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Fuel demand in Brazil in a dynamic panel data approach

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  • Santos, Gervásio F.

Abstract

The purpose of this paper is to evaluate the sensitivity of fuel consumers regarding price and income, taking recent changes in the Brazilian fuel market into account. In this market, new market rules, energy policy towards fuel diversification and introduction of flex-fuel engines have determined fuel competition among gasoline, ethanol and compressed natural gas. Using a dynamic panel data model, demand equations for these three fuels are econometrically estimated to obtain consumer adjustment coefficients, price, cross-price and income elasticities in the short and long-run. In addition, the effect of the introduction of flex-fuel engines in the market and the rationality of consumers towards efficiency constraints of the engines were tested. Apart from considerable competition, results show that the dynamics of the Brazilian fuel market revolves around ethanol instead of gasoline. While demands for gasoline and natural gas are inelastic to price, demand for ethanol is elastic in Brazil. Furthermore, after the introduction of the flex-fuel technology the sensitivity of consumers to fuel prices changed, and ethanol consumers take efficiency constrains into account when ethanol prices reach the threshold of 70% of gasoline prices.

Suggested Citation

  • Santos, Gervásio F., 2013. "Fuel demand in Brazil in a dynamic panel data approach," Energy Economics, Elsevier, vol. 36(C), pages 229-240.
    • Handle: RePEc:eee:eneeco:v:36:y:2013:i:c:p:229-240
    DOI: 10.1016/j.eneco.2012.08.012
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    as
    1. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Ethanol demand under the flex-fuel technology regime in Brazil," Energy Economics, Elsevier, vol. 33(6), pages 1146-1154.
    2. Jörg Breitung & Samarjit Das, 2005. "Panel unit root tests under cross‐sectional dependence," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(4), pages 414-433, November.
    3. Gang Liu, 2004. "Estimating Energy Demand Elasticities for OECD Countries. A Dynamic Panel Data Approach," Discussion Papers 373, Statistics Norway, Research Department.
    4. Joakim Westerlund, 2007. "Testing for Error Correction in Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(6), pages 709-748, December.
    5. Polemis, Michael L., 2006. "Empirical assessment of the determinants of road energy demand in Greece," Energy Economics, Elsevier, vol. 28(3), pages 385-403, May.
    6. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    7. Robert S. Pindyck & Julio J. Rotemberg, 1982. "Dynamic Factor Demands Under Rational Expectations," NBER Working Papers 1015, National Bureau of Economic Research, Inc.
    8. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    9. Eltony, M. N. & Al-Mutairi, N. H., 1995. "Demand for gasoline in Kuwait : An empirical analysis using cointegration techniques," Energy Economics, Elsevier, vol. 17(3), pages 249-253, July.
    10. Bentzen, Jan, 1994. "An empirical analysis of gasoline demand in Denmark using cointegration techniques," Energy Economics, Elsevier, vol. 16(2), pages 139-143, April.
    11. Espey, Molly, 1996. "Watching the fuel gauge: An international model of automobile fuel economy," Energy Economics, Elsevier, vol. 18(1-2), pages 93-106, April.
    12. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
    13. Molly Espey, 1996. "Explaining the Variation in Elasticity Estimates of Gasoline Demand in the United States: A Meta-Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 49-60.
    14. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    15. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    16. Espey, Molly, 1998. "Gasoline demand revisited: an international meta-analysis of elasticities," Energy Economics, Elsevier, vol. 20(3), pages 273-295, June.
    17. Daniel J. Graham & Stephen Glaister, 2002. "The Demand for Automobile Fuel: A Survey of Elasticities," Journal of Transport Economics and Policy, University of Bath, vol. 36(1), pages 1-25, January.
    18. Puller, Steven L. & Greening, Lorna A., 1999. "Household adjustment to gasoline price change: an analysis using 9 years of US survey data," Energy Economics, Elsevier, vol. 21(1), pages 37-52, February.
    19. Watkins, G. C., 1991. "Short-and long-term equilibria : Relationships between first and third generation dynamic factor demand models," Energy Economics, Elsevier, vol. 13(1), pages 2-9, January.
    20. Harris, Richard D. F. & Tzavalis, Elias, 1999. "Inference for unit roots in dynamic panels where the time dimension is fixed," Journal of Econometrics, Elsevier, vol. 91(2), pages 201-226, August.
    21. Baltagi, Badi H. & Griffin, James M., 1983. "Gasoline demand in the OECD : An application of pooling and testing procedures," European Economic Review, Elsevier, vol. 22(2), pages 117-137, July.
    22. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    23. Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
    24. Alves, Denisard C. O. & De Losso da Silveira Bueno, Rodrigo, 2003. "Short-run, long-run and cross elasticities of gasoline demand in Brazil," Energy Economics, Elsevier, vol. 25(2), pages 191-199, March.
    25. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    26. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
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    More about this item

    Keywords

    Fuel demand; Energy policy; Ethanol; Flex-fuel vehicle; Dynamic panel data model;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices

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