Estimation of TFP growth:a semiparametric smooth coefficient approach

This article uses a semiparametric smooth coefficient model (SPSCM) to estimate TFP growth and its components (scale and technical change). The SPSCM is derived from a nonparametric specification of the production technology represented by an input distance function (IDF), using a growth formulation. The functional coefficients of the SPSCM come naturally from the model and are fully flexible in the sense that no functional form of the underlying production technology is used to derive them. Another advantage of the SPSCM is that it can estimate bias (input and scale) in technical change in a fully flexible manner. We also used a translog IDF framework to estimate TFP growth components. A panel of U.S. electricity generating plants for the period 1986–1998 is used for this purpose. Comparing estimated TFP growth results from both parametric and semiparametric models against the Divisia TFP growth, we conclude that the SPSCM performs the best in tracking the temporal behavior of TFP growth

Estimation of Allocative Inefficiency and Productivity Growth with Dynamic Adjustment Costs

A substantial literature has been generated on the estimation of allocative and technical inefficiency using static production, cost, profit, and distance functions. We develop a dynamic shadow distance system that integrates dynamic adjustment costs into a long-run shadow cost-minimization problem, which allows us to distinguish static allocative distortions from short-run inefficiencies that arise due to period-to-period adjustment costs. The set of estimating equations is comprised of the first-order conditions from the short-run shadow cost-minimization problem for the variable shadow input quantities, a set of Euler equations derived from subsequent shadow cost minimization with respect to the quasi-fixed inputs, and the input distance function, expressed in terms of shadow quantities. This system nests within it the static model with zero adjustment costs. Using panel data on U.S. electric utilities, we contrast the results of static and dynamic shadow distance systems. First, the zero-adjustment-cost restriction is strongly rejected. Second, we find that adjustment costs represent about 0.42% of total cost, and about 1.26% of capital costs. Third, while both models reveal that labor is not utilized efficiently, the dynamic model indicates a longer period of over-use and less variance over time in the degree of inefficiency. With the dynamic model, productivity growth is larger but more stable.Allocative inefficiency, Dynamic estimation, Euler equations, Productivity change, Technical change, Technical inefficiency,

Adjustment and unobserved heterogeneity in dynamic stochastic frontier models

Stochastic frontier models with autocorrelated inefficiency have been proposed in the past as a way of addressing the issue of temporal variation in firm-level efficiency scores. They are justified using an underlying model of dynamic firm behavior. In this paper we argue that these models could have radically different implications for the expected long-run efficiency scores in the presence of unobserved heterogeneity. The possibility of accounting for unobserved heterogeneity is explored. Random- and correlated random-effects dynamic stochastic frontier models are proposed and applied to a panel of US electric utilitie

Quality of service, efficiency and scale in network industries: an analysis of European electricity distribution

Quality of service (QoS) is of major economic significance in natural monopoly infrastructure industries. In this article, we present an efficiency analysis of electricity distribution networks from seven European countries. We apply the stochastic frontier analysis method to multi-output translog input distance function models to estimate cost efficiency and scale economies. We show that introducing the quality dimension into the analysis affects estimated efficiency significantly, especially that smaller utilities' efficiency seems to decrease. Our results emphasize that QoS should be an integrated part of efficiency and economic analysis of regulated natural monopolies.

Dynamic Analysis of Production

A parable of economic life is that some factors can adjust rapidly while others adjust slowly in a given time scale. Focusing on production analysis in the dynamic setting leads us to emphasize the technology specification that permits the theoretical construction that can be translated and amenable to empirical implementation. A historical perspective of the framing the dynamic decision-making is reviewed. The adjustment cost model of the investment is the key conceptual feature as it can be incorporated into the formal structure of a production technology, which offers the opportunity to exploit primal-dual theory in both analysis and empirical implementation. An overview of empirical formulations in both econometric (parametric) and nonparametric settings is discussed. Dynamic production decision environment allows explicitly for the evolution of assets implying firms may not be in long-run equilibrium at a given point in time. The dynamic generalizations of modern production theory concepts measuring economic performance are reviewed given the need to properly account and value the factors that are out of equilibrium. Empirical nonparametric and parametric approaches are addressed at length. While these cases can be addressed relatively easily within a nonparametric, dynamic data envelopment analysis setting, econometric formulations are a greater challenge