Measuring the rebound effect with micro data: A first difference approach

Borger, Bruno De; Mulalic, Ismir; Rouwendal, Jan

doi:10.1016/j.jeem.2016.04.002

Cited by 63 publications

(21 citation statements)

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“…Most of these studies use aggregate data at the regional or national level. 3 More recently, several studies have estimated the elasticity of vehicle-miles-traveled with respect to the price of gasoline using disaggregated micro-level data, either from surveys or inspection odometer reading data (e.g., Linn, 2016;Bento et al, 2009;Knittel and Sandler, 2013;Gillingham, 2013Gillingham, , 2014Munk-Nielsen, 2015;De Borger, Mulalic, and Rouwendal, 2016b). In a notable contrast, estimates for drivers in the United States tend to be in the range of -0.05 to -0.30, while similar benchmark estimates for European drivers tend to show a much more elastic response.…”

Section: Introductionmentioning

confidence: 99%

A tale of two tails: Commuting and the fuel price response in driving

Gillingham

Munk-Nielsen

2019

Journal of Urban Economics

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The consumer price responsiveness of driving demand is central to the welfare consequences of fuel price changes. This study uses rich data covering the entire population of vehicles and consumers in Denmark to find a medium-run price elasticity of driving of-0.30. We uncover an important feature of driving demand: two small groups of much more responsive households that make up the lower and upper tails of the work distance distribution. The first group lives close to work in urban areas. The second group lives outside of major urban areas and has the longest commutes. Access to public transport appears to be the force behind the existence of the tails, enabling the switch away from driving. We find that a fuel price increase of 1 DKK/liter implies an average deadweight loss of 0.66 DKK/liter, but there is considerable heterogeneity and the tails bear a larger share of the loss.

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Section: Introductionmentioning

confidence: 99%

A tale of two tails: Commuting and the fuel price response in driving

Gillingham

Munk-Nielsen

2019

Journal of Urban Economics

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“…Previous analyses of the gas price elasticity of VMT demand (including those framed as estimates of the rebound effect) have instrumented key regressors, such as gas price (Hughes, Knittel et al 2006;Gillingham, Jenn et al 2015), vehicle characteristics (DeBorger, Mulalic et al 2016), and vehicle fuel economy (Linn 2013).…”

Section: Discussionmentioning

confidence: 99%