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Regional impact of changes in disposable income on Spanish electricity demand: A spatial econometric analysis

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  • Blázquez Gomez, Leticia M.
  • Filippini, Massimo
  • Heimsch, Fabian

Abstract

This paper presents an empirical analysis of residential electricity demand considering the existence of spatial effects. This analysis has been performed using aggregate panel data at the province level for 46 Spanish provinces for the period from 2001 to 2010. For this purpose, we estimated a log–log demand equation using a spatial autoregressive model with autoregressive disturbances (SARAR). The purpose of this empirical analysis is to determine the influence of price, income, and spatial spillovers on residential electricity demand in Spain. We are particularly interested in analyzing the impact of household disposable income variation across provinces observed during the economic crisis period 2009–2010. The estimation results show relatively low income elasticity and an inelastic demand to prices. Furthermore, the results show the presence of spatial effects in Spanish residential electricity consumption.

Suggested Citation

  • Blázquez Gomez, Leticia M. & Filippini, Massimo & Heimsch, Fabian, 2013. "Regional impact of changes in disposable income on Spanish electricity demand: A spatial econometric analysis," Energy Economics, Elsevier, vol. 40(S1), pages 58-66.
  • Handle: RePEc:eee:eneeco:v:40:y:2013:i:s1:p:s58-s66
    DOI: 10.1016/j.eneco.2013.09.008
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    as
    1. Alberini, Anna & Filippini, Massimo, 2011. "Response of residential electricity demand to price: The effect of measurement error," Energy Economics, Elsevier, vol. 33(5), pages 889-895, September.
    2. Leth-Petersen, Soren & Togeby, Mikael, 2001. "Demand for space heating in apartment blocks: measuring effects of policy measures aiming at reducing energy consumption," Energy Economics, Elsevier, vol. 23(4), pages 387-403, July.
    3. Francisco Alvarez-Cuadrado & Jose Maria Casado & Jose Maria Labeaga, 2016. "Envy and Habits: Panel Data Estimates of Interdependent Preferences," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 443-469, August.
    4. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2012. "Estimation of elasticity price of electricity with incomplete information," Energy Economics, Elsevier, vol. 34(3), pages 627-633.
    5. Heshmati, Almas, 2012. "Survey of Models on Demand, Customer Base-Line and Demand Response and Their Relationships in the Power Market," IZA Discussion Papers 6637, Institute of Labor Economics (IZA).
    6. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    7. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    8. Hsing, Yu, 1994. "Estimation of residential demand for electricity with the cross-sectionally correlated and time-wise autoregressive model," Resource and Energy Economics, Elsevier, vol. 16(3), pages 255-263, August.
    9. Filippini, Massimo & Pachauri, Shonali, 2004. "Elasticities of electricity demand in urban Indian households," Energy Policy, Elsevier, vol. 32(3), pages 429-436, February.
    10. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    11. Baltagi, Badi H. & Liu, Long, 2008. "Testing for random effects and spatial lag dependence in panel data models," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3304-3306, December.
    12. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    13. Xavier Labandeira & José M. Labeaga & Miguel Rodríguez, 2006. "A Residential Energy Demand System for Spain," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 87-112.
    14. Paul, Anthony & Myers, Erica & Palmer, Karen, 2009. "A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector," RFF Working Paper Series dp-08-50, Resources for the Future.
    15. Flaig, Gebhard, 1990. "Household production and the short- and long-run demand for electricity," Energy Economics, Elsevier, vol. 12(2), pages 116-121, April.
    16. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    17. Varian, Hal R., 1974. "Equity, envy, and efficiency," Journal of Economic Theory, Elsevier, vol. 9(1), pages 63-91, September.
    18. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    19. Narayan, Paresh Kumar & Smyth, Russell, 2005. "The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration," Energy Policy, Elsevier, vol. 33(4), pages 467-474, March.
    20. Azevedo, Inês M. Lima & Morgan, M. Granger & Lave, Lester, 0. "Residential and Regional Electricity Consumption in the U.S. and EU: How Much Will Higher Prices Reduce CO2 Emissions?," The Electricity Journal, Elsevier, vol. 24(1), pages 21-29, January.
    21. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    22. Hendrik S. Houthakker, 1980. "Residential Electricity Revisited," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    23. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    24. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    25. Hondroyiannis, George, 2004. "Estimating residential demand for electricity in Greece," Energy Economics, Elsevier, vol. 26(3), pages 319-334, May.
    26. Egger, Peter & Pfaffermayr, Michael & Winner, Hannes, 2005. "An unbalanced spatial panel data approach to US state tax competition," Economics Letters, Elsevier, vol. 88(3), pages 329-335, September.
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    More about this item

    Keywords

    Residential electricity demand; Aggregate panel data; Spatial economic effect; Economic crisis; Spatial econometrics;
    All these keywords.

    JEL classification:

    • D - Microeconomics
    • D2 - Microeconomics - - Production and Organizations
    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis

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