Estimating Marginal Cost of Quality Improvements: The Case of the UK Electricity Distribution Companies

The main aim of this paper is to develop an econometric approach to estimation of marginal costs of improving quality of service. We implement this methodology by way of applying it to the case of the UK electricity distribution networks. The estimated marginal costs allow us to shed light on the effectiveness of the current UK incentive regulation to improve quality, and to derive optimal quality levels and welfare losses due to sub-optimal quality levels. The proposed method also allows us to measure the welfare effect of the observed quality improvements in the UK between 1995 and 2003. Our results suggest that while the incentive schemes established by the regulator to encourage utilities to reduce network energy losses leads to improvement in sector performance, they do not provide utilities with sufficient incentives to avoid power interruptions. We find that the observed improvements in quality during the period of this study only represented 30% of the potential customer welfare gains, and hence there was still significant scope for quality improvements

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

Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks

Although supervised dimension reduction methods have been extensively applied in different scientific fields, they have hardly ever been used in production economics. Nonetheless, these methods can also be useful in regulation of natural monopolies, where firms’ cost and performance are affected by a large number of environmental factors. As economic theory suggests, at the presence of other relevant production or cost drivers, the traditional all-inclusive assumption is not satisfied. This paper shows that purging the data allows us to address this issue when analyzing the effect of weather and geography on efficiency in the context of the Norwegian electricity distribution networks

A U-shaped protection of altitude against mortality and infection of COVID-19 in Peru: An ecological study

Background The COVID-19 pandemic has affected the world in multiple ways and has been a challenge for the health systems of each country. From the beginning, risk factors for the severity and mortality of the disease were considered, as the spread of the virus was related to the living conditions of each population. Methods In this ecological study we have evaluated the role of geography, precisely the altitude above sea level in the incidence and mortality of COVID-19 in Peru. Incidence and mortality data were taken from the open-access database of the government of Peru until March 2021. COVID-19 cases and COVID-19 mortality were treated as cases/density population and 1000 x cases/inhabitants while altitude was treated as continuous and as a categorical variable divided in 7 categories. The relationship between COVID-19 cases or deaths for COVID-19 and altitude as continuous variable was determined using Spearman correlation test. Meanwhile when altitude was considered as a categorical variable, Poisson regression or negative binomial analyses were applied. Results A significant inverse correlation was found between COVID-19 cases by population density and altitude (r=-0.37 p < 0.001). By altitude categories, the lowest risk for infection was observed between 3,000 and 3,500 m (IRR 0.08; 95% CI 0.05,0.12). Moreover, we found an inverse correlation between altitude and COVID-19 mortality (r=-0.39 p < 0.001). Also, the lowest risk for mortality was observed between 3,000 and 3,500 m (IRR 0.12; 95%CI 0.08; 0.18). Similar results were found when analyses were adjusted for inhabitants and stratified by sex. Conclusion This study reports an inverse relationship between COVID-19 incidence and mortality with respect to the altitude of residence, particularly, a u-shaped protection is shown, with a highest benefit between 3000 and 3500 m. The possibility of using hypoxia as an alternative treatment requires more complex studies that should allow knowing the physiological and environmental mechanisms of the protective role

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