Publications

Prior to the global financial crisis, financial innovation was viewed very positively, resulting in a laissez-faire, deregulatory approach to financial regulation. Since the crisis the regulatory pendulum has swung to the other extreme. Post-crisis regulation, plus rapid technological change, have spurred the development of financial technology (FinTech). FinTech firms and data-driven financial service providers profoundly challenge the current regulatory paradigm. Financial regulators increasingly seek to balance the traditional regulatory objectives of financial stability and consumer protection with promoting growth and innovation. The resulting regulatory innovations include RegTech, regulatory sandboxes, and special charters. This Article analyzes possible new regulatory approaches, ranging from doing nothing (which spans being permissive to highly restrictive, depending on context), cautious permissiveness (on a case-by-case basis, or through special charters), structured experimentalism (such as sandboxes or piloting), and development of specific new regulatory frameworks. Building on this framework, we argue for a new regulatory approach, which incorporates these rebalanced objectives, and which we term ‘smart regulation.’ Our new automated and proportionate regime builds on shared principles from a range of jurisdictions and supports innovation in financial markets. The fragmentation of market participants and the increased use of technology requires regulators to adopt a sequential reform process, starting with digitization, before building digitally-smart regulation. This Article provides a roadmap for this process.

This report summarises the results of the study ‘Assessing the impacts of EU regulatory barriers on innovation’. To obtain the necessary data, a European-wide survey was conducted in 2016. This study focused on four sectors: energy, food, health and water. Each of these four sectors addressed the innovation system and regulatory framework, alongside questions on challenges, innovation drivers and barriers. While regulatory barriers to innovation were identified for the whole economy as well as within the four sectoral studies, the overall impact of regulation on innovation is predominantly neutral to positive. The main regulatory barrier identified concerns ‘conflicting regulation’ rather than a single type of regulation. The study also found that compliance with regulation creates innovation, with a net gain of EUR 3-6 billion per year in additional innovation investments and in terms of employment a net gain of 120 000 additional jobs. Removing existing regulatory barriers could release up to EUR 4-8 billion on general innovation investment per year.

This article presents the findings of an extensive multi‐method empirical study that explored the relationship between temporary legislation, better regulation, and experimentalist governance. Temporary (or “sunset”) legislation – statutory provisions enacted for a limited time and set to expire unless their validity is extended – is often hailed as a key tool for promoting experimental and better regulation. Despite the importance of temporary legislation and the burgeoning theoretical scholarship on the subject, there is still a dearth of empirical studies about how temporary legislation is used in practice. The lack of empirical evidence creates a lacuna in at least three areas of theoretical scholarship, concerning temporary legislation, better regulation, and experimentalist governance. This paper is a first step to fill this gap.

This is the second Activity Report on the work of the European Parliament in the fields of Ex-Ante Impact Assessment and European Added Value. (A first Activity Report, covering the period from June 2012 to June 2014, was published in September 2014). It provides an overview of the work carried out by the Directorate for Impact Assessment and European Added Value within the Parliament’s administration between June 2014 and December 2015, in support of oversight and scrutiny of the executive by parliamentary committees, in the fields specifically of: (i) ex-ante impact assessment; (ii) European added value; and (iii) ex-post evaluation. During the first 18 months of the current 2014-19 parliamentary term, a total of 145 pieces of work were published by the Directorate, encompassing some 6,500 pages of tex

On January 30, 2017, President Donald J. Trump signed Executive Order 13,771, which directs each agency to repeal at least two existing regulations before issuing a new regulation (referred to as the “one‐in‐two‐out” requirement) and imposes a regulatory budget that sets a cap on total incremental regulatory costs (set at zero for fiscal year 2017). The regulatory budget concept has been kicked around for decades, while the one‐in‐two‐out requirement is more recent and has been implemented in Canada, the United Kingdom, and Australia to various extents. Legal scholars and commentators have been quick to opine on the Order, with some pointing out ways in which it is irrational or impractical and others defending aspects of the Order such as the imposition of a regulatory budget. In this Essay, we take a somewhat different approach by evaluating how well the Order is likely to achieve its purpose of helping agencies “be prudent and financially responsible in the expenditure of funds.” Although vaguely laudable, this purpose is illaudably vague. We will therefore ground our analysis by defining the goal (or goals) of the Order according to priorities that have been adopted by prior administrations or promoted by scholars, commentators, or interest groups. For purposes of this analysis, we take no normative position on whether these end goals are desirable. The three potential goals that we evaluate the Order against are: (1) increasing the net benefits of regulations, (2) decreasing regulatory burdens, and (3) increasing presidential control over agencies. We then compare the Order against regulatory reform efforts in other countries. We conclude that the Order is unlikely to sensibly (much less prudently or responsibly) achieve any of these goals without significant changes

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Why should regulators be independent? How is it possible to reduce the gap between de iure and de facto independence? This OECD guidance addresses this topic by analysing the five dimensions in which the “culture of independence” should spread: role clarity and responsibility, transparency and accountability, financial independence, independence of leadership, and staff behaviour (Maria Bianca Armiento, Rassegna Osservatorio Air, n. 4/2019).

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others have the clear potential in the near-term to use algorithms to shape official decisions over both rulemaking and adjudication. It is no longer fanciful to envision a future in which government agencies could effectively make law by robot, a prospect that understandably conjures up dystopian images of individuals surrendering their liberty to the control of computerized overlords. Should society be alarmed by governmental use of machine learning applications? We examine this question by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law. At first glance, the idea of algorithmic regulation might appear to offend one or more traditional doctrines, such as the nondelegation doctrine, procedural due process, equal protection, or principles of reason-giving and transparency. We conclude, however, that when machine-learning technology is properly understood, its use by government agencies can comfortably fit within these conventional legal parameters. We recognize, of course, that the legality of regulation by robot is only one criterion by which its use should be assessed. Obviously, agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law, and in some cases, safeguards may be needed for machine learning to satisfy broader, good-governance aspirations. Yet in contrast with the emerging alarmism, we resist any categorical dismissal of a future administrative state in which key decisions are guided by, and even at times made by, algorithmic automation. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism over the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just.

The next generation of e-commerce will be conducted by digital agents, based on algorithms that will not only make purchase recommendations, but will also predict what we want, make purchase decisions, negotiate and execute the transaction for the consumers, and even automatically form coalitions of buyers to enjoy better terms, thereby replacing human decision-making. Algorithmic consumers have the potential to change dramatically the way we conduct business, raising new conceptual and regulatory challenges. This game-changing technological development has significant implications for regulation, which should be adjusted to a reality of consumers making their purchase decisions via algorithms. Despite this challenge, scholarship addressing commercial algorithms focused primarily on the use of algorithms by suppliers. This article seeks to fill this void. We first explore the technological advances which are shaping algorithmic consumers, and analyze how these advances affect the competitive dynamic in the market. Then we analyze the implications of such technological advances on regulation, identifying three main challenges.