Policy Diffusion: Seven Lessons for Scholars and Practitioners

Over the past 50 years, scholars have published nearly 1,000 research articles in political science and public administration journals about “policy diffusion.” This interest in how policies spread from one government to the next has been increasing among scholars and practitioners alike. Yet, although this focus has produced numerous insights into the policy-making process, the sheer volume of scholarship makes it difficult to identify and understand the key findings and lessons. Indeed, it is hard to see the forest through all of these trees.1 In this article, we step back and draw seven lessons from the literature and its current direction. Our review has three main purposes: First, this article may serve as an introduction for readers who are largely unfamiliar with policy diffusion. Second, practitioners may better understand diffusion pressures and their impacts on policy choices by focusing on key lessons. And finally, scholars who are interested in policy adoption, innovation, and diffusion may find new research directions in the takeaway points offered here. Thus, our goal is to provide insights to both practitioners and scholars, knowing that this necessarily entails sacrificing some depth and specificity in order to capture broad lessons of general interest.

In its most generic form, policy diffusion is defined as one government's policy choices being influenced by the choices of other governments. With this definition in hand, the importance of policy diffusion is undeniable. Those who wish to understand why governments adopt particular policies would be hard-pressed to find examples of policies that are selected entirely for internal reasons. Policy makers rely on examples and insights from those who have experimented with policies in the past. Government officials worry about the impact that the policies of others will have on their own jurisdictions. The world is connected today as never before, and those connections structure the policy opportunities and constraints faced by policy makers at the local, regional, state, national, and international levels.

In the American context, for example, health policy cannot be understood without assessing both the effects of state experiments on the formulation of national policies and the subsequent effects of those national policies on the states.2 Welfare reforms offer opportunities to learn from other governments’ earlier policies while trying to avoid becoming attractive to a needy population. Local and state governments compete for businesses with various tax incentives. The centralization of education policy in recent decades, with more funding provided and regulatory controls exerted by state and national governments, has dramatically altered local choices by superintendents and school boards. And the U.S. experience is not unique. External factors influence internal policy choices in every major policy area around the world. As just one example, pressure on European Union countries facing debt crises to adopt austerity measures by other member governments illustrates how policy diffusion considerations do not stop at national borders.

In today's interconnected world, understanding policy diffusion is crucial to understanding policy advocacy and policy change more broadly. For instance, given that state governments may learn from local antismoking experiences, is an antismoking group better served by targeting its limited resources toward advocating change at the local level or at the state level (see, e.g., Shipan and Volden 2006)? And, given numerous policy diffusion pressures, can scholars be confident in their explanations of policy choices without adequately accounting for external influences (see, e.g., Berry 1994)? The following lessons begin to answer the numerous questions that arise once scholars and practitioners turn their focus to policy diffusion.

Lesson 1: Policy Diffusion Is Not (Merely) the Geographic Clustering of Similar Policies

The spread of a policy innovation from one government to the next tends to bring to mind spatial imagery, such as ripples spreading from a pebble dropped in a pond. Indeed, early work on policy diffusion emphasized this sort of effect, usually conceived of as regional clustering (e.g., Walker 1969). This classic view of policy diffusion continued into recent decades. Even when the methodological sophistication of event history analysis began to allow external and internal determinants of policy choices to be examined simultaneously (Berry and Berry 1990), diffusion forces were often measured merely by the number of geographically neighboring states that had already adopted the given policy. Presumably, if scholars control for the internal reasons for a policy adoption and find evidence that earlier choices of neighbors still matter, then policy diffusion is relevant to understanding such adoptions.

Moreover, even when geographic clustering may be theoretically important, appearances of such clustering may be misleading. Similar governments often face the same types of problems and opportunities at about the same times. Which states were likely to reinstate the death penalty after the U.S. Supreme Court rulings of the 1970s (Mooney and Lee 1999)? Which governments around the world would adopt e-government and e-democracy practices when the relevant technologies became available (Lee, Chang, and Berry 2011)? How would states develop and modify enterprise zones given federal incentives (Mossberger 2000)? Because similar states tend to adopt similar policies, and because geographically neighboring states tend to have many political, economic, and demographic similarities, evidence of geographic policy clustering may have little to do with policy diffusion—that is, with one government's policy choices depending on others’ policies (Volden, Ting, and Carpenter 2008).

In today's world, with low barriers to communication and travel, the classic view of policy diffusion as geographic clustering is growing increasingly outdated. Over time, the lists of the most innovative American states have changed (Boehmke and Skinner 2011), and the rate at which innovations spread has accelerated (Boushey 2010). Whereas prior policy makers may have been limited to learning only from the experiences of nearby neighbors, today's sophisticated politicians and administrators have a much greater capacity to look far and wide for useful solutions to policy problems. Although these changes make detecting policy diffusion more difficult than merely exploring geographic clusters, they offer amazing opportunities for better policy choice and make the field of policy diffusion studies more interesting and significant than ever before.

Lesson 2: Governments Compete with One Another

Responding to claims that governments cannot be as efficient or innovative as the free market, Charles Tiebout (1956) presented a model in which local governments compete with one another, offering policies that are attractive to residents who sort themselves into jurisdictions based on their preferences for taxes and spending. This work launched a massive scholarly research stream of its own and drew attention to the idea of competition across governments.

In terms of policy diffusion, such competition affects the choices of other governments. A city that finds its middle-class residents moving to the suburbs for better schools may need to respond with education reforms of its own, or instead it may cater to other possible residents by focusing on altogether different alternatives, such as attracting a professional sports team or improving public transportation. This example illustrates the breadth of the concept of policy diffusion. Not merely the study of whether the same policies spread across governments, policy diffusion broadly encompasses the interrelated decisions of governments, even when one government's education policies influence another's transportation or entertainment policies.

While much of the economics literature that followed Tiebout focused on the wasteful nature of tax competition across states and localities (e.g., Wilson 1999), literatures in political science, public administration, and sociology turned to examples of public spending, regulation, and the production of public and private goods. For example, Berry and Berry (1990) demonstrate competition across state borders as one key determinant of state lottery adoption. Such competition is not merely reactive to the decisions of other states, but also can be strategic, anticipatory, and preemptive (e.g., Baybeck, Berry, and Siegel 2011).

It is in the realm of “redistributive” policies that competition-based policy diffusion has generated some of the most heated policy exchanges. Here, scholars and practitioners have focused on the possibility of a “race to the bottom” in social programs such as welfare. As articulated by Peterson and Rom (1990) in the American context, state policy makers worry about becoming “welfare magnets,” to which potential recipients move in order to receive higher benefits. Such fears may lead state governments to undercut one another in their redistributive services, eventually racing toward undesirable social safety nets. The race-to-the-bottom concept fueled major policy discussions about the likely impacts of welfare devolution in the mid-1990s and generated sizable scholarly literatures about why and where poor people move (e.g., Bailey 2005) and about the incentives of state policy makers (e.g., Volden 1997).

Although competition across states, localities, and countries exists in a wide range of policy areas, from taxes to welfare to trade, its importance for policy choices should not be overstated. For instance, the evidence that potential welfare recipients move across state lines for greater welfare benefits is mixed at best. For many other policy areas, ranging from county foster care policies, to state regulations on youth access to tobacco, to national disease control policies, governments have little or nothing to gain from competition. In many cases, governments set aside competition altogether, solving their problems collectively through interstate compacts or multilateral trade agreements. And more pernicious forms of competition across states have been explicitly disallowed; for example, the commerce clause of the U.S. Constitution keeps states from engaging in their own trade wars against one another.

Lesson 3: Governments Learn from Each Other

In his famous dissenting opinion in the case New State Ice Co. v Liebmann, U.S. Supreme Court Justice Louis D. Brandeis wrote, “It is one of the happy incidents of the federal system that a single courageous State may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country” (285 U.S. 262 [1932], 311). In order for governments to fully serve their roles as laboratories of democracy, policy makers must act as scientists, watching these experiments and learning from them. Indeed, the policy diffusion literature has recently provided substantial evidence of governments learning from one another's experiences.

Meseguer (2006), for example, finds that countries learn from the effectiveness of others’ trade liberalization policies and structure their own policies as a result. Volden (2006) shows that the American states that were best able to reduce their uninsured rates among poor children were most likely to have their children's health insurance programs copied by other states. Gilardi, Füglister, and Luyet (2009) establish that countries are more likely to change their hospital financing policies when they are ineffective and that these governments tend to adopt policies found to be effective elsewhere.

Such learning takes us far afield from the geographic clustering of policies. The best and most relevant experiments may be across the country or halfway around the world. Moreover, what is learned may have more to do with political opportunity than with policy effectiveness. Policies are complex, and the goals of policy makers vary from one government to the next. Success in containing costs may be more attractive to some than success in improving health outcomes, for example. Electorally minded politicians may care about the political success achieved rather than the policy success (Gilardi 2010), may look for political cover when adopting unpopular policies such as tax increases (Berry and Berry 1992), or may seek to learn not only about better policies but also about how better to compete with other governments (Guler, Guillén, and Macpherson 2002).

Given the political nature of policy choices, the multifaceted goals of policy makers, and the complexity of policies themselves, learning-based policy diffusion may be limited in a variety of ways. Weyland (2007), for example, demonstrates how national policy makers throughout Latin America were influenced by a series of biases and heuristics in developing their pension reform processes rather than making rational assessments based on all available information. Moynihan (2008) shows how policy makers rely on their networks to learn under uncertainty and during times of crisis. Learning about others’ policies and then effectively using lessons learned to solve one's own policy problems is time intensive and takes a high degree of skill. Time-pressed policy makers, those with limited staff support, and those generalists who have not had the opportunity to gain specialized expertise will not be able to take full advantage of others’ policy experiences.

Limits on the capacity to learn from others can be overcome, at least partially, by technological advances and by go-between actors. Low-cost communication and travel allow today's policy makers to attend conferences to exchange ideas, to venture forth on fact-finding trips, and to exchange information widely while sitting at their own desks. Interstate professional organizations such as the National Conference of State Legislatures or the National Governors Association offer clearinghouses of information about the policies adopted by other governments (e.g., Balla 2001). Similar organizations exist at other levels of government and around the world. Füglister (2012), for example, shows that membership in intergovernmental health policy conferences in Switzerland increases the likelihood that a canton will learn about and then adopt successful policies found in other cantons. Informal personal networks also help with the search for appropriate policies (e.g., Binz-Scharf, Lazer, and Mergel 2012). Additionally, policy advocates and entrepreneurs can step in to inform policy makers about policies that they believe would be attractive and effective in a new jurisdiction (e.g., Haas 1992; Mintrom 1997). However, although these groups and individuals may help overcome limits to learning, they also bring with them their own biases and limitations.

Lesson 4: Policy Diffusion Is Not Always Beneficial

Scholars have identified four main mechanisms of policy diffusion: competition and learning, as discussed earlier, but also imitation and coercion (e.g., Shipan and Volden 2008). Imitation is the copying of another government's policies without concern for those policies’ effects; thus, the extent of learning in these circumstances is merely the acknowledgment that a government that is perceived to be a leader has the policy and that it must, therefore, be something desirable. Imitation may be thought of as the policy diffusion equivalent of “keeping up with the Joneses,” with of all the associated negative aspects of such an approach. The voting public may demand the adoption of policies that they have seen or experienced elsewhere, regardless of whether those policies are ultimately suitable in their home community (e.g., Pacheco 2012). Cities where professional sports teams would not thrive seek them out nonetheless. State legislatures exactly copy bills written in other states, typos and all. Countries without the proper economic and educational foundations overbuild their infrastructure and industrial parks in the hope that doing so will attract businesses. Sometimes this spread of untested ideas works, but often, it results in inappropriate and understudied policy choices.

Coercion is the use of force, threats, or incentives by one government to affect the policy decisions of another. An extreme example is armed conflict, a concern that has generated its own sizable diffusion literature (e.g., Most and Starr 1980). But coercion need not rely on the threat of military conflict. Instead, economic power can provide the foundation for coercion, as seen in the recent attempt by Germany to bring about austerity measures in Greece. The example of International Monetary Fund incentives leading developing countries to adopt certain liberalization practices shows how international organizations can be used to facilitate policy diffusion. Coercion can also be seen in a top-down version of policy diffusion, such as when the U.S. federal government attaches restrictions to intergovernmental grants (e.g., Welch and Thompson 1980).4

As with other coercive activities, the use of grant incentives to influence policies at lower levels of government can be either beneficial or harmful. Given the intergovernmental competition (noted earlier) that could result from the underprovision of redistributive policies, the U.S. government has long used matching grants for programs such as welfare or Medicaid, encouraging a greater level of state funding by substantially increasing the bang from a state's buck. More direct vertical policy coercion comes in the form of unfunded mandates of states and localities or preemptive clauses restricting the policy discretion of states or localities. As an example of how localities prefer their own policy choices over statewide choices, Conlisk et al. (1995) note the case of North Carolina, which adopted statewide smoking restrictions in 1993 that would preempt any local laws passed after the following October 15. In the three months before the preemption took effect, the number of local antismoking restrictions soared from 16 to 105, indicating a strong preference for local control over state preemption. In sum, the policy diffusion concept captures the interrelated policy decisions across governments, whether they are based favorably on the normatively appealing concepts of cooperation and learning or less favorably on the manipulation of incentives.

Lesson 5: Politics and Government Capabilities Are Important to Diffusion

In earlier work (Shipan and Volden 2006), we explored an instance of bottom-up policy diffusion, asking what effect local antismoking policies might have on statewide antismoking policies. On one hand, there could be a “snowball effect,” whereby the momentum from the adoption of more local antismoking restrictions leads to a greater likelihood of state adoption. On the other hand, there could be a “pressure valve effect,” whereby the adoption of antismoking restrictions in all of the localities that really want them takes pressure off the state government to act.

Given that both of these effects seem plausible, we set out to learn which effect occurs and, if both do, which political features within a state might produce one effect instead of the other. We found that both of these effects do indeed take place, but which effect predominated was determined by interest group politics and by the capacity of the state legislature. For example, in states with an active and strong health lobby in the state legislature, local adoptions positively influenced the likelihood of state adoptions, as these lobbyists could point to favorable local experiences. States without strong health lobbyists were not only less likely to adopt antismoking restrictions overall, but even less likely still to do so if localities had already adopted a number of restrictions.

In terms of capacity, about a dozen states do not pay their legislators any annual salary at all, beyond covering per diem expenses; some legislatures do not meet for more than a few months every year or two; and many do not hire extensive legislative staffs. Such circumstances profoundly influence policy diffusion processes. Because of their lower capacity, these “less professional” state legislatures exhibit a strong pressure valve effect. If the localities adopted antismoking restrictions, that action removed the problem from the state policy agenda. Legislators could move on to more pressing business or return home to their primary jobs. In contrast, the most professional (and higher-capacity) states exhibited the strongest snowball effect, with state legislators clamoring to take local policies, extend them statewide, and use their policy achievements as grounds to advance their political careers.5

In a follow-up study (Shipan and Volden 2008), we assessed which diffusion mechanisms led localities to adopt these antismoking restrictions in the first place and discover that policy-making capacity once again had a significant impact. Larger cities learned greatly from earlier localities’ experiences and resisted preemptive pressures from their state government. In contrast, policy makers in smaller communities were less likely to learn and more likely to be buffeted by state policy-making decisions. Such small towns were also more susceptible to competition, fearful of losing diners to nearby neighbors if they adopted restaurant restrictions, and they were more likely to imitate the policies of larger cities, even those policies were inappropriate for their own communities.

These conditional factors and the mechanisms of diffusion may themselves change throughout the diffusion process (e.g., Kwon, Berry, and Feiock 2009). Competition matters more among early policy adopters (Mooney 2001), whereas coercion is a more potent factor among late adopters (Welch and Thompson 1980). And the effect of learning increases over time, as more evidence becomes available (Gilardi, Füglister, and Luyet 2009). Because late policy adopters tend to be poorer, smaller, and less cosmopolitan than early adopters (e.g., Crain 1966; Walker 1969), their political circumstances and policy-making capacities may well influence whether they take advantage of their learning opportunities, give in to coercion, or make no policy change at all. Finally, within any given government, diffusion mechanisms take on greater or diminished importance at different stages of the policy formation process, interacting with electoral and political constraints as a policy moves from the agenda-setting stage, to information gathering, to customization (Karch 2007).

Lesson 6: Policy Diffusion Depends on the Policies Themselves

The foregoing examples note a wide variety of policies that have spread from one government to another. Yet each policy is different, often in a variety of ways. For example, in criminal justice policy making, some policy changes, such as developing new RICO (Racketeer Influenced and Corrupt Organizations Act) standards to prosecute organized crime, are quite complex; others are straightforward, such as lowering the drunk driving standard from 0.10 to 0.08 percent blood alcohol content. Some changes, such as extending laws on theft to include credit card theft, are easily compatible with prior practices; others, such as “three strikes” laws, represent substantial breaks from the past. Do differences across the complexity or compatibility of laws affect the nature of policy diffusion?

To answer this question, Makse and Volden (2011) study the diffusion of 27 different criminal justice laws across the American states over a 30-year period. They rely on expert surveys to rate each policy on five dimensions: complexity and compatibility (as in the foregoing examples), as well as observability (whether the effects could be easily seen by others), relative advantage (whether the policy is perceived to have significant advantages over past policy), and trialability (whether the policy could be experimented with in a limited manner). The authors find that all five factors matter in explaining the spread of these policies. Complex policies spread more slowly, whereas compatible policies spread more quickly. Additionally, observability, relative advantage, and trialability all enhanced the rate of adoption and diffusion.

Perhaps more intriguingly, the nature of how these policies spread across the states was affected by the characteristics of the policies. For example, compared to policies whose effects were highly observable, those with low observability were half as likely to exhibit learning-based diffusion. Similarly, learning effects were cut in half for the most complex policies, for which one state's experiences may not translate well to other states. And there was no learning-based policy diffusion whatsoever observed among the set of policies that could be easily tried and abandoned, presumably because the internal trials served as a substitute for learning from the experiences of others.

These findings complement earlier results in the policy diffusion literature that demonstrate the role of innovation attributes in diffusion processes beyond the policy realm (Rogers 2003). Other ways of separating one policy from another, however, have produced mixed findings. For example, Mooney and Lee (1995, 1999) find that both morality policies and economic policies diffuse in similar ways, albeit for different reasons. Nicholson-Crotty (2009) shows that the salience of a policy increases its rate of diffusion. And Boushey (2010) explores how some policy adoptions occur as “-outbreaks,” where they are adopted so quickly across governments as to draw into question whether any diffusion processes were involved in their adoption at all.

Lesson 7: Decentralization Is Crucial for Policy Diffusion

Throughout the 1980s and 1990s, the U.S. federal government took steps to devolve control over some policy areas, such as welfare, to the state and local levels. More recently, such trends have reversed, with greater centralization in areas such as education and health care. Beyond the American experience, other federal systems have similarly been reassessing which levels of government should control which policy areas. Centralization has also played a major role on the international stage, such as through the creation and expansion of the European Union.

Some of the benefits of centralization include economies of scale and reduced redundancy in maintaining policy infrastructures, limits on harmful competitive practices across governments, and proper restrictions on negative spillovers (e.g., limiting harmful environmental pollutants that otherwise would be foisted on neighboring jurisdictions). Some of the costs of centralization include the loss of horizontal competition (along with its efficiency gains), reduced policy experimentation and learning, and a decreased ability to use local knowledge to match policies to heterogeneous local preferences. Most of these considerations involve key aspects of policy diffusion.

Building on the work of Oates (1968) and Musgrave (1969), Peterson (1995) argues that state and local governments are best able to handle “developmental” policies, such as education, in which local preferences vary and experimentation and learning are critical. In contrast, the national government is the best location for “redistributive” policies, such as Social Security or Medicare, because states or localities could be overwhelmed by competitive pressures and thus might adopt insufficient social safety nets in these areas.6 Such arguments draw deeply on policy diffusion ideas. In short, when the positive learning aspects of policy diffusion outweigh the negative competitive aspects, policy making is improved by taking advantage of such learning opportunities through decentralization. If a policy shifts from redistributive to developmental (e.g., as with the change from welfare-only to welfare-to-work programs in the mid-1990s), devolution may be appropriate, with the -possibility of learning about how to effectively encourage employment outweighing any remaining race-to-the-bottom concerns. As with all broad classifications, however, there are clearly exceptions, such as when competition around economic development has led states and localities to recruit businesses to their areas by using wasteful or ineffectual subsidies and tax incentives (e.g., Enrich 1996).

Decentralization can unleash the experimental power of policy diffusion, just as it can bring about healthy or unhealthy competition across governments. For example, in the first five years after the federal government granted control of new funds for children's health insurance to the states, state governments formally modified their Children's Health Insurance Programs more than 100 times, learning from one another's experiences (Volden 2006). In contrast, centralization can stifle local policy experimentation. For instance, when state governments acted in the antismoking policy arena, local policy adoptions fell to about 70 percent of their former adoption rates (Shipan and Volden 2008). Moreover, when the state government also included some preemptive language in its laws, local adoption rates fell by more than 90 percent.

The 2010 national health care reforms serve as an example of policy diffusion at work. The national model clearly built on some aspects of state policies, such as the individual health insurance mandate previously adopted in Massachusetts. Yet the adoption of national standards could cause state-level experimentation to be much more limited in the future. To attempt to address such limitations, the Patient Protection and Affordable Care Act includes provisions to try to recapture the beneficial elements of state-level competition and experimentation, such as mandated health insurance exchanges required in each state. Whether these provisions allow adequate flexibility and experiential learning to confront new and growing health care problems remains to be seen.

The Future of Policy Diffusion

Policy diffusion is not just a term to describe the geographic clustering of policies. Rather, it encompasses a broad array of interdependent policy choices across governments. The mechanisms of policy diffusion include competition across governments, learning from policy experiments, imitation, and coercion. Therefore, diffusion can be quite beneficial or ultimately harmful. How external diffusion pressures affect policy choices depends on the capacity of policy makers, political circumstances surrounding policy change, and the characteristics of the policies themselves. Such considerations are important not only for the selection of policies directly but also for the procedural choices regarding whether policies are formulated at the local, regional, national, or international levels.

These complexities all point to major challenges and opportunities for public administration scholars and practitioners. In understanding policy choices, scholars should be attuned to the relevant mechanisms of policy diffusion. They should consider the attributes of the policies that they are studying—whether the policies are simple or complex, whether the policies’ effects are opaque or easily observed, and so on. Scholars should assess the degree to which the relevant policy makers have the capacity to learn effectively from others’ experiences and the political will to resist competitive or coercive pressures.

Although scholars would be remiss in ignoring the policy diffusion literature while examining their chosen policy areas, we wish to present not merely a cautionary tale but also a call to action. Perhaps better than anyone else, scholars of public administration are well positioned to advance the literature on policy diffusion in new and exciting directions. For example, almost all policy diffusion work to date focuses on the adoption stage of the public policy process (but see Karch 2007 and Pacheco and Boushey 2012). Yet public administration scholars know that the process does not end at adoption—rather, policies evolve through their implementation. Indeed, implementation may present some of the most important opportunities for learning and imitation over time and across governments. Extending the policy diffusion literature beyond initial policy adoptions is warranted and long overdue.

In a similar vein, nearly all policy diffusion studies explore legislative adoption by state or national governments, while ignoring the equally important decisions made by executive agencies. In one notable exception, Volden (2006) surprisingly finds evidence that state legislatures learned from other states’ experiences much more than administrative agencies did. Future work could build on existing studies of the extent to which legislators delegate policy control to administrative agencies in order to gain from their capacity to learn (e.g., Gilardi 2009; Huber and Shipan 2003) or in order to take advantage of agencies’ lower likelihood of responding poorly to competitive or coercive forces. Such delegation decisions and subsequent policy choices could well be linked to the mechanisms of policy diffusion. Therefore, scholars could more fully confront the question of whether policy delegation and policy diffusion are complements or substitutes. Once again, scholars of public administration are well suited to addressing these sorts of issues, given their knowledge of and insights into agency decision making.

As one final academic example, too often the policies being considered in diffusion studies are dichotomized. Either a state has adopted a restriction on smoking in restaurants and bars or it has not. Yet public policy scholars know that policies are not mere binary choices. Some policies are more comprehensive than others. Some are coupled with limited enforcement. Many policies offer discretion to agencies or to other levels of government, while some restrict and preempt other policy makers. Studies of policy diffusion that consider these more nuanced policy elements may dramatically advance our understanding of which governments select which policies and why.

The lessons summarized here are not only of use to the scholarly community, but also of practical significance. Many practitioners already are well aware of the pressures and considerations presented here. That said, they may not be as well attuned to how these different diffusion mechanisms relate to one another. They may seek to learn from the experiences of other states; however, studies of policy diffusion have begun to point out that learning is just one of many potential mechanisms for diffusion. Thus, at times, it might make sense to push for a policy based on observing what other states have done, whereas at other times, learning might take a back seat to other potential mechanisms, such as economic competition. The literature on diffusion also illustrates for policy makers the ways in which decentralization and enhanced policy maker capacity can unleash the positive power of policy diffusion.

By no means do public administrators face these concerns and opportunities alone. At nearly every level of government around the world, policy makers find themselves in networks of other likeminded leaders. Professional associations promote policy diffusion through best practice reports, webinars, and workshops. They offer awards and rankings on such criteria as the “greenest city,” the “healthiest state,” or the “least corrupt nation.” Yet a bit of caution is also advised, as policy choices alone deserve only part of the credit for these accolades, and policy makers must consider whether the recommendations are merely fads or ideas that may not be well suited to the government's most pressing problems. What works well in one area may not succeed elsewhere. And smaller governments in particular should be cautious about using their limited resources on policies that may not be effective upon being rescaled to suit a smaller community.

Most generally, then, our advice to practitioners is simply to observe the policy-making world through the lens of policy diffusion. Diffusion processes are everywhere, and they are often insufficiently acknowledged. Recognizing these processes for what they are may make policy makers’ choices more transparent and more fully informed, potentially resulting in better public policies.