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An increasing gasoline price elasticity in the United States?
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 incr...
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Published in: | Energy economics 2021-03, Vol.95, p.104982, Article 104982 |
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container_title | Energy economics |
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creator | Goetzke, Frank Vance, Colin |
description | 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.
•We estimate the fuel price elasticity using the 2009 and 2017 waves of the NHTS.•The elasticity in 2009 is small – roughly −0.05 – but imprecisely estimated.•The elasticity in 2017 is statistically significant and larger in magnitude, −0.29.•The 2017 elasticity is further subject to heterogeneity by driving distance.•Those who drive the most are the least responsive to fuel prices. |
doi_str_mv | 10.1016/j.eneco.2020.104982 |
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•We estimate the fuel price elasticity using the 2009 and 2017 waves of the NHTS.•The elasticity in 2009 is small – roughly −0.05 – but imprecisely estimated.•The elasticity in 2017 is statistically significant and larger in magnitude, −0.29.•The 2017 elasticity is further subject to heterogeneity by driving distance.•Those who drive the most are the least responsive to fuel prices.</description><identifier>ISSN: 0140-9883</identifier><identifier>EISSN: 1873-6181</identifier><identifier>DOI: 10.1016/j.eneco.2020.104982</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Energy economics ; Estimates ; Estimation ; Fuel price elasticity ; Fuels ; Gasoline ; Heterogeneity ; Households ; Price elasticity ; Prices ; Robustness ; Taxation ; Vehicle miles traveled</subject><ispartof>Energy economics, 2021-03, Vol.95, p.104982, Article 104982</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Mar 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-929627e3787e89d89d101ba2be9116f6e372babf84a62d645e1bed4d3d6da39b3</citedby><cites>FETCH-LOGICAL-c396t-929627e3787e89d89d101ba2be9116f6e372babf84a62d645e1bed4d3d6da39b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27842,27900,27901,33199</link.rule.ids></links><search><creatorcontrib>Goetzke, Frank</creatorcontrib><creatorcontrib>Vance, Colin</creatorcontrib><title>An increasing gasoline price elasticity in the United States?</title><title>Energy economics</title><description>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.
•We estimate the fuel price elasticity using the 2009 and 2017 waves of the NHTS.•The elasticity in 2009 is small – roughly −0.05 – but imprecisely estimated.•The elasticity in 2017 is statistically significant and larger in magnitude, −0.29.•The 2017 elasticity is further subject to heterogeneity by driving distance.•Those who drive the most are the least responsive to fuel prices.</description><subject>Energy economics</subject><subject>Estimates</subject><subject>Estimation</subject><subject>Fuel price elasticity</subject><subject>Fuels</subject><subject>Gasoline</subject><subject>Heterogeneity</subject><subject>Households</subject><subject>Price elasticity</subject><subject>Prices</subject><subject>Robustness</subject><subject>Taxation</subject><subject>Vehicle miles traveled</subject><issn>0140-9883</issn><issn>1873-6181</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><sourceid>8BJ</sourceid><recordid>eNp9kE9LAzEQxYMoWKufwMuC5635s5tNDkVKsSoUPGjPIZvM1iw1W5NU6Lc3dT0LgYHMezPzfgjdEjwjmPD7fgYezDCjmJ5-KinoGZoQ0bCSE0HO0QSTCpdSCHaJrmLsMcY1r8UEzRe-cN4E0NH5bbHVcdg5D8U-OAMF7HRMzrh0zKIifUCx8S6BLd6SThAfrtFFp3cRbv7qFG1Wj-_L53L9-vSyXKxLwyRPpaSS0wZYIxoQ0uaXr241bUESwjueO7TVbScqzanlVQ2kBVtZZrnVTLZsiu7GufswfB0gJtUPh-DzSkXriokcUlZZxUaVCUOMATqVU3zqcFQEqxMn1atfTurESY2csms-uiAH-HYQVDQOvAHrApik7OD-9f8AB0Bw8g</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Goetzke, Frank</creator><creator>Vance, Colin</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TA</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>DHY</scope><scope>DON</scope><scope>FQK</scope><scope>JBE</scope><scope>JG9</scope><scope>SOI</scope></search><sort><creationdate>20210301</creationdate><title>An increasing gasoline price elasticity in the United States?</title><author>Goetzke, Frank ; Vance, Colin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-929627e3787e89d89d101ba2be9116f6e372babf84a62d645e1bed4d3d6da39b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Energy economics</topic><topic>Estimates</topic><topic>Estimation</topic><topic>Fuel price elasticity</topic><topic>Fuels</topic><topic>Gasoline</topic><topic>Heterogeneity</topic><topic>Households</topic><topic>Price elasticity</topic><topic>Prices</topic><topic>Robustness</topic><topic>Taxation</topic><topic>Vehicle miles traveled</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goetzke, Frank</creatorcontrib><creatorcontrib>Vance, Colin</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Materials Business File</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Environment Abstracts</collection><jtitle>Energy economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goetzke, Frank</au><au>Vance, Colin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An increasing gasoline price elasticity in the United States?</atitle><jtitle>Energy economics</jtitle><date>2021-03-01</date><risdate>2021</risdate><volume>95</volume><spage>104982</spage><pages>104982-</pages><artnum>104982</artnum><issn>0140-9883</issn><eissn>1873-6181</eissn><abstract>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.
•We estimate the fuel price elasticity using the 2009 and 2017 waves of the NHTS.•The elasticity in 2009 is small – roughly −0.05 – but imprecisely estimated.•The elasticity in 2017 is statistically significant and larger in magnitude, −0.29.•The 2017 elasticity is further subject to heterogeneity by driving distance.•Those who drive the most are the least responsive to fuel prices.</abstract><cop>Kidlington</cop><pub>Elsevier B.V</pub><doi>10.1016/j.eneco.2020.104982</doi></addata></record> |
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source | International Bibliography of the Social Sciences (IBSS); ScienceDirect Freedom Collection; PAIS Index |
subjects | Energy economics Estimates Estimation Fuel price elasticity Fuels Gasoline Heterogeneity Households Price elasticity Prices Robustness Taxation Vehicle miles traveled |
title | An increasing gasoline price elasticity in the United States? |
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