nep-reg New Economics Papers
on Regulation
Issue of 2023‒10‒16
eleven papers chosen by
Christopher Decker, Oxford University


  1. Where does the money go? An analysis of revenues in the GB power sector during the energy crisis By S. A. Maximov; P. Drummond; P. McNally; M. Grubb
  2. Pricing energy consumption and residential energy-efficiency investment: An optimal tax approach By Claude Crampes; Norbert Ladoux; Jean-Marie Lozachmeur
  3. The Micro-Aggregated Profit Share By Thomas Hasenzagl; Luis Perez
  4. Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems By Xingchen Xu; Stephanie Lee; Yong Tan
  5. The Emissions Reduction Potential for Freight Transport on a High-speed Rail Line Along the ‘European Silk Road’ By Erica Angers; Aleksandr Arsenev; Mario Holzner
  6. Procurement and Infrastructure Costs By Zachary Liscow; Will Nober; Cailin Slattery
  7. Empirical Analysis of Network Effects in Nonlinear Pricing Data By Liang Chen; Yao Luo
  8. Auctioning Long-Term Projects under Financial Constraints By Martimort, David; Arve, Malin
  9. The Rise, Fall, and Legacy of the Structure-Conduct-Performance Paradigm By Panhans, Matthew T.
  10. Open banking, shadow banking and regulation By Eccles, Peter; Grout, Paul; Zalewska, Anna; Siciliani, Paolo
  11. Deep learning model fragility and implications for financial stability and regulation By Kumar, Rishabh; Koshiyama, Adriano; da Costa, Kleyton; Kingsman, Nigel; Tewarrie, Marvin; Kazim, Emre; Roy, Arunita; Treleaven, Philip; Lovell, Zac

  1. By: S. A. Maximov (University College London); P. Drummond (University College London); P. McNally (University College London); M. Grubb (University College London)
    Abstract: The gas crisis has fed through to a huge impact on wholesale electricity prices in Britain. We use hourly price and generation data to estimate the impact on associated revenues to different types of generators. Given the extent of forward contracting, we complement simple results based on the day-ahead prices ("Case 1") with a more realistic case based on a representative, technology-specific assumptions on forward contracts ("Case 2"). We estimate that revenues to GB generators rose by almost £30bn, from about £20.5bn/yr (pre-Covid) to £49.5bn in 2022. About 70% of this accrued to gas generators (from about £6bn/yr to £19bn) and renewable generators with Renewable Obligation Certification (from £7.7bn to £15.5bn). There are various indications that the increase in revenues to gas plants significantly exceeded the rise in their input costs, and no reason to think the generating cost of these renewables significantly increased. Nuclear, and some other biomass and renewables also benefited. We find that the Electricity Generation Levy, introduced in Jan 2023, would have had limited impact on these numbers if it had existed in 2022 and is likely to have less impact in 2023. Finally, we discuss reasons and potential implications of the findings.
    Keywords: Electricity market design; energy crisis; renewable energy; CfD; long-run contracts; energy transition; energy poverty.
    JEL: L16 L51 L94 L98 Q4 Q28 Q58
    Date: 2023–05–16
    URL: http://d.repec.org/n?u=RePEc:thk:wpaper:inetwp207&r=reg
  2. By: Claude Crampes (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Norbert Ladoux (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Jean-Marie Lozachmeur (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique)
    Abstract: We analyze a Pareto optimal income tax problem à la Mirrlees (1971) in which households consume three types of goods: energy goods, energy efficient investments and non-energy goods. The two main ingredients of our normative analysis are: i) an indirect relationship between energy and the satisfaction of energy needs, as energy-efficient investments transform energy into services such as light, heating, and air conditioning; and, ii) imperfect information of the policy designer as regards the level of energy efficiency of households' housing and their labor market productivity. Each household differs with respect to these two latter characteristics, and the government designs a non-linear income tax combined with energy and energy efficient investment non linear pricing that maximizes a weighted sum of households' utilities. We show that a benevolent social planner should distort energy prices in a way that depends on the difference between the saturation of energy needs and the complementarity between energy and the level of energy efficiency in the provision of energy services. A sufficient condition for energy consumption to be subsidized is that the rebound effect is small. Second, when individuals can invest in energy efficiency on top of energy consumption, these investments should always be subsidized and the marginal subsidy should always be higher than the one on energy consumption.
    Keywords: Optimal income taxation, Indirect taxation, Energy services, Energy efficiency, Energy consumption
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-04189094&r=reg
  3. By: Thomas Hasenzagl; Luis Perez
    Abstract: How much has market power increased in the United States in the last fifty years? Using micro-level data from U.S. Compustat, we find that several indicators of market power have steadily increased since 1970. The aggregate markup has gone up from 10% of price over marginal cost in 1970 to 23% in 2020, and aggregate returns to scale have risen from 1.00 to 1.13. We connect these market-power indicators to profitability by showing that the aggregate profit share can be expressed in terms of the aggregate markup, aggregate returns to scale, and a sufficient statistic for production networks that captures double marginalization in the economy. We find that despite the rise in market power, the profit share has been constant at 18% of GDP because the increase in monopoly rents has been completely offset by rising fixed costs. Our empirical results have subtle implications for policymakers: overly aggressive enforcement of antitrust law could decrease firm dynamism and paradoxically lead to lower competition and higher market power.
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2309.12945&r=reg
  4. By: Xingchen Xu; Stephanie Lee; Yong Tan
    Abstract: Recent academic research has extensively examined algorithmic collusion resulting from the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e-commerce platforms employ recommendation algorithms to allocate exposure to various products, and this important aspect has been largely overlooked in previous studies on algorithmic collusion. Our study bridges this important gap in the literature and examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms. Specifically, two commonly deployed recommendation algorithms are examined: (i) a recommender system that aims to maximize the sellers' total profit (profit-based recommender system) and (ii) a recommender system that aims to maximize the demand for products sold on the platform (demand-based recommender system). We construct a repeated game framework that incorporates both pricing algorithms adopted by sellers and the platform's recommender system. Subsequently, we conduct experiments to observe price dynamics and ascertain the final equilibrium. Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives. Conversely, a demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals. Extended analyses suggest the robustness of our findings in various market scenarios. Overall, we highlight the importance of platforms' recommender systems in delineating the competitive structure of the digital marketplace, providing important insights for market participants and corresponding policymakers.
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2309.14548&r=reg
  5. By: Erica Angers; Aleksandr Arsenev (The Vienna Institute for International Economic Studies, wiiw); Mario Holzner (The Vienna Institute for International Economic Studies, wiiw)
    Abstract: This report estimates the CO2 emissions of freight transport on a hypothetical high-speed rail (HSR) line along the northern route, from Lyon to Warsaw, of a ‘European Silk Road’ (ESR). Using a methodology consisting of predictions regarding the freight-carrying capacity of the future HSR, and the commodity-level switchover, our results indicate that a best-case scenario, at a project lifecycle of 60 years, in which all trains run with 257 tonnes of load, provides for a reduction of 176.2 Mt of net CO2 emissions compared with current levels. These lifespan savings are comparable to a reduction of net emissions by close to 24% of the overall EU transport sector emissions (excluding air transport) of one year (as measured by the net emissions in 2018). The net negative emissions in the optimistic full-capacity scenario will compensate for the construction costs in 13 years. Thus, the potential for emission reduction along the northern route of the ESR is quite substantial, given that this is just one line, with limited capacity. This hints at the importance that bold missions, such as the construction of a pan-European HSR network, could have for the definition of a European Green Industrial Policy that is capable of supporting the fulfilment of the goals of the Paris Agreement on climate change.
    Keywords: Climate change, ecological efficiency, European Silk Road, European Union, green growth, green transition, high-speed rail (HSR), infrastructure, intermodal competition, life-cycle analysis (LCA), logistics, modal shift, train networks, transportation
    JEL: H54 L91 L92 Q42 Q50 Q51 Q55 Q56 Q58 R40 R41 R42
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:wii:rpaper:rr:472&r=reg
  6. By: Zachary Liscow; Will Nober; Cailin Slattery
    Abstract: Infrastructure costs in the United States are high and rising. The procurement process is one potential cost driver. In this paper we conduct a survey of procurement practices across the 50 states. We survey both employees at each state department of transportation (DOT) and the road builders that win contracts to build and maintain roads. With this survey we are able to create a new dataset of procurement rules and practices across the U.S. and understand what actors on the ground think drive costs. We then assemble a new dataset of project-level infrastructure costs. We correlate the survey practices with our new, detailed data on costs. We find that two important inputs in the procurement process appear to particularly drive costs: (1) the capacity of the DOT procuring the project and (2) the lack of competition in the market for government construction contracts.
    JEL: D44 H54 H57 H83 K40 L38 L91 O18 R42
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31705&r=reg
  7. By: Liang Chen; Yao Luo
    Abstract: Network effects, i.e., an agent's utility may depend on other agents' choices, appear in many contracting situations. Empirically assessing them faces two challenges: an endogeneity problem in contract choice and a reflection problem in network effects. This paper proposes a nonparametric approach to tackle both challenges by exploiting restriction conditions from both demand and supply sides. We illustrate our methodology in the yellow pages advertising industry. Using advertising purchases and nonlinear price schedules from seven directories in Toronto, we find positive network effects, which account for a substantial portion of the publisher's profit and businesses' surpluses. We finally conduct counterfactuals to assess the overall and distributional welfare effects of the nonlinear pricing scheme relative to an alternative linear pricing scheme with and without network effects.
    Keywords: Identification, Asymmetric Information, Network Effects, Nonlinear Pricing
    JEL: L11 L12 L13
    Date: 2023–09–25
    URL: http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-758&r=reg
  8. By: Martimort, David; Arve, Malin
    Abstract: We consider a procurement auction for the provision of a basic service to which an add-on must later be appended. Potential providers are symmetric, have private information on their cost for the basic service and the winning firm must also implement the add-on. To finance cost-reducing activities related to the add-on, this firm may need extra funding by outside financiers. Non-verifiable effort in reducing these costs creates a moral hazard problem which makes the firm’s payoff function for the second period concave in returns over the relevant range. This concavity has two effects: It makes it more attractive to backload payments to facilitate information revelation and uncertainty on the cost of the add-on introduces a background risk which requires a risk premium. In this context, we characterize the optimal intertemporal structure of payments to the winning firm, equilibrium bidding behavior and reserve prices in the first-price auction with bidders.
    Keywords: Auctions; procurement; financial constraints; dynamic mechanism design, asymmetric information; uncertainty; endogenous risk aversion.
    Date: 2023–09–18
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:128474&r=reg
  9. By: Panhans, Matthew T.
    Abstract: In 1982, Joe Bain was designated a Distinguished Fellow of the AEA, with an accompanying statement referring to him as “the undisputed father of modern Industrial Organization Economics.” The Structure-Conduct-Performance paradigm that Bain developed and deployed had been the core framework of industrial organization for two decades, and had a significant impact on competition policy from the 1950s through the 1970s. And yet by the time of Bain’s designation as a Distinguished Fellow, industrial organization was shifting away from SCP and instead relying on a foundation of game theory. This essay considers what made the SCP framework so influential in the United States, what shortcomings economists identified in the framework during the shift to the “new IO” in the late 1970s, and what lasting contributions the SCP research program made.
    Date: 2023–09–15
    URL: http://d.repec.org/n?u=RePEc:osf:socarx:dvm3e&r=reg
  10. By: Eccles, Peter (Bank of England); Grout, Paul (Bank of England); Zalewska, Anna (University of Leicester School of Business); Siciliani, Paolo (Bank of England)
    Abstract: We argue that open banking will create diverse banking models: competitive banks (serving depositors who adopt open banking) and monopolistic banks (serving the other depositors). In equilibrium, at the margin, the profit of competitive and monopolistic banks should be equal. Hence, the system-wide impact of any policy change cannot be judged solely by the impact on a typical monopolistic or competitive bank, the impact on relative profitability also matters since this can lead banks to move from one banking type to another. For example, an increase in capital requirements bites less on the profits of competitive than monopolistic banks. Some banks thus move to the (riskier) competitive sector which we show can increase overall risk in the system. A deposit rate ceiling dampens the impact of Bertrand competition, making competitive banks more profitable, so the (riskier) competitive sector grows. Hence, rather than making the system more stable, a marginal lowering of a deposit rate ceiling can increase risk. We also show that, in many scenarios, the regulator must choose between banks funding private sector projects or all banks being safe, the regulator cannot have both. This has implications for the optimal risk weights of sovereign debt. In our model, none of these effects are driven by the presence of unregulated assets/sectors nor on impacts on charter value, as is the case in papers that find outcomes that are the opposite of what was intended. We then introduce an unregulated, shadow banking sector into the model and show that the growth in shadow banking benefits monopolistic banks relative to competitive banks. This increases the size of the (low-risk) monopolistic sector, reducing overall risk in the system. We discuss policy implications.
    Keywords: Capital requirements; banking; open banking; shadow banking; competition; FinTech
    JEL: D43 G21 G28
    Date: 2023–09–08
    URL: http://d.repec.org/n?u=RePEc:boe:boeewp:1039&r=reg
  11. By: Kumar, Rishabh (Bank of England); Koshiyama, Adriano (University College London); da Costa, Kleyton (University College London); Kingsman, Nigel (University College London); Tewarrie, Marvin (Bank of England); Kazim, Emre (University College London); Roy, Arunita (Reserve Bank of Australia); Treleaven, Philip (University College London); Lovell, Zac (Bank of England)
    Abstract: Deep learning models are being utilised increasingly within finance. Given the models are opaque in nature and are now being deployed for internal and consumer facing decisions, there are increasing concerns around the trustworthiness of their results. We test the stability of predictions and explanations of different deep learning models, which differ between each other only via subtle changes to model settings, with each model trained over the same data. Our results show that the models produce similar predictions but different explanations, even when the differences in model architecture are due to arbitrary factors like random seeds. We compare this behaviour with traditional, interpretable, ‘glass-box models’, which show similar accuracies while maintaining stable explanations and predictions. Finally, we show a methodology based on network analysis to compare deep learning models. Our analysis has implications for the adoption and risk management of future deep learning models by regulated institutions.
    Keywords: Deep neural networks; fragility; robustness; explainability; regulation
    JEL: C45 C52 G18
    Date: 2023–09–01
    URL: http://d.repec.org/n?u=RePEc:boe:boeewp:1038&r=reg

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