Biofuels policies and fuel demand elasticities in Brazil

Energy Policy 128 (2019) 296–305

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Biofuels policies and fuel demand elasticities in Brazil a,⁎

b

c

c

Leonardo C.B. Cardoso , Maurício V.L. Bittencourt , Wade H. Litt , Elena G. Irwin a b c

T

Department of Rural Economics, Federal University of Viçosa (DER/UFV), Brazil Graduate Studies Program in Economic Development, Universidade Federal do Paraná (PPGDE/UFPR), Brazil Department of Agricultural, Environmental and Development Economics, The Ohio State University (OSU/AEDE), USA

ARTICLE INFO

ABSTRACT

JEL: Q41 Q4 C26

Biofuels are often seen by policymakers as solutions to concerns about the environment, energy diversification, and rural development. To understand the impacts of biofuel policy, however, it is important to understand demand elasticities. Brazil, a leader in biofuels, provides a unique setting to increase our knowledge about biofuel policy and the interactions within and between the gasoline and ethanol markets. We estimate own-price, cross-price, and income elasticities of the demand for ethanol and gasoline using a novel instrumental variable approach to control for the inherent endogeneity between supply and demand. This results in own-price elasticities for both fuels higher than previous literature suggests: approximately − 0.9 for gasoline and − 1.5 for ethanol. Income elasticities for both fuels are approximately 0.8. We also examine the elasticity impacts following the introduction of flex-fuel cars into the Brazilian market. By estimating the model with over 100 subsamples across time, we find that cross-price elasticities become positive, significant, and increasing, but only after larger market penetration of flex-fuel cars, which occurred approximately three years after their introduction.

Keywords: Ethanol Gasoline Demand Biofuels Instrumental Variables Endogeneity

1. Introduction Biofuels are often described as a solution to many of the world's most pressing challenges including climate change and energy security. Brazil, a leader in biofuel production and policies, provides an ideal setting to develop our understanding of the impacts of biofuels. Brazil began implementing biofuel policies as early as the 1970 s in efforts to achieve two primary goals. First, the country wanted to reduce their dependence on foreign oil, especially after a 500% spike in oil prices which followed the international oil crisis in 1973–74. Importing less fuel also would improve the country's trade balance. Second, the policies aimed to stimulate rural development. In particular, biofuels provided a second source of demand for sugarcane producers. As international oil prices receded in the 1980s, however, interest in biofuel policy diminished in Brazil and elsewhere.1 A new age of high

oil prices in the 2000s has renewed interest in biofuels policies once again, this time amid new concerns. Now, oil prices are more volatile than in the past and there is a growing interest in the environment and renewable energy. Food prices have risen, pitting the use of agricultural commodities for food against their use for energy. It is unsurprising, then, that policy interest in biofuels has returned, including in the United States and the European Union (OECD-FAO, 2013). Even within biofuels, there are debates about the environmental impacts of different fuel sources. Ethanol from sugarcane is superior to ethanol from corn or sugar beets in relation to the environmental footprint and the production costs. Table 1 shows examples of these advantages: ethanol from sugarcane has an energy balance2 six times larger than the energy balance from corn, and whereas CO2 emissions from sugarcane ethanol are 84% lower than CO2 emissions from gasoline, whereas corn ethanol emissions are only 30% lower (Goldemberg and Guardabassi, 2010). Regarding cost advantages.

Correspondence to: Campus Universitário, Departamento de Economia Rural, CEP: 36570.900, Viçosa, MG, Brazil. E-mail addresses: [email protected] (L.C.B. Cardoso), [email protected] (M.V.L. Bittencourt), [email protected] (W.H. Litt), [email protected] (E.G. Irwin). 1 Many countries including Argentina, Costa Rica, Malawi, Sweden, and Zimbabwe adopted similar public policies aimed at reducing oil dependence after the oil shocks in 1970s, followed by a decreased interest in the 1980s once oil prices fell. For more details see Johnson and Silveira (2014). 2 Energy balance is defined as the ratio between the energy contained in a given amount of ethanol and the fossil fuel required to grow, process, and transport the raw material and finished product (Goldemberg and Guardabassi, 2010). ⁎

https://doi.org/10.1016/j.enpol.2018.12.035 Received 1 August 2017; Received in revised form 19 December 2018; Accepted 21 December 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.

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Table 1 Ethanol features by different crops.

a

Energy Balance Production Cost (euro/100 liters) CO2 Reduction compared to gasolineb Production(liters/hectare)

Sugarcane (Brazil)

Corn (US)

Sugarbeet (Europe)

8.1–10 14.48 84% 6741

1.4 24.83 30% 4182

2.0 52.37 40% 5510

Source: (Goldemberg and Guardabassi, 2010) and (Macedo et al., 2008). Notes: (a) Energy

Balance =

Renewable Energy Output The Fossil Fuel Energy Input

total of fossil fuel energy accounting for production, harvesting and transportation. Therefore, with one

liter of fossil fuel in Brazil it is possible to produce around six times more ethanol than in the US. (b) Using life-cycle greenhouse gas emissions.

Additionally, Brazilian ethanol is more cost effective than ethanol from corn in US.3 Regardless of the goal of a biofuel policy, it is important to understand the elasticities in the markets for the impacted fuels. If a policy aims to reduce gasoline4 consumption to mitigate negative externalities like pollution, for example, policymakers must first understand whether gasoline demand is affected by price and, second, whether gasoline prices are the best prices to be changed. As such, an understanding of own-price and cross-price elasticities is important to develop public policies. Estimating elasticities, however, is problematic due to the simultaneity between supply and demand, and this endogeneity problem tends to create a bias in elasticity estimates (Davis and Kilian, 2011; Coglianese et al., 2017). Many techniques have been used in the literature to address this endogeneity problem, the most common of which is the Instrumental Variables (IV). Oil prices are a common instrument for gasoline prices and are most commonly used in time series estimation approach. When such estimations are based on panel data, however, a location-invariant instrument like oil prices is redundant. The same applies when to using sugar prices as an instrument for ethanol's demand. In this study we use a novel approach from the literature to construct retail price instruments with wholesale prices of ethanol, gasoline, and diesel; however, we follow the recent literature (Liu, 2014), by excluding the prices of neighboring states to avoid endogeneity issues that could be caused by demand-side factors, like border-state consumption, and by supply-side factors like cost correlations among states. The goal of this paper is to estimate the elasticities of gasoline and ethanol, focusing on the changes caused by the introduction of flex-fuel cars in Brazil in 2003. Flex-fuel cars created an arbitrage opportunity for consumers between gasoline and ethanol, therefore, we expect to find evidence of greater demand substitution between these fuels. We also hypothesize that the own-price and cross-price elasticities of ethanol and gasoline in Brazil increase in magnitudes after the introduction of flex-fuel cars. We investigate these hypotheses using monthly data from July 2001 to December 2014. This study makes several important contributions. First, we analyze the effect of an important policy change - the introduction of flex-fuel cars in Brazil - on price elasticities over time. Second, we make improvements upon the estimation of price elasticities using appropriate IVs to control for price endogeneity. Our IV approach results in elasticity estimates that are larger than previous estimates in the literature. Ethanol's own-price and cross-price demand elasticities are approximately −1.5 and 0.5,

respectively. Likewise, gasoline own-price and cross-price demand elasticities are approximately −0.9 and 0.2, respectively. Both demands have an income elasticity of approximately 0.8. Demand elasticities are a measure of the responsiveness of the quantity demanded to a change in an independent variable, so the −1.5 result in price ethanol demand indicates that an increase of 1% in ethanol's price reduces the demanded quantity in 1.5%. Third, we examine the impact of the introduction of flex-fuel cars using time subsamples and find evidence that this event shifted the demands for both gasoline and ethanol, increasing substitution between the fuels, as indicated by increased cross-price elasticities. This increased substitution has positive implications for consumer welfare, given that consumers are now less susceptible to a price increase in either market. The rest of this paper is organized as follows. Section 2 reviews the literature on biofuels and elasticity estimation, and provides a background on the demand for light fuels in Brazil. Section 3 discusses our estimation strategy and the instrumental variable approach we use to solve the endogeneity challenges of properly estimating the demands for ethanol and gasoline. Section 4 provides estimates for the demand elasticities and examines the impacts of the introduction of flex-fuel cars into the Brazilian market. Finally, Section 5 outlines the significance of our results, discusses relevant policy implications, and provides concluding remarks. 2. Background 2.1. Literature review Many studies examining the demand for light fuels show that gasoline is an inelastic good in the short and long-run. Elasticity tends to be larger in the long-run because there is a broader range of adjustment possibilities over a longer time horizon. These empirical results are in line with microeconomic theory since fuels have fewer alternatives in the short-run and more in the long-run. For example, if there is an unexpected and permanent increase in the price of gasoline, consumption in the following days likely would not change significantly. In the longer-run, however, consumers would rethink transportation strategies or residential location decisions, generating larger long-run changes to demand. Microeconomic theory also posits that the demand for light fuels is modeled using, at a minimum, price and income as explanatory variables. Burnquist and Bacchi (2002) and Cheung and Thomson (2004) only used these two variables in their empirical models. Two important surveys about gasoline demand (Dahl and Sterner, 1991 and (Espey, 1998)) show a large range of econometric techniques used to estimate the demand for light fuels, including time series, cross-sectional, panel data, and instrumental variable approaches. According to these surveys, estimated price elasticities fall between ( 0.12; 0.44) in the short-run and ( 0.23; 1.05) in the long-run. Income elasticities fall between (0.14; 0.58) in the short-run and (0.68; 1.31) in the long-run. For a comprehensive review of other papers' results we constructed a summary which includes elasticities from the United States, Europe, Brazil, and the average results of surveys.

3 Goldemberg and Guardabassi (2010), indicate that Brazilian sugarcane ethanol costs are 58% of ethanol costs from corn in USA, Shapouri and Salassi, show a smaller advantage, where Brazilian costs representing 78% of the costs in USA. Ajanovic and Haas (2014), pointed that projections for 2030 indicate that this advantage will remain. 4 Gasoline in Brazil could be called “gasohol” because it is a blend with 27% of anhydrous ethanol and 73% of gasoline (E27) since Mar/2015. Between 2001 and 2015, it ranged between E20 and E25. For a full description of this changes, see MAPA ().

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Table 2 Price and Income Demand Elasticities for Light Fuels. Referencesa

Local

Time

Fuel Type

Dahl and Sterner (1991)b Espey (1998)b Burnquist and Bacchi (2002) Alves and De-Losso (2003) Roppa (2005) Nappo (2007) Pock (2007)c Hughes et al. (2008) Serigati et al. (2010) Farina et al. (2010)d Farina et al. (2010) Souza (2010)c Souza (2010)c Freitas and Kaneko (2011)e Cardoso and Bittencourt (2013) Santos (2012) Santos (2012)

World World Brazil Brazil Brazil Brazil EU USA Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil

1929–1993 1929–1993 1973–1998 1974–1999 1979–2000 1994–2006 1990–2004 1974–2006 2001–2009 2001–2009 2001–2009 2001–2009 2001–2009 2003–2010 2001–2011 2001–2011 2001–2011

Gasoline Gasoline Gasoline Gasoline Gasoline Gasoline Gasoline Gasoline Ethanol Ethanol Gasoline Gasoline Ethanol Ethanol Ethanol Ethanol Gasoline

Short-run

Long-run

Price − 0.24 − 0.23 − 0.23 − 0.47 − 0.63 − 0.19 − 0.02; − 0.19

Income 0.80 0.30 0.96 0.12 0.16 0.68 0.03; 0.23

− 1.20; − 2.20 − 1.23 − 0.63 − 0.29; − 0.37 − 1.26; − 1.82 − 1.41 − 1.42 − 1.52 − 0.78

− 1.20; − 1.80 0.07; 0.32 0.20; 0.45 0.45 0.55 0.18

Price − 0.45 − 0.43

Income 1.16 0.81

− 0.12; − 0.84 − 0.30; − 0.43

0.16; 0.52 0.47; 0.54

− 1.80 − 3.30 − 8.45 − 1.18

2.82 3.72 0.52

Notes: (a) References are listed by year of publication; (b) These papers are surveys, hence we reported the mean of all studies; (c) Some authors have many estimates; for those, we reported the interval; (d) Income elasticity is not reported because they used average income of workers in conjunction with the unemployment rate and other variables as proxy to the level of economic activity (see Farina et al. (2010), p. 246; (e) (Freitas and Kaneko, 2011) did not find a significant income elasticity.

Results in Table 2 indicate that the Brazilian market usually has larger elasticities than in the U.S. and Europe. These differences are commonly explained by differences in income levels and preferences. One contribution of this paper is exploring the influence that the introduction of flex-fuel cars had in explaining the differences in Brazilian elasticities. There is also substantial variability in elasticity estimates within Brazil, ranging from − 0.29 (Souza, 2010) to − 0.78 (Santos, 2013) in gasoline market, for example. Most of this variation appears to be driven by measuring elasticities in different time periods, where earlier periods had, in magnitude, smaller price elasticities for both fuels. Newer econometric techniques also have a role in this explanation, but we cannot precisely say whether it is an upward or a downward bias. Environmental concerns continue to drive a strong and growing interest in using biofuels as a catalyst for change toward a low-carbon economy, but advances toward clean energy have been constrained by costs. Roughly speaking, we have the following trade-off: clean energy is not cheap and cheap energy is not clean. Ethanol from sugarcane has similar costs compared to gasoline in Brazil. Considering energy balances, however, sugarcane excels. Compared to corn and sugar beets, sugarcane can be produced for biofuels with lower costs, more efficient land intensity, and a better energy balance (Goldemberg and Guardabassi, 2010), (see Table 1). All these characteristics lead us to consider ethanol from sugarcane as a good option for biofuel production.

Fig. 1. Share of flex-fuel vehicles in total fleet in Brazil. Source: Authors, with data from Sindipeças (2017).

mixed into the gasoline, guaranteeing scale to producers; ii) Car subsidies - Taxes on Industrialized Products (IPI)6 are different between flex-fuel cars and gasoline cars. The only situation in which taxes are the same between these cars is when the engine size is 1000cc or less. For cars with larger engines, the IPI tax is higher for pure gasoline cars than for flex-fuel cars, increasing the demand for ethanol; iii) Direct subsidies on fuels - There is a higher tax burden on gasoline than on hydrated ethanol in Brazil (Jales and Costa, 2014). For the state of So Paulo, for example, taxes are roughly 21% of final price for ethanol and 42% for gasoline.

2.2. Light fuels in Brazil Brazil is a unique country for the study of light fuel markets. More than 60% of the Brazilian fleet is composed of flex-fuel cars (see Fig. 1), meaning that they can use ethanol, gasoline, or any blend of these fuels. As such, there is a need to include alternative fuel prices to estimate the demands for both ethanol and gasoline. This highlights the importance of cross-price elasticities when studying light fuel markets in Brazil and other countries with flex-fuel technology. Brazilian policies have resulted in one of the cleanest energy countries in the world. More than 46% of primary energy production came from renewable sources in 2015 (World Bank, 2018), of which 19% of total primary energy came from sugar cane products. Ethanol is competitive with gasoline in Brazil through institutional arrangements which are mainly composed of the following:

Ethanol also is not homogeneously competitive across Brazilian states, however. In Fig. 2 it is possible to see a high dispersion in the fuel price parity (Pe/Pg)5 in our data. This figure plots the monthly average gasoline and ethanol retail price ratios by state for the 27 states in Brazil, for each month from July/2001 to December/2014, in a total of 4374 observations. The bullets in Fig. 2 are organized from the lowest to the

6

Acronym for “Imposto sobre Produtos Industrializados (IPI)” in Portuguese. Owners of flex fuel cars in Brazil use the reference parity between the retail ethanol price (Pe) and retail gasoline price (Pg) to decide which fuel to buy. The 0.7 value of parity indicates the point where consumers are indifferent between both fuels due to energy content differences. 5

i) Government mandates - Even if consumers only buy gasoline, rather than hydrated ethanol, there is still 27% of anhydrous ethanol 298

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Fig. 2. Price Parity (Ethanol Price (Pe)/Gasoline Price (Pg)) in the sample from the lowest to the highest Pe/Pg. Note: 0.7 is the reference line to control for energy content differences between ethanol and gasoline in Brazil. Above the line, it is worthwhile for flex-fuel car owners to buy ethanol; below the line, it is not.

highest Pe/Pg ratio, showing that it is worthwhile the switch from gasoline to ethanol in only about 40% of observations. Distribution of price parities is not random across states either, Fig. 3 illustrates regional differences in relative prices. The variable used is the number of periods in our sample in which it is more worthwhile for flex-fuel car owners to buy ethanol rather than gasoline. The darkest states indicate that ethanol prices are competitive more than 80% of the time, whereas the lightest states indicate ethanol prices are competitive less than 3% of time. These relative price differences also reflect relative consumption. In the northern region, far from the largest producers, ethanol consumption is 1.41% of total consumption, while gasoline is 6.11%. Meanwhile, the state of So Paulo7 a major producer and consumer of fuels, consumes 60% of total ethanol and 27% of total gasoline. In sum, the northern region has gasoline-intensive fuel consumption, while the state of So Paulo has ethanol-intensive consumption.

Fig. 3. The proportion of time that ethanol is more price competitive than gasoline during the entire sample period, by state. In other words, the percentage of periods in which it is more worthwhile for flex-fuel car owners to buy ethanol than gasoline.

instruments. In Eq. (2), a relevant instrument would manifest itself in the 1 term being different than zero in the first stage:

xk =

Achieving equilibrium prices and quantities involves a naturally endogenous cycle between supply and demand. An increase in demand generates higher prices, higher prices increase supply, and a higher supply decreases prices, holding other variables constant. Because price and quantity are equilibrium points on both the supply and demand curves, and since they vary across time, this can result in endogeneity and biased estimates for the supply and demand curves. The most common remedy to this problem is to treat price as an endogenous variable in an instrumental variable (IV) approach, as pointed out by Liu (2014) and Hughes et al. (2008). In a simple equation, endogeneity can be visualized as follows:

E (µ ) = 0, j = 1, 2, …, k

1.

+

1 x1

+

2 x2

+

+

K 1 xK 1

+

1 z1

+ rK

(2)

Eq. (2) is considered the first stage of an IV approach and its estimation should have the following properties: E (rk ) = 0 and rk is uncorrelated with its explanatory variables. In fuel demand estimates using a time series approach, it is common to use oil prices as an instrument for gasoline demand and sugar prices for ethanol demand. Oil prices are one of the most important costs for gasoline and the sugar price is an important component in the opportunity cost of ethanol production. However, instrument choice is highly dependent upon data format. In a panel dataset with time fixed effects, there is no reason to include an instrument that contains no cross-sectional variation. Oil prices, therefore, would be redundant instruments in this context. Liu (2014) argues that prices from nonadjacent states can be used as instruments in a state-level panel, arguing that using these prices avoids the problem that state markets may not be completely separated. This approach is close in spirit to the use of spatially-lagged variables, but in spatial econometrics it is more common to use information from geographically closer states rather than nonadjacent states. We use the wholesale prices of diesel, gasoline, and ethanol from nonadjacent states to construct our Zd , Zg and Ze variables. These instruments are constructed by premultiplying the original wholesale price matrix (27×162) by a Weighting Matrix (W) (27 ×27) and are used in Eqs. (4) ^ and Pe ^ . Note that 27 are the number of states in and (5) to estimate Pg it it Brazil and 162 are the number of months in our sample. W is defined as:

3. Estimating the demand for light fuels

y = 0 + 1 x1 + 2 x2 + + k xk + µ , Cov (xj , µ ) = 0, Cov (xk , µ ) 0 ,

0

(1)

Note that Cov (xk , µ) 0 is not a problem just for k , but for all j . As such, we cannot consistently estimate Eq. (1) using OLS. Using an IV approach, we need at least one valid instrument (z1) for each endogenous variable (Wooldridge, 2010), p. 89. Valid instruments have to hold two primary properties: (i) the instrument is exogenous (Cov (z1, µ )) = 0) ; (ii) the instrument is relevant. Frequently, the practical concern is a trade-off between the relevance and exogeneity of

W = (imij ) where

z ij =

1 if states are not neighbors; 0 if i = j or if states are neighbors.

(3)

Because Brazilian gasoline is a blend of 27% anhydrous ethanol and 73% gasoline, ethanol prices may also be endogenous to gasoline demand. In the absence of a flex-fuel fleet, ethanol is a derived demand from the demand for gasoline. Neglecting this problem leads to biased ethanol prices in the gasoline demand and it might lead to insignificant price parameters. Hence, we estimate gasoline demands using

7 The state of So Paulo is the most populous in Brazil (22% of total), the richest in absolute terms (32.6% of Brazilian GDP), and the second in per-capita income.

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instrumented ethanol prices. There is no contemporaneous effect of ethanol demand into gasoline prices because ethanol is not a blend. In short, we treat own prices as endogenous in both demands, and the ethanol price as endogenous in gasoline demand. FirstStage: lnPgit = g 0 + lnPeit =

e0

+

g1 lnZjit

e1 lnZjit

+

+

g2 lnIncomeit

e2 lnIncomeit

Gasoline Second Stage: ^ + lnQgit = g 0 + g1lnPg it

^

g 2lnPeit

Ethanol Second Stage: ^ + lnQeit = e0 + e1lnPe it

+

+

+

g3 lnFleetit

e3 lnFleetit

g 3 lnIncomeit

e 2 lnPgit

+

+

+

+

g 4 lnPgit 1

e 4 lnPeit 1

+

g 4 lnFleetit

e3 lnIncomeit

+

+

+

(4)

git

(5)

eit

(6)

eit

e 4 lnFleetit

that own-prices are endogenous in ethanol and gasoline demand, as expected. We also have an intuition that ethanol price is endogenous in the gasoline demand, so shocks on the demand for gasoline could be transmitted to ethanol prices through the anhydrous ethanol channel. Hence, we also test the endogeneity of ethanol prices on gasoline demand. Another way to achieve the same result is with the control function approach, using the residuals of the first stage Eqs. (4) and (5) in the second stage. If the coefficient of the estimated residuals is different from zero, we also have endogeneity problems (Wooldridge, 2010), p. 130. Using both approaches, ethanol price is endogenous in both demands, and gasoline price is endogenous in its own demand. We expect more elastic own-price and cross-price parameters for ethanol demand than for gasoline. The intuition is that ethanol demand comes from an almost pure flex-fuel fleet,10 while gasoline demand can come from both flex-fuel and gasoline-only cars. Table 4 shows ethanol as an elastic good, which is different from fuel markets in other countries. Nearly all results in other countries, including some surveys, found fuel markets - gasoline in particular - as a price inelastic good (Ep < |1|) . It may be the case that ethanol markets have elastic own-price demands due to the arbitrage opportunities resulting from flex-fuel cars. Due to the log-linear functional form, the coefficients can be interpreted directly as elasticities, so ethanol has elasticities around ( 1.5) for the Brazilian market, which is at the top range of elasticity estimates from the literature dedicated to the study of the Brazilian market, ranging between ( 1.5) and ( 1.2) from the most recent papers (Santos, 2013; Farina et al., 2010; Cardoso and Bittencourt, 2013; Freitas and Kaneko, 2011). Income elasticity estimates are slightly less than one, indicating that an expansion of income has a large impact on ethanol demand. These results are larger than found in the U.S. and Europe. Hughes et al. (2008) (USA) and Pock (2007) (Europe), for example, did not find income elasticities larger than 0.52, even when considering long-run parameters. This is consistent with the intuition that increases in income should have smaller impacts in higher-income countries. Results are robust to the choice Zd , Ze , Zg or a combination of them (column 4 in Table 4), with an exception to e2 for columns 2 and 3 in Table 4, where gasoline wholesale prices of nonadjacent states are not used as instrument. Additionally, gasoline's cross-price elasticity on ethanol demand was approximately one fifth of ethanol's own-price effects (columns 1 and 4 in Table 4), indicating that ethanol demand is more sensitive to its own price than to the price of gasoline. The specification in Column (5) in Table 4 used the same estimator as the previous four columns, but considers ethanol price as exogenous. Not considering endogeneity appears to lead to larger own-price and crossprice elasticities, in magnitude. In the gasoline demand estimation (Table 5), all instruments performed similarly. The main difference is that in gasoline demand we used instruments for both the gasoline price and the ethanol price, due to gasoline containing some portion of anhydrous ethanol.11 We found higher own-price gasoline elasticities in Brazil than in international markets, likely due to a combination between higher arbitrage from flex-fuel cars and a smaller income in Brazil (transportation represents a higher share of income in middle and low-income countries). Comparing our results with other evidence from Brazil, we found parameters close to Santos (2013)(Ep = 0.78) , for example. Comparing both markets, gasoline markets seem to respond less to price. This is an expected result because ethanol demand is almost totally composed by flex-fuel cars - consumers have choice to change fuel type anytime - whereas gasoline demand is only partially flex-fuel. Income elasticity parameters were almost the same for ethanol and

+

eit

(7)

lnPgit and lnPeit are the natural logarithm of real prices of gasoline and ethanol by state in Brazil. lnQgit and lnQeit are the natural logarithm of total amounts of each fuel sold, measured in barrel of oil equivalent. All four variables Pgit , Peit , Qgit and Qeit , prices and quantities, can be found at the National Petroleum, Natural Gas and Biofuel Agency (ANP) website. In Eqs. (4) and (5), j can be g, e and d to represent the instrumental variables created using gasoline, ethanol, and diesel wholesale prices, respectively, of nonadjacent states. The quantities and instruments are also log-transformed. An inflation index is used to transform nominal prices into real prices and it can be found at Brazil's Central Bank website. lnIncome is the are the natural logarithm of amount of taxes from each state (ICMS8). Tax information comes from the Brazilian Treasury's website. Another common proxy for income is electrical consumption, however we chose taxes for two primary reasons. First, electrical consumption is not available at the state level. Second, energy consumption may not be a good proxy for long panels, since technological changes towards energy-savings and environmental concerns decreases the correlation between GDP and energy consumption. It is common to include population as a control for fuel demand, but this is not a good option to capture the real fleet effects in Brazil. Because it is a middle-income country, the relative size of Brazil's fleet has been changing in relation to the population. In 2000 there were 8.4 people/vehicle; 11 years later this ratio was around 4 people/vehicle. To compare, the U.S. has a ratio of approximately 1.25 people/vehicle, behind only Monaco and San Marino.9 ^ are, respectively, the fitted values of lnPg and lnPe in Eqs. ^ and lnPe lnPg it it (4) and (5). The final data set is a panel (NT ) where N equals 27 (the number of states in Brazil) and T equals 162 (the number of months in our sample), resulting in a panel with 4374 observations. Our sample goes from July 2001 to December 2014. Summary statistics can be found in Table 3. 4. Results 4.1. Preliminary Results There are two basic questions used to address potential endogeneity: i) Is there an endogeneity problem? ii) Is there a valid instrument? We attempt to answer first question using the Durbin-Wu-Hausman approach, testing consistency through differences between Ordinary Least Squares (OLS) and Instrumental Variables (IV). Results indicated 8

Abbreviation in Portuguese for Imposto sobre Circulao de Mercadorias e Servios. 9 The data of US, Monaco and San Marino is available at World Bank Database. For Brazil see the National Motor Vehicle and Traffic Department Database (Denatran).

10 Production of cars designed to run solely on ethanol ceased in 2006. They represent less than two percent of the vehicle fleet in 2017. 11 Gasoline in Brazil is a blend containing 27% anhydrous ethanol in the most of our sample.

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2005m12) went from zero to 10.8% of total cars. At the end of the second period (2010m6) flex-fuel cars reached 37.4%, and at the end of our sample, this proportion was approximately 54%. The evolution of flex-fuel fleet in Brazil can be seen in the Fig. 1. Another important event in 2006 was that flex-fuel car production exceeded the production of gasoline cars. Table 6 shows the results of the estimated coefficients after re-parametrization. Ethanol's own-price and cross-price elasticities had a large increase from period one (2001m1-2005m12) to period two (2006m1-2010m6). Another important result is that in period one and period two, the crossprice coefficient of ethanol demand shows an insignificant result, but in the last period it showed positive and significant coefficient. Gasoline demand displayed the same behavior, with a larger increase in own-price elasticities from the first to the second period, but with a slight decrease from the second to the third. Again, the cross-price coefficient was not significant in the first period, but became significant in the following periods. These cross-price elasticity results for both demands have an interesting interpretation. In period one, when flex-fuel cars made up less than 10% of the fleet, the lack of substitution power between the two fuels result in insignificant estimates of cross-price elasticities. In the third period, upon a much larger adoption of flex-fuel cars, the estimates became significant, and with the expected positive signs. As a robustness check for the timing thresholds used in the dummy variables, we also constructed approximately 100 regressions for each demand consisting of consecutive three-year subsamples. Because it is a moving window sample, observations in the first subsample go from 2001m7 to 2004m6; the second goes from 2001m8 to 2004m7, and so on, using Eqs. (4), (5), (6), and (7). With this approach, we can identify when the cross-price elasticities become significant, i.e., the point at which the confidence interval of cross-price elasticities is above zero. This occurs with the subsample starting from 2006m7 for the ethanol demand (Fig. 4) and from 2007m1 for gasoline demand (Fig. 5). Using the significance of the elasticity parameters as a signal of integration, the light fuel markets take approximately three years to achieve integration after the introduction of flex-fuel cars. Figs. 4 and 5 indicate that there is no large variation of income elasticities and that own-price elasticities for both demands increased over time. This increase of own-price elasticities, combined with significant cross-price elasticities demonstrate an increase in consumers’ bargaining power.

Table 3 Summary statistics of the main variables in level. Variable

Obs

Mean

Std. Dev.

Units

Retail Ethanol Price (Pe) Retail Gasoline Price (Pg ) Retail Diesel Price (Pd) Wholesale Ethanol Price Wholesale Diesel Price Wholesale Gasoline Amount Ethanol (Qe ) Amount Gasoline (Qg ) Income Fleet Inflation Index

4374

1.841

0.426

R$

4374

2.525

0.381

R$

4374 4364

1.894 1.553

0.440 0.413

R$ R$

4370 4370 4374 4374 4291 4374 4374

1.551 2.163 103,794.8 468,475.5 688,526.2 2.67e+ 07 1.468

0.397 0.328 332,049.6 707,858.9 1,304,074 5,680,398 0.225

R$ R$ barrel of oil equivalent barrel of oil equivalent R$ number of cars Index (July−2001 = 1)

gasoline demand, indicating that the demand impacts from increases in income would be roughly the same for ethanol and gasoline markets. Not considering the endogeneity in gasoline markets appears to lead to a negative cross-price elasticity, whereas we would expect a positive value. 4.2. Elasticities across time and the role of flex-fuel cars Most of the studies in the literature used log-linear specifications for light fuel demand estimation. In addition to facilitating the direct interpretation of the parameters as elasticities, this specification form also creates the imposition that elasticities are constant for the whole sample. We will relax this assumption, not with a different specification, but by using sub-samples to allow for different own-price and cross-price elasticities over time. We would expect higher cross-price elasticities after the introduction of flex-fuel cars, and we believe that there are mixed effects which result in elasticities that are larger than those found in recent research. First, the older literature does not take endogeneity into account, and will tend to underestimate elasticities. Second, introducing flex-fuel cars increases arbitrage opportunities between both fuels, increasing cross-price and own-price elasticities and making fuel more price sensitive. In order to account for changes in elasticities over time, we interacted own-price and cross-price parameters with time dummies. We used time dummy variables to split our sample into three equal time periods. The proportion of flex-fuel cars in the first period (2001m1-

4.3. Comparing results with literature Our analysis results in larger elasticity estimates, in magnitude, compared to those for the United States and Europe. This is not surprising

Table 4 Ethanol demand estimations using different instruments. (1) Ethanol Price ( Gasoline Price ( Income ( Fleet (

e1)

e2 )

e3)

e4 )

Etanol Price treated as Endogenous Variables used as instruments for ethanol price: lnPeit 1 lnIncome lnFleet lnZg

lnZe lnZd N

− 1.494 * **

(2)

(3)

− 1.561 * **

− 1.358 * **

(4)

(5)

− 1.439 * **

− 2.285 * **

(2.64) 0.789 * **

(13.85) 0.621 * **

(−16.08) 0.296 *

(−17.98) 0.204

(−16.48) 0.029

(−16.36) 0.329 * *

(43.51) 0.541 * **

(40.43) 0.655 * **

(51.53) 0.378 * **

(42.37) 0.606 * **

(33.94) 1.132 * **

Yes

Yes

Yes

Yes

No

Yes Yes Yes Yes

Yes Yes Yes No

Yes Yes Yes No

Yes Yes Yes Yes

No No No No

(2.37) 0.792 * **

(4.69)

No No 4156

(1.69) 0.761 * **

(5.25)

Yes No 4003

(0.24) 0.872 * **

(3.51)

No Yes 4156

(4.90)

Yes Yes 4003

(−31.58) 1.623 * **

(8.92)

No No 4291

Notes: Zg , Ze and Zd are, respectively, gasoline, ethanol and diesel wholesale prices of nonadjacent states; (5) estimation ignored price endogeneity (only for comparison purposes); t statistics in parentheses; First stage is not reported here, but instruments were highly significant; * p < 0.05, * * p < 0.01, * ** p < 0.001. 301

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Table 5 Gasoline demand estimations using different instruments.

Gasoline Price ( Ethanol Price ( Income ( Fleet (

g1) g2 )

g3)

g4 )

Ethanol Price treated as Endogenous Gasoline Price treated as Endogenous Variables used as instruments for gasoline price: lnPgit 1

lnIncome lnFleet lnZg

lnZe lnZd N

(1)

(2)

(3)

(4)

(5)

− 0.962 * **

− 0.937 * **

− 0.958 * **

− 0.894 * **

− 0.906 * **

(−10.71) 0.218 * **

(−10.64) 0.205 * **

(−11.02) 0.199 * **

(−10.45) 0.192 * **

(−11.63) − 0.090 *

(3.92) 0.820 * **

(3.73) 0.825 * **

(3.69) 0.826 * **

(3.55) 0.819 * **

(−1.98) 0.805 * **

(129.25) 0.166 * **

(134.04) 0.164 * **

(135.30) 0.161 * **

(130.74) 0.180 * **

(134.73) 0.280 * **

(3.94)

(4.01)

(4.00)

(4.34)

(7.27)

Yes Yes

Yes Yes

Yes Yes

Yes Yes

No No

Yes

Yes

Yes

Yes

No

No No 4003

Yes No 4003

No Yes 4003

No Yes 4003

No No 4291

Yes Yes Yes

Yes Yes No

Yes Yes No

Yes Yes Yes

No No No

Notes: Zg , Ze and Zd are, respectively, gasoline, ethanol and diesel wholesale prices of nonadjacent states; (4) estimation used two instruments (gasoline and diesel wholesale prices); (5) estimation ignored price endogeneity (only for comparison purposes); t statistics in parentheses; First stage is not reported here, but instruments were highly significant; * p < 0.05, * * p < 0.01, * ** p < 0.001.

These elasticities are higher than those found in the U.S. and Europe; as such, public policies affecting prices could be applied and may be more effective than policies in other parts of the world. ii) Cross-price elasticities are positive and significant in both demands, demonstrating that ethanol and gasoline are substitute goods in the Brazilian market. Any public policy targeting one market, therefore, should take into account spillovers to the other market. It is also clear that this effect occurred after the introduction of flex-fuel cars in 2003. By dividing the data into three time subsamples, we observe cross-price elasticities to be no different from zero in the first subsample, when the flex-fuel fleet represented only a small share of the light vehicle fleet. In the third subsamples, once flex-fuel cars gained significantly more market penetration, cross-price elasticities became significant, and with the expected positive sign. iii) Using a moving window sample, we find that cross-price elasticities became statistically different from zero for ethanol on the subsample initiated on 2006m7, and for gasoline on the subsample initiated on 2007m1. Since flex-fuel cars began to be sold in 2003m5, we conclude that it took approximately three years until cross-price elasticities became significant, due to the small share of flex-fuel cars during that time. As such, introducing flex-fuel cars in Brazil resulted in increased substitution between ethanol and gasoline, but only after a delay. This result emphasizes the importance of considering the timing and potentially delayed effects of public policies. iv) After the introduction of flex-fuel cars, there was an increase in the own-price elasticities in demands for both ethanol and gasoline, indicating that flex-fuel cars increased arbitrage opportunities in the demands for both fuels. v) Accounting for endogeneity generated larger elasticity coefficients for gasoline compared to the previous literature related to the Brazilian market, but similar results for ethanol.Our price elasticity estimations demonstrate how fuel prices respond to shocks. It is possible that the nature of the shock is important for demand responses. For instance, Coglianese et al. (2017) argue that changes in taxes could have a larger effect than changes in costs; in particular, a 10% reduction in demand caused by a shock to the cost of oil would result in a smaller reduction in demand than the same increase driven by taxes. Reasons for this may include the persistence of shocks from taxes, heightened media exposure, and tax aversion by consumers.

Table 6 Price Elasticities Across Time.

Ethanol Price (time - 1) Ethanol Price (time - 2) Ethanol Price (time - 3) Gasoline Price (time - 1) Gasoline Price (time - 2) Gasoline Price (time - 3)

(1)

(2)

Ethanol Demand − 0.624 * ** * (−5.10) − 2.905 * ** (−21.34) − 4.594 * ** (−19.45)

Gasoline Demand 0.083 (1.09) 0.329 * ** (3.82) 0.320 * * (2.83)

− 0.106 (−0.73) 0.131 (0.44) 2.323 * ** (6.87)

− 0.695 * ** (−6.11) − 1.769 * ** (−8.24) − 1.390 * ** (−7.18)

Notes: Intervals: Time 1: 2001m1-2005m12; Time 2: 2006m1-2010m6; Time 3: 2010m7-2014m12. Values reported are final elasticities. Estimations used Eqs. (4), (5), (6) and (7), with the instruments being the specified in column 4 in Tables 4 and 5, for ethanol and gasoline demand, respectively.

because as countries become richer, smaller shares of income are spent on fuel, decreasing price sensitivity. We also estimate our model during similar time periods in relation to previous studies, as seen in Table 7. Regarding income elasticities, we expected larger variations due to different proxies used in each study. For example, electricity consumption and taxes are used in panel estimations and GDP is used in time series. In Fig. 6 we plot the estimates found in the literature against our estimates for the same period. A 45-degree is plotted to be used as reference, so distance to this line indicates differences between our estimations and the ones found in the cited literature. 5. Final remarks Using wholesale prices of non-neighbors as price instruments, we estimated the demands for ethanol and gasoline in Brazil. The most important findings are as follows: i) Own-price elasticities for ethanol (Ep = 1.5) and gasoline (Ep = 0.9) indicate that Brazilian consumers are price sensitive. 302

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Fig. 4. Ethanol Demand Coefficients. Source: Authors. Notes: a) This is a moving window estimation, which moves forward one month in each new estimation; b) Each subsample has 36 observations.

Fig. 5. Gasoline Demand Coefficients. Source: Authors. Notes: a) This shows a moving window estimation, which moves forward one month in each new estimation; b) Each subsample has 36 observations.

In Brazil, there are differences in taxes for diesel (30%), ethanol (35%), and gasoline (45%).12 Cars are also prohibited from using diesel,13 generating a clear market separation between cars and trucks. Because the country relies on a large trucking transportation network and because car ownership is primarily concentrated among the higher-income population,

the separation between the diesel and gasoline markets allows the government to make targeted policies. A tax on diesel will indirectly affect the entire population as taxes pass through the trucking companies, whereas a tax on gasoline primarily affects car owners. If the government increases the tax difference between gasoline and diesel, it is important to realize this will also impact the market for hydrated ethanol. As in most demand studies, our results are exposed to the Lucas Critique. Even with short-run estimates, the accuracy of the model depends on the extent of market changes, where severe shocks increase the possibility of parameter changes, the accuracy of predictions, and the impacts of public policy. The results of this paper show that the markets for gasoline and

12 We calculate tax burden using prices from ANP and taxes per liter from Fecombustveis (2018). 13 Due to the energy crisis in 1970s, Brazil approved a law (349/1976) banning diesel-powered cars. To use diesel, cars should have the capacity to transport more than one ton. In practice, small cars are not allowed to use diesel.

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Table 7 Comparing elasticity estimates with the literature. References

Period

Type of Fuel

PEa

OPEb

IEc

OIEd

Burnquist and Bacchi (2002) Alves and Bueno (2003) Roppa (2005) Nappo (2007) Serigati (2010) Farina et al. (2010) Souza (2010) Souza (2010) Santos (2012) Santos (2012) Freitas and Kaneko (2011) Farina et al. (2010) Our Estimates Our Estimates

1973–1998 1974–1999 1979–2000 1994–2006 2001–2009 2001–2009 2001–2009 2001–2009 2001–2011 2001–2011 2003–2010 2001–2009 2001–2014 2001–2014

gasoline gasoline gasoline gasoline ethanol ethanol ethanol gasoline ethanol gasoline ethanol gasoline gasoline ethanol

0.23 0.47 0.63 0.19 1.2 1.23 1.26 0.29 1.52 0.78 1.41 0.63 0.94 1.64

* * * * 1.08 1.08 1.08 0.78 1.62 0.94 1.36 0.78 0.94 1.64

0.96 0.12 0.16 0.68 1.2 ** 0.20 0.32 0.55 0.18 ** ** 0.82 0.74

* * * * 0.78 0.78 0.78 0.84 0.78 0.82 0.83 0.84 0.82 0.74

2

Notes: * are models that we cannot estimate due to our sample begins in 2001. * * are studies that did not report income elasticities (see notes on Table 2). (a) Price Elasticity (PE). (b) Our Price Elasticity (OPE) was calculated using our model for the same period of cited studies; (c) Income Elasticity (IE); d) Our Income Elasticity (OIE).

Our Estimates (eta)

Authors' original estimates .5 1 1.5

Souza (2010) (eta) Farina et. al (2010) (eta)

Freitas and Kaneko (2011) (eta) Santos (2012) (eta)

Serigati (2010) (eta) Our Estimates (gas)

Farina et. al (2010) (gas)

0

Santos (2012) (gas) Souza (2010) (gas)

0

.5

1 Our estimate of elasticity

1.5

2

45 degrees line Fig. 6. Comparing elasticities estimates with other studies. Source: Authors. Our estimates were performed using the same time subsamples of cited studies. We report only the own-price elasticities. “eta” and “gas” indicate ethanol and gasoline, respectively.

ethanol are fully integrated in Brazil. Every policy that potentially changes ethanol prices will have spillover effects in the demand for gasoline, and policies primarily designed to impact one fuel market can surely affect the markets for other fuels. Because ethanol leads to competition among crops for food and energy, food and land markets may also be affected. For example, a policy designed to alter fuel consumption by changing the price of either gasoline or ethanol will have demand effects on both fuels. The change in the demand for ethanol will, in turn, affect land and food markets, as ethanol-producing crops require land to grow and, especially in the case of corn, face competition for use as food. Downstream effects can continue into fertilizers, freight, and other markets; these interconnected spillover effects across markets offer many opportunities for continued research.

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