Publications

​

​

​

Observers across the ideological spectrum have criticized benefit–cost analysis for as long as it has been part of the rulemaking process. Still, proponents and detractors agree that analysis has morphed into a mechanism often used by agencies to justify regulatory decisions already made. We argue that a simpler analysis of more alternatives conducted earlier in the regulatory process can resuscitate it as a tool to inform policy. Recognizing that requiring a procedure does not ensure that regulators will follow it, we offer possible remedies, including strengthening or relaxing subsequent review of proposed rules, which raise the cost of circumventing the reform or lower the cost of following it.

​

Despite efforts to ensure that new regulations provide net benefits to citizens, the accumulation of regulations threatens economic growth and well-being. As a result, Congress is exploring the possibility that applying fiscal budgeting concepts to regulation could bring more accountability and transparency to the regulatory process. This Essay examines the advantages and challenges of applying regulatory budgeting practices and draws some preliminary conclusions based on successful experiences in other countries

President-elect Trump committed to “a requirement that for every new federal regulation, two existing regulations need to be eliminated” or what could be called a “two-for-one” requirement. The implementation of such a regulatory “pay as you go” process raises a number of issues including: what constitutes a “new regulation”; how offsets should be measured; estimating and managing additional analytic and administrative workload; enforcement; and how to increase the likelihood such a policy survives the next presidential transition. This working paper presents the advantages and disadvantages of various options for implementing a two-for-one policy, and also discusses the role of regulatory benefits estimates in such a system.

Innovation is an essential element of the internal market. Defined by the objective of a “highly competitive social market economy, aiming at full employment and social progress, and a high level of protection and improvement of the quality of the environment” (Article 3 (3) TEU), innovation is a precondition of “sustainable and job-creating growth”.1 It leads to higher productivity and competitiveness2 while yielding social and environmental benefits.

This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(2008). Nudge. London: Penguin Books] such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytic nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence ‘hypernudge’). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints.

​