Fuel demand elasticities to determine consumer responses to tax increases or price shocks are typically based on aggregate data. The literature generally provides one elasticity estimate for each country, assuming similar response for all households. However, it is possible that different households can have different responses to the same stimuli depending on the household characteristics. Assuming a single elasticity for all households may fail 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 characteristics in the USA. We find substantial heterogeneity in price and income elasticities based on demographic groupings and income groups. Results of a distributional analysis for a gasoline tax are also presented using the heterogeneous responses.
ArchibaldR.GillinghamR. (1980). “An analysis of short-run consumer demand for gasoline using household survey data.” The Review of Economics and Statistics62 (4): 622-628.
2.
ArchibaldR.GillinghamR. (1981). “A decomposition of the price and income elasticities of the consumer demand for gasoline.” Southern Economic Journal47: 1021-1031.
3.
ArellanoM.BondS. R. (1991). “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations.” Review of Economic Studies58: 277-297.
4.
BalestraP.NerloveM. (1966). “Pooling cross section and time-series data in the estimation of a dynamic model: The demand for natural gas.” Econometrica34: 585-612.
5.
BaltagiB. H. (2005). Econometric analysis of panel data. West Sussex: John Wiley & Sons Ltd.
6.
BaltagiB. H.GriffinJ. M. (1983). “Gasoline demand in the OECD: An application of pooling and testing procedures.” European Economic Review22: 117-137.
7.
BaltagiB. H.WuP. X. (1999). “Unequally spaced panel data regressions with AR(1) disturbances.” Econometric Theory15 (6): 814-823.
8.
BassoL. J.OumT. H. (2007). “Automobile fuel demand: A critical assessment of empirical methodologies.” Transport Reviews27 (4): 449-484.
9.
BeckN. (2001). “Time-series cross-section data: What have we learned in the past few years?” Annual Review of Political Science4: 271-293.
10.
BeckerG. (1965). “A theory of the allocation of time.” Economic Journal September: 493-517.
11.
BlowL.CrawfordI. (1997). “The distributional effects of taxes on private motoring.” The Institute for Fiscal Studies Commentary 65, London.
12.
BombergM.KockelmanK. M. (2007). “Traveler response to the 2005 gas price spike.” Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington DC.
13.
BreuschT.PaganA. (1980). “The LM test and its applications to model specification in econometrics.” Review of Economic Studies47: 239-254.
14.
Bureau of Labor Statistics (1997-2002). “Consumer Expenditure Survey: Public Use Micro Data.” US Department of Labor, Accessed at <http://www.bls.gov/cex/home.htm>
15.
Bureau of Labor Statistics (2005). “Consumer Expenditure Survey: Current Standard Tables: 19842003.” US Department of Labor, Accessed at <http://www.bls.gov/cex/home.htm>
16.
Bureau of Labor Statistics (2007). “Consumer Price Indexes.” US Department of Labor, Accessed at <http://www.bls.gov/cpi/home/htm>
17.
DahlC. (1995). “Demand for transport fuels: A survey of demand elasticities and their components.” The Journal of Energy Literature1 (2): 3-27.
18.
DahlC.SternerT. (1991). “Analyzing gasoline demand elasticities: A survey.” Energy Economics13: 203-210.
19.
DargayJ.GatelyD.SommerM. (2007). “Vehicle ownership and income growth, worldwide: 1960-2030.” The Energy Journal28 (4): 143-170.
20.
de JongG.GunnH. (2001). “Recent evidence on car cost and time elasticities of travel demand in Europe.” Journal of Transport Economics and Policy35 (2): 137-160.
21.
Energy Information Administration (2006). “Motor gasoline sales to end users prices.” US Department of Energy, July, Accessed at <http://tonto.eia.doe.gov/dnav/pet/pet_pri_top.asp>.
22.
Federal Highway Administration (2006). “Monthly motor fuel reported by states.” various years, US Department of Transportation, July, Accessed at <http://www.fhwa.dot.gov/ohim/mmfr/mmfrpage.htm>.
23.
FriedmanM. (1957). A Theory of the Consumption Function. Princeton: Princeton University Press.
24.
GertlerP.LocayL.SandersonW. (1987). “Are user fees regressive? The welfare implications of healthcare financing proposals in Peru.” NBER Working paper, No. 2299.
25.
GolobT. F.KimS.RenW. (1996). “How households use different types of vehicles: A structural driver allocations and usage model.” Transportation Research A Policy and Practice30 (2): 103-118.
26.
GoodwinP.DargayJ.HanlyM. (2004). “Elasticities of road traffic and fuel consumption with respect to price and income: A review.” Transport Reviews24 (3): 275-292.
27.
GrahamD. J.GlaisterS. (2002a). “The demand for automobile fuel: A survey of elasticities.” Journal of Transport Economics and Policy36 (1): 1-26.
28.
GrahamD. J.GlaisterS. (2002b). “Review of income and price elasticities of demand for road traffic.” Department for Transport, London.
29.
GreeneW. H. (2005). Econometric Analysis, New Delhi: Pearson Education Inc.
30.
GreeneD. L.HuP. S. (1985). “The influence of the price of gasoline on vehicle use in multivehicle households.” Transportation Research Record988: 19-24.
31.
GreeneD. L.HuP. S. (1986). “A functional form analysis of the short-run demand for travel and gasoline by one-vehicle households.” Transportation Research Record1092: 10-15.
32.
GreeningL. A.JengH. T.FormbyJ. P.ChengD. C. (1995). “Use of region, life-cycle and role variable in the short-run estimation of the demand for gasoline and miles travelled.” Applied Economics27: 643-656.
33.
GujaratiD. N. (2003). Basic Econometrics. New York: McGraw Hill Companies, Inc., pp. 478.
34.
HalvorsenR.PalmquistR. (1980). “The interpretation of dummy variables in semilogarithmic equations.” American Economic Review70 (3): 474-475.
35.
HausmanJ. A. (1978). “Specification tests in Econometrics.” Econometrica46: 1251-1371.
36.
HausmanJ.NeweyW. (1995). “Nonparametric estimation of exact consumer surplus and deadweight loss.” Econometrica63: 1445-1476.
37.
HeavenrichR. M. (2006). “Light-duty automotive technology and fuel economy trends: 1975 to 2006.” Office of Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor.
38.
HildrethC.HoukJ. P. (1968). “Some estimators for a linear model with random coefficients.” Journal of the American Statistical Association63: 584-595.
39.
HsiaoC. (2003). Analysis of Panel Data. Cambridge: Cambridge University Press.
40.
HsiaoC.SunB. H. (2000). “To pool or not to pool panel data.” In KrishnakumarJ.RonchettiE. eds., Panel Data Econometrics: Future Directions, Papers in Honor of Professor Pietro Balestra Amsterdam: North Holland, pp. 181-198.
41.
HughesJ. E.KnittelC. R.SperlingD. (2007). “Evidence of a shift in the short-run price elasticity of gasoline demand.” Proceedings of the 86th Annual Meeting of the Transportation Research Board Washington, DC
42.
KayserH. A. (2000). “Gasoline demand and car choice: estimating gasoline demand using household information.” Energy Economics22: 331-348.
43.
LancasterK. (1966). “A new approach to consumer theory.” Journal of Political Economy April: 132-157.
44.
Medlock IIIK. B.SoligoR. (2001). “Economic development and end-use energy demand.” The Energy Journal22 (2): 77-105.
45.
MetcalfG. E. (1993). “The Lifetime Incidence of State and Local Taxes: Measuring Changes During the 1980s.” NBER Working Paper No. 4252, Cambridge.
46.
NicolC. J. (2003). “Elasticities of demand for gasoline in Canada and the United States.” Energy Economics25: 201-214.
47.
ParryI. A. H.SmallK. A. (2005). “Does Britain or the United States have the right gasoline tax?” The American Economic Review95 (4): 1276-1289.
48.
PlumperT.TrgerV. E. (2007). “Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyzed with unit fixed effects.” Political Analysis Accessed at <http://pan.oxfordjournals.org/cgi/content/abstract/mp002v1>
49.
PoterbaJ. (1991). “Is the Gasoline Tax Regressive?” Tax Policy and the Economy5: 145-164.
50.
PucherJ.RenneJ. L. (2003). “Socioeconomics of urban travel: evidence from the 2001 NHTS.” Transportation Quarterly57 (3): 49-77.
51.
PullerS. L.GreeningL. A. (1999). “Household adjustment to gasoline price change: An analysis using 9 years of US survey data.” Energy Economics21: 37-52.
52.
RobinsonJ. (1969). The Economics of Imperfect Competition. London: MacMillan.
53.
SantosG.CatchesidesT. (2004). “Distributional consequences of gasoline taxation in the United Kingdom.” Transportation Research Record1924: 103-111.
54.
SchmaleneseeR.StokerT. M. (1999). “Household gasoline demand in the United States.” Econo-metrica67 (3): 645-662.
55.
SipesK. N.MendelsohnR. (2001). “The effectiveness of gasoline taxation to manage air pollution.” Ecological Economics36: 299-309.
56.
SternN. (2007). The Economics of Climate Change: The Stern Review. Cambridge: Cambridge University Press.
57.
SternerT. (2007). “Gasoline Taxes a useful instrument for climate policy.” Energy Policy35(6): 3194-3202.
58.
SternerT.DahlC. A. (1992). “Modelling transport fuel demand.” In SternerT. ed., International Energy Economics London: Chapman and Hall, pp. 65-79.
59.
SwamyP. A. V. B. (1970). “Efficient inference in random coefficient regression model.” Economet-rica38: 311-323.
60.
WadudZ. (2008). “Personal tradable carbon permits for road transport: Heterogeneity of demand responses and distributional analysis.” PhD Dissertation, Department of Civil and Environmental Engineering, Imperial College London.
61.
WadudZ.GrahamD. J.NolandR. B. (in press). “Modelling fuel demand for different socioeconomic groups.” Applied Energy.
62.
WallsM.KrupnickA. (1993). “The Distributional and Environmental Implications of an Increase in the Federal Gasoline Tax.” Resources For the Future Discussion Paper ENR93-24.
63.
WestS. E. (2004). “Distributional effects of alternative vehicle pollution control policies.” Journal of Public Economics88: 735-757.
64.
WestS. E.Williams IIIR. C.(2004). “Estimates from a Consumer Demand System: Implications for the Incidence of Environmental Taxes.” Journal of Environmental Economics and Management47 (3): 535-558.
65.
WilsonS. E.ButlerD. M. (2007). “A lot more to do: The sensitivity of time-series cross-section analyses to simple alternate specifications.” Political Analysis Accessed at <http://pan.oxfordjourn-als.org/cgi/content/abstract/mp1012v1>.
66.
YatchewA.NoJ. A. (2001). “Household gasoline demand in Canada.” Econometrica69 (6): 1697-1709.
67.
ZellnerA. (1962). “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias.” Journal of the American Statistical Association57 (298): 348-368.
