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Ten Global Trends in Regulation: A Future Outlook

  • Jeroen van der HeijdenEmail author
  • Graeme Hodge
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Abstract

The regulatory regimes that public servants are involved in today are largely shaped by five global trends in regulation that emerged in the last decades of the twentieth century. They include performance-based regulation, risk regulation, responsive regulation, smart regulation, and better regulation. This chapter provides introductions into and discussions of these trends. From there on, the chapter explores five key trends in regulation that have emerged since the start of the twenty-first century and that will likely be dominant in regulatory governance over the next decades to come. They include experimental regulation, behavioral insights informed regulation (“nudging”), regulatory intermediaries, regulatory orchestration, and regulatory stewardship.

Keywords

Regulatory governance Regulatory reform Risk-based regulation Responsive regulation Behavioral insights informed regulation 

Introduction

The introduction to this section of the Handbook, by Jeroen van der Heijden, has sketched, in broad brushstrokes, the distant and recent history of regulatory reform. The previous chapter, by Graeme Hodge, has discussed the foundations of regulatory theory and practice with a specific focus on the public service. In this chapter, we join forces and move on from our earlier discussions and explore ten of the most promising global trends in regulation that will likely dominate regulatory governance for the critical decades that lie ahead of us. In each case, we hint at the strengths of the trends as well as weaknesses. The trends covered in this chapter extend from roughly the last two decades of the twentieth century to the first two of the twenty-first century.

Performance-Based Regulation

One classic direct regulatory governance intervention has long been statutory regulations. An example is building regulations that set requirements for the structural safety of a building. Such requirements are often expressed in standards that seek to steer behavior in such a way that harmful results are prevented or in such a way that a specific outcome is achieved. These standards have historically been formulated in prescriptive terms, generally referred to a prescriptive regulation or prescriptive standards. Prescriptive standards seek to prevent harmful events, for instance, the collapsing of a building, by stating the exact requirements the particular parts of a building, its construction, or even its design process have to meet. Typically, prescriptive building regulations set standards to the loadings for buildings, for instance, how much load a floor should be able to hold, to the structural use of concrete or other building materials, or to the process of calculating its structural safety. An example is: “no floor enclosed by structural walls on all sides exceeds 70 m2” (HM Government 2010, 18). Prescriptive standards require governments to possess far-reaching technical expertise to formulate. In building regulations, and other areas, this particular issue is however often overcome by referring to (international) standards that are developed by nongovernmental organizations. For instance, the German Federal Building Code relies on many standards developed by the nongovernmental German Institute for Standardization (DIN, Deutsches Institut für Normung), while building regulations in many other countries refer to standards developed by the International Organization for Standardization (ISO). Besides, prescriptive standards leave little room for flexibility or innovation, which is an oft-heard critique (Duncan 2005). One side effect of this situation is that challenges may arise where a novel technology cannot be applied because the prescriptive regulations do not allow for it, for example, novel technology that allows for reusing household water for watering the garden and safe water at household and city level. Such technology could conflict with health regulations that require that all water used by households meets the high standards of potable water.

To overcome problems of rigidity that may come with prescriptive regulation, many governments around the globe have moved to performance-based regulation and standards since the 1980s. These standards specify the performance of a good or service but do not specify how that performance is to be achieved. Such standards are normally considered to give those regulated an incentive to find a solution that is both effective in terms of meeting the standard and efficient in terms of costs (May 2003). In the Netherlands, for example, the Dutch Building Decree sets a design target for the energy efficiency of (future) houses but does not stipulate how this target is to be achieved (Beerepoot and Beerepoot 2007). Since its introduction in 1995, the target has been expressed stringently, and it is to be expected that houses built from 2020 onward have to meet a zero-energy target. The introduction of performance-based regulation implies a move away from technical norms toward legal norms. The former “draws its force from the scientific knowledge of an object it intends to put to use”; the latter “draws its force from shared faith in the projected realm that the norm aims to realize” (Supiot 2007, 149). The regulatory focus is no longer on how compliance is reached, but that compliance is reached. Still, scholars express concerns.

The danger in this type of regulation may lie in its highly complex nature (Spence 2004), a missing link between regulation and methods to test compliance, and the lack of clarity for regulatees on how to reach compliance (Deighton-Smith 2008). The overall accountability of the regime has also been criticised (Meacham et al. 2005) because it is left to regulatees to prove that compliance is reached. These concerns are further supported by empirical research. An example is a comparative study on building safety in New Zealand and fire safety in the United States (May 2007). This study found that evaluation criteria to assess performance were missing; government agencies responsible for compliance assessment were lacking the expertise to carry out enforcement; and accountability of the systems was questioned due to issues in professional judgment and the exercise of professional discretion. A solution to potential issues with performance-based regulation is sometimes found in adding “deemed-to-satisfy” or “deemed to comply” provisions to performance-based codes (Van der Heijden 2014). Provisions that include examples of materials, components, design factors, and construction methods, which if used, will result in compliance. Yet, some argue that in practice regulatees often are unwilling to depart from such provisions because these at least state how to reach compliance. Such behavior undermines the rationale for performance-based regulation and in practice implicates a move back to prescriptive regulation.

Risk-Based Regulation

From the 1980s onward, risk reduction was given a more and more important role in discussions on regulation, and a shift toward risk governance and risk-based regulation can be perceived (van der Heijden 2019b). Risk-based regulation differs from traditional regulation because it is not based upon the input of an activity – prescribing what to do, or which standards to meet – but based upon its output – the risk it causes. Another difference between traditional regulation is its nondeterministic character: traditional regulation aims at reducing noncompliance to zero, whereas risk-based regulation accepts that risks do exist and that some risks are inevitable but tries to reduce these risks to a minimum (Seiler 2002). The fundamental principle of risk-based regulation is that regulatory regimes “should use comprehensive risk assessment to concentrate resources on the areas that need them most” (Hampton 2005, 7). Risk-based regulatory regimes therefore “aim to control relevant risks, not to secure compliance with sets of rules” (Baldwin et al. 2012, 281). They aim to provide a logical structure within which the regulator’s enforcement decisions occur and can be understood. Risk-based regulation can be understood as a utilitarian approach to regulation. It seeks to provide a systematic (and ideally transparent and accountable) approach to allocating the finite regulatory means (mainly staff and funds) in such a way that the largest harms and risks can be addressed. Typically, regulators, therefore, go through a process in which a range of potential harms and impacts are judged and the probability of these occurring is specified. A risk ‘quantum’ is then established. The notion of risk regulation has a strong inherent appeal. After all, we have been dealing well with quantitative risk assessments in fields such as road accidents, gun deaths, drinking water quality, and food related illnesses for some time now. And its rationality promised much to the enforcement narrative. So far, so good.

However, risk-based regulation in practice has been far less certain. For a start, many commentators argue that although much regulation seeks to address “harm” (whether to health, welfare, safety, property, or the environment), the concept of harm is not just an issue of damage or injury. Such scholars argue that risk is a social construct and its assessment is not an objective undertaking (Rosa 1998; Aven 2010; Renn 2008). In a similar vein, and more directly addressing the notion of risk, others argue that the concept of risk in our regulatory discourse includes three different threads, not one (Haines 2017). The first part, an “actuarial risk,” is the possibility of harm such as a fall from a worksite or toxic effluent from a factory. The second part, “sociocultural risk,” is both tangible and intangible threats to the human collective, perhaps through new technologies or through changing social order, conflicting values, inequalities, or concerns about security or belongingness. The third type of risk is “political risk,” which sees risk as “not only as risks to the government of the day, but also to the legitimacy of a political system within a particular setting” (Haines 2017, 185). The point being made here is crucial. Risk in regulatory narrative is only in part a quantitative calculation – it is as much a social and political entity as it is a scientific number (For an elaborate review of the literature touching on different approaches to conceptualize risk, see van der Heijden 2019b, or de Sousa 2020).

Risk-based regulation is said to have both advantages and disadvantages. It is often perceived as more effective and efficient than traditional approaches to regulatory governance as priority is given to certain enforcement activities and as more legitimate, as certain choices are more analytically based (Hutter 2005). Nevertheless, these choices are themselves the downside of risk-based regulation, as it is impossible to determine a risk objectively (Baldwin et al. 2012). Besides, the analytical approach of defining risks, by combining chance and effect, may give a false sense of security (Rothstein et al. 2006). Furthermore, such a false sense of security may be strengthened when the model is “too literally and slavishly believed in” (Hutter 2005, 13) and, once risks are determined, the model might be blind for new risks (Baldwin 2006). Finally, it is questionable if risk-based regulation has to be experienced as an (other) enforcement strategy or “a methodical tool into which political judgments may be explicitly incorporated” (Flüeler and Seiler 2003, 228) – i.e., a tool for allocating resources. Some critics even warn that such tools for risk regulation, and related risk management may become a “cult” of standard setting (Durant 1998). Perhaps, ultimately, what public servants (and their Ministers) may be least comfortable with is the matter of transparency and legitimacy. The “politically contentious judgments [can be] hidden behind neutral risk assessment language,” and the use of risk-based regulation frameworks can ultimately reveal high levels of discretion, along with significant resourcing limitations and sweeping trade-offs (Baldwin et al. 2012, 293).

Responsive Regulation

In the 1990s, Ian Ayres and John Braithwaite (1992) introduced what may be considered one of the major paradigm shifts in our thinking of regulation: the model of responsive regulation. Under a traditional regulatory regime, the government sets regulations and enforces these. The most traditional structure is a command-and-control regime based on negative incentives (Kagan 1984). This structure has, however, been subject to much criticism. Both capture and overregulation are possible, and compliance standards are difficult to set and difficult to enforce (Baldwin et al. 2012). Critics of this regime, therefore, promote alternative regimes in which different strategies are used, preferably a mix of strategies (Hawkins and Thomas 1984; Parker 2000; Tyler 1990). Indeed, Ayres and Braithwaite state that on the one hand, rejecting punitive regulation is naïve, but so too is a total commitment to it, because this might lead to unnecessary employment of means. Based upon prior empirical research in pharmaceutical companies and coal mining companies by Braithwaite (1984, 1985) and Australian business regulatory agencies by Grabosky and Braithwaite (1986), the authors of the responsive regulation model state that a strategy based upon punishment as the first choice is unaffordable, unworkable, and counterproductive. Instead of aiming at compliance through deterrence-based strategies, the authors promote the use of different, less punitive and less restrictive, strategies and preferably a mix of different strategies: “the trick of successful regulation is to establish a synergy between punishment and persuasion” (Ayres and Braithwaite 1992, 25). Responsive regulation differs from the traditional command-and-control regime in what triggers a regulatory response and what this response will be. The relation between controller and subject and the controller’s ability to choose between different sanctions is regarded the strength of this model (Ayres and Braithwaite 1992; Braithwaite 2002).

The responsive regulation model is widely used internationally – and well-known for its pyramids of regulatory strategies and sanctions. The enforcement pyramids shown in Fig. 1 illustrate this. The model suggests that regulation in practice includes a range of possible actions from hard law through to soft law. Ayres and Braithwaite’s analysis broke away from the recurring long-term policy battles between “deterrence versus compliance” and examined the regulatory games played between regulators and those subject to regulation. They suggested that the behavior of the regulator ought to depend to a large degree on the behavior of the regulatee and that in the first instance, the philosophy of the effective and “responsive” regulator is to initially encourage compliance and try the least cost measure. If these measures do not work, then actions taken by the regulator are then “escalated” upward so that increasingly punitive and increasingly legal methods are adopted to ensure compliance. The implication of this is that much regulatory time is spent on establishing systems of compliance for “normal behavior” (e.g., through licensing and accreditation schemes) and on measuring and monitoring, as well as in regulatory conversations, assessing and reporting, as opposed to formal court proceedings. Ayers and Braithwaite observed that the regulatory game “could produce public interest outcomes that were compatible with, and even heightened by deliberation, trust-building and empowerment” (Morgan and Yeung 2007, 54). Moreover, rather than deepening existing scholarly tensions, they were inspired by the idea that through regulatory encounters and conversations, cooperation could pay off. Ayers and Braithwaite’s cross-disciplinary ideas remain as refreshing and relevant today as when they appeared almost three decades ago, and the “responsive regulation” model has stood the test of time (Parker 2013). Having said this, their model acknowledged that cooperation between regulators and regulatees could ultimately encourage the evolution of corruption and capture and that as a result, an ongoing and strong involvement of ‘publc interest groups’ was needed (Morgan and Yeung, 2007; Van der Heijden 2017). Also, while the model has been both widely applied and applauded, it has had multiple criticisms. These have included being inappropriate for severe risks (such as terrorism), the assumption of an ongoing relationship or dialogue between regulator and regulatee, and the inability to de-escalate down the pyramid once escalation has occurred (Windholz 2018).
Fig. 1

Visualisations of the responsive regulation model (Ayres and Braithwaite 1992). (Reprinted with permission from John Braithwaite)

Smart Regulation

Innovative as they may be, both risk-based regulation and responsive regulation keep regulatory governance within the public service. Implicitly they ascribe regulatory tasks, and specifically regulatory enforcement, as a task for public agencies and public inspectors. A model that embraces the possibility of involving parties other than public agencies in the regulatory process is Neil Gunningham and Peter Grabosky’s (1998) smart regulation. In their work, Gunningham and Grabosky divide the regulatory process into parties, roles, and interactions. They argue that up to the late 1990s, the regulatory process was seen too much as “a dance between two participants – government and business” (Gunningham and Grabosky 1998, 93). Central to the smart regulation model is to have those actors involved in the regulatory process that are best fit to enforce regulations. This may sometimes be through traditional government agencies, sometimes through self-regulatory or co-regulatory initiatives in which private sector actors enforce rules against members of their bodies, and sometimes through third parties, such as consumer interest groups or insurance companies, which act as “surrogate regulators” (Gunningham and Grabosky 1998, 106). Gunningham and Grabosky ascribe most value to surrogate regulators when large companies are involved and when noncompliance is easy to identify (for an extensive discussion, see Van der Heijden 2016). A well-known example of success by surrogate regulators is Greenpeace’s 1999 campaign against Home Depot, the then-largest supplier of do-it-yourself products in the United States. Through their campaign Greenpeace sought to ensure that do-it-yourself suppliers in the United States moved away from supplying old growth wood and had all their wooden products certified under the voluntary Forest Stewardship Council certification regime for timber products. By carefully targeting the largest supplier, Home Depot, first and by seeking media attention for this campaign, Greenpeace could convince other suppliers of do-it-yourself products to participate in the Forest Stewardship Council’s certification scheme with a mere phone call (Domask 2003).

The extensive empirical research of Gunningham and Grabosky has confirmed both the importance of “surrogate regulators” being large companies and the need for noncompliance to be easily noticeable. For instance, for an ordinary citizen, it might be easy to notice violation of planning regulations when a building is built where it is not supposed to. Yet, violation of technical building regulations when the wrong type of glazing is used might be hard or even impossible to notice as that same citizen does not have the necessary technical knowledge or experience to do so. Although Gunningham and Grabosky and others ascribe much potential value to surrogate controllers, for instance, “whistle-blowers” that report misconduct in their firm or branch (Rothschild and Miethe 1999), others find less potential. The likelihood of surrogate controllers reporting violations to (external) government agencies may depend on the type of violation; the impact reporting this violation may have to the surrogate controller and the background of the violator (Feldman and Lobel 2008). In short, many moving parts must align to make this model achieve its desired results. Also, introducing third parties into regulatory enforcement may come with complications. A study on the role of third-party regulators in the enforcement of construction regulation in Australia and Canada highlights some of these (Van der Heijden 2015a). In both countries, regulatees have the choice of involving either a government or a private sector inspector in the monitoring and enforcement of their compliance with regulations. The research indicates that involving private sector inspectors in certain monitoring tasks makes the regulatory enforcement process more effective and efficient as a result of specialization but that too much private sector involvement comes with accountability problems. Once private sector actors operate at great distance from governments, shirking and cutting corners were found to be a considerable risk. That is, without sufficient governmental oversight of the enforcement tasks of these private sector inspectors, the latter may bend to the wishes of their clients – which implies that they may certify construction work as being compliant with regulatory requirements and issue building and occupancy permits where standards have not been met.

We should note here that the smart regulation terminology was briefly used by the European Commission between 2010 and 2014 and has since been replaced by “better regulation” terminology (Scott 2018). The Commission’s understanding of smart regulation differs significantly from that in Gunningham and Grabosky’s work. It implied “[t]o ensure that EU action is effective, the Commission assesses the impact of policies, legislation, trade agreements and other measures at every stage – from planning to implementation and review” (cited and further discussed in Van der Heijden 2016).

Better Regulation

In a sense, all these changes to regulatory governance have been seeking “better regulation” and working toward a goal of “good regulation.” Rhetorically, these phrases are little more than magic concepts for policy advertising (Pollit and Hupe 2011). After all, no sensible person would prefer “worse regulation” and pursue a goal of “bad regulation.” The journey to “good” and “better” regulation that we observe in many countries around the globe began with the advent of stagflation in the late 1970s. This period of high inflation with stagnant economic growth saw calls from American economists for “deregulation” to solve the problem – albeit that deregulation often implied regulatory reconfiguration of regulatory regimes and reregulation (Claude and Chertman 2009; Freiberg 2010). In retrospect, this was somewhat naive and optimistic, but it represented an important step in the ongoing policy battle between capital owners and those preferencing social purposes. Economists also reacted to the long-held criticism that they seemed more intent on abolishing regulation (because of its inherent “market inefficiency”) rather than assuming Parliamentary sovereignty and examining how to make it work better. Their answer came in the form of US President Ronald Reagan’s “Regulatory Impact Statement” (RIS). The central proposition was that more attention needed to be paid to the anticipated costs and benefits of regulatory proposals. RIS, employing benefit-cost analysis, has since become popular around the globe and is now an important tool for regulatory reform in OECD countries.

Over time, then, the deregulation reform had morphed into the RIS (or “regulatory impact review”) reform. The application of RIS in practice is a large domain for discussion, and, as with all benefit-cost analysis, there is much to say on the matter. It is suffice to comment here that there certainly is a role for benefit-cost analysis in decision-making but there is a risk that (cost-)efficiency as a public value will overshadow others, such as transparency, accountability, and equity (Baldwin et al. 2012). These early efforts also subsequently morphed into efforts under more general rhetorical banners such as “regulatory reform,” “better regulation,” and “rethinking regulation.” “Reducing red tape” and “reducing administrative burdens” on business using “Standard Cost Models” (originally out of the Netherlands) were also crucial to this policy evolution path. Red tape reform agendas launched by governments have been a visible feature of the political landscape over the past two decades, and many states have now had red tape reduction targets for several years.

Systematic work in this area was carried out by the UK Better Regulation Task Force (BRTF), between 1997 and 2005. The BRTF aimed to help “[improving] regulation as it affects the private, public and voluntary sectors [in the United Kingdom]” (BRTF 2005, 7). The BRTF promulgated an initial set of principles for good regulation. These have long since been popularized globally and include proportionality, accountability, consistency, transparency, and targeting. The BRTF’s work and agenda later informed the European Union’s comprehensive Better Regulation program, launched in 2002 (Renda 2016). Better regulation seeks to increase competitiveness and reduce regulatory burdens, as well as increase the legitimacy of regulation by requiring transparency of the processes of developing and implementing regulation (Radaelli and Meuwese 2009). Since its introduction, the program has expanded and now requires ex post evaluations, fitness checks, cumulative costs assessments, and retrospective reviews. The program is ambitious in that it allows policy-makers to combine diverse policy objectives in different areas and has renewed attention toward evidence-informed policy-making (Eliantonio and Spendzharova 2017). While this is a good start, there still is a risk that (cost-)efficiency overshadows other ambitions of regulatory governance. Leading regulatory scholars Robert Baldwin, Martin Cave, and Martin Lodge argue that a more comprehensive, contribution to our thinking on good and better regulation is required. They made the important point that the extent to which a regulatory action is “good” depends on the democratic issue of whether the action is worthy of support and is seen as “legitimate” by citizens. “Good regulation” to them involves five tests: legislative mandate, accountability, due process, expertise, and efficiency (Baldwin et al. 2012). They emphasize that while these five dimensions each require complex judgments, much of which comes down to qualitative assessment; they are morally far superior to setting a single, quantitative criteria such as wealth maximization, for instance.

Experimental Regulation

Recent scholarship points toward a current trend of willing experimentation in regulatory governance (Halpern 2019). It observes that many of today’s regulatory challenges are too complex to address with traditional regulatory interventions and that conventional, generic, one-size-fits-all regulatory interventions easily result in under- or overregulation. They call for a careful exploration of tailored interventions that are mindful of their contexts. Their work follows a long tradition of scholarship that is interested in experimentalism in policy design (Campbell 1969), evaluating the effectiveness of policy inititiatives (Hodge 2010), learning policy lessons (Rose, 1993) and in evidence-based policy-making (Pawson 2002). An ideal-type experiment is (i) a recursive process of regulatory design and implementation which is subject to constant observation and adaptation to local conditions and unexpected circumstances; with (ii) ongoing learning about the effectiveness and efficiency of the regulatory intervention which informs its further development, adaptation or abolishment; and (iii) collaboration between the developers, administrators and those subject to the regulatory intervention (Sabel and Zeitlin 2011). This co-operative approach to learning represents a fresh emphasis on evaluating what works best in regulatory decision making. It builds on a need for those attempting to influence the behaviours of others to learn across disciplines which have long sought policy learning and improved regulatory governance through careful evaluation. It also re-emphasises the need for regulators to better monitor their actions for effectiveness and be pro-active in their work. Experimentation in regulatory governance as discussed in the recent literature has emphasized highly localised processes of testing, piloting, or demonstrating of a regulatory intervention seeking to learn about its potential in overcoming regulatory problems (Van der Heijden 2015b).

Key to experimentation in regulatory governance is the drawing of lessons about outcomes that may be expected when the experiment is formalized and included in (future) policy. There is an extensive literature on policy learning (Rose 1993; Dolowitz and Marsh 2000; Petts 2007; Radaelli 2012) and scholars have been careful in considering how policy lessons are learned and the value of these lessons. There is clearly a need to not only seek rigorous evaluations and include multiple sources and interpretations of ‘evidence’ (Parsons 2002), but also learn more effectively across numerous evaluations and evidence bases. Scholars also point to the need for mechanisms to collect and store lessons so that these can be shared among different actors and to actively seek to share lessons with a broad group of (possible) participants in the regulatory experiment and beyond (e.g., Vreugdenhil et al. 2010).

Central to the recent popularity of experimentation in regulatory governance is the concept of randomized control trials (RCT). RCTs build on the same logic as the testing of new medications. Evaluators have always been able to adopt a wide range of experimental and quasi-experimental designs to ensure rigor in evaluation studies (Hodge 2010), however the RCT experimental design followed by robust peer review both remain gold standards in evaluation. In a nutshell, an RCT follows the following steps: (1) People or organizations participating in the experiment are randomly allocated to one or more groups that are subject to the intervention or interventions to be tested or to a group that will not be subject to any intervention (the control group). (2) The groups are followed for a period in the same way, and the only difference between them is the intervention to which they are subject. (3) After the trial is completed, observations are compared between the groups to understand whether the behavior of the group or groups that received the intervention is (statistically significantly) different from that of the control group (Haynes et al. 2012). For example, aiming to increase the on-time payment of traffic fines, the New South Wales Government in Australia carried out an RCT in 2012. One group of people (the control group) received the traditional payment notice, and another group (the intervention group) received a redesigned payment notice. The redesigned notice had an obvious “PAY NOW” stamp on it, used simple language, and clearly communicated the consequences of not paying the fine. People who received the redesigned notice (the intervention group) were about 3% more likely to pay their fines on time, which was reflected in revenue of over AUD 1 million for the New South Wales government and 9,000 fewer people losing their licences (NSW Government 2018).

Behavioral Insights Informed Regulation (“Nudging”)

The above example of testing an improved traffic fine payment notice that used highly salient information is illustrative for another trend in regulatory governance also: the use of insights from the behavioral sciences in regulatory governance (Kosters and van der Heijden 2015; van der Heijden 2019a). In the past, much regulatory intervention has built on a neoclassical understanding of human behavior. This has particularly been the case in commercial and financial regulation. In a nutshell, humans were assumed to be rational beings who have “stable, coherent and well-defined preferences rooted in self-interest and utility maximisation that are revealed through their choices” (McMahon 2015, 141). This presumption of traditional mainstream economic models that people are ‘utility maximisers’ (the ‘Homo Economicus’ stereotype), has long suffered considerable criticism, however. Polymath Herbert Simon (1945) warned us half a century ago that we are bound by our own habits, take short cuts in our thinking and that we satisfice from a limited number of choices when making decisions. And scholars of public policy and political philosophy have long called for a more realistic understanding of human behaviour in the development and implementation of regulation (Boston 1991; Hayek 1982).

Overall, then, it has been common knowledge throughout most humanities disciplines that individual people have cognitive and attentional limitations, and that ‘the social environment and prevailing social norms matter for individual choices’, as Sibony and Alemanno (2015, 4) say. People often want to be benevolent because this is fairer than selfish behaviour (bounded self-interest). People may desire one thing, such as eating healthy, and yet choose to do something else such as smoking or eating unhealthy food. This occurs because their willpower is bounded - we have a preference for instant pleasures and an immediate payoff, even if these offer poorer long term outcomes (Hodge and Greve 2019). Our decisions are also highly sensitive to the way a problem is framed. We are strongly influenced by people in authority or people we like. And we feel a loss more strongly than an equivalent gain. We are indeed all subject to a set of heuristics and biases—using mental shortcuts and using psychological anchors of dubious integrity—when making decisions. The major contribution made by contemporary behavioral scientists such as Daniel Kahneman (2011), Roberto Cialdini (2009), and Dan Ariely (2008) has been to bring such matters to mainstream economic thinking and to provide strong evidence as to just how important these various personal traits are. Armed with these insights, behavioral scientists and regulatory scholars and practitioners have begun to develop regulatory interventions that seek to steer people’s behavior taking into account the heuristics and biases we are all subject to. This application of behavioral insights is what Richard Thaler and Cass Sunstein (2009) refer to as changing the “choice architecture” of those who are governed in their famous book Nudge: Improving Decisions about Health, Wealth and Happiness. To them, the answer to the question “why use insights from the behavioral science in regulatory governance?” (or “why nudge?”) is that the application of behavioral insights should result in more effective regulatory governance and, therefore, should make people happier. For government, the answer is similar in some respects, in that effective regulatory governance and regulations are obviously preferable to ineffective ones.

Inspired by these ideas, governments around the world have begun to incorporate these insights into regulatory interventions (OECD 2017a, b, 2018). Whether these interventions improve the effectiveness of regulatory governance is now an area of much debate (van der Heijden 2019a). Organizations dedicated to testing behavioral insights informed regulatory interventions, such as the Behavioural Insights Team in the United Kingdom, are actively involved in tests and (randomized control) trials to understand whether a specific intervention has the desired outcomes. They often report that the regulatory interventions they have developed are effective in changing the behavior of those they target. But the extent to which the optimistic promises of these reformers will be met over time, has been questioned. The central finding of the UK House of Lords (2011), for instance, was that using a range of interventions was often necessary for effective public policies, and that ‘non-regulatory measures used in isolation, including “nudges”, are less likely to be effective…’ They also reported ‘a lack of applied research at a population level to support specific interventions to change the behaviour of large groups of people’. The findings of the broader academic literature, likewise, have not been clear-cut. The empirical studies of academics have found that interventions building on these insights sometimes have desirable effects and sometimes do not. They stress that, currently, we lack robust evidence to make generic statements about the extent to which regulatory interventions informed by behavioral insights live up to their expectations. When considering the theoretical success and legitimacy of behavioural interventions in the context of public governance, there has likewise been considerable scepticism (Jones et al. 2013). Thus, despite much research, neither academics nor practitioners currently know if the behavioural approach will work across the board (Baldwin 2014; Wright and Ginsburg 2012). In this light, one perspective of the potential of RCT and nudging techniques is that those most keen on expanding such activities are simply the most recent contemporary examples of a long line of ‘truth ‘ junkies (Sullivan 2011). On the other hand, perhaps a more measured perspective is that even gaining small improvements may be worthwhile for society as a whole given the low cost of using behavioural insights.

Regulatory Intermediaries

Governments often involve nongovernmental individuals and organizations to develop and implement regulation (Levi-Faur 2013). They can do this by outsourcing regulatory tasks, by collaborating with regulated parties, or by allowing parties to self-regulate. In these situations, regulation is no longer a two-party interaction between a regulator and its target but is an interaction between three or more parties: regulators, regulatory intermediaries, and targets of regulation. Scholars have for a long time been interested in the involvement of nongovernmental individuals and organizations in the development and implementation of regulation, but it is only recently that an all-encompassing research program on regulatory intermediaries has been started by Kenneth Abbott, David Levi-Faur, and Duncan Snidal (2017). The notion of regulatory intermediaries advances the role of nongovernmental individuals and organizations in the model of smart regulation (discussed above). Recall, in smart regulation, Neil Gunningham and Peter Grabosky (1998) embrace the possibility of involving parties other than public agencies in the regulatory process. They do so specifically for the enforcement of regulation. Some two decades after introducing their ideas, it now appears hard to imagine a regulatory regime in which non-state actors do not take up regulatory roles. Organic food certification is often carried out by private sector organizations, conduct standards in many professions are developed and monitored by industry associations, the roadworthiness of vehicles is tested by private sector inspectors, standards for medical equipment are developed by dedicated private sector standards-setting organizations, and so on (Auld 2014). All these non-state actors and organizations do not remove governmental actors and agencies from regulatory regimes. They rather act as intermediaries between government as regulator and firms, citizens, and others as the subjects or targets of regulation.

The R → I → T model (regulator, intermediary, target) model (Abbott et al. 2017) is specifically interested in the new dynamics in regulatory regimes that have emerged with the growth of regulatory intermediaries over the last decades. As previously noted, regulation was traditionally viewed as a two-party relationship between regulator and target (R → T). Here the regulator seeks to influence the behavior of the target, and the target sometimes seeks to influence the behavior of the regulator – the latter is extensively discussed in the classic literature on regulatory capture (Etzioni 2009). The R → I → T model acknowledges that contemporary regulatory regimes are subject to more complex dynamics. For example, the target may seek to capture the intermediary, the intermediary may seek to capture the regulator, or the regulator may seek to capture the intermediary (Abbott et al. 2017). These additional relationships make it more complex for regulators and those subject to regulation to get a clear understanding of the performance of the regulatory regime. Also, with the growth of intermediaries in regulatory regimes, traditional feedback loops in regulatory regimes are altered. For example, the traditional feedback loop about regulatory performance between the government regulatory inspector and the government regulatory developer may get lost when regulation is enforced by private, for-profit inspection firms (Van der Heijden 2008).

The R → I → T model was introduced to explore the role of intermediaries introduced in regulatory regimes by government regulators. It seeks, in part, to understand the bright sides of intermediation: the embracing of their skills and expertise to increase the effectiveness and efficiency or regulatory design and implementation, as well as an opportunity to increase the accountability of regulatory regimes. It does, however, also allow researchers to study why often those subject to regulation (targets) actively seek support from intermediaries (rather than from government regulators) and why and how intermediaries seek to enter regulatory regimes independently and expand their influence once they have become involved out of self-interest (Van der Heijden 2017). In other words, the global turn to regulatory intermediaries comes with darker sides as well. Governments may become too dependent on intermediaries and lose the skills and expertise required to understand the performance of regulatory regimes. Intermediaries may become too involved in the setting of standards and regulation, which is particularly problematic as they are less publicly accountable for their actions than are governmental actors and agencies. Moreover, intermediaries may raise barriers against competitors entering or operating in the market for regulatory services (Davis 2007) or even seek to crowd out the government provision of services (Van der Heijden 2010).

Conclusion

In this chapter we have provided an overview of the most promising trends in regulatory governance that we feel will be central to addressing the key challenges that lie ahead of us: climate change, global inequalities across the world population, and rapidly evolving disruptive technologies. Our discussions of each trend are snapshots, or bird’s-eye views, at best. Space limited us in how deeply we could zoom into each trend. We trust, however, that the extensive references provided allow interested readers to explore the various trends to a greater extent.

That leaves us then with pointing at two developments in regulatory governance we feel may have a big impact over the decades to come but that have not seen as much scholarly inquiry as the eight trends already discussed. The first is regulatory orchestration. The idea of regulatory orchestration involves the mobilization of intermediaries in the governance task, and has been drawn from the context of international organisations. It draws our attention to the much-needed coordination of all individuals and organizations involved in regulatory governance, the ways they are involved, and the way they fill in their tasks and responsibilities (Abbott et al. 2016; Abbott and Snidal 2010). Considering the trends we have discussed before, and keeping in mind how fast they have developed, regulatory orchestration asks us to think about what parties are best positioned to carry out such coordination and what orchestration strategies will yield the best results. The second is regulatory stewardship. The notion of stewardship is common to legal scholars (e.g., constitutional stewardship), environmental scholars (e.g., environmental stewardship), and management scholars (e.g., stewardship theory) (Cossin and Boon Hwee 2016). The concept has, however, not had much attention in regulatory scholarship (Bratspies 2009; Brownsword 2011). Stewardship asks regulators to think about their role of protecting or safeguarding the regulatory regimes they work with, to improve them, and to be in service to those working in regulatory regimes and those subject to them. In a nutshell, the notion of regulatory stewardship moves away from looking for yet another novel (or modified) regulatory tool, instrument, or strategy and considers whether and how changes to the agency of regulators may improve the performance of regulatory regimes.

On this last point, New Zealand is a world leader in that it made regulatory stewardship a statutory obligation for all government departments in 2017 (NZ Treasury 2017). What regulatory stewardship could look like, and how to achieve it in practice, is not yet crystal clear for the New Zealand government. However, as we will see in the next chapter, they are actively working with the idea to make improvements to their regulatory regimes that may ultimately help other countries to make much-needed improvements too.

Summary

Ten key global trends in regulation that will likely affect regulation in the twenty-first century are identified and explored in some depth: performance-based regulation, risk-based regulation, responsive regulation, smart regulation, better regulation, experimental regulation, behavioral insights informed regulation (“nudging”), regulatory intermediaries, regulatory orchestration, and regulatory stewardship. For each trend, theoretical underpinnings are addressed and illustrated with international examples, and documented performance outcomes are discussed.

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Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of GovernmentVictoria University of WellingtonWellingtonNew Zealand
  2. 2.Faculty of LawMonash UniversityMelbournceAustralia

Section editors and affiliations

  • Jeroen van der Heijden
    • 1
  1. 1.School of GovernmentVictoria University of WellingtonWellingtonNew Zealand

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