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on Regulation |
| By: | Michael G Pollitt; Daniel Duma; Andrei Covatariu; Paul Nillesen |
| Keywords: | Distribution System Operators, energy transition, regulation |
| JEL: | L94 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:enp:wpaper:eprg2610 |
| By: | Karissa Moothoo Padayachie Nair (University of Johannesburg); Reena Das Nair (University of Johannesburg) |
| Abstract: | Kenya, often referred to as Africa’s “Silicon Savannah”, has rapidly evolved to become the regional digital business hub in East Africa. While Kenya’s approach to regulation has led to some market-driven competition, it faces similar competition-related challenges that many jurisdictions around the world face in the constantly evolving digital markets landscape. There have been competition concerns raised by entrepreneurs and businesses with respect to accessing markets and competing against the large domestic and global players that have already entered and taken up lead positions in Kenya’s digital ecosystem. This led to the Competition Authority of Kenya (CAK) introducing the Competition Amendment Bill in 2024 which is targeted at addressing platform dominance in digital ecosystems. South Africa adopted a different approach to regulating the digital economy using market inquiries as the primary tool to address potential harm to competition. The market inquiries highlighted concerns surrounding platform dominance and barriers to entry in e-commerce, amongst other concerns. |
| Keywords: | Digital platforms, Competition Policy, Digital Market regulation |
| JEL: | L41 K21 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:rza:ersawp:338 |
| By: | Chang-Koo Chi (Yonsei University); Jay Pil Choi (Michigan State University); Jong-Hee Hahn (Yonsei University); Seongkyun Kim (Yonsei University) |
| Abstract: | This paper studies self-preferencing incentives by vertically integrated platforms that operate both marketplaces and affiliated retail businesses. We show that self-preferencing and transaction fees are substitute instruments for profit extraction, implying that restrictions on self-preferencing may induce offsetting increases in transaction fees and thereby generate unintended consequences for consumer welfare. We characterize the platform’s optimal choice of self-preferencing and transaction fees and evaluate the welfare effects of behavioral and structural remedies. We also extend the analysis to settings with platform competition and consumer search, examining how market forces shape self-preferencing incentives and evaluating the robustness of our main results. |
| Keywords: | self-preferencing, vertically integrated platforms, transaction fees, regulation, hierarchical Hotelling model, search |
| JEL: | L2 L5 D2 D8 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2026rwp-294 |
| By: | Comisión Nacional de los Mercados y la Competencia (CNMC) (Comisión Nacional de los Mercados y la Competencia (CNMC)) |
| Abstract: | Rail passenger transport is essential for economic and environmental sustainability and for social and territorial cohesion. This study identifies possible barriers that may affect the process of opening up services subject to public service obligations (PSO) to competition. To minimise these obstacles and ensure that the potential benefits of opening up are realised, it is recommended, first, to adopt a pro-competitive approach in the application of the regulations regarding the designation of PSO and their award, according to an ambitious timetable. Second, to establish an appropriate institutional framework, decoupling public operators providing services from infrastructure managers and tendering authorities, while strengthening independent oversight of the process. Third, ensure that new operators can access the relevant inputs (including infrastructure, rolling stock, workshops, driving personnel) and the information needed to compete effectively. |
| Keywords: | Railway, Public Service Obligations, Regulation, Competition, Passenger Transport, Liberalisation, Tenders |
| JEL: | K23 L5 L43 L92 R4 |
| Date: | 2026–05–04 |
| URL: | https://d.repec.org/n?u=RePEc:awo:epaper:e/cnmc/001/24_eng |
| By: | Hendrik Theine (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria; Socio-Ecological Transformation Lab, Johannes Kepler University Linz, Austria; Darmstadt University of Applied Sciences, Germany); Steffen S. Bettin (Department of Socioeconomics, Vienna University of Economics and Business, Vienna, Austria) |
| Abstract: | Generative AI presents a puzzle for political economy. Leading firms accumulate structural advantages, lock in users, and shape technical standards at unprecedented speed, while unit economics remain negative and no clear path to profitability has emerged. This puzzle, we argue, can only be made sense of through an explicit analysis of corporate power, for which mainstream frameworks centred on market concentration alone are ill-equipped. Drawing on heterodox economics and cultural political economy (Boyer, 2022; Galbraith, 1984; Rothschild, 2002; Sum and Jessop, 2013), we develop a multi-dimensional heuristic distinguishing market power, as strategic control within markets, from the power to shape the wider cultural political economy. The rules of the game and the relationship to the state. We map market concentration across three layers of the genAI stack (GPU infrastructure, hyperscalers, foundation models), examine its distinctive cost structure, and analyse the emerging state–capital configuration. High costs and negative unit economics generate strong concentration, pointing toward an AI oligopoly. Politically and culturally, firms deploy the familiar Big Tech playbook (lobbying, academic capture, hegemony production), recruited around two narratives: AI nationalism and the AGI imaginary. Yet we identify a structural break from the platform era. Where platform firms sought to bypass the state, frontier AI firms actively court state procurement and patronage. What is emerging, we argue, is a state project in the making: a configuration in which states adopt the survival and dominance of specific AI firms as their own objectives. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:set:wpaper:6 |
| By: | Lucas W. Davis |
| Abstract: | It may seem like a distant memory now, but as of the mid-2000s, U.S. natural gas production had been flat for a decade, and the U.S. was importing liquefied natural gas (LNG), with plans to import much more. Then shale gas happened. Advances in hydraulic fracturing and horizontal drilling caused U.S. natural gas production to increase significantly, and the U.S. went from being a net importer of natural gas to being the world's largest exporter. This paper calculates how much shale gas has saved U.S. natural gas consumers. Using price differences between the United States, Europe and Japan, we calculate that U.S. natural gas consumers have saved $3.1-$4.3 trillion between 2007 and 2025, equivalent to $164-$227 billion annually. Access to low-price U.S. natural gas has been particularly valuable during major supply shocks such as the war in Ukraine, and the benefits of shale gas have been experienced broadly across sectors and states. |
| JEL: | Q41 Q42 Q48 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35245 |
| By: | Leight, Jessica; Clark, Anne Angsten; Reynolds, Katherine |
| Abstract: | Community-driven development (CDD) reliably delivers locally prioritized public infrastructure and services, often at a lower cost and with less leakage than traditional government-managed projects, and it has proven particularly effective in challenging contexts affected by institutional fragility, conflict, and violence. CDD effectively supports communities in prioritizing projects, producing a wide range of positive local outcomes. Yet, the diversity of choices makes generalizing the efficacy of CDD across settings difficult, highlighting the need for more research and better approaches to generating evidence and identifying impact. Emerging evidence suggests that infrastructure built by communities through CDD programs often endures longer than development projects that employ more traditional “top-down” approaches, with positive effects persisting for a decade or more. |
| Keywords: | development aid; community development; infrastructure; public services |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:prnote:182190 |
| By: | Christopher Clayton; Antonio Coppola |
| Abstract: | We study whether AI methods applied to large-scale portfolio holdings data can improve macroprudential financial regulation. We build a graph-based deep learning model tailored to security-level data on the holdings of financial intermediaries. The architecture incorporates economic priors and learns latent representations of both assets and investors from the network structure of portfolio positions. Applied to the universe of non-bank financial intermediaries, covering nearly $40 trillion in wealth, the model substantially outperforms existing approaches in out-of-sample forecasts of intermediary trading behavior, including in crisis episodes. The model has more than ten times the explanatory power for the cross-sectional variation in asset returns during stress events compared to traditional approaches, and it outperforms existing systemic risk metrics at the institution level. Its learned representations show that the holdings network encodes rich, economically interpretable information about fire- sale vulnerability. The architecture is fully inductive, producing informative estimates even when entire asset classes or investors are withheld from training. We embed our empirical approach into a macroprudential optimal policy framework to formalize why these objects matter for policy and welfare. We show that even in an equilibrium environment subject to the Lucas critique, the predictive information from the model improves welfare by sharpening the cross-sectional targeting of policy interventions, and we demonstrate a complementarity between prediction and structural knowledge. |
| JEL: | C4 G1 G2 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35227 |
| By: | Dana Golden; Aruna Balasubramanian; Niranjan Balasubramanian |
| Abstract: | Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) and power purchase agreements (PPAs) hyperscalers use to claim carbon neutrality remains unclear. We develop a game-theoretic model in which a data center operator chooses among RECs, PPAs, and behind-the-meter colocation while generators make entry decisions under endogenous financing costs. The model identifies a timing wedge -- the mismatch between consumption and credited renewable generation -- as a central mechanism through which AI demand degrades reliability, raises prices, and increases emissions even when RECs cover 100% of annual consumption. Colocation with storage addresses this wedge directly and induces the greatest renewable entry by eliminating generator revenue risk. We test these predictions by exploiting the staggered release of large language models as a natural experiment, using difference-in-differences on a novel dataset linking AI activity to local grid outcomes. AI demand significantly increases fossil generation, wholesale prices (up to 25% in treated PJM zones), and outage frequency (0.5--1 additional outages per year) near data centers, with impacts scaling in model size. Data centers with on-site generation exhibit a sign reversal in power-quality effects, consistent with the model's prediction that behind-the-meter capacity absorbs demand spikes. Counterfactual analyses show that edge inference, spatial reallocation, and colocated storage each substantially mitigate grid impacts, while REC-only strategies do not. Together, our results demonstrate that the externalities of AI to the grid are tightly coupled to procurement design and the spatial organization of data center infrastructure. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.00811 |
| By: | Nicolas Pasquier |
| Abstract: | Traditional firms competing in a primary market may expand into a secondary market that generates user data and enhances the quality of the primary product. This paper examines how competition between such rival ecosystems affects market outcomes and welfare. Using a Hotelling framework with two symmetric ecosystems that each offer a primary product and a secondary data-rich product, I show that the size of the secondary market is key. When the secondary market is small, ecosystems invest less in quality than in a benchmark with only a primary market and earn higher profits at the expense of consumers. As the secondary market grows, quality investment rises and the welfare ranking can reverse. I further show that expansion into a secondary market need not create a trade-off between profits and consumer surplus: when the ecosystems’ secondary products are sufficiently differentiated, both profits and consumer surplus can exceed their benchmark levels. These findings inform policy debates on digital adoption, market structure, and ecosystem regulation. |
| Keywords: | Competing Ecosystems, Quality Investment, Data-Driven Quality |
| JEL: | L13 L51 D43 O31 Q16 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:gbl:wpaper:2026-03 |
| By: | Andrea Pannone; Francesco Giancaterini; Tiziano Bacaloni; Andrea Bernardini; Alessio Abeltino |
| Abstract: | The energy sector is a cornerstone of national strategic autonomy, yet its increasing financialization has transformed ownership structures into complex networked configurations. This paper investigates the distribution of economic power in the Italian energy sector by introducing two sector-level extensions of the Network Power framework: the Aggregate Network Power Index (A-NPI) and the Aggregate Network Power Flow (A-NPF). Unlike traditional macro-level measures, these indices aggregate firm-level control and influence into a systemic framework that accounts for the relative economic weight of each operator. Applying this framework to the Italian case reveals a "Governance Paradox": while the State retains formal majority ownership, the sector's deepening reliance on global capital markets and the pervasive presence of common ownership by transnational institutional investors have progressively hollowed out public strategic direction. The results show that capital centralization enables global financial actors to internalize sectoral competition, fostering a regime of tacit strategic convergence in the management of critical infrastructure. This configuration challenges European strategic autonomy, raising questions about the adequacy of traditional Foreign Direct Investment (FDI) screening and antitrust tools in addressing the systemic influence exerted through networked ownership structures. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.25555 |
| By: | Alex Chan |
| Abstract: | I study medical liability when artificial intelligence acts as a doctor rather than as a passive clinical tool. The central object is the legally usable medical record: the inputs, logs, warnings, prescriptions, follow-up instructions, and outcomes on which courts, contracts, insurers, and regulators can condition responsibility. I show that AI medical liability is an institutional design problem under imperfect legal information. If the record separates AI-controllable error from patient nonadherence and natural disease progression, high-powered AI-fault liability implements the standard accident-law ideal. If the record is coarse, the first best may be infeasible: the same transfer that disciplines the AI also insures the patient's hidden action. With joint causation, the relevant object is a marginal-responsibility score rather than a posterior cause label. I characterize the feasible set of liability incentives generated by the record and show when the optimal rule is no liability, strict liability, negligence, a safe harbor, comparative fault, or a continuous warranty. I then study algorithmic defensive design, through which AI developers can design not only medical recommendations but also the record on which future liability depends. Adoption, learning, enterprise liability, insurance, no-fault compensation, and regulation enter as ways to change the record, the liable entity, or the financing of compensation. The framework yields conditional implications rather than a one-size-fits-all rule. |
| JEL: | D47 D82 D86 D9 G22 I1 I13 I18 K12 K13 L51 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35321 |
| By: | Lohawala, Nafisa (Resources for the Future); Teng, Xuan |
| Abstract: | Flight-booking websites, such as Google Flights and Skyscanner, increasingly display estimated CO2 emissions for flight itineraries, but little is known about whether this information affects booking decisions. We study how emissions disclosure affects consumers’ flight choices using US domestic flight data from 2018 to 2022 and a discrete-choice model and find that it increases consumers’ sensitivity to flight emissions. In our preferred specification, the absolute value of the emissions elasticity of demand increases from 0.23 in the predisclosure period to 0.28 in the period following the first disclosure. Expressed in willingness-to-pay (WTP) terms, the implied WTP for emissions reductions is $33 per ton higher in the postdisclosure period. Counterfactual simulations suggest that mandating emissions disclosure across all flight-booking platforms would further strengthen consumers’ responsiveness to emissions information.Keywords: Willingness to pay, Carbon emissions disclosure, Discrete-choice model, AviationJEL codes: D12, D83, L93, Q58 |
| Date: | 2026–06–11 |
| URL: | https://d.repec.org/n?u=RePEc:rff:dpaper:dp-26-08 |
| By: | Ferracane, Martina F.; Hoekman, Bernard; Shepherd, Ben; Shingal, Anirudh |
| Abstract: | This paper examines the experience in Argentina and Uruguay with EU data adequacy decisions. Building on existing evidence showing that data adequacy can boost bilateral trade in digitally deliverable services, the analysis shows that both countries recognized its trade-facilitating potential, in addition to benefits from data protection from a rights perspective. In addition, their experience with regulatory reform, based on the objective of obtaining an adequacy decision, has supported ongoing efforts more broadly in the region to develop data protection standards. There have been significant spillovers in a regulatory sense, as well as institutional adaptations on a regional level through the development of EU-based and non-EU-based adequacy clubs. A quantitative analysis using a structural gravity model that incorporates the latest developments in the causal analysis literature supports these claims. It shows that the effect of data adequacy on bilateral trade builds over time, potentially taking five years or more to fully develop. In Argentina, adequacy led to an increase of around 28 percent in bilateral exports of digitally deliverable services with other adequate countries. In Uruguay, the effect was smaller but still substantial, at 11 percent. |
| Keywords: | data protection; international trade; trade in services |
| JEL: | L86 F15 F14 |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138702 |