The Direct Costs and Benefits of US Electric Utility Divestitures

This paper studies the impact of divestiture on the efficiency and costs of electric utilities. The empirical literature shows that there exist economies of scope for electric utilities and that divestiture decreases distribution efficiency but increases generation efficiency. This paper is to bring together these different results. Our analysis covers distribution, transmission, and power sourcing. Our data is an unbalanced panel of about 138 US electric utilities for the years 1994 to 2006 over which we observe 30 divestitures between 1997 and 2003. First, we regress firmlevel efficiencies for distribution and power sourcing on various divestiture indicators. Second, we compare the weighted cost between divested and non-divested firms and calculate a net present value for the entire sample of divestitures. Last, we regress net benefits from divestiture on the distribution side on the net benefit for power sourcing to see whether individual firms successfully off-set any costs of divestiture. We find that divestiture reduces distribution efficiency but increases power sourcing efficiency. Both effects depend on the amount of own nuclear generation output but not fossil-fuel or hydro output. The net present value for all divestitures in our sample is $11.3 billion. It seems that relatively lower costs of power outweigh losses in economies of scope as well as other restructuring costs. However, lower costs of power might be the result of favourable contracts put in place at the time of divestiture. Our study complements traditional studies of economies of scope and shows that divestitures might well be worth it

Estimating economies of scale and scope with flexible technology

The final publication is available at Springer via http://dx.doi.org/10.1007/s11123-016-0467-1Economies of scope are typically modelled and estimated using a cost function that is common to all firms in an industry irrespective of their type, e.g. whether they specialize in a single output or produce multiple outputs. Instead, we estimate a flexible technology model that allows for type-specific technologies and show how it can be estimated using linear parametric forms including the translog. A common technology remains a special case of our model and is testable econometrically. Our sample, of publicly owned US electric utilities, does not support a common technology for integrated and specialized firms. Our empirical results therefore suggest that assuming a common technology might bias estimates of economies of scale and scope. Thus, how we model the production technology clearly influences the policy conclusions we draw from its characteristics

Mind the Gap: Measuring Academic Underachievement Using Stochastic Frontier Analysis

We propose using Stochastic Frontier Analysis to estimate pupils’ academic underachievement. We model underachievement as the gap between expected achievement and actual achievement, not due to a learning disability. Our data are a panel for 2,228 Belgian pupils observed over 6 years of primary education. We found that the average underachievement gap is 23.5%. That is, the average pupil does not exploit about one fourth of their potential. Gifted pupils appear to underachieve as much as non-gifted pupils. We also found that class size is a determinant of underachievement. The association between class size and underachievement is non-monotonic, with an underachievement minimum at a class size of about 20 pupils

Mind the Gap: Measuring Academic Underachievement Using Stochastic Frontier Analysis

We propose using Stochastic Frontier Analysis to estimate pupils’ academic underachievement. We model underachievement as the gap between expected achievement and actual achievement, not due to a learning disability. Our data are a panel for 2,228 Belgian pupils observed over 6 years of primary education. We found that the average underachievement gap is 23.5%. That is, the average pupil does not exploit about one fourth of their potential. Gifted pupils appear to underachieve as much as non-gifted pupils. We also found that class size is a determinant of underachievement. The association between class size and underachievement is non-monotonic, with an underachievement minimum at a class size of about 20 pupils