Use of region, life-cycle and role variables in the short-run estimation of the demand for gasoline and miles travelled

Greening, Lorna A.; Jeng, Hann Tarn; Formby, John P.; Cheng, David C.

doi:10.1080/00036849500000054

Cited by 31 publications

(24 citation statements)

References 21 publications

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“…Along another stream of the literature, Greening et al [23] recognised that the price and income elasticities can be dependent on household structure and life-cycle and derived demand for different groups based on various household characteristics. They used 1990 CEX data and determined that in general, retired or unemployed households are least responsive to fuel price changes, while households with traditional family structures have the largest price elasticities.…”

Section: Previous Studiesmentioning

confidence: 99%

Modelling fuel demand for different socio-economic groups

2009

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Section: Previous Studiesmentioning

confidence: 99%

Modelling fuel demand for different socio-economic groups

2009

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“…Archibald and Gillingham (1980) first utilized such a framework, which was later followed by Greening et. al.…”

Section: The Econometric Modelmentioning

confidence: 99%

“…It is assumed that preferences are functions of households' demographic characteristics. Demographic variables that have been found to affect the household consumption of gasoline are the size and composition of the household, number of earners in the household, age, race, gender, and education of the household, among other possible factors (Archibald and Gillingham 1980, 1981, Greening et. al.…”

Section: The Econometric Modelmentioning

confidence: 99%

“…These enable an understanding of the aggregate impact on demand due to a rise in fuel price or taxes; however, they likely miss the detailed distributional effect of price changes on different socio-economic groups. Recent studies of gasoline and travel demand suggest that there could be significant heterogeneity in the price sensitivity of different socio economic groups or geographic areas (Archibald and Gillingham 1980, 1981, Greening et. al.…”

Section: Introductionmentioning

confidence: 99%

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Gasoline Demand with Heterogeneity in Household Responses

Wadud

Graham

Noland³

2010

The Energy Journal

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Fuel demand elasticities are typically based on aggregate data to determine consumer responses to tax increases or price shocks. However, this fails to capture the detailed distributional effect on different socio-economic groups, which is often needed to fully understand the impact of fuel tax measures. This paper presents results from a household level gasoline demand model which accommodates variation in price and income elasticity with increasing income as well as for different socio-economic groups in the USA. We find substantial heterogeneity in price and income elasticities based on demographic groupings and income groups.

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“…Micro-level information is expensive to obtain and is usually gathered using surveys conducted far less frequently than annually, which is the frequency used in most of the studies utilizing aggregate data. See Greening (1995), Archibald & Gillingham (1981), Table 6) for an overview.…”

Section: Micro-level Datamentioning

confidence: 99%

Income elasticity of gasoline demand: A meta-analysis

Havránek

Kokeš

2015

Energy Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn this paper we quantitatively synthesize empirical estimates of the income elasticity of gasoline demand reported in previous studies. The studies cover many countries and report a mean elasticity of 0.28 for the short run and 0.66 for the long run. We show, however, that these mean estimates are biased upwards because of publication bias-the tendency to suppress negative and insignificant estimates of the elasticity. Using mixed-effects multilevel meta-regression we filter out publication bias from the literature. Our results suggest that the income elasticity of gasoline demand is smaller than commonly thought: the corrected estimate is 0.1 for the short run and 0.46 for the long run.

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Use of region, life-cycle and role variables in the short-run estimation of the demand for gasoline and miles travelled

Cited by 31 publications

References 21 publications

Modelling fuel demand for different socio-economic groups

Modelling fuel demand for different socio-economic groups

Gasoline Demand with Heterogeneity in Household Responses

Income elasticity of gasoline demand: A meta-analysis

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