An empirical evaluation of the effect of working from home on waste behavior

Sammanfattning

We evaluate the effect of working from home on waste generated by individuals both at and away from their homes. To that 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 location of work. Our results suggest that working from home reduces organic and residual waste by 20% and 12%, respectively.

The article can be accessed here.

Network Regulation under electoral competition

Artikel (med peer review)Publikation

Leroux, A., & Söderberg, M.

Publiceringsår

2023

Publicerat i

Energy Economics, 106614.

Sammanfattning

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.

Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data

Artikel (med peer review)Publikation

Duras, T., Javed, F., Månsson, K., Sjölander, P., & Söderberg, M.

Publiceringsår

2023

Publicerat i

Energy Economics, 106621.

Sammanfattning

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.

Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market

Artikel (med peer review)Publikation

Zeebari, Z., Månsson, K., Sjölander, P., & Söderberg, M.

Publiceringsår

2023

Publicerat i

Journal of Productivity Analysis, 59(1), 79-97.

Sammanfattning

In stochastic frontier analysis, the conventional estimation of unit inefficiency is based on the mean/mode of the inefficiency, conditioned on the composite error. It is known that the conditional mean of inefficiency shrinks towards the mean rather than towards the unit inefficiency. In this paper, we analytically prove that the conditional mode cannot accurately estimate unit inefficiency, either. We propose regularized estimators of unit inefficiency that restrict the unit inefficiency estimators to satisfy some a priori assumptions, and derive the closed form regularized conditional mode estimators for the three most commonly used inefficiency densities. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimators outperform the conventional (unregularized) estimators when the inefficiency is greater than its mean/mode. Based on real data from the electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators provide substantially different results for highly inefficient companies.

The article can be accessed here.

Sammanfattning

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