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Necessity or Luxury Good? Household Energy Spending and Income in Britain 1991-2007

Helena Meier, Tooraj Jamasb, and Luis Orea

Year: 2013
Volume: Volume 34
Number: Number 4
DOI: 10.5547/01956574.34.4.6
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Abstract:
In recent years, many households around the world have experienced reductions in real incomes and higher energy prices, both of which have important demand and welfare implications. A better understanding of the socio-economic determinants of household energy demand and spending is therefore important from a welfare perspective. This is particularly useful in the case of liberalised energy markets where there is a need to devise new and innovative energy policies for the residential sector. This paper explores British household spending on energy in total and on electricity and gas separately. As the relative importance of essential or luxury services of energy varies with income, we focus our analysis on this driver of energy spending and estimate Engel spending curves using static and dynamic models for a panel dataset comprising over 77,000 observations for the 1991-2007 period. The lack of household level price data is common in liberalized retail energy markets. This issue is addressed by a new modeling approach based on within and between differences in regional energy prices. We find that the Engel spending curves are S-shaped. Income elasticities for energy spending are, however, U-shaped and smaller than unity, suggesting that energy services are a necessity for households.



Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks

Luis Orea and Tooraj Jamasb

Year: 2017
Volume: Volume 38
Number: Number 4
DOI: 10.5547/01956574.38.4.lore
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Abstract:
Since the 1990s, electricity distribution networks in many countries have been subject to incentive regulation. The sector regulators aim to identify the best performing utilities as frontier firms to determine the relative efficiency of firms. This paper develops a nested latent class (NLC) model approach where unobserved differences in firm performance are modelled using two `zero inefficiency stochastic frontier' (ZISF) models nested in a `latent class stochastic frontier' (LCSF) model. This captures the unobserved differences due to technology or environmental conditions. A Monte Carlo simulation suggests that the proposed model does not suffer from identification problems. We illustrate the proposed model with an application to Norwegian distribution network utilities for the period 2004-2011. We find that the efficiency scores in both LCSF and ZISF models are biased, and some firms in the ZISF model are wrongly labelled as inefficient. Conversely, inefficient firms may be wrongly labelled as being fully efficient by the ZISF model.



A Spatial Stochastic Frontier Model with Omitted Variables: Electricity Distribution in Norway

Luis Orea, Inmaculada C. Álvarez, and Tooraj Jamasb

Year: 2018
Volume: Volume 39
Number: Number 3
DOI: 10.5547/01956574.39.3.lore
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Abstract:
An important methodological issue in efficiency analysis for incentive regulation of utilities is how to account for the effect of unobserved cost drivers such as environmental factors. We combine a spatial econometric approach with stochastic frontier analysis to control for unobserved environmental conditions when measuring efficiency of electricity distribution utilities. Our empirical strategy relies on the geographic location of firms as a source of information that has previously not been explored in the literature. The underlying idea is to utilise data from neighbouring firms that can be spatially correlated as proxies for unobserved cost drivers. We illustrate this approach using a dataset of Norwegian distribution utilities for the 2004-2011 period. We show that the lack of information on weather and geographic conditions can be compensated with data from surrounding firms. The methodology can be used in efficiency analysis and regulation of other utilities sectors where unobservable cost drivers are important, e.g. gas, water, agriculture, fishing.





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