Luis Orea

Parametric Decomposition of a Generalized Malmquist Productivity Index

This paper provides a parametric decomposition of a generalized Malmquist productivity index which takes into account scale economies. Unlike Balk (2001), the contribution of scale economies to productivity change is evaluated without recourse to scale efficiency measures, which are neither bounded for globally increasing, decreasing, or constant returns to scale technologies nor for ray-homogeneous technologies. An empirical application using panel data from Spanish savings banks is included. This application shows the advantages of the suggested method compared to Balks approach. The results show an increase of total factor productivity which can be mainly attributed to technical progress and the positive effect of returns to scale.

Mergers and technical efficiency in Spanish savings banks: A stochastic distance function approach

Abstract The aim of this paper is to test the temporal variation of technical efficiency of Spanish savings banks during the period 1985–1998. Furthermore, we test whether merged and non-merged firms have different levels and temporal patterns of technical efficiency. A stochastic output distance function (R.W. Shephard, Theory of Cost and Production Functions, Princeton University Press, Princeton, NJ) is employed to accommodate multiple output technology. The distance function provides the advantage that it does not need information about prices, so it can accommodate the multi-product nature of the financial sector only using the quantities as data (an important point when the assumptions about perfectly competitive markets are unlikely to be met). The temporal variation of efficiency is modeled extending the Battese and Coelli (Journal of Productivity Analysis 3 (1992) 153–169) approach in two ways: relaxing the monotonicity of the temporal variation pattern of the efficiency term, and allowing for different patterns of efficiency change between merged and non-merged firms.

Necessity or Luxury Good? Household Energy Spending and Income in Britain 1991-2007

The residential demand for energy is growing steadily and the trend is expected to continue for the foreseeable future. Household spending on energy services tends to increase with income. We explore household total spending on energy and on electricity and gas separately. We use an extensive British household panel data with more than 77,000 observations for the 1991-2007 period to explore the determinants of energy spending. We analyse income as a main driver of spending on energy and draw Engel spending curves for these. The lack of household level price data in liberalized retail energy markets is addressed by a new modelling approach to reflect within and between regional differences in energy prices. Also, long run changes in energy spending of households are approximated by exploring unit effects. The main results show the Engel spending curves are S-shaped. Income elasticities for energy spending are U-shaped and lower than unity, suggesting that energy services are a necessity for households. Moreover, the findings show that the income elasticity of energy spending is somewhat higher in the long run. Finally, we find a dynamic link between energy spending and income changes rather than a fixed budget threshold where basic needs are met. Hence, we suggest policy approaches that enable households to find their individual utility maximizing energy spending levels.Keywords: Burr distribution; Durations; Range; Score; Un-observed components; Weibull distribution

Estimating Market Power in Homogenous Product Markets Using a Composed Error Model: Application to the California Electricity Market

This study contributes to the literature on estimating market power in homogenous product markets. We estimate a composed error model, where the stochastic part of the firm’s pricing equation is formed by two random variables: the traditional error term, capturing random shocks, and a random conduct term, which measures the degree of market power. Treating firms’ conduct as a random parameter helps solving the issue that the conduct parameter can vary between firms and within firms over time. The empirical results from the California wholesale electricity market suggest that realization of market power varies over both time and firms, and reject the assumption of a common conduct parameter for all firms. Notwithstanding these differences, the estimated firm-level values of the conduct parameter are closer to Cournot than to static collusion across all specifications. For some firms, the potential for realization of the market power unilaterally is associated with lower values of the conduct parameter.

Efficiency and Environmental Factors in the US Electricity Transmission Industry

The electricity industry in most developed countries has been restructured over recent decades with the aim of improving both service quality and firms’ performance. Regulated segments (e.g. transmission) still provide the infrastructure for the competitive segments and represent a notable amount of the total price paid by final customers. However there is a lack of empirical studies that analyze firms’ performance in the electricity transmission sector. We conduct an empirical analysis of the US electricity transmission companies for the period 2001-2009. We use stochastic frontier models that allow us to identify determinants of firms’ inefficiency and to control for weather conditions, potentially one of the most decisive uncontrollable factors in electricity transportation. Our results suggest that there is room for improvement in the performance of the US electricity transmission system. Regulators should also take into account that more adverse conditions generate higher levels of inefficiency and that achieving long-term efficiency improvements tends to deteriorate firms’ short-term relative performance.

Weather Factors and Performance of Network Utilities: A Methodology and Application to Electricity Distribution

Incentive regulation and efficiency analysis of network utilities often need to take the effect of important external factors, such as the weather conditions, into account. This paper presents a method for estimating the effect of weather conditions on the costs of electricity distribution networks using parametric techniques. It examines whether the use of popular statistical variable reduction techniques is conceptually and econometrically sound for analyzing the effect of weather on the network costs. In this paper we estimate cost functions with the whole set of weather variables, identifying, when necessary, a subset of variables that can accurately reflect the effects of weather conditions. We show that weather conditions significantly affect distribution costs and the absence of weather variables has a downward biased impact on the effect of quality on costs. Also, the performance of statistical weather composites to capture this effect is poor. Finally, we show that there is a distinction between the effects of persistent and time varying weather conditions.

A Parametric Approach to Estimating Eco-Efficiency

The concept of eco-efficiency is becoming increasingly popular as a tool to capture economic and environmental aspects of agricultural production. The literature to date has exclusively used the Data Envelopment Analysis (DEA) approach to measure producers’ eco-efficiency. We show that it can also be estimated using a Stochastic Frontier Analysis (SFA) approach. Our approach not only allows controlling for random noise in the data but also permits an analysis of the potential substitutability between environmental pressures. We provide an empirical application of our model to data on a sample of Spanish dairy farms.

Measuring Eco-efficiency Using the Stochastic Frontier Analysis Approach

The concept of eco-efficiency has been receiving increasing attention in recent years in the literature on the environmental impact of economic activity. Eco-efficiency compares economic results derived from the production of goods and services with aggregate measures of the environmental impacts (or ‘pressures’) generated by the production process. The literature to date has exclusively used the Data Envelopment Analysis (DEA) approach to construct this index of environmental pressures, and determinants of eco-efficiency have typically been incorporated by carrying out bootstrapped truncated regressions in a second stage. We advocate the use of a Stochastic Frontier Analysis (SFA) approach to measuring eco-efficiency. In addition to dealing with measurement errors in the data, the stochastic frontier model we propose allows determinants of eco-efficiency to be incorporated in a one stage. Another advantage of our model is that it permits an analysis of the potential substitutability between environmental pressures. We provide an empirical application of our model to data on a sample of Spanish dairy farms which was used in a previous study of the determinants eco-efficiency that employed DEA-based truncated regression techniques and that serves as a useful benchmark for comparison.

Using a spatial econometric approach to mitigate omitted variables in stochastic frontier models: An application to Norwegian electricity distribution networks

An important methodological issue for the use of efficiency analysis in incentive regulation of regulated utilities is how to account for the effect of unobserved cost drivers such as environmental factors. This study combines the spatial econometric approach with stochastic frontier techniques to control for unobserved environmental conditions when measuring firms’ efficiency in the electricity distribution sector. Our empirical strategy relies on the geographic location of the firms as a useful source of information that has previously not been explored in the literature. The underlying idea in our empirical proposal is to utilise variables from neighbouring firms that are likely to be spatially correlated as proxies for the unobserved cost drivers. We illustrate our approach using the data of Norwegian distribution utilities for the years 2004 to 2011. We find that the lack of information on weather and geographic conditions can likely be compensated with data from surrounding firms using spatial econometric techniques. Combining efficiency analysis and spatial econometrics methods improve the goodness-of-fit of the estimated models and, hence, more accurate (fair) efficiency scores are obtained. The methodology can also be used in efficiency analysis and regulation of other types of utility sectors.