TY - JOUR
T1 - A Spatial Stochastic Frontier Model with Omitted Variables
T2 - Electricity Distribution in Norway
AU - Orea, Luis
AU - Álvarez, Inmaculada C.
AU - Jamasb, Tooraj
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Spatial econometrics
KW - Stochastic frontier models
KW - Environmental conditions
KW - Electricity distribution networks
KW - Spatial econometrics
KW - Stochastic frontier models
KW - Environmental conditions
KW - Electricity distribution networks
UR - https://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=954921379761&rft.object_portfolio_id=&svc.holdings=yes&svc.fulltext=yes
U2 - 10.5547/01956574.39.3.lore
DO - 10.5547/01956574.39.3.lore
M3 - Journal article
VL - 39
SP - 93
EP - 116
JO - The Energy Journal
JF - The Energy Journal
SN - 0195-6574
IS - 3
ER -