The Microeconometric Approach to Modelling Energy Demand: Some Results for Uk Households

Baker, Paul; Blundell, Richard

doi:10.1093/oxrep/7.2.54

Cited by 66 publications

(39 citation statements)

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“…In addition, a number of studies also stress the need for sub-national disaggregation when estimating elasticities of residential energy demand -e.g. Garcia-Cerrutti (2000) using data across California, Hanemann et al (2013) using data in Spain, Baker and Blundell (1991) using data in the UK, and Bernstein and Griffin (2006), Uri (1983) and Hsing (1992) using data across the US.…”

Section: Model and Its Limitationsmentioning

confidence: 99%

“…Many other papers control for substitution between gas and electricity and some make important contributions to measuring price elasticities across wealth quintiles. But they do not consider other energy substitutes including wood, liquid fuel or LPG simultaneously, meaning that they too overestimate the potential effects of energy tariff increases -for example, the studies by Baker and Blundell (1991) in the UK, Zhang (2011) in Turkey and Vásquez et al (2011) in the US. 4 The results here illustrate that in estimating the potential effects of this significant gas price reform in Armenia while not controlling for substitution between all the energy sources of gas, electricity, LPG, wood and liquid fuel overestimates the welfare loss in total consumption for households in the bottom quintile by an estimated 36%.…”

Section: Introductionmentioning

confidence: 99%

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How natural gas tariff increases can influence poverty: Results, measurement constraints and bias

Krauß

2016

Energy Economics

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Abstract. Energy tariff increases are generally essential to address environmental and fiscal concerns but they can also push households into poverty. This paper estimates the expected poverty and distributional effects of a significant natural gas tariff reform in the context of Armenia that increased the country's residential tariff by about 40%. It is the first paper in the literature on energy tariff reforms to simultaneously try and control for substitution between all major energy sources (not just some), to take into account the seasonality of consumption over the full annual cycle, and to apply different methods to assess changes in household consumption on natural gas and shifts in natural gas between main and supplementary heating sources. Existing papers thus generally overestimate the potential effects of energy price increases on household welfare. The results here -which face, like any statistical study, a set of important methodological constraints -suggest nonetheless that this significant tariff increase led to an estimated 8% of households shifting away from gas, mainly towards wood, as their heating source. It consequently resulted in an estimated 2.8% of households falling below the national poverty line, while likely also influencing non-monetary human welfare that cannot be well captured econometrically. Finally, methodological assumptions and limitations in assessing these relationships, as well as potential policy implications are outlined. This article has been published and is available online at http://dx

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Section: Model and Its Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How natural gas tariff increases can influence poverty: Results, measurement constraints and bias

Krauß

2016

Energy Economics

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“…10 Northern Ireland's Utility Regulator (URegNI) (2010) provides data on annual Gas demand in the residential sector in NI and we assume that the daily profile follows that of ROI. Many studies including Baker and Blundell (1991) and Berkhout et al (2004) find that the elasticity of demand for gas in the residential sector is inelastic. We use an elasticity of -0.16, estimated by Asche, Nilsen and Tveterås (2008).…”

Section: Consumer Surplusmentioning

confidence: 99%

“…We use an elasticity of -0.16, estimated by Asche, Nilsen and Tveterås (2008). We also carry out a sensitivity analysis in which we decrease the elasticity to -0.5 as this is the elasticity estimated for UK households (Baker and Blundell, 1991). We also increase the elasticity to -.01.…”

Section: Consumer Surplusmentioning

confidence: 99%

The cost of natural gas shortages in Ireland

Leahy¹,

Devitt²,

Lyons³

et al. 2012

Energy Policy

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This paper investigates the economic implications of disruptions of one to ninety days to the supply of natural gas in Ireland. We assess the impact of a hypothetical gas supply disruption in both winter and summer in 2008 (with observed market characteristics) and in 2020 (with projected market characteristics). The cost of a natural gas outage includes the cost of natural gas being unavailable for heating and other purposes in the industrial and commercial sectors, lost consumer surplus in the residential sector, the cost of lost electricity in all sectors and lost VAT on the sale of gas and electricity. Ireland produces much of its electricity from natural gas and the loss of this electricity accounts for the majority of the cost of a natural gas outage. Losing gas-fired electricity would cost 0.1 to 1.0 billion euro per day, depending on the time of week, the time of year, and rationing of electricity. Industry should be rationed before households to minimize economic losses, but current emergency protocols favour industry. If gas-fired electricity is unavailable for three months, the economic loss could be up to 80 billion euro, about half of Gross Domestic Product. Losing gas for heating too would add up to approximately 8 billion euro in economic losses. We also discuss some options to increase Ireland's security of supply, and find that the cost is a small fraction of the avoided maximum damage. ESRI working papers represent un-refereed work-in-progress by researchers who are solely responsible for the content and any views expressed therein. Any comments on these papers will be welcome and should be sent to the author(s) by email. Papers may be downloaded for personal use only.

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“…Although in disaggregated models one representative consumer per group is used, still the process remains arbitrary and judgmental. Assimakopoulos (1992) suggested an approach of endogenously obtaining homogeneous groups of consumers using a two stage process: a 33 See Dubin and McFadden (1984), Baker and Blundell (1991) and Baker et al (1989) as well. structural analysis of households using statistical techniques and then modeling demand equations.…”

Section: Econometric Approachmentioning

confidence: 99%

Energy Demand Models For Policy Formulation: A Comparative Study Of Energy Demand Models

Bhattacharyya¹,

Timilsina²

2009

Policy Research Working Papers

127

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This paper critically reviews existing energy demand forecasting methodologies highlighting the methodological diversities and developments over the past four decades in order to investigate whether the existing energy demand models are appropriate for capturing the specific features of developing countries. The study finds that two types of approaches, econometric and end-use accounting, are used in the existing energy demand models. Although energy demand models have greatly evolved since the early 1970s, key issues such as the poor-rich and urban-rural divides, traditional energy resources, and differentiation between commercial and non-commercial energy This paper-a product of the Environment and Energy Team, Development Research Group-is part of a larger effort in the department to study climate change and clean energy issues. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gtimilsina@worldbank.org.commodities are often poorly reflected in these models. While the end-use energy accounting models with detailed sector representations produce more realistic projections compared with the econometric models, they still suffer from huge data deficiencies especially in developing countries. Development and maintenance of more detailed energy databases, further development of models to better reflect developing country context, and institutionalizing the modeling capacity in developing countries are the key requirements for energy demand modeling to deliver richer and more reliable input to policy formulation in developing countries. Energy Demand Models for Acronyms and Definitions DefinitionsA few terms appear recurrently in this report: tool, model, method, methodology, approach and framework. We give the dictionary meaning of these terms and indicate how we have used them in this paper.A "tool" is "the means whereby some act is accomplished" or "an implement used in the practice of a vocation". In this paper, we use the term to imply an implement or a kit that is used in analyzing energy demand or forecasting demand or in analyzing the energy system in its part or whole.A "model" is defined as "a simplified description of a complex entity or process". In this paper, we use models to imply a simplified representation of a complex problem or process, often in mathematical terms, that helps us in conceptualizing and analyzing the problem. In this sense, a model can be a tool or can employ a number of tools in a systematic way.A "framework" is defined as "a structure supporting or containing something" or "a simplified description of a complex entity or process". In this paper, we have used this term interchangeably with a model.A "method" is defined as "a way of doing something, especially a systematic way; implies an orderly logical arrangement (usually in steps)". We have used the term to mean a logical, systematic approach for accomplishing a task.An "approach" is defined as the "ideas or actions intended to deal with a problem or situatio...

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The Microeconometric Approach to Modelling Energy Demand: Some Results for Uk Households

Cited by 66 publications

References 8 publications

How natural gas tariff increases can influence poverty: Results, measurement constraints and bias

How natural gas tariff increases can influence poverty: Results, measurement constraints and bias

The cost of natural gas shortages in Ireland

Energy Demand Models For Policy Formulation: A Comparative Study Of Energy Demand Models

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