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Dynamic adjustment of ethanol demand to crude oil prices: implications for mandated ethanol usage

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Abstract

This paper examines the dynamic behavior of ethanol blenders with a focus on the substitution possibilities between ethanol and crude oil in the fuel blending sector. The estimation results of the dynamic linear logit model reveal that the dynamic adjustment in the input demand system is very sluggish. In addition, the estimation results show that an increase in gasoline output no longer raises the share of ethanol after imposing the mandated percentage standard for ethanol. The estimated price elasticities of input demand offer statistical evidence that ethanol demand is less responsive to crude oil prices in the post-ethanol-mandate period than in the pre-ethanol-mandate period. The decompositions of cross-price elasticities strongly support that the reduced elasticities of ethanol demand with respect to the price of crude oil are more attributable to the mandated percentage standard for ethanol rather than the relative price of ethanol to crude oil.

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  1. There was skepticism about corn-based ethanol. Unlike the arguments of ethanol advocates, many studies on ethanol concluded that corn-based ethanol would be technically and environmentally limited (Pimentel 2003; Pimentel and Patzek 2005; Eaves and Eaves 2007; Searchinger et al. 2008; Thompson et al. 2011; Lankoski and Ollikainen 2011). The technical transformation of corn into ethanol might create large production costs, while the increase in ethanol demand might fail to produce significant environmental benefits. Moreover, welfare analyses on ethanol did not clearly support the ethanol policy (Gardner 2007; Gardner and Tyner 2007; Martinez-Gonzalez et al. 2007; Schmitz et al. 2007; Babcock 2008; Gorter and Just 2009a, b; Du et al. 2009). The ethanol policy would be beneficial to corn producers as well as ethanol producers, but the benefits might be offset by the large costs that government and consumers would pay.

  2. The EPA defines ethanol blenders as any persons who own, lease, operate, control, or supervise ethanol blending plants; the plants imply any refineries where gasoline is produced by adding ethanol to gasoline.

  3. While the literature has given no attention to the substitution possibilities between ethanol and crude oil, a few past studies focused on the substitution of ethanol for gasoline (Rask 1998; Gardner 2007; Babcock 2008; Luchansky and Monks 2009; Anderson 2012). However, their results were mixed with respect to the role of ethanol, showing a wide range of price elasticities. The estimated own-price elasticities of ethanol demand varied from −0.22 (inelastic) to −2.82 (elastic), and the estimated gasoline-price elasticities of ethanol demand ranged from \(-\)2.13 (complements) to 5.05 (substitutes).

  4. Most studies on input substitution employed flexible functional forms based on the duality approach to the demand system for inputs (Diewert 1971; Fuss 1977; Griffin 1977; Pindyck 1979; Guilkey and Lovell 1980; Guilkey et al. 1983; Hall 1986; Diewert and Wales 1995; Fisher et al. 2001; Serletis et al. 2010). However, the dynamic mechanisms with flexible functional forms frequently failed to obey the theoretical conditions (Considine and Mount 1984). In particular, the empirical applications of dynamic flexible forms often yielded unexpected signs of price elasticities.

  5. The Chow test indicates that there exist statistical differences in the estimates between the two periods; the statistic is 18.33 which is significant at the 5 % level (Chow 1960). In addition, there is statistical evidence that a structural change occurs during the full-sample period from 1993 to 2015 (see “Appendix” for detailed results). The results presented in “Appendix” indicate that there exists a structural change due to the Energy Policy Act of 2005, implying that the ethanol mandate may lead to a change in the substitution possibilities.

  6. Frondel and Schmidt (2006) decomposed the observed differences in the cross-price elasticities between capital and energy. Considering the estimates as factual cross-price elasticities, they established counterfactual cross-price elasticities to use the Oaxaca–Blinder decompositions; their analysis applied the decompositions of wage differences to changes in the cross-price elasticities (Blinder 1973; Oaxaca 1973; Oaxaca and Ransom 1994). For the dynamic analysis, Steinbuks (2012) applied the decompositions to the estimates of the dynamic linear logit model in order to identify the technology differences in interfuel substitution.

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Authors and Affiliations

  1. Food and Resource Economics Department, University of Florida, Gainesville, FL, USA

    Dong Hee Suh & Charles B. Moss

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  1. Dong Hee Suh
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Correspondence to Dong Hee Suh.

Appendix: Dynamic linear logit model and structural change

Appendix: Dynamic linear logit model and structural change

Additional explanatory variables are added to know whether the ethanol mandate induces a structural break in the dynamic linear logit model (Considine 1989). A dynamic linear logit model is specified as

$$\begin{aligned} \ln \left( \frac{s_c}{s_e}\right) _t&= \alpha _c - \left[ \beta _{cg}^{*} s_{gt}^{*} + \beta _{ce}^{*} \left( s_{ct}^{*} + s_{et}^{*} \right) \right] \ln \left( \frac{w_c}{w_e}\right) _t \nonumber \\&\quad + \left( \beta _{cg}^{*} - \beta _{ge}^{*} \right) s_{gt}^{*} \ln \left( \frac{w_g}{w_e}\right) _t + \gamma _c \ln y_t \nonumber \\&\quad + \theta \ln \left( \frac{x_c}{x_e}\right) _{t-1} + \delta _{c} \ln \left( \frac{w_c}{w_e}\right) _t d_t + \psi _{c} \ln \left( \frac{w_g}{w_e}\right) _t d_t \nonumber \\&\quad + \rho _{c} \ln y_t d_t + \varphi _{c} d_t + \left( \epsilon _c - \epsilon _e \right) _t \end{aligned}$$
(16a)
$$\begin{aligned} \ln \left( \frac{s_g}{s_e}\right) _t&= \alpha _g - \left[ \beta _{cg}^{*} s_{ct}^{*} + \beta _{ge}^{*} \left( s_{gt}^{*} + s_{et}^{*} \right) \right] \ln \left( \frac{w_g}{w_e}\right) _t \nonumber \\&\quad + \left( \beta _{cg}^{*} - \beta _{ce}^{*} \right) s_{ct}^{*} \ln \left( \frac{w_c}{w_e}\right) _t + \gamma _g \ln y_t \nonumber \\&\quad + \theta \ln \left( \frac{x_g}{x_e}\right) _{t-1} + \delta _{g} \ln \left( \frac{w_c}{w_e}\right) _t d_t + \psi _{g} \ln \left( \frac{w_g}{w_e}\right) _t d_t \nonumber \\&\quad + \rho _{g} \ln y_t d_t + \varphi _{g} d_t + \left( \epsilon _g - \epsilon _e \right) _t \end{aligned}$$
(16b)

where \(d_t\) is equal to 1 if the ethanol mandate is effective (i.e., 2006–2015 period), and equal to 0 otherwise (i.e., 1993–2005 period). The maintained hypothesis is that the Energy Policy Act of 2005 has no effect on the relative cost shares of inputs, which implies the null hypothesis is \(\varphi _{c}=\varphi _{g}=0\). In Table 7, the estimation results reject the null hypothesis, showing that the implementation of the ethanol mandate significantly affects the relative cost shares of inputs.

Table 7 Structural change in fuel blending sector, Jan 1993–Aug 2015
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Suh, D.H., Moss, C.B. Dynamic adjustment of ethanol demand to crude oil prices: implications for mandated ethanol usage. Empir Econ 52, 1587–1607 (2017). https://doi.org/10.1007/s00181-016-1112-6

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