Network Regulation under electoral competition
Leroux, A., & Söderberg, M. (2023). Network Regulation under electoral competition. Energy Economics, 106614.
Leroux, A., & Söderberg, M. (2023). Network Regulation under electoral competition. Energy Economics, 106614.
Academics and policymakers generally agree that energy infrastructure should be subject to price regulation. More and more critics of modern regulatory approaches, however, point to the apparent failures of these mechanisms to achieve competitive pricing in practice. Some have suggested that customers ought to be involved in the regulatory process, but it is uncertain how customers’ perspectives can best be incorporated. In this study, we evaluate how electoral competition influences monopoly pricing by extending well-known regulatory laboratory experiments. We show that electoral competition has a significant and negative impact on prices. This effect disappears when electoral competition is implemented jointly with incentive regulation, implying substitutability rather than complementarity of regulation and electoral competition.
The article can be accessed in full here.
Leroux, A., & Söderberg, M.
2023
Energy Economics, 106614.
2023
Ratio Working Paper Series
Increasing waste levels, combined with ambitious environmental targets, are exerting upward pressures on the cost for municipal solid waste in many countries. The purpose of this study is to investigate what municipalities can do to counteract this development. We collect information about population, cost and waste from 225 Swedish and Norwegian municipalities and empirically investigate how waste bin structure/type of waste collection system and population affect municipalities’ waste cost. Results indicate that 4-compartment bins is the most expensive bin structure (+13%) and using the same bin types in detached and multi-family dwellings leads to coordination savings (-18%). The cost minimising population is slightly above 600,000 inhabitants. Several of the surveyed municipalities have substantially fewer inhabitants than that and cost per inhabitant can be reduced by up to 30% in several locations through collaborations with larger neighbours. In Sweden, transferring the responsibility for solid waste from the municipalities (290 in total) to the regions (20 in total) would eliminate almost all scale inefficiencies.
2023
SSRN 4333193.
We evaluate the effect of working from home on waste generated by individuals both at and away from their homes. To this end, we collect a unique dataset that matches administrative household-level waste data from Sweden with survey data on how many hours individuals work from home. A novel identification approach allows us to link waste generated away from home to the choice of work location. Our results suggest that working from home reduces organic and residual waste by 20% and 12%, respectively.
2023
Energy Economics, 106621.
Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.
The article in total can be read here.