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

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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Energy Specializations: Electricity – Local Distribution; Electricity – Distributed Generation; Electricity – Policy and Regulation

JEL Codes: Q54: Climate; Natural Disasters and Their Management; Global Warming, Q56: Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth, C51: Model Construction and Estimation, R15: General Regional Economics: Econometric and Input-Output Models; Other Models, R12: Size and Spatial Distributions of Regional Economic Activity, C52: Model Evaluation, Validation, and Selection, Q48: Energy: Government Policy, Q24: Renewable Resources and Conservation: Land, Q21: Renewable Resources and Conservation: Demand and Supply; Prices, D22: Firm Behavior: Empirical Analysis

Keywords: spatial econometrics, stochastic frontier models, environmental conditions, electricity distribution networks

DOI: 10.5547/01956574.39.3.lore

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Published in Volume 39, Number 3 of the bi-monthly journal of the IAEE's Energy Economics Education Foundation.

 

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