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Objective vs. Subjective Fuel Poverty and Self-Assessed Health

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  • Manuel Llorca

    () (Durham University Business School, Durham University)

  • Ana Rodríguez-Álvarez

    (Oviedo Efficiency Group, Department of Economics, University of Oviedo, Spain)

  • Tooraj Jamasb

    (Durham University Business School, Durham University.)

Abstract

Identification of fuel poverty and its impact on individuals is a growing social issue. Classifying households using subjective measures of fuel poverty yields different results than when objective measures are used. Moreover, there are assessment-related difficulties in establishing the effects on health and wellbeing, which hinders policy design to tackle this problem. In this paper, we propose a latent class ordered probit model to control for subjectivity when analysing the influence of fuel poverty on self-reported health. This methodology is applied to a sample of 25,000 individuals in 11,000 households for the 2011–2014 period in Spain, where 5.1 million people (11% of the population) could not afford to heat their homes to an adequate temperature in 2014. The results show that poor housing conditions, low income, material deprivation, and fuel poverty, have a negative impact on health. We also find that the effect of objective fuel poverty and other poverty-related factors on health are stronger when we control for unobserved heterogeneity among individuals. Since objective measures alone may not fully capture the adverse effect of fuel poverty on health, we advocate policy approaches that combine both objective and subjective measures and its application by policymakers.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Manuel Llorca & Ana Rodríguez-Álvarez & Tooraj Jamasb, 2018. "Objective vs. Subjective Fuel Poverty and Self-Assessed Health," Working Papers EPRG 1823, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
  • Handle: RePEc:enp:wpaper:eprg1823
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    as
    1. Andrew Clark & Fabrice Etilé & Fabien Postel-Vinay & Claudia Senik & Karine Van der Straeten, 2005. "Heterogeneity in Reported Well-Being: Evidence from Twelve European Countries," Economic Journal, Royal Economic Society, vol. 115(502), pages 118-132, March.
    2. Euan Phimister, Esperanza Vera-Toscano and Deborah Roberts, 2015. "The Dynamics of Energy Poverty: Evidence from Spain," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 1).
    3. Raffaele Miniaci & Carlo Scarpa & Paola Valbonesi, 2008. "Distributional Effects of Price Reforms in the Italian Utility Markets," Fiscal Studies, Institute for Fiscal Studies, vol. 29(1), pages 135-163, March.
    4. Waddams Price, Catherine & Brazier, Karl & Wang, Wenjia, 2012. "Objective and subjective measures of fuel poverty," Energy Policy, Elsevier, vol. 49(C), pages 33-39.
    5. Fernandez-Blanco, Victor & Orea, Luis & Prieto-Rodriguez, Juan, 2009. "Analyzing consumers heterogeneity and self-reported tastes: An approach consistent with the consumer's decision making process," Journal of Economic Psychology, Elsevier, vol. 30(4), pages 622-633, August.
    6. Deller, David, 2018. "Energy affordability in the EU: The risks of metric driven policies," Energy Policy, Elsevier, vol. 119(C), pages 168-182.
    7. Ürge-Vorsatz, Diana & Tirado Herrero, Sergio, 2012. "Building synergies between climate change mitigation and energy poverty alleviation," Energy Policy, Elsevier, vol. 49(C), pages 83-90.
    8. Rodriguez-Alvarez, Ana & Orea, Luis & Jamasb, Tooraj, 2019. "Fuel poverty and Well-Being:A consumer theory and stochastic frontier approach," Energy Policy, Elsevier, vol. 131(C), pages 22-32.
    9. Roberts, Deborah & Vera-Toscano, Esperanza & Phimister, Euan, 2015. "Fuel poverty in the UK: Is there a difference between rural and urban areas?," Energy Policy, Elsevier, vol. 87(C), pages 216-223.
    10. Fahmy, Eldin & Gordon, David & Patsios, Demi, 2011. "Predicting fuel poverty at a small-area level in England," Energy Policy, Elsevier, vol. 39(7), pages 4370-4377, July.
    11. Thomson, Harriet & Snell, Carolyn, 2013. "Quantifying the prevalence of fuel poverty across the European Union," Energy Policy, Elsevier, vol. 52(C), pages 563-572.
    12. Dorothée Charlier & Bérangère Legendre, 2016. "Fuel Poverty: A Composite Index Approach," Policy Papers 2016.06, FAERE - French Association of Environmental and Resource Economists.
    13. Anderson, Will & White, Vicki & Finney, Andrea, 2012. "Coping with low incomes and cold homes," Energy Policy, Elsevier, vol. 49(C), pages 40-52.
    14. Peter Heindl, 2015. "Measuring Fuel Poverty: General Considerations and Application to German Household Data," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 71(2), pages 178-215, June.
    15. Moore, Richard, 2012. "Definitions of fuel poverty: Implications for policy," Energy Policy, Elsevier, vol. 49(C), pages 19-26.
    16. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2015. "A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand," Energy Economics, Elsevier, vol. 49(C), pages 599-609.
    17. William H. Greene & Mark N. Harris & Bruce Hollingsworth, 2015. "Inflated Responses in Measures of Self-Assessed Health," American Journal of Health Economics, MIT Press, vol. 1(4), pages 461-493, Fall.
    18. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Measuring energy poverty in Greece," Energy Policy, Elsevier, vol. 94(C), pages 157-165.
    19. Thomas F. Crossley & Federico Zilio, 2018. "The health benefits of a targeted cash transfer: The UK Winter Fuel Payment," Health Economics, John Wiley & Sons, Ltd., vol. 27(9), pages 1354-1365, September.
    20. Legendre, Bérangère & Ricci, Olivia, 2015. "Measuring fuel poverty in France: Which households are the most fuel vulnerable?," Energy Economics, Elsevier, vol. 49(C), pages 620-628.
    21. Clinch, J. Peter & Healy, John D., 2001. "Cost-benefit analysis of domestic energy efficiency," Energy Policy, Elsevier, vol. 29(2), pages 113-124, January.
    22. Dorothee Charlier and Berangere Legendre, 2019. "A Multidimensional Approach to Measuring Fuel Poverty," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    23. Ormandy, David & Ezratty, Véronique, 2012. "Health and thermal comfort: From WHO guidance to housing strategies," Energy Policy, Elsevier, vol. 49(C), pages 116-121.
    24. Papada, Lefkothea & Kaliampakos, Dimitris, 2018. "A Stochastic Model for energy poverty analysis," Energy Policy, Elsevier, vol. 116(C), pages 153-164.
    25. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    26. Recalde, Martina & Peralta, Andrés & Oliveras, Laura & Tirado-Herrero, Sergio & Borrell, Carme & Palència, Laia & Gotsens, Mercè & Artazcoz, Lucia & Marí-Dell’Olmo, Marc, 2019. "Structural energy poverty vulnerability and excess winter mortality in the European Union: Exploring the association between structural determinants and health," Energy Policy, Elsevier, vol. 133(C).
    27. Maciej Lis & Katarzyna Salach & Konstancja Swiecicka, 2016. "Heterogeneity of the fuel poor in Poland – quantification and policy implications," IBS Working Papers 08/2016, Instytut Badan Strukturalnych.
    28. Scarpellini, Sabina & Alexia Sanz Hernández, M. & Moneva, José M. & Portillo-Tarragona, Pilar & Rodríguez, María Esther López, 2019. "Measurement of spatial socioeconomic impact of energy poverty," Energy Policy, Elsevier, vol. 124(C), pages 320-331.
    29. Antonio Alvarez & Julio del Corral, 2010. "Identifying different technologies using a latent class model: extensive versus intensive dairy farms," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 37(2), pages 231-250, June.
    30. Maciej Lis & Agata Miazga & Katarzyna Salach, 2016. "Location, location, location. What accounts for regional variation of fuel poverty in Poland?," IBS Working Papers 09/2016, Instytut Badan Strukturalnych.
    31. Lawson, Rob & Williams, John & Wooliscroft, Ben, 2015. "Contrasting approaches to fuel poverty in New Zealand," Energy Policy, Elsevier, vol. 81(C), pages 38-42.
    32. Vilches, Alberto & Barrios Padura, Ángela & Molina Huelva, Marta, 2017. "Retrofitting of homes for people in fuel poverty: Approach based on household thermal comfort," Energy Policy, Elsevier, vol. 100(C), pages 283-291.
    33. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    34. Florian Fizaine & Sondès Kahouli, 2018. "On the power of indicators: how the choice of fuel poverty indicator affects the identification of the target population," Post-Print halshs-01957436, HAL.
    35. John Hills, 2012. "Getting the measure of fuel poverty: Executive summary," CASE Briefs 31, Centre for Analysis of Social Exclusion, LSE.
    36. Liddell, Christine & Morris, Chris, 2010. "Fuel poverty and human health: A review of recent evidence," Energy Policy, Elsevier, vol. 38(6), pages 2987-2997, June.
    37. Miniaci, Raffaele & Scarpa, Carlo & Valbonesi, Paola, 2014. "Energy affordability and the benefits system in Italy," Energy Policy, Elsevier, vol. 75(C), pages 289-300.
    38. Hills, John, 2011. "Fuel poverty: the problem and its measurement," LSE Research Online Documents on Economics 39270, London School of Economics and Political Science, LSE Library.
    39. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    40. Scarpellini, Sabina & Rivera-Torres, Pilar & Suárez-Perales, Inés & Aranda-Usón, Alfonso, 2015. "Analysis of energy poverty intensity from the perspective of the regional administration: Empirical evidence from households in southern Europe," Energy Policy, Elsevier, vol. 86(C), pages 729-738.
    41. repec:aen:journl:eeep4_1_phimister is not listed on IDEAS
    42. repec:dau:papers:123456789/14815 is not listed on IDEAS
    43. Florian Fizaine & Sondès Kahouli, 2018. "On the power of indicators: how the choice of the fuel poverty measure affects the identification of the target population," Policy Papers 2018.01, FAERE - French Association of Environmental and Resource Economists.
    44. Dubois, Ute, 2012. "From targeting to implementation: The role of identification of fuel poor households," Energy Policy, Elsevier, vol. 49(C), pages 107-115.
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    Cited by:

    1. Marlena Piekut, 2020. "Patterns of Energy Consumption in Polish One-Person Households," Energies, MDPI, Open Access Journal, vol. 13(21), pages 1-31, October.

    More about this item

    Keywords

    Fuel poverty in Spain; self-assessed health; latent class ordered probit model.;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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