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PublicationWorking paper

Social Preferences and Environmental Externalities

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Abstract

Standard economic theory assumes that consumers ignore the externalities they create, such as emissions from burning fossil fuels and generating waste. In an incentivized study (N = 3, 718), we find that most people forgo substantial gains to avoid imposing negative externalities on others. Using administrative data on household waste, we show a clear link between such prosociality and waste behavior: prosociality predicts lower residual waste generation and higher waste sorting. Prosociality also predicts survey-reported pro-environmental behaviors such as lowering indoor temperature, limiting air travel, and consuming eco-friendly products. These findings highlight the importance of considering social preferences in environmental policy.

Campos-Mercade, P., Ek, C., Söderberg, M., & Schneider, F. H. (2025). Social preferences and environmental externalities (Working Papers No. 2025:6). Department of Economics, School of Economics and Management, Lund University.

Details

Author
Campos-Mercade, P., Ek, C., Söderberg, M., & Schneider, F. H.
Publication year
2025
Published in

Working paper series: Department of Economics, School of Economics and Management, Lund University.

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  • Professor

    Magnus Söderberg

    magnus.soderberg@ratio.se

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Published in

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Abstract

The growing amount of waste worldwide has led to policies requiring cost-effective waste management. Consequently, municipalities responsible for providing waste services are under greater pressure to do so efficiently for their residents. Using data from 225 Swedish and Norwegian municipalities, we investigate how the waste bin structure and population affect the cost of MSW management. Results indicate that 4-compartment bins are the most expensive (+13 %), while 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, and the cost per inhabitant can be reduced by up to 30 % in several locations through collaborations with larger neighbours.

Article (with peer review)

Benchmarking in the energy sector: Implications for practice and policy

Månsson, J., Månsson, K., & Söderberg, M.

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Abstract

Efficiency benchmarking has become a cornerstone of energy sector regulation and management, providing tools to evaluate and compare the performance of utilities, power plants, and even national energy systems. Regulators use methods like Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) to set performance targets, incentivize cost reduction, and guide policy. Yet, applying benchmarking in real-world contexts is challenging. Energy sector stakeholders – regulators, firms, and policymakers – often grapple with complex models, data limitations, and evolving policy goals (such as reliability and decarbonization) that benchmarking must address. This editorial reviews the contributions of papers in this Energy Economics special issue on “Benchmarking,” highlighting their implications for practical benchmarking in the energy sector. We synthesize what these studies reveal about pressing regulatory challenges, unresolved problems in implementation, and needed future developments to strengthen the use of benchmarking in policy and industry. The focus is on translating technical advances into insights for practitioners and decision-makers in the energy field.

Working paper

A penalization approach for estimating inefficiency in stochastic frontier panel models

Tchatoka, F. D., Söderberg, M., Hakeem, M. A.
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Publication year

2025

Published in

University of Adelaide, School of Economics and Public Policy Working Paper.

Abstract

Efficiency analysis is essential for evaluating the performance of entities that deliver essential or standardized services. The estimator proposed by Jondrow et al. (1982) is widely used in this context, but it has been criticized for several shortcomings: it tends to bias inefficiency estimates toward the mean, distorts the distribution, and misrepresents the conditional distribution of inefficiency—especially in cross-sectional data.

Zeebari et al. (2023) propose a regularization-based alternative that aligns sample and theoretical moments; however, this method is primarily designed for cross-sectional applications and does not extend naturally to panel data.

In response, this paper introduces a penalized mode estimator for unit inefficiency in panel data. The estimator accounts for heteroskedasticity in both inefficiency and idiosyncratic errors. A closed-form expression is derived, and Monte Carlo simulations demonstrate its superior performance compared to existing methods. An empirical application using data from electricity providers in Australia, Canada, and New Zealand highlights the practical advantages of the proposed approach.

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