Demand for gasoline is more price-inelastic than commonly thought

Abstract

One of the most frequently examined statistical relationships in energy economics has been the price elasticity of gasoline demand. We conduct a quantitative survey of the estimates of elasticity reported for various countries around the world. Our meta-analysis indicates that the literature suffers from publication selection bias: insignificant or positive estimates of the price elasticity are rarely reported, although implausibly large negative estimates are reported regularly. In consequence, the average published estimates of both short-and long-run elasticities are exaggerated twofold.Using mixed-effects multilevel meta-regression, we show that after correction for publication bias the average long-run elasticity reaches −0.31 and the average short-run elasticity only −0.09.

Interdependencies between Biofuel, Fuel and Food Prices: The Case of the Brazilian Ethanol Market

“…In fact, biofuel may compete for both renewable and non-renewable resources and this may therefore have an impact on its sustainability and that of food [13][14][15][16][17][18][19][20]. In order to be sustainable, biofuels should be carbon neutral, especially considering the necessity of fossil fuel substitution and global warming mitigation.…”

A Meta-Analysis of the Income Elasticity of Money Demand

“…Také odhady, které vykazují opačné znaménko, než které pro daný parametr předpokládá teorie, mohou být méně často vybírány k publikaci. Tento druh publikační selektivity dokumentujeme v článku Havránek et al (2012), který se zabývá cenovou elasticitou poptávky po benzínu. Protože málo výzkumníků předpokládá, že benzín je Giffenovým statkem, téměř žádný z nich nereportuje pozitivní odhady cenové elasticity.…”

Meta-analysis of the income and price elasticities of gasoline demand: public policy implications for Latin America

“…This is represented in equation (4) (Stanley, 2001;Havranek and others, 2012;Doucouliagos and Stanley, 2009):…”

“…Thus, the set of estimated elasticities are distributed randomly (in the absence of publication bias) around the real elasticity value, irrespective of the standard error (equation (3)) (Doucouliagos and Stanley, 2009;Stanley, 2008): where b i represents the estimated elasticities and β 0 the actual elasticity value, and w i is the error term. In cases where the estimated elasticities present a publication bias, it is possible to use a meta-regression with mixed effects and Heckman's two-step procedure (Havranek and others, 2012). This procedure uses the characteristics of a control sample to estimate potential bias with a probit or logit model, so that in a second step the Mills ratio can be included in the final equation (Heckman, 1979;Angrist and Pischke, 2009).…”

“…There are different types of metaregression estimates; this study uses a Heckman-type meta-regression based on the mixed-effects model that corrects for publication bias (Van den Bergh and others, 2010;Cumming, 2012;Havranek and others, 2012). Also reported for reference purposes are estimates of fixed, random and Huber-White effects, as these can be used to correct for heteroskedasticity and autocorrelation problems due to production clusters (Williams, 2000;Wooldridge, 2002).…”