Estimating the effect of a gasoline tax on carbon emissions
dc.contributor.author | Davis, Lucas W. | en_US |
dc.contributor.author | Kilian, Lutz | en_US |
dc.date.accessioned | 2011-11-10T15:31:13Z | |
dc.date.available | 2013-01-02T16:31:52Z | en_US |
dc.date.issued | 2011-11 | en_US |
dc.identifier.citation | Davis, Lucas W.; Kilian, Lutz (2011). "Estimating the effect of a gasoline tax on carbon emissions." Journal of Applied Econometrics 26(7): 1187-1214. <http://hdl.handle.net/2027.42/86812> | en_US |
dc.identifier.issn | 0883-7252 | en_US |
dc.identifier.issn | 1099-1255 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86812 | |
dc.description.abstract | Recently the proposal has been made to raise gasoline taxes in the United States to curb carbon emissions. The existing literature on the sensitivity of gasoline consumption to changes in price may not be appropriate for evaluating the effectiveness of such a tax. First, most of these studies fail to address the endogeneity of gasoline prices. Second, the responsiveness of gasoline consumption to a change in tax may differ from the responsiveness of consumption to an average change in price. We address these challenges using a variety of methods including traditional single‐equation regression models, estimated by least squares or instrumental variables methods, and structural vector autoregressions. Our preferred approach exploits the historical variation in US federal and state gasoline taxes. Our most credible estimates imply that a 10‐cent per gallon increase in the gasoline tax would reduce carbon emissions from vehicles in the United States by about 1.5%. Copyright © 2010 John Wiley & Sons, Ltd. | en_US |
dc.publisher | John Wiley & Sons, Ltd | en_US |
dc.title | Estimating the effect of a gasoline tax on carbon emissions | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Economics, University of Michigan, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Haas School of Business, University of California, Berkeley, CA, USA | en_US |
dc.contributor.affiliationother | National Bureau of Economic Research, Cambridge, MA, USA | en_US |
dc.contributor.affiliationother | Centre for Economic Policy Research, London, UK | en_US |
dc.contributor.affiliationother | Haas School of Business, Berkeley, University of California, CA 94 720‐1900, USA. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86812/1/jae1156.pdf | |
dc.identifier.doi | 10.1002/jae.1156 | en_US |
dc.identifier.source | Journal of Applied Econometrics | en_US |
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dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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