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date: 25 February 2020

Disproportionate Policy Response

Summary and Keywords

Disproportionate policy response is understood to be a lack of ‘fit’ or balance between the costs of a public policy and the benefits derived from this policy, and between policy ends and means. The study of this phenomenon and its two anchor concepts, namely, policy over- and underreaction, has been inspired by the insight that inefficiencies in the allocation of attention in policymaking leads policymakers to react disproportionately to information. This theory of information processing appears to be broadly accepted and has generated a large body of research on agenda setting. However, little attention has been devoted to actual policy over- and underreaction and how it affects the public. The latest developments are conceptual in nature and include a conceptualization and dimensionalization of policy over- and underreaction, as well as an early-stage development of a preference-driven approach to disproportionate policy response. These issues are fundamental to developing understanding of the formulation, implementation, and evaluation of disproportionate policy response. They are also valuable to those who want to better understand the processes through which policy over- and underreaction occur and are of considerable interest to practitioners who want to understand how to manage disproportionate policy responses more effectively.

Although disproportionate policy response poses methodological challenges because it is time-bound, context-sensitive and has a problematic counterfactual (i.e., proportionate policy response), it deserves academic attention. This is because the insight of the punctuated equilibrium theory—that policy responses oscillate between periods of underreaction to the flow of information coming from the environment into the system and overreaction due to disproportionate information processing—implies that policy oscillation is the norm rather than the rarity. To probe research questions related to the topic at hand, disproportionate policy response can be measured as individuals’ perceptions of what they think about the proportionality of policy. Alternatively, scholars may employ vignette survey experiments, sophisticated cost-benefit analysis and a comparison of policy outcomes with (national or international) standards developed by experts. Scholars may also undertake experimental manipulation using risk unfolding over time, combined with varying types of warnings.

The study of disproportionate policy response is a gateway to some of the most significant aspects of public policy. Global and domestic threats coupled with relatively skeptical publics about politicians and political institutions and rising negativity and populism in democratic politics imply that policy overshooting is increasingly required for the public to perceive policy action as sufficient and politicians as competent, at least in the short term. Not only has disproportionate policy response been a focal point for political actors seeking decisive and swift policy change in times of real or manufactured crisis or no change at all, but such action has time and time again also made a dramatic impact upon the direction and the character of policy and politics. Classic examples are the U.S. response to 9/11 and the federal response to Hurricane Katrina. So far the literature on policy change has not responded to the emergence of the stream of research aimed at fully understanding the complex phenomenon of disproportionate policy response, but a robust research agenda awaits those answering this article’s call for action.

Keywords: policy overreaction, policy underreaction, risk, policy change, context, policy design, policy formulation


The study of disproportionate policy response and its two anchor concepts, namely, policy over- and underreaction, has been inspired by the insight that inefficiencies of attention allocation in policymaking lead policymakers to react disproportionately to information (Jones, 2001). This theory of disproportionate information processing appears to be broadly accepted and has generated a large body of research on agenda setting (e.g., Jones & Baumgartner, 2005a, 2012). Initial studies have been motivated by the punctuated equilibrium theory, which suggests that policy responses will oscillate between periods of underreaction to the flow of information coming from the environment into the system and overreaction due to disproportionate information processing (Jones & Baumgartner, 2005a). More recently, scholars have begun to pay attention to “too much” and “too soon” patterns of state activity, as well as patterns of “too little” and “too late,” due to mistakes of omission or commission in the diagnosis and the prescription stages of decision-making (Walker & Malici, 2011). Collectively, this work set the boundaries of discussions about public policies in a way that includes modes of disproportionate policy response as well as the factors that lead to their sustainment over an extended period of time, termed policy bubbles (Jones, Thomas, & Wolfe, 2014; Maor, 2014b, 2016c).

This article provides a review of research addressing disproportionate policy response and adds some conceptual nuance in describing the distinction between selective and non-selective policy overreaction as well as the embeddedness of disproportionate policy response in context. Disproportionate policy response is understood to be “a lack of ‘fit’ or balance between the costs of a public policy and the benefits that are derived from this policy, and/or between a policy’s ends and means” (Maor, 2016a, p. 3). Whereas proportionality is easily understood as a standard that requires perfect balance between policy costs and benefits as well as between policy ends and means, disproportionate policy response violates this standard. The aim of this article is to introduce the reader to the latest developments in the study of disproportionate state activity and to do so by being explicit about its two anchor concepts and cognizant of how these concepts compare with related ones, for example, of over- and underinvestment. These developments are conceptual in nature and involve abstraction from reality rather than from other models. So they all connect with rather than lose sight of reality. They inform the literature with respect to how disproportionate policy response is designed, implemented, and evaluated but also highlight the need for a clear understanding of what we mean when we study disproportionate policy response and when we claim this area is important. These issues are fundamental for understanding the occurrence of deliberate disproportionate policy response. These developments are also of considerable interest to practitioners who want to better understand how to manage disproportionate policy responses more effectively.

The article begins with an intriguing event that has triggered my interest in conceptualizing policy over- and underreaction. The first section briefly elaborates on the response of Prime Minister Benjamin Netanyahu to the Mount Carmel fire, which highlights a unique type of policy overreaction, namely preemptive policy overreaction. The second section introduces the definitional grounds with regard to policy over- and underreaction. The third section introduces belief-based conceptualizations of policy over- and underreaction. The fourth discusses a potential preference-based conceptualization of policy over- and underreaction and introduces the distinction between a selective and a non-selective mode of policy overreaction. The fifth section introduces the embeddedness of disproportionate policy response in context. The sixth section focuses on measurement issues, and the final section concludes by proposing directions for future research.

Challenging Incrementalism

Perhaps for too long, the main challenge to incrementalism, which fosters proportionality in public policy when policy problems change gradually (e.g., Lindblom, 1959), came from attempts to explain patterns of attention, measured in terms of the kind of issues politicians and other policy actors focus upon and budgetary expenditures over time. Most notable is the aforementioned policy oscillation insight (Jones & Baumgartner, 2005a). The reasons for the emergence of periods of shifting attention include the bounded nature of human activity (Jones, 2001, 2003), exogenous shocks (e.g., Breunig, 2011; Jones & Baumgartner, 2012), the slow build-up of policy problems that require intense attention (Jones & Baumgartner, 2005a), and the framing of issues by policy entrepreneurs (Baumgartner & Jones, 1993; Jones & Baumgartner, 2005a). However perceptive this insight has been, it provides little nuance regarding what the government is actually doing and how it affects the public (Dowding, Hindmoor, & Martin, 2016).

Insofar as disproportionate state activity is concerned, some studies on British government claim that governments overrespond to crises and create policy disasters (Butler, Adonis, & Travers, 1994; Dunleavy, 1995; Finer, 1975; Hood, 1994; Moran, 2003). Other studies have coined valuable terms. Hogwood and Peters (1985) have devised the terms oversteering, which refers to “moving back past the correct path to another set of mistakes in the opposite direction” (p. 83); ove-targeting, which refers to “[t]he use of as many instruments as there are objectives” (p. 167), and overinstrumenting, which refers to a case in which “many instruments all target the same client group or objective” (pp. 167–168). And Howlett & Kemmerling (2016) have referred to underdesign, which occurs when suboptimal designs are adopted, and to overdesign, which emerge when “the reverse occurs and resources are wasted in ‘over engineering’ a design vis à vis the significance of the problem” (p. 5). However, no attempt has been made to bring definitional clarity to these terms and integrate them in an analytical perspective or framework of disproportionate policy response.

The main reason for the little attention devoted to disproportionate policy response probably lies in an impression among policy scholars that policy over- and underreaction are policy mistakes; policies not carefully thought out; policies not carefully implemented; policies based on strategic misperceptions; policies that are bound to fail; policies that provide inferior solutions to recognized economic, social and other problems; policies that are derived from policymakers who are driven by cognitive biases and emotions; and policies that are the result of flawed decision making processes, such as groupthink (Janis, 1982) and polythink (Mintz & Wayne, 2016). For example, U.S. presidents’ foreign policy mistakes were categorized into “too much” policy, which occurs following a mistake of commission in the diagnosis stage of decision-making, and policy that is implemented “too soon,” which is the result of a mistake of omission in the prescription stage of decision-making (Walker & Malici, 2011). Another example is the new policy design paradigm (Howlett, 2014), which revolves around the notion of “adaptive policy-making” (Hoffmann, 2011) and is premised on the need “… to cope with the risk of under-investing and over-investing (or wrongly investing) in future policies” (Walker, Marchau, & Swanson, 2010, p. 922). Further, the study of disproportionate policy response is considered by many policy scholars as an academic minefield because it is time-bound, context-sensitive, has problematic counterfactual (i.e., proportionate policy response), and because of difficulties involved in measuring proportionality in public policy. It is therefore no surprise that policy scholars have largely ignored the study of this phenomenon and how it affects the public despite the fact that it is the norm rather than the rarity, as Jones and Baumgartner’s (2005a) policy oscillation insight indicates.

My interest in this area emerged coincidentally following a puzzling observation—the response of Prime Minister Benjamin Netanyahu to the Mount Carmel fire. This event highlighted the characteristics of preemptive policy overreaction, which aims, among others, at gaining a strategic advantage in an allegedly unavoidable swing of public mood (Maor, 2012). It gave me an understanding that it may be only one among many types of policy overreactions. Let me briefly describe this event, the insight it generated, and the process of theorizing it initiated. Readers who may be interested in the conceptual development of the area of disproportionate policy response may skip to the next section.

On December 2010, the biggest and deadliest fire in Israel’s history broke out on Mount Carmel. Approximately 1,500 ill-equipped firefighters were called on to fight the forest fire. Once the government had been informed that there was no firefighting material and equipment, government officials, including Netanyahu, called on other countries to assist in the firefighting efforts. Consequently, some 30 firefighting aircraft and three helicopters from 15 countries arrived to aid forces on the ground. Soon afterward, the Israeli government decided to enlist the services of the world’s largest firefighting aircraft. The “Evergreen” Boeing 747 supertanker, brought in from the United States, was hailed by government officials and the Israeli press as the ultimate firefighting measure, even though by the time the supertanker had arrived, three days into the fire, the blaze was already waning (Haaretz Service, 2010).

With almost 10,000 acres of forest, farmland, and homes devoured by the fire and 42 people dead, the Israeli government utilized and encouraged moment-by-moment news coverage of the supertanker, from its recruitment and arrival to its first day on the job. It seemed to me at that time that there was a concerted attempt by Netanyahu to redirect the public’s attention away from the continuing fire and the damage it was causing and toward the spectacular efforts he was making to acquire what was being billed as the ultimate firefighting measure (Haaretz Editorial, 2011; Ravid, 2010). The supertanker itself was publicized to such a degree that while the aircraft was waiting at Ben Gurion Airport after landing, news outlets sent crews to film the prime minister visiting the plane and then ran stories that focused the news on the plane itself, rather than on the continuing fire (Katz, 2010). Even when news stories began to publicize the exorbitant price estimates for the use of the supertanker, which was suggested to be around $200,000 per hour of operation (Katz, 2010), the expense was used to illustrate the prime minister’s willingness to go to any lengths in order to extinguish the fire quickly and effectively rather than as an unnecessary and frivolous allocation of public resources.

This event immediately turned my attention to the phenomenon of policy overreaction. I soon discovered that economists, psychologists, and political scientists have devoted a great deal of attention to the emergence of overreaction. A large body of laboratory research has shown that people do not exhibit the perfect rationality that economists commonly assume (Simon, 1982) and that many of their intuitive predictions are governed by heuristics, or rules of thumb (e.g., Tversky & Kahneman, 1974). Kahneman and Tversky (1973), for example, have proposed that the representativeness heuristic, which refers to judgments based on stereotypes, leads to overreaction. And Sunstein (2002) has suggested that public overreaction to highly politicized, low-probability risks could be explained by probability neglect, which occurs when people focus on the worst possible scenario. The strong emotions that are then triggered lead to the failure to inquire into the small probability that the worst case will occur. The finding that overreaction is driven, among other things, by cognitive factors, has also surfaced in the political science literature, and more specifically in the agenda-setting arena. Jones and Baumgartner (2005a) have argued that bounded rationality leads to disproportionate information-processing whereby “signals are ignored, responses are delayed, and ineffective strategies are deployed” (p. 17). Focusing on selective attention—“far and away the most important cognitive limitation that influences political choice” (p. 16)—they claimed that “[e]motion is the gateway to selective attention: when emotion is roused, attention follows” (p. 16). Their model of how information is used thus leads to a sequence of delay followed by overreaction. “Once evidence or pressure accumulates indicating that some new dimension observed deserved more attention, the system often overresponds to this in turn” (p. 17).

Focusing on the realm of public policy, the following question was raised: If, as the aforementioned streams have indicated, an assessment of information is not particularly simple, why should we assume that there is only one mode of policy overreaction? The fact that the concept of policy overreaction is defined in terms of another concept—action, that is, an authoritative activity that emerges around a policy problem—further emphasizes the need to “cover” more thoroughly the terminological and empirical terrain. The concept of policy overreaction may contain a number of “faces” and deserves therefore an attempt to achieve a higher degree of adequacy (Gerring, 2001). I therefore think that there is a need to move towards concrete, if not actually observational, concept of policy overreaction (and, of course, policy underreaction). But before embarking upon this challenge, definitional clarity has to be obtained.

The Definitional Ground

In an attempt to infuse the concept of policy over- and underreaction with a robust conceptual identity, that is, one that goes beyond the analytical reach of the terms “too much” and “too soon” or “too little” and “too late” (Walker & Malici, 2011), a few concepts have recently been introduced. Policy overreactions “are policies that impose objective and/or perceived social costs without producing offsetting objective and/or perceived benefits” (Maor, 2012, p. 235). Costs and benefits may be incurred by policymakers, target populations, and/or the general public, depending on whose assessment of policy overreaction, or its lack thereof, is sought by the researcher. In addition, what appears to be an overreaction or underreaction at a certain point in time may differ later as the magnitude of a policy problem or relevant events become more apparent. One manifestation of policy overreaction is the clear-cut concept of policy overinvestment, which occurs “when government overinvests in a single policy instrument beyond its instrumental value in achieving a policy goal” (Jones, Thomas, & Wolfe, 2014, p. 149). A similar distinction applies in the case of policy underreaction, which refers to “systematically slow and/or insufficient response by policymakers to increased risk or opportunity, or no response at all” (Maor, 2014a, p. 426). This implies that it is “a policy whose actual net utility … is smaller than a counterfactual net utility” (Maor, 2014a, p. 428). Adapted from Jones, Thomas, and Wolfe (2014), policy underinvestment occurs when policymakers underinvest in a single policy instrument below its instrumental value in achieving a policy goal. Whereas the definitions of policy over- and underreaction appeal to considerations of public interest broadly defined, including, for example, economic efficiency, social welfare, social justice, sustainability, and individual well-being, the definitions of policy over- and underinvestment, although easily measurable and tractable, often appeal to narrow considerations of economic efficiency.

Examples of policy over- and underreaction can be gauged following a dilemma faced by policymakers regulating a financial system comprised of a diverse set of financial institutions that provide credit and deposit-like services to households and businesses as well as facilitating financial transactions across the financial system (e.g., banks, insurance companies, pension funds, financial companies, and the like). From the perspective of financial stability, policymakers may limit speculation in a particular area (e.g., the housing market) by imposing restrictions in that particular area. However, doing so may lead financial institutions to adapt to these regulatory changes by relocating particular financial activities in order to bypass the restrictions. Alternatively, policymakers fearful of a financial crisis quickly spreading from a particular area to others because of interconnections may apply a systemic policy tool, such as an increase in the interest rate, to maintain the stability of the financial system. Increasing the interest rate when no evidence exists of salient (known) risk to the stability of the financial system—validated by tools such as an annual stress test for banks—and no financial crisis emerges falls under the category of policy overreaction. An increase in the interest rate in order to curb rapidly escalating risk falls under the category of proportionate response. Regulatory inaction following rapidly escalating risk which leads to systemic financial crisis falls under the category of policy underreaction.

Different disproportionate policy responses have different social, economic and other substantive effects, as well as varied cognitive and emotional effects. What the government is actually doing affects markets, people, other countries, and so on. A focus on policy over- and underreaction, especially in the run-up and during rare, high-impact events, provides opportunities to assess the formation and implementation of disproportionate policy response. This is because policy responses during such events, whether real or manufactured, revolve primarily around expectation management by governments. For example, in a policy system already in the throes of a broad panic and public fears—manifested by wholesale withdrawal of deposits that could, absent government intervention, destroy otherwise solvent banks, the “use of overwhelming force to quell panics” (Geithner, 2014, p. 397) and restore confidence may be required. Overwhelming force is understood to be “a credibly large amount of committed resources available to use with discretion during a […] crisis” (Gorton, 2015, p. 976). But before researchers engage in empirical examination, some form of a conceptual or mental map should be in place.

The approaches that may be employed to advance a better understanding of policy over- and underreaction can be categorized based on whether they focus on policymakers’ beliefs (e.g., the representativeness heuristics; overconfidence) or on policymakers’ preferences. The next section briefly summarizes a belief-based conceptualization of policy over-and underreaction, emphasizing the multidimensional nature of these constructs.

Belief-Based Conceptualization of Policy Over- and Underreaction

As noted, overreaction is commonly viewed as arising from individual’s cognitive and emotional shortcomings. Drawing on robust findings from these fields of research, Maor (2012) has posed a question regarding the likely policy outcomes in situations whereby policymakers believe that they are more talented and competent than they actually are, have more control over the event at hand than they in fact do, have greater chances of success in solving the policy problems than they genuinely do, and perceive the information that they possess as more precise than it actually is (Kahneman, 2011). Based on two key dimensions of policy overreaction, namely (1) the effects of positive and negative events and (2) the effects of overestimation and accurate estimation of information, Maor (2012) has identified four distinct modes of policy overreaction that reflect differences in the nature of implemented policy.

Preemptive overreaction emerges when policymakers overestimate information regarding a negative event (e.g., a misperception that a risk poses an imminent threat). The subsequent policy comprises attempts by government to neutralize the threat by, for example, a visually-striking policy display. A classic example is the slaughter of around 5 million animals for precautionary reasons following the bovine spongiform encephalopathy (BSE) crisis in the United Kingdom. Regulatory overreaction occurs when policymakers accurately estimate information regarding a negative event (e.g., a realistic recognition of the scope and intensity of a threat and of the urgent need to gather information regarding the threat). The subsequent policy comprises attempts by government to significantly increase the jurisdiction and powers of law enforcement agencies. An example is the U.S. government response to 9/11. “Calibrated” overreaction emerges when policymakers overestimate information regarding a positive event (e.g., a misperception that a new policy idea, model, or theory precisely mimics some particular parameters of reality). The subsequent policy comprises, among others, attempts by government to ensure that all relevant policies are implemented in accordance with the new model. An example is the implementation of radical, far-reaching programs of public management reforms in New Zealand during the late 1980s and 1990s. Nearly mandatory overreaction occurs when policymakers accurately estimate information regarding a positive event (e.g., a realistic recognition regarding the deeply contested nature of a scientific innovation and the derived implications in terms of the speedy implementation of policy and the aggressive information-gathering in light of a potential public backlash).The subsequent policy comprises, among others, attempts by government to thrust responsibility for policy implementation onto individuals (e.g., parents) and state-funded organizations, as well as allowing wide opt-outs from the policy. An example is the school-based human papillomavirus (HPV) vaccination in the United Kingdom, Canada, and Australia (Maor, 2012). Surely there are many other types of policy overreactions, but Maor (2012) has addressed the ones that may be derived from policymakers’ overconfidence.

Regarding policy underreaction, the emergence of this phenomenon has attracted scholarly attention from a diverse set of disciplines, including economics, psychology, and political science (e.g., Jones & Baumgartner, 2005b; Tversky & Kahneman, 1973; Slovic, 2007, 2010). For example, studies have shown that individuals underestimate the cumulative effect of events (Bar-Hillel, 1973; Cohen, Chesnick, & Haran, 1972). Surprisingly, there has been little diffusion of this research into the study of public policy. Notable exceptions are Jones and Baumgartner (2005b), Walker and Malici (2011), and Bazerman and Watkins (2008), but these studies do not delve into the multidimensional nature of policy underreaction.

The belief-based conceptualization that has provided the most explicit discussion of policy underreaction revolves around two key elements of decision making in situations of risk unfolding over time: (1) policymakers’ underestimation and accurate estimation of increased risks and (2) intra- and extra-organizational sources of policy persistence (Maor, 2014a). Based on these dimensions, four distinct modes of policy underreaction were identified which reflect differences in the nature of implemented policy.

Directed underreaction “emerges when policymakers accurately estimate increased risk but are predominantly influenced by intra-organizational sources of policy persistence. The subsequent policy comprises (long/short sequences of) self-initiated small/partial adjustments in the same direction” (Maor, 2014a, p. 426). An example is the 2010 Turkish Mavi Marmara incident outside Israel’s territorial waters during the preparation of which there had been an accurate estimation by the Israeli Army chief of staff that there would be a violent response from the flotilla passengers, coupled with disorganized decision-making processes in government as well as an organizational culture that accorded great weight to the military as opposed to other response options. Forced underreaction “emerges when policymakers accurately estimate increased risk but view the policy at hand as primarily subject to extra-organizational constraints, such as the expected response from other players with a dominant position in the relevant system. The policy implemented revolves around policymakers’ giving in to, or being forced to adapt to or comply with external constraints, but still being able to initiate small policy adjustments over the dominant dimension of risk” (Maor, 2014a, p. 426). An example is the Israeli decision not to launch a preemptive attack a few hours before the 1973 Yom Kippur War, fearing the U.S. response to such a move that would have denied military and diplomatic support to Israel during the war, and the resulting call-up of the reserves instead. Symbolic underreaction “emerges when policymakers underestimate increased risk (i.e., do not recognize the need for a substantial policy change) and are predominantly influenced by intra-organizational sources for policy persistence, such as organizational and cultural imperatives, leading to ‘tunnel vision’ and routine modes of thought. The policy implemented involves willingness to express full support for the issue at hand and engagement in a disingenuous or surface-level attempt to change policy” (Maor, 2014a, p. 426). An example is the response of the U.S. Securities and Exchange Commission to the 2007–2008 financial crisis. And no action “emerges when policymakers underestimate increased risk and are predominantly influenced by extra-organizational constraints” (Maor, 2014a, p. 426). An example is the Swedish government’s underestimation of the risk faced by Swedes who were caught up in the 2004 Asian tsunami and remained stranded in Thailand and elsewhere, and especially the risk faced by those in need of medical treatment.

Each of the aforementioned types of policy underreaction may be framed in ways that hide its true meaning. For example, inaction may be framed as ‘astute politics’ or ‘dereliction of responsibilities’ (McConnell & ’t Hart, 2014). This ‘presentation strategy’ (Hood, 2011) is tied to the fact that it is much harder to blame policymakers for doing nothing than for doing something.

Having brought some clarity to the core constructs of disproportionate policy response from a belief-based perspective, the next section brings a bit more breathing room to the same constructs, but from another perspective.

Preference-Based Conceptualization of Policy Over- and Underreaction

Although there are currently no theoretically-solid preference-based conceptualizations of policy over- and underreaction, future analytical frameworks may be based on insights generated in the area of behavioral finance. One theory, for example, focuses on reduced risk aversion following earlier gains from investment decisions as the reason underlying investors’ decisions to buy an asset more enthusiastically, thereby pushing its price up even further (Barberis, Huang, & Santos, 2001; Thaler & Johnson, 1990). One could easily think of cases where policymakers’ satisfaction with policy outcomes as well as with the derived political benefits leads them to become less risk averse (i.e., to deliberately over- or underreact). Having experienced policy success, they are less concerned about future policy failures because they assume the impact of such failures can be cushioned by the prior policy-specific and political gains. This, in turn, leads them to adopt riskier policies. A case in point is the long period of profitable growth in banking in the United States after the 1990s and the success of the banks in withstanding the burst of the dotcom bubble. Against this background, universal home ownership was expanded, among others, by banks which purchased subprime residential mortgage-backed securities, and consequently, a crisis-prone financial environment was created. Risk-averse policymakers could have pushed forward regulatory changes—such as a significant increase in capital requirements in the commercial and shadow banking systems coupled with tightening regulatory enforcement in order to ensure that banks solely securitized mortgages that involved creditworthy borrowers—in order to avert the 2007–2008 financial crisis. However, the policy response chosen was no action.

As noted earlier, one of the great challenges in public policy is to understand what the government is actually doing and how it affects the public (Dowding et al., 2016).Recognizing the importance of developing an evidence-based conceptualization in response to this challenge, Maor (2016a) has recently distinguished between two types of disproportionate policy options, namely, rhetoric and doctrine, and has developed the disproportionate policy perspective (Maor, 2016b). However insightful the aforementioned conceptualizations may be when fully developed, the idea in this section is not to construct a new conceptualization but rather to add some nuances to potential preference-based conceptualization. Focusing on the deliberate design of disproportionate policy options—i.e., those which are backed by a credibly large (low) amount of committed resources, and are perceived by policymakers as overwhelming (underwhelming) response, the need emerges to distinguish among different types of this phenomena in order to increase analytical sharpness. A useful distinction with regard to the design of policy overreaction options can be drawn between selective and non-selective use of overwhelming government force (Maor, 2016a). The balance between these two modes is a major concern for policymakers wishing to overreact, for example, in order to quell public fears and panic by cognitively and emotionally overwhelm target populations. Let me elaborate on each of the aforementioned modes.

In a non-selective mode of policy overreaction, the government provides resources for individuals and institutions in need. In a systemic banking crisis, for example, the government may lend freely to banks in distress and to other weak financial institutions. In financial terms, the government may follow Bagehot’s (1873) rule or doctrine that in a crisis the central bank should lend freely, at a high rate, and on good collateral. During the 2007–2008 financial crisis, for example, the Federal Reserve followed Bagehot’s rule (Bernanke, 2014a, b), and so did the Bank of England (King, 2010) and the European Central Bank (Draghi, 2013). In the non-selective mode, therefore, the government relies on individuals and/or institutions in dire need to come forward and seek assistance and operates by every possible means and mode to provide this assistance. Another example is the use of mass vaccination, rather than a targeted vaccination campaign, as an initial response employed by policymakers before information is obtained regarding the actual risk posed by the contagious disease to the overall population.

To facilitate the non-selective use of overwhelming government force, government may also delay the eligibility test of those seeking assistance until the crisis is over and/or suppress information regarding the identities of weak individuals or institutions seeking government assistance in order to overcome their reluctance to do so because of fears that others will view this as a sign of weakness.

A selective mode of policy overreaction includes the design of more complex programs or methods that allow policymakers to be selective in the use of overwhelming policy response. Selectivity is gained by designing a mechanism that separates the fundamentally healthy institutions or individuals from the terminally ill or weak, and that is accompanied by a government commitment to make resources available through this mechanism to selected individual or institution unable to meet their needs independently.

Whether or not this type of response falls under the category of proportionate response or is an overreaction depends on whether the threshold separating the fundamentally healthy institutions or individuals from the terminally ill or weak is constructed with relatively narrow margins of safety or, on the other hand, with large or very large margins of safety. It is reasonable to suggest that during a crisis involving panic and public fears, the government will deliberately select a threshold with large or very large margins of safety so that all those in need and those that are border cases will be assisted. To increase the perceived credibility of this move, the government will be disinclined to publicize the threshold or any other information regarding it.

A case in point is the stress tests of the 19 largest bank holding companies which were innovatively designed and introduced in the United States during the financial crisis of 2007–2008. According to Timothy Geithner (2014), who served as president of the Federal Reserve Bank of New York (and vice chairman of the Federal Open Market Committee of the Federal Reserve System) and then as U.S. Treasury Secretary:

[T]he stress test would provide a form of triage, separating the fundamentally healthy [banks] from the terminally ill.

(p. 12)

[I]f an unhealthy firm couldn’t raise enough [capital] from private investors, government would forcibly inject the missing capital. (p. 11) The plan aimed to impose transparency on opaque financial institutions and their opaque assets in order to reduce the uncertainty that was driving the panic.

(p. 286)

According to Gorton (2015, p. 988), the process was as follows:

The banks were instructed to calculate losses, profits, and loan loss reserves over the next nine quarters under a baseline scenario and under a more adverse (i.e., stress) scenario.… The regulators independently made their own such projections under each scenario. Comparing a bank’s capital projection to that of the regulators produced the “capital gap.” If there was a significant gap, compared to required capital, then the gaps were required to be filled with capital plans filed by the banks, privately-produced capital, and if that could not be done then through the [U.S. Treasury’s] Capital Assistance Program (CAP).

Interestingly, although the results of these stress tests were made public, the model or process employed by the Fed in order to achieve the results was not.1 A substantial reason for this strategy may be the attempt by policymakers to hide the size of the safety margin employed by the mechanism at hand in order to limit public information regarding the scale of the crisis. In addition, the mechanism (the Term Auction Facility) aimed at assisting the major banks was designed specifically for the purpose of avoiding stigma by using auctions, thereby making loans in secret, without publicly revealing borrowers’ identities (Geithner, 2014; Gorton, 2015).

Going back to our example regarding mass vaccination as an initial response in cases of contagious disease, a selective policy overreaction would include immunization of young children, pregnant women, and older adults as an initial response while applying a broad definition of “young” and “old.” If policymakers wish to quell the hysteria of the general public by announcing that mass vaccination may immediately be implemented when there is justifying evidence (i.e., immunizations for the whole population have been bought and are in stock), they may use Put Option when buying the vaccinations, so that they can announce their non-selective approach in order to quell panic while maintaining the right to return a specified amount of vaccinations to the manufacturers and get a refund.

In sum, the behavioral finance literature highlights theories that are based either on investors’ beliefs or their preferences. Potential conceptualizations of policy overreaction could draw on this insightful demarcation. The relevance of two types of policy overreaction, selective and non-selective ones, was also highlighted here. This demarcation emphasizes that policymakers wishing to overreact in pursuit of policy goals and/or political benefits should carefully weigh the balance between the two. It also directs scholarly attention to the policy processes during which the balance between these two modes is considered as part of the calculus of policy overreaction.

Attempts to produce systematic knowledge related to policy over- or underreaction require close attention to context and contextual effects. A substantial reason for this is the fact that policy over- and underreaction are context-sensitive and time-bound. Our attention now turns to an elaboration and illustration of this point.

The Embeddedness of Disproportional Policy Response in Context

Recognizing contextual effects facilitates an understanding of the full spectrum of causes leading to disproportionate policy response. I refer here especially to the underestimation of context as a contributing factor in the emergence of policy under- and overreaction. Specifically, modes of disproportional policy response require an awareness of the policy problem at hand and its severity. This awareness may be tied to contextual factors. Take, for example, the emergence of the Ebola outbreak in West Africa during the mid-2010s. The peculiarities of this context, especially a population distrustful of government and more inclined to seek out traditional healers, have caused a delayed response by government. According to Dr. Margaret Chan, WHO Director-General of the World Health Organization:

With the benefit of hindsight, the mistrust is a major problem.… Instead of sending patients to a treatment center as early as possible, people in the community kept their loved ones at home and nursed them. It was like a peat fire spreading underground.… Information was not flowing up. That is a big problem. You cannot manage what you don’t see and what you don’t know. I think all of us underestimated the context.… An old disease in a new context gave us surprises.

(Quoted in Kupferschmidt, 2015, p. 495)

This example clearly shows that society might matter. Policymakers have to equip themselves with rich societal knowledge in order to effectively take account of how it might actually matter to the awareness and knowledge of the policy problem and its enormity.

Other aspects of different contexts in which public policy occurs, such as psychology, culture, history, demography, and technology, might also matter. One of the insights of social psychology, for example, points to the power of social situations and social structures to overwhelm individual dispositions, to the point of committing extraordinary, demonic acts of harm and evil (e.g., Lifton, 1986; Milgram, 1974). Erecting death camps and devising and executing genocide are perhaps extreme examples, but they underline the technical and political state capacity (Hupe & Hill, 2014) to overreact. Relatedly, certain contexts characterized by the existence of a large ethnic minority may manifest a salient ethnic category, thus strengthening ethnic identity (e.g., Mullen, Brown, & Smith, 1992). Once ethnic identity is more pronounced, aspects of public policy addressing this target population may reflect ingroup bias (i.e., positive reactions to the ingroup relative to the outgroup), thereby triggering disproportionate policy response. Similarly, culture bias in the form of “social construction of reality” (Berger & Luckman, 1967) falls squarely under the environmental factors that may generate and maintain disproportionate policy response for an extended period of time.

Blame culture and the derived consequence of presidents and prime ministers being subject to enormous and intense political pressure to do something in order not to be blamed for doing nothing may also play a critical role in disproportionate policy response. For example, during the severe acute respiratory syndrome (SARS) pandemic of 2003, Canada, Australia, and Singapore have instituted border screening measures . They screened hundreds of thousands of passengers and did not detect even one confirmed case (Samaan, Patel, Spencer, & Roberts, 2004; St. John et al., 2005; Wilder-Smith, Goh Kee, & Paton, 2003). Similarly, during the Ebola virus disease epidemic of 2014, the U.S. government decided to screen travelers from three West African countries at five airports for signs of Ebola. Other than giving President Obama substantial media coverage, the screening did not detect one confirmed case (U.S. Centers for Disease Control and Prevention, 2014). Another contextual aspect which may shape disproportionate policy response is the geographic one. The dynamics of place—for example, its violent logic (Therborn, 2006, p. 523)—may also be of importance. The Clinton administration fatally neglected Rwanda and opted instead to concentrate military intervention in the Balkans. This policy underreaction made the genocide of the Tutsi by the Hutu possible (Therborn, 2006, p. 523).

Ideational aspects of the environment may also shape disproportionate policy response. But ideas must be seen in the context of other factors shaping policy choices and outcomes. Germany’s deflationary policy adopted in 1930–1932 provides a classic example. A majority of German economists rejected the view that the Great Depression could be ameliorated by government-generated demand. A substantial reason for this was their support of the idea that the market economy tends toward optimal equilibria rather than getting stuck in a stable underuse of resources. Bureaucrats held a similar view regarding deficit financing and fear of panic following the injection of a large quantity of money into the local financial system. However,

[t]he most important factors shaping the policy … were political. This was driven by the fear of returning to the rampant inflation that had characterized the first years after the First World War. Following the collapse of Imperial Germany, a coalition of labor, business, and government responded to the threat of chaos and political instability with inflationary policies that eventually resulted in the “hyperinflation” of 1922–3. It was this negative policy legacy that was the strongest factor leading to the deflationary policy adopted in 1930–2.

(Rueschemeyer, 2006, p. 241)

This policy overreaction undermined most groups in German society, led to the collapse of the banking system in 1931, and made Hitler’s promises to defeat unemployment and stabilize prices attractive to many segments of German society. It was this policy overreaction in the early 1930s that brought Hitler to power.

The discussion so far indicates that a commitment to utilize the concepts of policy over- and underreaction, especially when a comparative perspective undergirds the research in question, should pull researchers toward greater respect for identifying and describing the particular and the unique. Significant differences between contexts should become the starting point of detailed comparative research into disproportionate policy response.


A fundamental component of good research is that of sound measurement. Good measures advance theory development and testing and clarify the evaluation of (dis)proportionality in public policy. Although the concept of proportionality has been extensively studied by legal and philosophy scholars, little empirical knowledge is available about proportionality in public policy and the ways in which it informs and shapes the calculus of policy choice. To probe research questions related to the topic at hand, the concept can be measured as individuals’ perceptions of what they think about the proportionality of policy. “Individuals” may refer to policymakers, members of target populations and the general public. Opting for this strategy implies that, in terms of the breadth of this measure, proportionality is measured on the basis of an overall assessment of a given policy. Respondent perceptions are aggregated ex post into what one may call “meta-proportionality.” Respondents may furthermore be clustered into homogeneous groups for reporting and further statistical analysis. An overall assessment of proportionality presents an easier task for respondents. If they are also asked to compare the similarity of proportionality across policies within the same policy sector or across sectors, statistical routines could be used to gauge the dimensions of judgment that differentiate among policies with regard to their level of proportionality. Measuring changes in the proportionality of policy may be undertaken by using this methodology.

Alternatively, scholars may employ vignette survey experiments, for example, using experts and laypersons or other samples. Respondents may be randomly assigned one of numerous descriptions of a public policy which differ with regard to policy goals, policy tools, and policy outcomes and then asked to evaluate the proportionality of the aforementioned aspects. Numerous experimental conditions could be designed, each combining one of the policy goals, policy tools, and/or policy outcomes. This methodology has been widely used in social sciences, and its findings have been found to have strong external validity in predicting the behavior of both citizens (Hainmueller, Hangartner, & Yamamoto, 2015) and professionals (Peabody, Tozija, Munoz, Nordyke, & Luck, 2004).

Relatedly, scholars may also undertake experimental manipulation using risk unfolding over time, combined with varying types of warnings. Warnings may be classified, for example, as tactical or strategic (Davis, 2009, p. 173), as a point estimate or a range, or as qualitative or quantitative (Bulkley & Herrerias, 2005, p. 604), and according to their level of credibility. The idea is to use an experimental design framework within which escalating repeated warnings and their outcomes are manipulated in order to ascertain policy responses by participants or, preferably, by policymakers.

Policy over- and underreaction may also be assessed by sophisticated cost-benefit analysis. They may be measured as well by a comparison of policy outcomes with (national or international) standards developed by experts. For example, a government air quality policy may be compared with the World Health Organization’s recommendations for air quality standards, which were developed by an international group of experts (e.g., Tosun, 2013).


Policy response is complex, and many different models are necessary to capture the richness of observed policy response across many different settings. However, policy process models have so far left out modes of disproportionate policy response. This deficiency is sufficiently jarring to warrant asking whether we miss something deeper about the significance of such response if we confine our attention too narrowly to routine policy processes or if we limit our measures to statistical scorecards of attention. Throughout this article I have tried to show that the study of disproportionate policy response is a gateway to some of the most significant aspects of public policy. Not only has it been a focal point for political actors seeking decisive change or no change at all, but such action has time and time again also made a dramatic impact upon the direction and the character of policy and politics (e.g., Maor, 2012, 2014a, b, 2016a, b, c; Maor & Gross, 2015). Classic examples are the U.S. response to 9/11 and the federal response to Hurricane Katrina.

Although significant advances have been made in defining policy over- and underreaction, much work still needs to be done in dimensionalizing these concepts in specific policy sectors. The idea is to gauge the dimensions that are the most important in accounting for a change of perceptions from policy underreaction to proportionate response and to policy overreaction. Research that empirically addresses this question will be theoretically and practically valuable. Part and parcel with dimensionalizing policy over- and underreaction in specific policy sectors, more work is needed to understand the underlying behavioral antecedents: How are policy over- and underreaction created? What are the underlying processes that allow it to create or destroy (perceived) value for policymakers? Are arrangements that prevent disproportionate policy response accidental? Such research will be useful for increasing our understanding of how the perceived value of a given policy is created or destroyed and thus managing it more effectively. Readers have to recognize that policy over- and underreaction may be poorly calibrated policies but also intentional ones. As noted earlier, policymakers may deliberately formulate off-the-shelf and specially-designed policy overreaction strategies as “last-resort” options, emergency options, and as an attempt to signal target populations that policymakers “may go mad” if they encounter certain behavior of the target populations (Maor, 2016a). In this regard, policy scholars must pay greater heed to issues of temporality, in both the short and long term. The area of disproportionate policy response would also benefit greatly from field semi-experimentation (e.g., Grant & Wall, 2009) that systematically tests the emergence of policy over- and underreaction, for example, under escalating repeated warnings. Finally, scholars have begun to explore processes of sustained policy over- and underreaction propelled by self-reinforcing forces (Jones, Thomas, & Wolfe, 2014; Maor, 2014b, 2016c). More work is needed to fully understand these processes. To date, much of this research has been qualitative, offering the benefits of inductive theorizing. Going forward, research in this area would also benefit greatly from quantitative and experimental analyses.

Finally, there is mounting criticism that revolves around the lack of empirical research addressing disproportionate policy response. Undoubtedly, students of disproportionate policy responses face more daunting challenges for empirical analysis than students of other aspects of policy dynamics. Yet it is hard to argue that disproportionate policy response is less amenable to theory. Indeed, given the difficulties associated with empirical work, theory arguably plays an at least as useful, if not more useful, role. It can guide scholars as to what sorts of data they should be collecting from government documents and other sources. It can identify institutional incentives, thereby enabling researchers to distinguish these incentives from personal tendencies (e.g., overconfidence) or other factors. And when data are classified or otherwise unavailable, theory can still provide insight. The area of disproportionate policy response is still in its infancy, and the lack of empirical investigation clearly reflects this fact. It is hoped that the theoretically meaningful constructs established so far will offer guidance that facilitates future empirical inquiry


Bagehot, W. (1873). Lombard Street: A description of the money market. London: Henry S. King.Find this resource:

Bar-Hillel, M. (1973). On the subjective probability of compound events, Organizational Behavior and Human Performance, 9(3), 396–406.Find this resource:

Bar-Joseph, U. (2005). Watchman fell asleep: The surprise of Yom Kippur and its sources. New York: State University of New York Press.Find this resource:

Barberis, N., Huang, M., & Santos, T. (2001). Prospect theory and asset prices. Quarterly Journal of Economics, 116, 1–53.Find this resource:

Baumgartner, F. R., & Jones, B. D. (1993). Agendas and instability in American politics. Chicago: University of Chicago Press.Find this resource:

Bazerman, M. H., & Watkins, M. D. (2008). Predictable surprises: The disasters you should have seen coming, and how to prevent them. Boston: Harvard Business School Press.Find this resource:

Berger, P., & Luckman, T. (1967). The social construction of reality. Garden City, NY: Doubleday.Find this resource:

Bernanke, B. S. (1983). Nonmonetary effects of the financial crisis in the propagation of the Great Depression. American Economic Review, 73, 257–276.Find this resource:

Bernanke, B. (2014a, January). Central banking after the great recession: Lessons learned and challenges ahead: A discussion with Federal Reserve Chairman Ben Bernanke on the Fed’s 100th Anniversary. Washington, DC: Brookings Institution.Find this resource:

Bernanke, B. (2014b). The Federal Reserve: Looking back, looking forward. Retrieved from

Blinder, A. S. (2013). After the music stopped: The financial crisis, the response, and the work. New York: Penguin Press.Find this resource:

Brändström, A., Kuipers, S., & Daléus, P. (2008). The politics of tsunami responses: Comparing patterns of blame management in Scandinavia. In A. Boin, A. McConnell, & P. ‘t Hart (Eds.), Governing after crisis: The politics of investigation, accountability and learning (pp. 114–147). Cambridge, U.K.: Cambridge University Press.Find this resource:

Breunig, C. (2011). Reduction, stasis, and expansion of budgets in advanced democracies. Comparative Political Studies, 44, 1060–1088.Find this resource:

Bulkley, G., & Herrerias, R. (2005). Does the precision of news affect market underreaction? Evidence from returns following two classes of profit warnings. European Financial Management, 11, 603–624.Find this resource:

Butler, D., Adonis, A., & Travers, T. (1994). Failure in British government: The politics of the poll tax. Oxford: Oxford University Press.Find this resource:

Cohen, J., Chesnick, E. I., & Haran, D. A. (1972). A confirmation of the inertial-Y effect in sequential choice and decision. British Journal of Psychology, 63, 41–46.Find this resource:

Davidoff, S. M., & Zaring, D. (2009). Regulation by deal: The government’s response to the financial crisis. Administrative Law Review, 61, 463–480.Find this resource:

Davis, J. (2009). Strategic warning: Intelligence support in a world of uncertainty and surprise. In L. K. Johnson (Ed.), Handbook of intelligence studies (pp. 173–188). London: Routledge.Find this resource:

Dowding, K., A. Hindmoor, & Martin, A. (2016). The Comparative Policy Agendas Project: Theory, measurement and findings Journal of Public Policy, 36(1), 3–25.Find this resource:

Draghi, M. (2013, May 23). Building stability and sustained prosperity in Europe. Speech at the event entitled “The Future of Europe in the Global Economy.” Retrieved from this resource:

Dunleavy, P. (1995). Policy disasters: Explaining the UK’s record. Public Policy and Administration, 10, 52–70.Find this resource:

Federal Open Market Committee (FOMC). (2008). Meeting of the Federal Open Market Committee, October 28–29. Washington, D.C. Retrieved from this resource:

Financial Crisis Inquiry Commission. (2011). The financial crisis: Inquiry report. Washington, DC: U.S. Government Printing Office.Find this resource:

Finer, S. (1975). Introduction: Adversary politics and electoral reform. In S. Finer (Ed.), Adversary politics and electoral reform (pp. 3–32). London: Anthony Wigram.Find this resource:

Fitzgerald, D. (2013). Learning to forget: US Army counterinsurgency doctrine and practice from Vietnam to Iraq. Stanford, CA: Stanford University Press.Find this resource:

Geithner, T. F. (2014). Stress test: Reflections on financial crises. New York: Crown Publishers.Find this resource:

Gerring, J. (2001). Social science methodology: A critical framework. Cambridge, U.K.: Cambridge University Press.Find this resource:

Gorton, G. (2015). Stress for success: A review of Timothy Geithner’s financial crisis memoir. Journal of Economic Literature, 53(4), 975–995.Find this resource:

Grant, A. M., & Wall, T. D. (2009). The neglected science and art of quasi-experimentation: Why-to, when-to, and how-to advice for organizational researchers. Organizational Research Method, 12, 653–686.Find this resource:

Haaretz Editorial. (2011, March 9). Government reins are slipping through Netanyahu’s hands. Haaretz.Find this resource:

Haaretz Service. (2010, December 4). Fire chief: We find ourselves in the best situation since start of Carmel blaze. Haaretz,Find this resource:

Hacker, J. S., & Pierson, P. (2010). Drift and democracy: The neglected politics of policy inaction. Paper prepared for the 2010 meeting of the American Political Science Association, Washington, DC.Find this resource:

Hainmueller, J., Hangartner, D., & Yamamoto, T. (2015). Validating vignette and conjoint survey experiments against real-world behavior. Proceedings of the National Academy of Sciences of the United States of America, 112, 2395–2400.Find this resource:

Hoffmann, M. J. (2011). Climate governance at the crossroads: Experimenting with a global response after Kyoto. Oxford: Oxford University Press.Find this resource:

Hogwood, B. W., & Peters, G. B. (1985). The pathology of public policy. Oxford: Clarendon Press.Find this resource:

Hood, C. (1994). Explaining economic policy reversal. Buckingham, U.K.: Open University Press.Find this resource:

Hood, C. (2011). The blame game: Spin, bureaucracy, and self-preservation in government. Princeton, NJ: Princeton University Press.Find this resource:

Howlett, M. (2014). From the ‘old’ to the ‘new’ policy design: Design thinking beyond markets and collaborative governance. Policy Sciences, 47, 187–207.Find this resource:

Howlett, M., & Kemmerling, A. (2016). Calibrating Climate Change Policy: The Causes and Consequences of Sustained Policy Under-Design, Paper prepared for presentation at the HKU-USC-IPPA Conference on Public Policy, June 10–11, Hong Kong, China.Find this resource:

Hupe, P., & Hill, M. (2014). Delivery capacity. In M. Lodge & K. Wegrich (Eds.), The problem-solving capacity of the modern state: Governance challenges and administrative capacities (pp. 25–40). Oxford: Oxford University Press.Find this resource:

Israel State Comptroller. (2012). Report on the implementation of the National Security Council law and the handling of the Turkish flotilla. Jerusalem: State Comptroller.Find this resource:

James, H. (1989). What is Keyesian about deficit financing? The case of interwar Germany. In P. A. Hall (Ed.), The political power of economic ideas: Keynesianism across nations (pp. 231–262). Princeton, NJ: Princeton University Press.Find this resource:

Janis, I. L. (1982). Victims of groupthink: A psychological study of foreign-policy decisions and fiascos (2d ed.). Boston: Houghton Mifflin.Find this resource:

Jones, Bryan D. (2001) Politics and the architecture of choice. Chicago: University of Chicago PressFind this resource:

Jones, B. D. (2003). Bounded rationality in political science: Lessons from public administration. Journal of Public Administration Theory and Research, 13, 395–410.Find this resource:

Jones, B. D., & Baumgartner, F. R. (2005a). The politics of attention. Chicago: University of Chicago Press.Find this resource:

Jones, B. D., & Baumgartner, F. R. (2005b). A model of choice for public policy. Journal of Public Administration Research and Theory, 15, 325–351.Find this resource:

Jones, B. D., & Baumgartner, F. R. (2012). From there to here: Punctuated equilibrium to the general punctuation thesis to a theory of government information processing. Policy Studies Journal, 40, 1–20.Find this resource:

Jones, B. D., Thomas, H. F., III, & Wolfe, M. (2014). Policy bubbles. Policy Studies Journal, 42, 146–171.Find this resource:

Kahneman, D. (2011). Thinking fast and slow. New York: Farrar, Straus and Giroux.Find this resource:

Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.Find this resource:

Katz Y. (2010, December 5). Supertanker officials: We could have doused flames sooner. The Jerusalem Post.Find this resource:

King, M. (2010). Banking—from Bagehot to Basel, and back again. Speech at the Second Bagehot Lecture, Buttonwood Gathering, New York, October 25. Retrieved from this resource:

King, G., Murray, C. J. L., Salomon, J. A., & Tandon, A. (2004). Enhancing the validity and cross-cultural comparability of measurement in survey research. American Political Science Review, 98, 191–207.Find this resource:

Kozaryn, L. D. (2001). Families, friends honor Desert Storm fallen. DoD news, U.S. Department of Defense. Retrieved from

Kupferschmidt, K. (2015). Crisis manager with 194 bosses. Science, 350(6260), 495.Find this resource:

Lifton, Robert J. (1986). The Nazi Doctors. New York: Basic Books.Find this resource:

Lindblom, C. E. (1959). The science of “muddling through.” Public Administration Review, 19, 79–88.Find this resource:

Maor, M. (2012). Policy overreaction. Journal of Public Policy, 32, 231–259.Find this resource:

Maor, M. (2014a). Policy persistence, risk estimation and policy underreaction. Policy Sciences, 47, 425–443.Find this resource:

Maor, M. (2014b). Policy bubbles: Policy overreaction and positive feedback. Governance, 27, 469–487.Find this resource:

Maor, M. (2015). Policy entrepreneurs in policy valuation processes: The case of the Coalition for Environmentally Responsible Economies. Paper prepared for presentation at the INOGOV workshop on Novel Approaches to Climate Governance and the Role of Entrepreneurship, May 18–19, Amsterdam.Find this resource:

Maor, M. (2016a). Rhetoric and doctrines of policy over- and underreactions in times of crisis. Paper prepared for presentation at the HKU-USC-IPPA Conference on Public Policy, June 10–11, Hong Kong, China.Find this resource:

Maor, M. (2016b). The implications of the emerging disproportionate policy perspective for the new policy design studies. Policy Sciences.Find this resource:

Maor, M. (2016c). Emotion-driven negative policy bubbles. Policy Sciences, 49(2), 191–210.Find this resource:

Maor, M., & Gross, J. (2015). Emotion regulation by emotional entrepreneurs: Implications for political science and international relations. Paper presented at the 73rd Annual Conference of the Midwest Political Science Association, April 16–19, 2015, Chicago.Find this resource:

Maoz, Z. (2006). Defending the holy land: A critical analysis of Israel’s security & foreign policy. Ann Arbor: University of Michigan Press.Find this resource:

McConnell, A. (2011). Success? Failure? Something in-between? A framework for evaluating crisis management. Policy and Society, 30, 63–76.Find this resource:

McConnell, A., & ’t Hart, P. (2014). “Public Policy as Inaction: The Politics of Doing Nothing” Available at SSRN:

Milgram, Stanley. (1974). Obedience to Authority. New York: Harper & Row.Find this resource:

Mintz, A., & Wayne, C. (2016). The Ploythink syndrome: U.S. foreign policy decisions on 9/11, Afghanistan, Iraq, Syria, and ISIS. Stanford, CA: Stanford University Press.Find this resource:

Moran, M. (2003). The British regulatory state: High modernism and hyper innovation. Oxford: Oxford University Press.Find this resource:

Mullen, B., Brown, R., & Smith, C. (1992). Ingroup bias as a function of salience, relevance and status: An integration. European Journal of Social Psychology, 22, 103–122.Find this resource:

Peabody, J. W., Tozija, F., Munoz, J., Nordyke, R. J., & Luck, J. (2004). Using vignettes to compare the quality of clinical care variation in economically divergent countries. Health Services Research, 39, 1951–1970.Find this resource:

Powell, C. L. (1992). U.S. forces: Challenges ahead. Foreign Affairs, 72, 32–45.Find this resource:

Ravid, B. (2010, December 4). Netanyahu: Battle to stop Carmel fire will be determined in the air. Haaretz.Find this resource:

Rueschemeyer, D. (2006). Why and how ideas matter. In R. E. Goodin & C. Tilly (Eds.), The Oxford handbook of contextual political analysis (pp. 227–251). Oxford: Oxford University Press.Find this resource:

Simon, H. A. (1982). Models of bounded rationality, 3 vols. Cambridge, MA: MIT Press.Find this resource:

Samaan, G., Patel, M., Spencer, J., & Roberts, L. (2004). Border screening for SARS in Australia: what has been learnt? Medical Journal of Australia, 180, 220–223.Find this resource:

Slovic, P. (2007). If I look at the mass i will never act: Psychic numbing and genocide. Judgment and Decision Making, 2, 79–95.Find this resource:

Slovic, P. (2010). The more who die, the less we care. In E. Michael-Kerjan & P. Slovic (Eds.), The irrational economist: Decision making in a dangerous world (pp. 30–40). New York: Public Affairs Press.Find this resource:

Spence, M. (1974). Market signaling: Information transfer in hiring and related screening processes. Cambridge, MA: Harvard University Press.Find this resource:

Sunstein, C. R. (2002). Probability neglect: Emotions, worst cases and law. Yale Law Journal, 112, 61–107.Find this resource:

St. John, R. K., King, A., de Jong, D., Bodie-Collins, M., Squires, S. G., & Tam, T. W. (2005). Border screening for SARS. Emerging Infectious Diseases Journal, 11, 6–10.Find this resource:

Swedish Tsunami Commission. (2005). Sweden and the tsunami: Evaluation and proposals. Stockholm: Swedish Tsunami Commission.Find this resource:

’t Hart, P., Rosenthal, U., & Kouzmin, A. (1993). Crisis decision making: The centralization thesis revisited. Administration & Society, 25, 12–45.Find this resource:

Thaler, R., & Johnson, E. (1990). Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice. Management Science, 36, 643–660.Find this resource:

Therborn, G. (2006). Why and how place matters. In R. E. Goodin & C. Tilly (Eds.), The Oxford handbook of contextual political analysis (pp. 509–534). Oxford: Oxford University Press.Find this resource:

Tosun, J. (2013). Environmental policy change in emerging market democracies—Central and Eastern Europe and Latin America compared. Toronto: University of Toronto Press.Find this resource:

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 4, 207–232.Find this resource:

Tversky A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1130.Find this resource:

U.K. Department for Environment, Food & Rural Affairs (DEFRA). (2005). Adaptation policy framework: A consultation by the Department for Environment, Food and Rural Affairs. PB11431. London: DEFRA.Find this resource:

U.S. Centers for Disease Control and Prevention (CDC). (2014). Airport exit and entry screening for Ebola—August–November 10, 2014. Morbidity and Mortality Weekly Report, 63(49), 1163–1167.Find this resource:

U.S. Government Accounting Office (GAO). (2009). Financial markets regulation: Financial crisis highlights need to improve oversight of leverage at financial institutions and across system. Retrieved from

Walker, S. G., & Malici, A. (2011). U.S. presidents and foreign policy mistakes. Stanford, CA: Stanford University Press.Find this resource:

Walker, W. E., Marchau, V. A. W. J., & Swanson, D. (2010). Addressing deep uncertainty using adaptive policies: Introduction to section 2. Technological Forecasting and Social Change, 77, 917–923.Find this resource:

Weinberger, C. W. (1990). Fighting for peace: Seven critical years in the Pentagon. New York: Warner Books.Find this resource:

Wilder-Smith, A., Goh Kee, T., & Paton, N. I. (2003). Experience of severe acute respiratory syndrome in Singapore: importation of cases, and defense strategies at the airport. Journal of Travel Medicine, 10, 259–262.Find this resource:

Zeira, E. (2004). Myths versus reality: The October 1973 War—failures and lessons (in Hebrew). Tel Aviv: Miskal.Find this resource:


(1.) TARP stabilized banking and rescued the auto industries, provided loan guarantees, and helped stave off mortgage foreclosures (Blinder, 2013). Once the banking sector and the economy had stabilized, calibration of the disproportionate policy response took place. Although Congress initially authorized $700 billion for TARP, that authority was reduced to $475 billion by the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act).