetry given by:

5 푈푆 5 푈푆 5 푈푆 Adding up: ∑푖=1 훼푖 = 1, ∑푖=1 훽퐼 = 0 , ∑푖 훾푖 = 0, (4.2.3)

5 푈푆 Homogeneity: ∑푗=1 훾푖푗 = 0, (4.2.4)

푈푆 푈푆 Symmetry: 훾푖푗 = 훾푖푗 , (4.2.5)

The uncompensated price elasticities are given by:

훾푈푆 푈푆 푈푆 푈푆 푈푆 푈푆 푖푗 훽푖 훼푖 훽푖 5 푈푆 푈푆 휂푖푗 = −훿푖푗 + 푈푆 − 푈푆 − 푈푆 ∑푖=1 훾푘푗 푙푛푝푘 , (4.2.6) 푤푖 푤푖 푤퐼

푈푆 푈푆 where 훿푖푗 = 1 푖푓 푖 = 1 푎푛푑 훿푖푗 = 0 표푡ℎ푒푟푤푖푠푒 The compensated price elasticities are given by:

푈푆 ∗푈푆 푈푆 푈푆 훽푖 휂푖푗 = 휂푖푗 + 푤푗 (1 + 푈푆). (4.2.7) 푤푖

Finally, the income elasticity is given by:

푈푆 푈푆 훽푖 푒푖 = 푈푆 + 1. (4.2.8) 푤푖

In addition, the following reduced form equations, relating the price of each vegetable oil to the quantity of ethanol consumed, are simultaneously estimated along with the budget share equations.

푈푆 푈푆 푙푛푝푖 = 휆0푖 + 휆1푖푙푛푄푒푡ℎ푎푛표푙 + 푣푖 i=1, 2… 5, (4.2.9) where v is random error term.

For the European Union, five budget share equations are simultaneously with five reduced form equations relating the price of each vegetable oil to the quantity of biodiesel consumed given by:

3 The adding up restriction is generally satisfied by deleting one share equation from the system and using the restrictions to recover the parameters of the deleted equation Here, I assume that the consumption of these five vegetable oils is separable from the consumption of the rest of the food items.

Texas Tech University, Turker Dogruer, March 2016

퐸푈 퐸푈 푙푛푝푖 = 휃0푖 + 휃1푖푙푛푄푏푖표푑푖푒푠푒푙 + Ϛ푖 i=1, 2… 5, (4.2.10) where Ϛ is a random error term.

In the case of Brazil, the following two equations are estimated:

퐵푟푎푧푖푙 퐵푟푎푧푖푙 푙푛푄푠푢푔푎푟 = 훿0 + 훿1푙푛푝푠푢푔푎푟 + 훿2푙푛퐺퐷푃퐵푟푎푧푖푙 + 휇 (4.2.11)

퐵푟푎푧푖푙 퐵푟푎푧푖푙 푙푛푝푠푢푔푎푟 = 휋0 + 휋1푙푛푄푠푢푔푎푟 + 푣 (4.2.12) where and 휇 and v are random error terms.

The effect of expansion of the production of biofuels on the consumption of vegetable oils and sugar is given in an elasticity format by the following equations:

푈푆 푈푆 휕푄푖 푄푒푡ℎ푎푛표푙 5 푈푆 U.S: 푈푆 푈푆 = ∑푗=1 휂푖푗 휆1푗 (4.2.13) 휕푄푒푡ℎ푎푛표푙 푄푖

퐸푈 퐸푈 휕푄푖 푄푏푖표푑푖푒푠푒푙 5 퐸푈 E.U: 퐸푈 퐸푈 = ∑푗=1 휂푖푗 휃1푖 (4.2.14) 휕푄푏푖표푑푖푒푠푒푙 푄푖

퐵푟푎푧푖푙 퐵푟푎푧푖푙 휕푄푠푢𝑔푎푟 푄푒푡ℎ푎푛표푙 Brazil: 퐵푟푎푧푖푙 퐵푟푎푧푖푙 = 훿1휋1 (4.2.15) 휕푄푒푡ℎ푎푛표푙 푄푠푢𝑔푎푟

The welfare effects of the policies aiming at expanding the production of biofuels. Using a second order Taylor approximation of the compensating variation, the CV is given by

푈푆 푈푆 푈푆 푈푆 ∗푈푆 푄푖 푈푆 2 US: 퐶푉푖 ≈ 푄푖 ∆푝푖 + 0.5 × 휂푖푖 푢푠 (∆푝푖 ) (4.2.16) 푃푖0

퐸푈 퐸푈 퐸푈 퐸푈 ∗퐸푈 푄푖 푈푆 2 EU: 퐶푉푖 ≈ 푄푖 ∆푝푖 + 0.5 × 휂푖푖 푢푠 (∆푝푖 ) (4.2.17) 푃푖0

퐵푟푎푧푖푙 퐵푟푎푧푖푙 퐵푟푎푧푖푙 퐵푟푎푧푖푙 푄푠푢𝑔푎푟 퐵푟푎푧푖푙 2 Brazil: : 퐶푉푖 ≈ 푄푖 ∆푝푖 + 0.5 × 훿1 퐸푈 (∆푝푠푢푔푎푟 ) (4.2.18) 푃푖0

Welfare changes are explored as follows: it should be noted that S1 is the supply curve

before the price changes and S2 is the shift in supply curve after the price changes. Before

any changes in the welfare, the consumer surplus consists of sections A, B, C, and D shown

in the graph. The supplier surplus is F and G. After the change in welfare, due to the

Texas Tech University, Turker Dogruer, March 2016 changes in price, consumer surplus shrank and reduced to A (Consumers losses B, C and

D). The supplier surplus increased, and sections B and C were added to the supplier surplus.

Sections D and G on the other hand, are the dead weigh lost for the economy. This can be illustrated by the US corn prices changes and other commodities used in this study. For example the US corn welfare loss is 472 million dollars due to changes in price of corn.

P2 S1 B

C D P1

G F

Q Q1 Q2

Texas Tech University, Turker Dogruer, March 2016

CHAPTER 5 RESULTS 4.1 Estimation Results

The LA-AIDS model results for United State, Brazil, and Europe. Table 5.1, 5.2 and 5.3 indicates the parameter estimates for price (gij ) for three region. Highlighted estimates are statistically significant at the α=0.10 level or better.

Table 5.1

Parameter Estimates for price (LA-AIDS) Model (Europe)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel oil oil oil

Corn -0.020344 -0.1597 0.058454 0.084005 -0.00597 -0.00001

Rapeseed oil 0.005297 -0.08482 0.096501 -0.02081 0.001075 -6.93E-6

Soybean oil 0.004157 0.5378 -0.24298 -0.30516 0.012012 0.000038

Sunflower oil -0.00714 -020982 0.062663 -0.161327 -0.00637 -9.47E-6

Ethanol 0.000732 0.130955 -0.07347 -0.06255 -0.000698 -1.48E-6

Biodiesel -0.02339 -0.04401 -0.06446 0.130429 -0.0012 -0.00001

Table 5.2

Parameter Estimates for price (LA-AIDS) Model (United States)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel oil oil oil

Corn -0.08085 0.03326 -0.12573 -0.01493 0.02686 -3.24E-6

Rapeseed oil -0.00857 -0.072552 -0.06717 0.00377 -0.00067 1.824E-6

Soybean oil -0.0654 -0.03453 -0.125506 0.020084 -0.04623 -0.00002

Sunflower oil 0.007773 -0.00493 -0.01612 -0.0065 0.007276 -6.11E-7

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Ethanol 0.005533 -0.02733 -0.01513 0.020025 -0.016225 4.467E-6

Biodiesel -0.02019 -0.02383 0.024628 -0.01013 0.028159 -0.00003

Table 5.3

Parameter Estimates for price (LA-AIDS) Model (Brazil)

Commodities Ethanol Sugar Cane

Ethanol 0.251484 0.283709

Sugar Cane -0.21728 0.283709

Table 5.4, 5.5, and 5.6 indicates the parameter estimates for income (bi) and constant coefficient

(ai )of LA-AIDS model. Similarly Tables 3 and 4 indicate the parameter estimates for price

(gij )income (bi) and constant coefficient (ai) for the Full-AIDS model.

Table 5.4

Parameter Estimates for income and constant coefficient (LA-AIDS) Model (Europe)

Parameters Estimations Parameters Estimations

A1 0.423741 B1(INC) -0.05615 A2 -0.18418 B2(INC) 0.023245 A3 0.284431 B3(INC) 0.000564 A4 0.474066 B4(INC) 0.032088 A5 -0.0012 B5(INC) -0.00016 A6 0.000158 B6(INC) -0.00002

Texas Tech University, Turker Dogruer, March 2016

Table 5.5

Parameter Estimates for income and constant coefficient (LA-AIDS) Model (United States)

Parameters Estimations Parameters Estimations

A1 0.131404 B1(INC) -0.02139 A2 -1.08119 B2(INC) 0.103722 A3 2.27798 B3(INC) -0.11763 A4 0.155394 B4(INC) -0.01897 A5 -0.49123 B5(INC) 0.055087 A6 0.0003334 B6(INC) -0.00003

Table 5.6

Parameter Estimates for income and constant coefficient (LA-AIDS) Model (Brazil)

Parameters Estimations Parameters Estimations

A1 1.116259 B1(INC) 0.083027

A3 -0.52339 B3(INC) -0.01265

Elasticities of income and price for LA-AIDS model is compared following tables. Table 5.7,

5.8, and 5.9 compares the compensated and uncompensated elasticities of the two models for each countries and Table, 5.10, 5.11, and 5.12 compares the income elasticities of the LA-AIDS model for three countries.

Table 5.7.1

LA-AIDS Model Uncompensated (Marshallian) price elasticity (Europe)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel

oil oil oil

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Corn -1.1419 -14.7706 6.514449 9.6664 -0.48501 0.041669

Rapeseed oil -1.25863 0.20332 -0.07227 0.002022 -0.00048

Soybean oil -2.45336 -1.50569 0.059226 0.000174

Sunflower oil -0.59165 -0.01931 -0.00084

Ethanol -0.6358 -0.00011

Biodiesel -2.7373

Table 5.7.2

LA-AIDS Model compensated (Hicksian) price elasticity (Europe)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel

oil oil oil

Corn -1.0970 -16.4974 5.72160 8.32763 -0.4919 0.04163

Rapeseed oil -0.7183 0.416962 0.288476 0.00388 -0.0005

Soybean oil -1.99562 -1.16271 0.060993 0.000182

Sunflower oil -0.19832 -0.01738 -0.00083

Ethanol -0.60168 -0.0001

Biodiesel -2.7432

Table 5.8.1

LA-AIDS Model compensated (Marshallian) price elasticity (US)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel oil oil oil

Corn -0.9665 -0.04387 -0.68422 -0.21192 0.039244 -0.07522

Rapeseed oil -0.00511 -2.42206 -0.12643 0.413887 -0.00095

Texas Tech University, Turker Dogruer, March 2016

Soybean oil -0.90158 0.048651 -0.05338 0.003983

Sunflower oil -0.76291 -0.14154 0.041507

Ethanol -0.555261 -0.1019

Biodiesel -0.9498

Table 5.8.2

LA-AIDS Model compensated (Hicksian) price elasticity (US)

Commodities Corn Rapeseed Soybean Sunflower Ethanol Biodiesel

oil oil oil

Corn -0.9064 -0.03698 -0.12596 -0.19629 0.05562 -0.0752

Rapeseed oil -0.22559 -0.8998 -0.08339 0.458541 -0.0009

Soybean oil -0.18679 0.068655 -0.03242 0.004006

Sunflower oil -0.76382 -0.14249 0.041506

Ethanol -0.640697 -0.1018

Biodiesel -0.9499

Table 5.9

LA-AIDS Model compensated and uncompensated price elasticity (Brazil)

Uncompensated LA-AIDS Compensated LA-AIDS

M11 (Ethanol) -0.60937 H11(Ethanol) -0.081793

M33(Sugar C) -0.434383 H33(Sugar C) -0.606539

M13 -0.15633 H13 -0.053712

Texas Tech University, Turker Dogruer, March 2016

As it can be seen in Table 7, Europe uncompensated (Marshallian) and compensate

(Hicksian) own price elasticities in LA-AIDS model are negative and most of them significant expect soybean oil and ethanol, and that is, what is expected to be seen.

From Table 8, it can be observed that LA-AIDS model for United States show uniform results in terms of both, compensated and uncompensated own price elasticities, being negative and significant except for the rapeseed oil and biodiesel. Table 9 indicates that Brazil has similar compensate (Hicksian) and Uncompensated (Marshallian) own price elasticities as both, Europe and United States. Own price elasticities are negative and that is as it was expected.

Looking at the two countries (United State and Brazil) and Europe alone, the own price elasticities, for Europe, as it can be observed, many of the combinations for compensated and uncompensated cross price elasticities are negative, but not significant except the cross price elasticities for sunflower oil and ethanol. This indicates that sunflower oil and ethanol is complement to each other. After all, results of cross price elasticities in Europe, do not show significant effect on price except one for Hicksian and Marshallian.

Further estimation reveals that, the compensate and uncompensated elasticities shows that in the United States, cross price elasticities are generally negative and not significant, but cross price elasticities for rapeseed oil and soybean oil, corn and sunflower seed oil, and also ethanol and biodiesel are negative and statistically significant . This indicates that these six commodities are complement to each other, respectively. With respect to compensated

(Hicksian) demand, Brazil has negative cross price elasticities, which means goods are complement for each other.

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Table 5.10

Income Elasticities for LA-AIDS Model (Europe)

Corn Rapeseed Soybean Sunflower Ethanol Biodiesel

Oil Oil Oil

LA-AIDS -3.9099 1.053586 1.001685 1.093715 0.915819 -1.90975

Table 5.11

Income Elasticities for LA-AIDS Model (United States)

Corn Rapeseed Soybean Sunflower Ethanol Biodiesel

Oil Oil Oil

LA-AIDS 0.737059 1.966347 0.945774 0.061073 3.68506 -0.37067

The income elasticities (Table 5.10, 5.11) in the LA-AIDS model for the four commodities

(rapeseed, soybean, sunflower, and ethanol) are positive which measures the percentage change in demand caused by a percent change in income. The positive income elasticities are clear indications that these four good are considered a normal good. Apart from that corn and biodiesel has negative income effect that shows these two commodities are inferior good.

Table 5.12

Income Elasticities for LA-AIDS Model (Brazil)

Ethanol Sugarcane

LA-AIDS 1.136526 0.931539

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Similarly, Table 12 shows the income elasticities for both commodities are positive and so normal good

As it can be seen in Table 7, Europe uncompensated (Marshallian) and compensate

(Hicksian) own price elasticities in LA-AIDS model are negative and most of them significant expect soybean oil and ethanol, and that is, what is expected to be seen.

Further estimation reveals that, the compensate and uncompensated elasticities shows that in the United States, cross price elasticities are generally negative and not significant, but cross price elasticities for rapeseed oil and soybean oil, and also ethanol and biofuel are negative and statistically significant . This indicates that these four commodities are complement to each other, respectively. With respect to compensated (Hicksian) demand, Brazil has negative cross price elasticities, which means goods are complement for each other.

Texas Tech University, Turker Dogruer, March 2016

According to best Choice, the best fit model for this data can be based on various parameter estimates. One of widely used criteria is assessing the fit regression models on the basis of RMSE

(Root mean square errors) and R-square values. Below are the results for RMSE, R-square and adjusted R-square. These results are obtained from the LA-Aids model that was mentioned above. The results from the LA-Aids are from the analysis of demand for six commodities that are shown in the table below.

Table 5.13

R-Square values for LA-AIDS Model (Europe)

Parameters LA-AIDS

W1 (Corn) 0.9862

W2 (Rapeseed Oil) 0.9616

W3 (Soybean Oil) 0.6811

W4 (Sunflower Oil) 0.9692

W5 (Ethanol) 0.8739

W6 (Biodiesel) 0.9572

Table 5.14

R-Square values for LA-AIDS Model (United States)

Parameters LA-AIDS

W1 (Corn) 0.9512

W2 (Rapeseed Oil) 0.8880

W3 (Soybean Oil) 0.8925

W4 (Sunflower Oil) 0.7872

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W5 (Ethanol) 0.9187

W6 (Biodiesel) 0.9296

Table 5.15

R-Square values for LA-AIDS Model (Brazil)

Parameters LA-AIDS

W1(Ethanol) 0.8342

W2(Sugar cane) 0.7120

Table 5.16

Adj R-Square values for LA-AIDS Model (Europe)

Parameters LA-AIDS

W1 (Corn) 0.9823

W2 (Rapeseed Oil) 0.9482

W3 (Soybean Oil) 0.5695

W4 (Sunflower Oil) 0.9584

W5 (Ethanol) 0.8298

W6 (Biodiesel) 0.9422

Table 5.17

Adj R-Square values for LA-AIDS Model (United States)

Parameters LA-AIDS

W1 (Corn) 0.9372

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W2 (Rapeseed Oil) 0.8488

W3 (Soybean Oil) 0.8549

W4 (Sunflower Oil) 0.7127

W5 (Ethanol) 0.8902

W6 (Biodiesel) 0.9049

Table 5.18

Adj R-Square values for LA-AIDS Model (Brazil)

Parameters LA-AIDS

W1(Ethanol) 0.8054

W2(Sugar cane) 0.6619

Table 5.19

RMSE (Root Mean Square Error) values for LA-AIDS Models (Europe).

Parameters LA-AIDS

W1 (Corn) 0.00453

W2 (Rapeseed Oil) 0.0298

W3 (Soybean Oil) 0.0290

W4 (Sunflower Oil) 0.0187

W5 (Ethanol) 0.000730

W6 (Biodiesel) 3.153E-6

Texas Tech University, Turker Dogruer, March 2016

Table 5.20

RMSE (Root Mean Square Error) values for LA-AIDS Models (United States).

Parameters LA-AIDS

W1 (Corn) 0.00559

W2 (Rapeseed Oil) 0.060

W3 (Soybean Oil) 0.0240

W4 (Sunflower Oil) 0.00514

W5 (Ethanol) 0.00662

W6 (Biodiesel) 3.756E-6

Table 5.21

RMSE (Root Mean Square Error) values for LA-AIDS Models (Brazil).

Parameters LA-AIDS

W1(Ethanol) 0.0459

W2(Sugar cane) 0.0439

4.1.2 Welfare Analysis

Now the question becomes that, to be able to produce more biofuel and ethanol, how much would the selected regions have to pay the residents to keep them as well of as they were before in terms of vegetable oil consumption? We can expect that any price change on the six commodities would affect the welfare of the individual, and as a result of the price change of these commodities, it would cause the individuals to be worse off. How much worse off? There is only one way that allows us to answer this question, and that is, how much we have to compensate individuals to move the new budget constrain to take individuals back to where they

Texas Tech University, Turker Dogruer, March 2016 were originally. Table 22 shows the results of the welfare measures. These results provide a monetary measures of the welfare effects of the four vegetable oils, biofuel and ethanol prices.

From the results, we can see how much money is needed to be given to the effected consumers to bring them to where they were before. The unit for the monetary measures in table 22 is the

U.S dollar.

Table 5.22

Compensating Variation Estimation

United States Europe Brazil

Corn -1.18644 -3162.7 NA

Rapeseed Oil -3.08743 -0.41213 NA

Soybean Oil -113.789 -0.0191 NA

Sunflower Oil -2.94641 -6.60434 NA

Ethanol -0.46437 -0.49184 -0.60373

Biodiesel -0.91516 0.905246 NA

Sugar NA NA -0.83298

Table 5.22 illustrates the results for the CV resulted from the price changes. Also, the total welfare loss can be seen in table 5.23, where Unites states incurred approximately 702.1 million dollars total welfare lost due to changes in the corn price. Other welfare losses include approximately 742.5 million dollars due to changes in rapeseed oil prices, 1008.7 million dollars in soybean oil, 1416.7 million dollars sunflower, 4304.7 million dollars ethanol, and 567.9 million dollars biodiesel due to changes in price. Results of the CV for other region in the study show similarly welfare loss for all commodities. The results generated by this analysis are consistent with the previous studies, estimating the social cost of using corn for ethanol production. For example, studying

Texas Tech University, Turker Dogruer, March 2016 the impact of ethanol production in the context of social cost, Taylor (2009) estimated the social cost for the US to be in the range of $472 to $952 million. The results of this analysis are illustrated in table 5.23.

Table 5.23

Total Welfare Loss US (Million Dollars)

United Corn Rapeseed Soybean Sunflower Ethanol Biodiesel Sugar Oil Oil Oil States

Period 2 $702.1 $742.5 $1008.7 $1416.7 $4304.7 $567.9 NA

Table 5.23.1

Total Welfare Loss EU (Million Dollars)

Europe Corn Rapeseed Soybean Sunflower Ethanol Biodiesel Sugar Oil Oil Oil

Period 2 $420.6 $504.5 $946.6 $435.2 $245.1 $758.2 NA

Table 5.23.2

Total Welfare Loss EU (Million Dollars)

Brazil Ethanol Sugar

Period 2 $293.3 $564.1

4.2 Conclusion

This research examines the demand for different vegetable oils and the welfare consequences of the price changes in Europe, United States, and sugar canes and ethanol in

Texas Tech University, Turker Dogruer, March 2016

Brazil. Firstly, we want to investigate vegetable oils demand in Europe, United States, and sugar demand in the U.S and Brazil at the consumer level. This is done by LA-AIDS model that provided own price elasticities, cross price elasticities, and income effect for vegetable oils categories and biofuel in three different regions. Secondly, we analyze the welfare effect of biofuel production expansion on consumer behavior. In order to understand this effect, compensate variation is calculated and through this, it is also indicated how much consumers are needed to be compensated for what they lost and be at what they were before the effect.

The hypothesis is that the price of vegetable oils and sugar depends on the quantity biofuel production. Our expectations were that the quantity demanded of vegetable oils depended on the quantity produced of biofuel. And the price of each vegetable oils depends on the quantity of biofuel as well. That is, we expected that the prices of vegetable oils will rise with the increase of biofuel production.

Looking at the results, they indicate that vegetable oils demand in three region will effect consumer level not significantly for most of commodities. It is also shown that own price elasticities in LA-AIDS model for two countries and Europe are negative, and statistically significant, and that is what was expected to be seen. However, Most of the cross price elasticities for biofuel and vegetable oils are negative but, statistically not significant. Results of the cross price elasticities for the sugar cane and ethanol in Brazil were positive but not significant. On the other hand, the own price elasticities were found to be negative and significant, which was expected.

According to the CV results, Unites states experienced approximately 702.1 million dollars total welfare lost due to changes in the corn oil price. Other welfare losses include, approximately 742.5 million dollars due to changes in rapeseed oil prices, 821 million dollars in

Texas Tech University, Turker Dogruer, March 2016 soybean oil, 1008.7 million dollars sunflower oil million dollars in soybean oil, 1416.7 million dollars sunflower, 4304.7 million dollars ethanol, and 567.9 million dollars biodiesel due to changes in price due to changes in price. The CV results for other regions in the study show similar trend of welfare loss for all commodities that were analyzed in this study and are shown in table 5.22.

Texas Tech University, Turker Dogruer, March 2016

5.1 Appendix

LA-AIDS Model (United States)

Uncompensated LA-AIDS Compensated LA-AIDS

M11 -0.96656* H11 -0.90064*

M22 -0.005113 H22 -0.22559

M33 -0.90158* H33 -0.18679*

M44 -0.76291* H44 -0.76382*

M55 -0.555261 H55 -0.640697

M66 -0.94984* H66 -0.94985*

M12 -0.04387 H12 0.036989

M13 -0.68422 H13 -0.12596

M14 -0.21192* H14 -0.19629*

M15 0.039244 H15 0.05562

M16 -0.07522 H16 -0.075215

M23 -2.42206* H23 -0.89980*

M24 -0.126345 H24 -0.08339

M25 0.413887 H25 0.458541

M26 -0.00095 H26 -0.0009

M34 0.048651* H34 0.068655

M35 -0.05338* H35 -0.03242

M36 0.003983 H36 0.00406

M45 -0141154 H45 -0.14249

M46 0.041507 H46 0.0.04150

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M56 -0.10196* H56 -0.10186*

*indicates significance at 5% level

LA-AIDS Model (Europe)

Uncompensated LA-AIDS Compensated LA-AIDS

M11 -1.14196 H11 -1.09704

M22 -1.25863* H22 -0.7183*

M33 -2.45336 H33 -1.99562

M44 -0.59165* H44 -0.19832*

M55 -0.6358 H55 -0.60168

M66 -2.73738* H66 -2.74325*

M12 -14.7706* H12 -16.4974*

M13 6.514449 H13 5.721602

M14 9.6664 H14 8.32763

M15 -0.48501 H15 -0.4919

M16 0.041669 H16 0.041639

M23 0.20332 H23 0.416962

M24 -0.07227 H24 0.288476

M25 0.002022 H25 0.00388

M26 -0.00048 H26 -0.00047

M34 -1.50569 H34 -1.16271

M35 0.059226 H35 0.060993

M36 0.000174 H36 0.000182

M45 -0.01931 H45 -0.01738

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M46 -0.00084* H46 -0.00083*

M56 -0.00011 H56 -0.0001

*indicates significance at 5% level

Parameter Estimates for price LA-AIDS Model (Brazil)

Uncompensated LA-AIDS Compensated LA-AIDS

M11 -0.60937* H11 -0.081793*

M33 -0.434383 H33 -0.606539

M13 -0.15633 H13 -0.053712

*indicate significance at 5% level

Texas Tech University, Turker Dogruer, March 2016

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