TITLE: Regulating Local Monopolies in
Electricity Transmission: A Real-world Application of the StoNED Method

SPEAKER:  Andrew Johnson

ABSTRACT:

The Finnish electricity market
has a competitive energy generation market and a monopolistic transmission
system. To regulate the local monopoly power of network operators, the
government regulator uses frontier estimation methods (e.g., Stochastic
Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA)) to
identify excessive transmission costs, taking into account outputs and the
operating environment. We describe the new regulatory system developed for the
Finnish regulator, which is based on the method Stochastic Non-smooth
Envelopment of Data (StoNED) and utilizes panel data to detect the excessive
costs from random noise.

The literature of
productive efficiency analysis is divided into two main branches: the parametric
SFA and nonparametric DEA. StoNED is a new frontier estimation framework that
combines the virtues of both DEA and SFA in a unified approach to frontier
analysis. StoNED follows the SFA approach by including a stochastic component. In
contrast to SFA, however, the proposed method does not make any prior
assumptions about the functional form of the production function. In that
respect, StoNED is similar to DEA, and only imposes free disposability,
convexity, and some returns to scale specification.

The main advantage of
the StoNED approach to the parametric SFA approach is the independence of the ad
hoc parametric assumptions about the functional form of the production function
(or cost/distance functions). In contrast to the flexible functional forms, one
can impose monotonicity, concavity and homogeneity constraints without
sacrificing the flexibility of the regression function. Additionally, the main
advantage of StoNED to the nonparametric DEA approach is robustness to
outliers, data errors, and other stochastic noise in the data. In DEA the
frontier is spanned by a relatively small number of efficient firms, however,
in our method all observations influence the shape of the frontier. Also many
standard tools from parametric regression such as goodness of fit statistics
and statistical tests are directly applicable in our approach. This is collaborate
work with Timo Kuosmanen of Aalto University in Finland.

 Andrew L Johnson is an Assistant
Professor in the Department of Industrial and Systems Engineering at Texas
A&M University. He obtained his B.S. in Industrial and Systems Engineering
from Virginia Tech and his M.S. and Ph.D. from the H. Milton Stewart School of
Industrial and Systems Engineering from Georgia Tech. His research interests
include productivity and efficiency measurement, warehouse design and
operations, material handling and mechanism design. He is a member of the
INFORMS, National Eagle Scout Association, and German Club of Virginia Tech.