Informal Benchmarks as a Source of Regulatory Threat in Unregulated Utility Sectors

This paper investigates to what extent unregulated local monopolies attempt not to evoke the introduction of a formal price regulation by conforming to customers’ and authorities’ expectations. It is argued that utilities can meet expectations by setting prices that imitate neighbours’ prices. The empirical evaluation rests on a cross-sectional data set representing all Swedish district heating utilities, and on a flexible nonlinear IV specification. It is found that while utilities’ price setting schemes are insensitive to customer complaints, they are significantly influenced by the passive monitoring by authorities. The spillover effect from the 5-6 closest neighbours is around 40 %.regulatory threat, spatial correlation, price, district heating, Sweden

"The influence of decision-maker effort and case complexity on appealed rulings subject to multi-categorical selection"

This study extends the standard econometric treatment of appellate court outcomes by 1) considering the role of decision-maker effort and case complexity, and 2) adopting a multi-categorical selection process of appealed cases. We find evidence of appellate courts being affected by both the effort made by first-stage decision makers and case complexity. This illustrates the value of widening the narrowly defined focus on heterogeneity in individual-specific preferences that characterises many applied studies on legal decision-making. Further, the majority of appealed cases represent non-random sub-samples and the multi-categorical selection process appears to offer advantages over the more commonly used dichotomous selection models.Appeal, Decision-maker effort, Case complexity, Selection bias. JEL classification:K41, C34

Informal benchmarks as a source of regulatory threat in unregulated utility sectors

This paper investigates to what extent unregulated local monopolies attempt not to evoke the introduction of a formal price regulation by conforming to customers' and authorities' expectations. It is argued that utilities can meet expectations by setting prices that imitate neighbours' prices. The empirical evaluation rests on a cross-sectional data set representing all Swedish district heating utilities, and on a flexible nonlinear IV specification. It is found that while utilities' price setting schemes are insensitive to customer complaints, they are significantly influenced by the passive monitoring by authorities. The spillover effect from the 5-6 closest neighbours is around 40 %

Spatial price homogeneity as a mechanism to reduce the threat of regulatory intervention in locally monopolistic sectors

We claim that a reason for why unregulated investor-owned local monopolies do not always charge the monopoly price is that they are threatened by customer complaints that may lead to retaliations from local elected officials. When investor-owned monopolies are exposed to this threat they will mimic the price(s) of their neighbour(s); the stronger the threat, the higher the spatial price correlation. The threat increases when elected officials have pro-consumer preferences and neighbours are geographically close. The empirical analysis, based on a complete cross-sectional data set from the Swedish district heating sector in 2007, confirms the theoretical predictions

The influence of decision-maker effort and case complexity on appealed rulings subject to multi-categorical selection

This study extends the standard econometric treatment of appellate court outcomes by 1) considering the role of decision-maker effort and case complexity, and 2) adopting a multi-categorical selection process of appealed cases. We find evidence of appellate courts being affected by both the effort made by first-stage decision makers and case complexity. This illustrates the value of widening the narrowly defined focus on heterogeneity in individual-specific preferences that characterises many applied studies on legal decision-making. Further, the majority of appealed cases represent non-random sub-samples and the multi-categorical selection process appears to offer advantages over the more commonly used dichotomous selection models

Spatial price homogeneity as a mechanism to reduce the threat of regulatory intervention in locally monopolistic sectors

We claim that a reason for why unregulated investor-owned local monopolies do not always charge the monopoly price is that they are threatened by customer complaints that may lead to retaliations from local elected officials. When investor-owned monopolies are exposed to this threat they will mimic the price(s) of their neighbour(s); the stronger the threat, the higher the spatial price correlation. The threat increases when elected officials have pro-consumer preferences and neighbours are geographically close. The empirical analysis, based on a complete cross-sectional data set from the Swedish district heating sector in 2007, confirms the theoretical predictions.

Regulatory behaviour under threat of court reversal: theory and evidence from the Swedish electricity market

This paper investigates how regulators influence outcomes in regulated markets when their decisions are subject to the threat of court review. We develop a theoretical model that provides a number of behavioural implications when (i) all regulators' dislike having their decisions overturned by courts, (ii) inexperienced regulators care more about not having their decisions overturned than experienced regulators, and (iii) experienced regulators also care about consumer surplus. The theoretical implications are tested using a database of Swedish regulatory decisions from the electricity distribution sector. We provide empirical evidence that inexperienced regulators are more likely to set higher regulated prices than experienced regulators, and as the complexity of the case increases, there are on average more overturned decisions and higher prices for inexperienced regulators. The links between experience, complexity and regulatory outcomes are both statistically and economically significant. Simulations show that if those decisions that were not appealed had been appealed, then the court would have lowered the prices by 10% on average

Titrating Polyelectrolytes - Variational Calculations and Monte Carlo Simulations

Variational methods are used to calculate structural and thermodynamical properties of a titrating polyelectrolyte in a discrete representation. The Coulomb interactions are emulated by harmonic repulsive forces, the force constants being used as variational parameters to minimize the free energy. For the titrating charges, a mean field approach is used. The accuracy is tested against Monte Carlo data for up to 1000 monomers. For an unscreened chain, excellent agreement is obtained for the end-to-end distance and the apparent dissociation constant. With screening, the thermodynamical properties are invariably well described, although the structural agreement deteriorates. A very simple rigid-rod approximation is also considered, giving surprisingly good results for certain properties.Comment: 22 pages, PostScript, 9 figure

Consumer myopia, imperfect competition and the energy efficiency gap: evidence from the UK refrigerator market

The empirical literature on the energy efficiency gap concentrates on demand inefficiencies in the energy-using durables markets and finds evidence that consumers underestimate future energy costs when purchasing a new appliance. We take a broader view and also consider the impact of imperfect competition. Using data on the UK refrigerator market (2002-2007), we find that the average energy consumption of appliances sold during this period was only 7.2% higher than what would have been observed under a scenario with a perfectly competitive market and non-myopic consumers. One reason for this small gap is that market power actually reduces energy use

Are Natural Domain Foundation Models Useful for Medical Image Classification?

The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OpenCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistently outperforms the standard practice of ImageNet pretraining. However, other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024