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on Regulation |
| By: | Christos Genakos; Mario Pagliero; Lorien Sabatino; Tommaso Valletti (Cambridge Judge Business School, University of Cambridge) |
| Abstract: | Fixed book price (FBP) agreements are a form of resale price maintenance applied to books in various countries. FBP restricts retail price competition with the aim of promoting book production variety. Yet, despite its popularity and adoption in many countries, there is no empirical evidence on its effects. We offer systematic evidence on the impact of FBP on book variety and prices using a detailed new dataset from Italy that includes the universe of books published and bought, before and after the introduction of FBP. Our results indicate that FBP raises prices without significantly affecting the number of new books published in the marketplace. However, it also increases considerably the variety of books actually bought, especially from independent bookstores. We estimate a structural demand model that accounts for both effects, finding that consumers overall benefit from the regulation. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:jbs:wpaper:202501 |
| By: | Jeremy Proz; Martin Huber |
| Abstract: | Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel detection algorithm to two cartel cases in the Italian electricity market and evaluates its out-of-sample performance. Specifically, we consider an ensemble machine learning method that uses statistical screens constructed from the offer price distribution as predictors for the incidence of collusion among electricity providers in specific regions. We propose novel screens related to the capacity-withholding behavior of electricity providers and find that including such screens derived from the day-ahead spot market as predictors can improve cartel detection. We find that, under complete cartels - where collusion in a tender presumably involves all suppliers - the method correctly classifies up to roughly 95% of tenders in our data as collusive or competitive, improving classification accuracy compared to using only previously available screens. However, when trained on larger datasets including non-cartel members and applying algorithms tailored to detect incomplete cartels, the previously existing screens are sufficient to achieve 98% accuracy, and the addition of our newly proposed capacity-withholding screens does not further improve performance. Overall, this study highlights the promising potential of supervised machine learning techniques for detecting and dismantling cartels in electricity markets. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09885 |
| By: | Catherine Bobtcheff; Philippe De Donder; François Salanié |
| Abstract: | We set up a static model of electricity provision in which delivery to consumers is only imperfectly reliable. Blackouts can be either rolling or systemic; in both cases a price cap becomes active on the wholesale market. We show that for any given value of the price cap, one can decentralize optimal allocations thanks to two types of regulatory instruments: a retail tax, and capacity subsidies. Some properties follow. If demand is affected by multiplicative shocks only, capacity subsidies are exactly financed by the revenues from the retail tax. If moreover the distribution of systemic blackouts is exogenous, a price cap is sufficient, provided it is set at the value of lost load. In all other cases, all instruments are needed, and capacity subsidies need to be differentiated, based on the correlation between available capacity and its social value. We also discuss the impacts of a carbon tax on supply, demand, and optimal regulation. |
| Keywords: | electricity, reliability, renewables, climate change |
| JEL: | D24 Q41 Q42 Q48 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12092 |
| By: | Bergman, Aaron (Resources for the Future); Peplinski, McKenna (Resources for the Future); Rennert, Kevin (Resources for the Future); Roy, Nicholas (Resources for the Future) |
| Abstract: | The Inflation Reduction Act replaced an assortment of technology-specific tax credits for clean electricity with two “technology-neutral” tax credits, the 45Y and 48E tax credits (named after their sections in the tax code). The 45Y tax credit is a “production” tax credit, which pays a set amount for every unit of electricity generated, while the 48E tax credit is an “investment” tax credit that pays a fraction of the capital cost for a qualifying generation or storage technology. Unlike previous iterations, these tax credits apply to any technology that can produce electricity with zero emissions. For more details, see On Deck for Treasury: The Inflation Reduction Act’s New Approach to Clean Electricity Tax Credits and The US Department of the Treasury’s Proposed Guidance for the Tech-Neutral Tax Credits Importantly, the expiration of these tax credits is based on the overall carbon intensity of the electricity sector rather than any specific year.As the new administration and Congress contemplate proposals for the budget reconciliation process, these and other tax credits in the Inflation Reduction Act are on the table for potential repeal. In this issue brief, we explore the consequences of a repeal of these tax credits for retail electricity prices, consumer electricity bills, government expenditures, clean electricity, and emissions.In addition to our reference case, we examine three additional scenarios to assess the impacts of high and low natural gas prices, as well as high electricity demand, on the consequences of a repeal. These scenarios encompass the main parameters known to affect electricity prices. Natural gas prices have displayed wide variation historically, and greater exports of natural gas would put upward pressure on electricity prices. Increased electricity demand, driven by electrification of end-uses or to power data centers and artificial intelligence, would also put upward pressure on electricity prices. We use a high-demand scenario taken from the National Renewable Energy Laboratory’s Electrification Futures Study to account for these factors.We find that repealing these tax credits is modeled to:Increase nationally averaged electricity rates by roughly 5–7 percent across modeled scenarios in 2030, reaching a peak of 6–10 percent higher in 2035. These rate impacts translate into a $75–$100 increase in national average annual electricity bills in 2030, with a peak increase of $100–$150 per year (real 2023 dollars). Rate increases differ significantly by region, with the highest impact seen in the upper plains states ($300–$400 per year increases in the West North Central census region).Reduce tax expenditures by $227–$315 billion dollars over the ten-year budget window (2025–2034, cumulative nominal dollars). After 2035, the annual reduction in tax expenditures is $48–$63 billion per year, declining to $24–$47 billion per year in 2040.Increase power sector carbon dioxide emissions by 350 Mt–400 Mt CO₂ in 2035, with a cumulative increase in power sector emissions of 3, 500 Mt–4, 500 Mt CO₂ between 2025 and 2040.Reduce wind generation capacity in 2035 by 125 GW–225 GW and solar capacity in 2035 by approximately 175 GW. This is a coincidental convergence in 2035. The range increases to 175–225 GW in 2036 and remains at that level through the end of the projection period. |
| Date: | 2025–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:rff:ibrief:ib-25-06 |
| By: | Hangcheng Zhao; Ron Berman |
| Abstract: | Online sellers have been adopting AI learning algorithms to automatically make product pricing and advertising decisions on e-commerce platforms. When sellers compete using such algorithms, one concern is that of tacit collusion - the algorithms learn to coordinate on higher than competitive. We empirically investigate whether these concerns are valid when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. Our empirical strategy is to analyze competition with multi-agent reinforcement learning, which we calibrate to a large-scale dataset collected from Amazon.com products. Our first contribution is to find conditions under which learning algorithms can facilitate win-win-win outcomes that are beneficial for consumers, sellers, and even the platform, when consumers have high search costs. In these cases the algorithms learn to coordinate on prices that are lower than competitive prices. The intuition is that the algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices. Our second contribution is an analysis of a large-scale, high-frequency keyword-product dataset for more than 2 million products on Amazon.com. Our estimates of consumer search costs show a wide range of costs for different product keywords. We generate an algorithm usage and find a negative interaction between the estimated consumer search costs and the algorithm usage index, providing empirical evidence of beneficial collusion. Finally, we analyze the platform's strategic response. We find that reserve price adjustments will not increase profits for the platform, but commission adjustments will. Our analyses help alleviate some worries about the potentially harmful effects of competing learning algorithms, and can help sellers, platforms and policymakers to decide on whether to adopt or regulate such algorithms. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.08325 |
| By: | Hellenkamp, Detlef |
| Abstract: | Die regulatorische Agenda 2025+ stellt den europäischen Bankensektor vor eine signifikante Konvergenz komplexer Anforderungen, darunter die Finalisierung von Basel III (CRR III/CRD VI), der Digital Operational Resilience Act (DORA), die Markets in Crypto-Assets Regulation (MiCAR), das neue Paket zur Bekämpfung von Geldwäsche und Terrorismusfinanzierung (AML/CFT) mit der Einrichtung der AMLA sowie die fortschreitende Implementierung von ESG-Regulierungen (CSRD/ESRS, Taxonomie). Die Ergebnisse dieser Arbeit zeigen, dass trotz der unbestreitbaren Notwendigkeit zur Stärkung der Resilienz und Integrität des Sektors die aggregierte regulatorische Komplexität, die erheblichen Implementierungskosten sowie potenzielle normative Inkonsistenzen, substanzielle Herausforderungen für die Wettbewerbs- und Innovationsfähigkeit der Institute konstituieren. Insbesondere Interaktionen im Kontext der digitalen Transformation, die Gewährleistung regulatorischer Proportionalität sowie eine datenschutzkonforme Handhabung umfangreicher Datenmengen erfordern eine präzise austarierte Kalibrierung im Sinne einer differenziert und kohärent ausgestalteten („smarteren“) Regulierung. Die aufsichtlichen Prioritäten der Europäischen Zentralbank (EZB) und der Europäischen Bankenaufsichtsbehörde (EBA) reflektieren diese Herausforderungen und erfordern tiefgreifende strategische Reorientierungen innerhalb der Geschäftsmodelle und Risikomanagement-Frameworks der Institute. Der Ausblick deutet auf eine anhaltend hohe Regulierungsdynamik hin, die zunehmend von der Notwendigkeit geprägt sein wird, die komplexen Wechselwirkungen zwischen finanzstabilitätsbezogenen Zielsetzungen, technologischer Innovationsfähigkeit und nachhaltigkeitsorientierten Anforderungen systematisch zu steuern. |
| Abstract: | The 2025+ regulatory agenda presents the European banking sector with a significant convergence of complex requirements, including the finalisation of Basel III (CRR III/CRD VI), the Digital Operational Resilience Act (DORA), the Markets in Crypto-Assets Regulation (MiCAR), the new Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) package with the establishment of the AMLA, and the ongoing implementation of ESG regulations (CSRD/ESRS, EU Taxonomy). The results of this work show that, despite the undeniable need to strengthen the resilience and integrity of the sector, the aggregated regulatory complexity, the considerable implementation costs and potential normative inconsistencies constitute substantial challenges for the competitiveness and innovative capacity of institutions. In particular, interactions in the context of digital transformation, ensuring regulatory proportionality and handling large volumes of data in compliance with data protection regulations require precise calibration in the sense of differentiated and coherent (‘smarter’) regulation. The supervisory priorities of the European Central Bank (ECB) and the European Banking Authority (EBA) reflect these challenges and require far-reaching. The outlook points to a persistently high regulatory dynamic that will be increasingly characterised by the need to systematically manage the complex interactions between financial stability-related objectives, technological innovation capability and sustainability-oriented requirements. |
| Keywords: | AI Act, AML/CFT package (AMLA), Supervisory Priorities, Basel III (CRR III/CRD VI), Corporate Sustainability Reporting Directive (CSRD), Digitalisation, Digital Operational Resilience Act (DORA), Digital Transformation, ESG Regulation, European Sustainability Reporting Standards (ESRS), Markets in Crypto-Assets Regulation (MiCAR), Proportionality in banking regulation, RegTech & SupTech, Smart Regulation |
| JEL: | G28 G21 K23 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:324490 |
| By: | Domeshek, Maya (Resources for the Future) |
| Abstract: | In November of 2023, the governor of Michigan signed a package of climate bills (Executive Office of the Governor 2023) that sets a 100 percent clean electricity target for 2040, one of the earliest in the United States (CESA n.d.). In 2020, the governor issued an executive order setting a goal for the state to reach net zero carbon emissions by 2050 (Executive Directive 2020-10 2019); in 2022, the MI Healthy Climate Plan was released, outlining actions the state could take by 2030 to work toward that goal (EGLE 2022). The state’s largest investor-owned utilities had previously set voluntary decarbonization targets: DTE for 2050 (DTE Energy 2019) and Consumers Energy for 2040 (Consumers Energy 2020). Fall 2023’s legislative package included six pieces of legislation: SB 271 on clean electricity, HB 5120 on renewables permitting, SB 277 on siting solar on farmland, SB 273 on efficient electrification, SB 519 on helping workers transition out of the fossil sector, and SB 502 on the utility integrated resource planning (IRP) process. This brief compares the provisions of the legislation with the state’s existing plan and utility plans to evaluate the progress the state has made toward institutionalizing electricity decarbonization. |
| Date: | 2024–07–23 |
| URL: | https://d.repec.org/n?u=RePEc:rff:ibrief:ib-24-07 |
| By: | Cesare Carissimo; Fryderyk Falniowski; Siavash Rahimi; Heinrich Nax |
| Abstract: | This paper proposes a fresh `meta-game' perspective on the problem of algorithmic collusion in pricing games a la Bertrand. Economists have interpreted the fact that algorithms can learn to price collusively as tacit collusion. We argue instead that the co-parametrization of algorithms -- that we show is necessary to obtain algorithmic collusion -- requires algorithm designer(s) to engage in explicit collusion by algorithm orchestration. To highlight this, we model a meta-game of algorithm parametrization that is played by algorithm designers, and the relevant strategic analyses at that level reveal new equilibrium and collusion phenomena. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.14766 |
| By: | Osswald do Amaral, Francisco; Zetzmann, Steffen |
| Abstract: | We examine how rising energy costs affect rental housing markets and inequality. Using listing data for the 30 largest German cities from 2015-2024, we find that higher energy prices are passed through to net rents in high-rent segments, where inefficient properties see significant rent reductions, but not in lower-priced segments. This asymmetry reflects tighter markets and lower demand elasticity in the affordable segment. Consequently, low-income households face much larger increases in total housing costs. Our results show how segmented housing markets can amplify inequality when energy prices rise, highlighting important distributional implications for climate policy. |
| Keywords: | Housing Markets, Energy Prices, Climate Change, Inequality |
| JEL: | R31 Q41 Q54 D31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkwp:324656 |