Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Drawing on the 2009 and 2017 waves of the National Household Transportation Survey, this paper is concerned with modeling the fuel price elasticity, allowing for differential estimates in its magnitude over time and across households. We find an elasticity close to zero for the year 2009, which increases to upwards of −0.3 by the year 2017. We explore the robustness of this result to different model specifications and estimation techniques, including instrumental variable estimation to account for the possible endogeneity of fuel prices, as well as quantile regression to account for heterogeneity according to driving intensity. While a similar pattern of increasing elasticity over time emerges across all these models, the quantile model suggests an inverse relationship between the magnitude of the elasticity and miles driven in 2017. As demonstrated with a back of the envelope calculation, one implication of this pattern is a more muted effectiveness of fuel taxation than implied by the estimates of a standard mean regression.