Coveting More Than Thy Neighbor: Beyond Geographically Proximate Explanations of Postsecondary Policy Diffusion Brian Sponsler The George Washington University

In theorizing about how and why higher education policy spreads among the states, scholars have consistently assumed that the existence of policy in a nearby state increases the likelihood that other states will subsequently adopt a similar policy. However, limited empirical evidence supports this theoretical perspective. To advance scholarship, state-level postsecondary education policy innovation studies should employ causal mechanisms of policy diffusion—policy learning, competition, coercion, and socialization—seldom utilized in extant literature to construct more precise models of the diffusion process. Theoretically sound models of diffusion in this domain lay a foundation for investigating the inßuence of policy type, ideology, regional and national actors, and stages of the policymaking process on the spread of postsecondary policy ideas among the states.

Sponsler, B. (2010). Coveting more than thy neighbor: Beyond geographically proximate explanations of postsecondary policy diffusion. Higher Education in Review, 7, 81-100.

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Coveting More Than Thy Neighbor: Beyond Geographically Proximate Explanations of Postsecondary Policy Diffusion Over the last decade, researchers have attempted to identify internal and external state conditions that serve as antecedents to postsecondary policy innovations. Studies have investigated a range of substantive policy areas, including Þscal reforms, accountability efforts, and governance changes (e.g., Doyle, 2006; Fowles, 2009; McLendon, Heller, & Young, 2005; Mokher & McLendon, 2009). Collectively, this stream of scholarship has identiÞed a number of intrastate characteristics as inßuencing policy adoptions. Research has had limited success, however, in accurately modeling and explaining the process of postsecondary policy diffusion, perhaps due in part to the near uniform theoretical approach taken in research to date. When theorizing about how and why higher education policy spreads among the states, scholars have consistently asserted that policymakers draw lessons from the experiences of other polities, and that this lesson drawing is driven by geographic proximity. This geography-based approach to research has consistently relied on an assumption that the existence of a particular policy in a nearby state increases the likelihood that other states will subsequently adopt a similar policy. Problematically, there is limited empirical evidence to support this theoretical contention. The lack of empirical evidence suggests the process of postsecondary policy diffusion may be more complex then extant research presumes. To capture the process of policy diffusion more accurately, scholars should expand the theoretical conceptions employed in postsecondary policy innovation research. The purpose of this article is to support the extension of theoretical conceptions of diffusion pressures in state-level higher education policy innovation studies. Accordingly, four causal mechanisms of policy diffusion drawn from the broader political science literature—policy learning, competition, coercion, and socialization—are discussed. Collectively these four mechanisms provide a diverse theoretical menu from which scholars may choose when attempting to capture postsecondary policy diffusion empirically. Beginning with a brief overview of postsecondary policy innovation scholarship, the article notes the emphasis research has placed on explaining policy adoptions. Next, discussion focuses on the theoretical justiÞcations put forth in this line of scholarship for how and why policy spreads among the states; doing so highlights the near uniform reliance on a policy learning-geographic explanation of the diffusion process. I then turn to alternative theoretical conceptions of diffusion, suggesting that scholars more fully examine the inßuence policy learning, competition,

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coercion, and socialization may have on the diffusion of higher education policy. I conclude with suggested directions for future research. Policy Innovation and Postsecondary Policy One promise of American federalism is the ability of states to function as policy laboratories through enactment of new policies, and in so doing to provide evidence from which other polities may learn. Scholars have investigated policy innovations with a focus on (a) identifying withinstate conditions that inßuence policy adoptions and (b) identifying the mechanisms that drive the diffusion of policy ideas (Berry, 1994; Berry & Berry, 1990, 1992; Karch, 2007; Volden, 2006; Volden, Ting, & Carpenter, 2008). Policy innovation studies, therefore, typically focus on two interrelated processes: adoption and diffusion. Although these two processes share an intellectual foundation, distinctions can be made between the two. Adoption research attempts to explain the process by which a political jurisdiction enacts a policy new to the polity (Karch, 2006; Walker, 1969). Diffusion research, on the other hand, seeks to uncover conditions that drive the spread of new policy ideas (Gray, 1973; Volden, 2006). Although a sub-process of the larger adoption dynamic, there may exist unique conditions and drivers that cause policy diffusion—it is not a necessity that the conditions that give rise to adoption of new policy are the same conditions that drive policy spread. An extensive network analysis by Graham, Shipan, and Volden (2008) identiÞes three studies in particular as most inßuential in U.S. state-level policy innovation scholarship: Walker’s (1969) conceptualization and primary test of diffusion; Gray’s (1973) identiÞcation of the emergent S-curve representing the pattern of policy adoptions; and Berry and Berry’s (1990) study of state lottery adoption that pioneered the use of advanced methodological techniques. Outcomes of these works provide a foundation for the study of policy innovations. First, wealthier, more urban, and more industrial states are more likely to adopt new programs and policies than their less wealthy, less urban, and less industrial peers (Walker, 1969). Second, innovativeness varies across policy areas; a state’s likelihood of innovating ßuctuates as the substance of policy deliberations shifts (Gray, 1973). Finally, use of advanced methodological techniques allow for the combination of internal and external determinants into single models of analysis (Berry & Berry, 1990; see also Box-Steffensmier & Jones, 2004; Singer & Willet, 2003). Building on this foundation, innovation studies have mostly adhered to the ideas put forth in these three works while investigating new factors that inßuence adoption and diffusion such as policy entrepreneurs (Mintrom, 1997) and the role of policy success (Shipan & Volden, 2008; Volden, 2006).

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Explaining Diffusion in Postsecondary Policy Scholarship In the area of postsecondary education policy, research has relied heavily on the Walker-Gray-Berry-and-Berry framework to investigate new policy innovations (e.g., Doyle, 2006; McLendon, Hearn, & Deaton, 2006; Mokher & McLendon, 2009). In so doing, research has sought to identify conditions within and between states that serve as antecedents to postsecondary policy adoptions. Studies have investigated budgetary and Þnancial reforms in higher education (McLendon, Heller, & Young, 2005), merit-based student Þnancial aid programs (Doyle, 2006), state performance-accountability policies (McLendon et al., 2006), dual enrollment policies (Mokher & McLendon, 2009), and postsecondary governance reforms (Fowles, 2009). Collectively, this stream of research has identiÞed internal determinants of state policy adoptions, pointing to the structure of statewide higher education coordinating boards, government ideology, electoral competitiveness, state Þscal conditions, demographics, and political party strength as impacting adoptions. Limited insight, however, has been gained into the causes of postsecondary policy diffusion, as research has typically focused on explaining policy adoptions. Moreover, the theoretical justiÞcation for inclusion of a diffusion hypothesis in research has been nearly uniform. When theorizing about why and how higher education policies may spread among the states, scholars have consistently asserted that policymakers draw lessons from the experiences of other polities, and that this lesson drawing is driven by geographic proximity. Put another way, studies to date have focused exclusively on neighboring states as the motivator of policymaker lesson drawing, and have done so at the expense of other explanations of why and how policies may diffuse. In relying on geographic proximity as a proxy to capture policy learning, higher education scholars have followed a well established tradition in diffusion research. Ever since McVoy (1940) Þrst posited that public policies spread in concentric circular patterns, scholars have attempted to capture the geographic pattern of policy diffusion. Subsequent studies recognized that policies may diffuse for a variety of reasons, but continued to emphasize the role of geography by focusing on nearby states as proxies to capture the effect of prior adoptions on a given state’s policy behavior. Several reasons exist for why geography may impact the spread of policy. First, state policymakers may be inclined to look to nearby states for policy examples due to a belief that cultural, demographic, educational, or economic conditions are similar to conditions in their own state (Berry & Berry, 2007; Walker, 1969). Second, geographic proximity may facilitate avenues of communication that support the transfer of policy ideas among

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elected ofÞcials (Crain, 1966; Foster, 1978). Finally, media markets often transcend political boundaries to disseminate information on policy ideas and actions across jurisdictional borders, transmitting information to diverse populations of decision-makers in unique polities; historically these media markets have been local or regional in nature (Hays & Glick, 1997; Winter & Eyal, 1981). Despite the rationales offered for how geography may inßuence policy spread, this line of reasoning is open to critique. For instance, the rise of national media, political forces that operate in a multitude of states, and professional networks and associations that exist to transmit policy ideas suggest that diffusion may be a more national process than scholarship to date has represented (Balla, 2001; Gray, 1994; Lieberman & Shaw, 2000; Winder & LePlant, 2000). Nonetheless, a review of policy innovation studies in the domain of postsecondary education policy reveals a nearly uniform reliance on geographically proximate states as motivators of policy learning and lesson drawing. This geographic approach to research has consistently relied on an assumption that the existence of a particular policy in a nearby state provides an example from which decision-makers in other polities can draw lessons. Moreover, the modeling of the diffusion process in postsecondary education innovation research has assumed that lesson drawing is uniformly positive, positing adoption of a policy in a nearby state increases the likelihood that other states will subsequently take similar action. Table 1 provides an overview of diffusion hypotheses, theoretical conceptions of the mechanisms of diffusion, operationalization of diffusion variables, and diffusion-speciÞc Þndings from nine representative postsecondary policy innovation studies. Table 1 illustrates three underlying assumptions informing extant investigations, including a consistent theoretical justiÞcation for why diffusion is likely to occur; a uniform hypothesis that diffusion pressures are positive and therefore likely to lead to an increase in future adoptions; and consistent use of a geographic measure to capture diffusion pressures. Each is subsequently discussed. Scholars have consistently theorized that diffusion is driven by a loosely deÞned process of policy learning, whereby polities learn from each other as they search for policy ideas. However, this conception is somewhat unsatisfying on two fronts. First, research has yet to move beyond a general policy learning proposition to articulate how this process unfolds. Discussion has failed, for example, to adequately identify conductors in the policymaking space that serve to transmit lessons from one set of policymakers to another. Second, discussion of exactly what policymakers are learning is limited; current literature posits that learning

Policy Focus

Six financial and accountability innovations: performance-funding; performancebudgeting; mandated assessment of undergraduates; meritbased scholarships; prepaid tuition plans; college savings plans.

Prepaid tuition and college savings plans.

Merit-based student grant programs.

A range of policies including: performance-funding; performancebudgeting; performance-reporting.

Author/Study Title

McLendon, M. K., Heller, D. E., & Young, S. P. (2005). State Postsecondary Policy Innovation: Politics, Competition, and the Interstate Migration of Policy Ideas.

Doyle, W. R., McLendon, M. K., Hearn, J. C. (2005). The Adoption of Prepaid Tuition and Savings Plans in the American States: An Event History Analysis.

Doyle, W. R. (2006). Adoption of Merit-Based Student Grant Programs: An Event History Analysis.

McLendon, M. K., Hearn, J. C., & Deaton, R. (2006). Called to Account: Analyzing the Origins and Spread of State PerformanceAccountability Policies for Higher Education.

“States whose neighbors have already adopted a higher-education performance policy will be more likely to adopt the same policy” (p. 8).

“States with more neighbors that have a merit aid program will themselves be more likely to adopt a merit aid program” (p. 266).

“States with more neighbors that have either type of program (prepaid tuition or savings plan) will themselves be more likely to adopt a merit aid program” (p. 19).

“States with innovative neighbors will be more likely to adopt postsecondary policy innovations” (p. 374).

Diffusion Hypothesis

“Several distinct diffusion models exist. The most prevalent holds that states are most likely to emulate their immediate neighbors, meaning those with which they share a border” (p. 8).

“In the case of merit aid programs, I posit that this is a case of interstate competition for highly capable young people—a competition among states to retain human capital” (p. 265).

“A key concept in analyses of policy adoption in the fifty states has been diffusion—the idea that states emulate the previous policy behavior of their neighbors or peers” (p. 18).

“The prevalent approach holds that states are most likely to emulate their immediate neighbor, meaning those states with which they share a contiguous border” (p. 374).

Theoretical Conception of Diffusion Mechanism

Describing Diffusion in Postsecondary Education Policy Innovation Studies

Table 1

2. Percentage of states in defined regional groupings of states based on higher education compacts.

1. The number of contiguous states that have already adopted an innovation

2. Number of states in defined regional groupings of states based on higher education compacts.

1. The number of contiguous states that have already adopted an innovation.

1. The number of contiguous states that have already adopted a particular innovation.

2. Mean number of years since adoption of a contiguous state’s innovation.

1. The number of contiguous states that have already adopted a particular innovation.

Operationalization of Diffusion Variable

2. insignificant

1. insignificant (negative for performance funding; neutral for performance budgeting.)

2. insignificant (negative)

1. insignificant (negative)

2. insignificant for college saving plans (positive)

1. significant for prepaid tuition plans (negative)

2. insignificant for accountability innovations (positive).

1. significant (for any innovation and only financial innovations) (positive)

Significance (direction)

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Policy Focus

Statutory higher education governance reforms.

State-level student unit-record systems (SUR).

State-level dual enrollment policies.

Statutory higher education governance reforms

In-state tuition policy for undocumented students.

Author/Study Title

McLendon, M. K., Deaton, R., & Hearn, J. C. (2007). The Enactment of Reforms in State Governance of Higher Education: Testing the Political Instability Hypothesis.

Hearn, J. C., McLendon, M. K., Mokher, C. G. (2008). Accounting for Student Success: An Empirical Analysis of the Origins and Spread of State Unit-record Systems.

Mokher, C. G., & McLendon, M. K. (2009). Uniting Secondary and Postsecondary Education: An Event History Analysis of State Adoption of Dual Enrollment Policies.

Fowles, J. (2010). Modeling State Decentralization of Higher Education Governance: A Hazard Model Approach.

Sponsler. B. A. (2010). State Adoption of Undocumented Student Tuition Policy: An Event History Analysis.

Table 1 continued

“States with more neighbors that have adopted a policy . . . will themselves be more likely to adopt such a policy” (p. 56).

“Given the mixed support for regional diffusion … I have no a priori expectation for this variable” (p.24).

“States will be more likely to adopt dual enrollment policies if their neighbors have already adopted the policy” (p. 256).

“States whose regional neighbors have already adopted a SUR system will themselves be more likely to adopt one” (p. 669).

“States whose regional neighbors have already adopted a governance reform for higher education will themselves be more likely to adopt one” (p.656).

Diffusion Hypothesis

“Diffusion of undocumented student tuition policy is theorized to be a result of policy learning, where states…look to previously adopting neighbors to identify potential policy solutions” (p. 56).

“…a positive and statistically significant coefficient…provides evidence that states are borrowing policy solutions from their neighbors” (p. 24).

“As noted policy diffusion refers to the tendency of states to emulate the policy behavior of their neighbors. States may engage in policy mimicry for a variety of reasons….we believe that states might copy one another because of normative pressures…” (p. 256).

“Presumably propelled by regional associations and other formal and informal informational and peer contacts have been shown to occur in higher education” (p. 669).

“Our final explanation for reform points beyond conditions within states and toward the emulative influences between and among them” (p. 656).

Theoretical Conception of Diffusion Mechanism

1. insignificant (negative)

2. insignificant (positive)

2. Percentage of contiguous neighbors having adopted.

1. Percentage of contiguous neighbors that have previously adopted an undocumented student tuition policy.

1. insignificant (negative)

1. insignificant (negative)

1. insignificant (negative)

1. insignificant (negative)

Significance (direction)

1. Percentage of states in regional higher education compacts.

1. Number of neighboring states with a dual enrollment policy.

1. The number of neighboring states with a SUR system.

1. Percentage of states in defined regional groupings of states based on higher education compacts.

Operationalization of Diffusion Variable

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is occurring, with minimal articulation of the substance of this theorized lesson-drawing. Given this lack of clarity, it is not surprising empirical evidence demonstrating a signiÞcant inßuence of diffusion on adoptions is absent from a plurality of studies reviewed. Additionally, investigations have consistently assumed that diffusion is a positive process. In each study reviewed, the explicit assumption has been that a state would be more likely to adopt a policy if their neighbors had previously adopted a similar policy. This conception assumes that diffusion is a unidirectional process and that lesson drawing by policymakers is only afÞrming; policymakers are presumed to be taking only positive policy cues from the experiences of nearby states. However, although this may be an accurate assumption in many cases, it is also possible that policy learning results in a reduction of the likelihood of adoption. For example, if the adoption of a policy by one polity demonstrates negative political consequences for elected ofÞcials, policy adoption may actually be less likely in other jurisdictions. Policy learning, therefore, may be a more complex process than extant research has acknowledged. Finally, the reviewed studies have relied on a measurement of the number of neighboring states that have previously adopted a policy to capture diffusion pressures. The assumption has been that geographic distance is the most appropriate measurement of diffusion pressures and that as neighboring states adopt a policy the pressure to adopt increases. The consistent reliance on this measurement, though understandable from a methodological standpoint, has produced an empirical record that provides minimal support for the effect of geographic proximity on the diffusion of postsecondary education policy. As illustrated in Table 1, only 2 of 15 variables analyzed resulted in statistically signiÞcant results. Moreover, one of the signiÞcant Þndings indicated a negative inßuence, suggesting that polities with neighbors who had previously adopted were less likely themselves to adopt. At a more granular level, a plurality of results, although not statistically signiÞcant, suggest a negative inßuence on the likelihood of additional adoptions—a contradiction to the investigations diffusion-related hypotheses. Though research to date has made signiÞcant contributions to the literature by identifying antecedents of postsecondary policy adoptions, empirical evidence points to a process of diffusion in this domain that may be more complex then research has acknowledged. One way to advance scholarship in hopes of more fully modeling and capturing diffusion pressures is by explicitly describing the causal mechanisms of diffusion that may inßuence the postsecondary policy innovation process.

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Rethinking Diffusion: Lessons from the Literature Four forces—learning, competition, coercion, and socialization— drawn from the broader Þeld of political science have the potential to offer more precise and convincing theoretical explanations of why and how postsecondary education policies diffuse among the states. Clear theoretical conceptions of diffusion in this domain support continued investigation into geographic and non-geographic-based diffusion pressures. The intent of the following discussion is to offer options for higher education researchers to consider when attempting to identify diffusion in a postsecondary context. Policy Learning Extant postsecondary policy innovation studies have for the most part relied on a loosely deÞned notion of policy learning as the theoretical justiÞcation for inclusion of a diffusion variable in analytic models. However, limited reference to precisely what policymakers are learning, or why they are drawing the lessons that they are presumed to be drawing, is evident in discussion. A fuller explanation of the process of policy learning would enrich scholarship. Policy learning is organic to the process undertaken by decisionmakers as they attempt to craft effective policy solutions and lies at the heart of the conception of the 50 states as independent laboratories of democracy (Brandeis, 1932; Dye, 1990). As policymakers seek evidence to solve problems, they may look to some set of reference states for policy examples (Berry & Berry, 2007; Berry & Baybeck, 2005; Boehmke & Witmer, 2004). When policy is judged to be effective at addressing targeted conditions, and other polities learn of its success, a process of diffusion may naturally follow (Volden, 2006). Policy learning, however, may be about more than just determining policy success, and can also refer to efforts by decision-makers to determine the political viability of a policy idea, the Þnancial implications of enactment, or the possible intended and unintended consequences of implementation. One way in which scholarship could be advanced is through more precise explanations of why policy learning takes place. Two distinct types of policy learning—imitation and emulation—provide fuller explanations of this process. Imitation. Postsecondary policy may diffuse as a result of an imitative process. Imitative diffusion occurs because policymakers believe they share policy-relevant characteristics with prior adopters. The recognition of shared characteristics among states serves as a heuristic shortcut for politicians searching for policy solutions and can facilitate policy learning

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(Karch, 2007). Commonalties may be recognized by policymakers among states for a variety of reasons. Policymakers, for example may determine which states to imitate by assessing demographic, political, economic, and structural resemblances. Once policymakers recognize common attributes, a process of policy imitation may follow. Imitation, therefore, may drive policy spread when decision-makers focus on who (which set of states) adopt a speciÞc policy. Emulation. Policy may also diffuse because state policymakers learn of a successful version in another polity and try to replicate its success in their own state (Volden, 2006; Walker, 1969). Emulation is a speciÞc form of policy learning driven by potential adopters’ perception of the policy to achieve desired goals (Karch, 2007; Volden, 2006). In emulative diffusion processes, later adopters attempt to capture positive outcomes of early adopters—a process congruent with a conception of the 50 states as policy laboratories (Dye, 1990). Of primary concern is the policy itself and its associated outcomes, not contextual characteristics of states. As with imitation, emulative diffusion pressures need not be theoretically limited by geography; policymakers may identify successful innovations in states distant from their own, particularly given the expansion of national organizations and channels of communication. Emulation, therefore, has the possibility of supporting more precise explanations of why postsecondary innovations diffuse, particularly when there is reason to assume that decision makers are focused speciÞcally on what policy is being enacted, rather than the states that are enacting it. Competition A policy may diffuse due to interstate competition. In such cases, policies spread because policymakers believe a competitive advantage may be gained or a disadvantage avoided (Boehmke & Witmer, 2004; Karch, 2006). For example, states may compete for positive economic conditions that lead to growth. Business Þrms and private citizens are presumed to be attracted to states where the ratio of costs to services received is most favorable (Peterson, 1981). As a result, policymakers may feel pressure to adopt policy that exists in other jurisdictions if (a) the policy affects their state’s relative attractiveness and (b) the policy can help avoid conditions under which Þrms and citizens may be inclined to relocate (Karch, 2007). Alternatively, states may adopt policies to repel groups that policymakers deem undesirable. For example, research has investigated the presence of a “race to the bottom” in welfare policymaking, whereby states seek to offer lower beneÞt levels in hopes of dissuading low-income citizens from moving to their state (Bailey, 2005; Peterson & Rom, 1990; Volden, 2002). Thus, competition to attract desirable populations and repel

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undesirable populations provides a potential theoretical justiÞcation for why policies spread among states. Moreover, this competitive dynamic suggests an ongoing process of interaction among state governments as adjustments are made to policies in response to changing conditions and the actions of other polities. Coercion Coercion—the process through which one form of government attempts to impose a preferred policy solution on another—may also be a mechanism of policy diffusion. Coercion may operate in a vertical (e.g. federal-state or state-local district) or horizontal (e.g. state-state) process. Vertical coercion occurs when actors from previously adopting polities— or governments considering adoption themselves—attempt to impose policy preferences onto a potential adopter (Graham, Shipan, & Volden, 2008). To exert pressure in this way, the coercive actor may use grants, preemptive policy, or regulation to incentivize desired policy actions and punish undesirable policy actions (Karch, 2006). Coercion can also be a horizontal process, where in the case of the states, one government applies pressure in some manner on another until the targeted polity adjusts or adopts policy preferable to the coercive government. To exert horizontal pressure in such a way, states may engage in policy issue linkage, where policy cooperation among jurisdictions in one area is reliant upon policy actions preferable to the coercive government in another policy domain (Graham et al., 2008). Although coercion may prove difÞcult to model theoretically and capture in the domain of higher education, scholars should be cognizant of the role this mechanism could play in the diffusion of policy and attempt to identify opportunities to utilize it in research. Socialization Socialization is a fourth potential mechanism of policy diffusion. Socialization deÞnes efforts made by members of a network to change norms and preferences for types of policies, as opposed to dictation of a speciÞc policy innovation (Graham et al., 2008). Notions of socialization are evident in early diffusion scholarship. Walker (1969), for example, identiÞed larger, more urban, and wealthier states as policy leaders and other states as policy laggers—those states that follow in the policy footsteps of early adopters. Extending Walker’s line of reasoning, states identiÞed as policy leaders could be viewed as drivers of socialization by setting norms for the network of states. Within the networks of states, particular states may emerge as more active leaders of policy innovations, serving in part to drive a process of socialization.

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Synopsis Four mechanisms of policy diffusion—learning, competition, coercion, and socialization—have been discussed to support more precise explanations for why postsecondary policy innovations could diffuse among the states. Utilizing any one of these mechanisms in research would advance the standard approach taken to-date in scholarship and provide a foundation from which diffusion processes in postsecondary education policy can be more accurately modeled and captured. Two notable examples of attempting to more clearly deÞne the process of diffusion in the postsecondary domain are evident in extant literature. An investigation by Mokher and McLendon (2009) attempts to capture the potential inßuence of socialization on the spread of postsecondary policy. The authors, in developing a rationale for why diffusion pressures may inßuence state adoption of dual enrollment policies, note the tendency of states to “copy one another because of normative pressures” (Mokher & McLendon, 2009, p. 256); socialization thus becomes the theoretical driver of diffusion. In another example, Doyle (2006) investigates state adoption of student merit-aid programs. Built on research suggesting that states may be in competition for human capital, Doyle (2006) theorizes that states are in competition to retain academically talented students, and that this competition is the cause for the investigated policies diffusion. In each example, diffusion is theorized to be more than a general and passive process of policymakers learning from the actions of other states. Despite taking a more nuanced theoretical approach to diffusion, these two studies continue to rely on geographic-based variables when modeling the diffusion processes. This need not be the case. For instance, if in fact states are competing for human capital, and merit-aid programs are an appropriate policy response to this dynamic, one could presume that states would adjust the market value of these aid awards in response to other state actions, whether states were neighbors or not. Moreover, rather than investigating whether or not a state adopts such a policy, it may be more fruitful to investigate how states that are net exporters or importers of college-bound students respond with like policies given the actions of some set of reference states. Are states that experience a “brain drain” more or less likely to adopt a merit-aid program than states that receive more human capital then they export? Does the relative value of merit-aid scholarships to in-state tuition levels ßuctuate across states in response to student migration patterns? Conversely, are these policies not inßuenced by enrollment trends and instead emerge given political conditions in states? Pursuing answers to these types of questions could lead to different conceptualizations of “neighboring,” as states would be presumed to respond to the policy actions of states perceived to be siphoning off

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human capital rather than those that are geographically close; there is little reason to assume that only geographically proximate states would be in competition with one another. By clearly articulating why a policy is likely to diffuse, investigations need not rely on geographic proximity as a proxy to capture diffusion pressures. Although geography proximity may be an appropriate measurement to pair with any of the discussed causal mechanisms, it is a theoretical requirement for none. More nuanced conceptions of why policy is likely to spread may in turn lead to use of measurements to capture diffusion pressures that do not rely on geography, an as yet untried approach to research in the postsecondary policy domain. New Directions in Postsecondary Policy Innovation Research To build on the four causal mechanisms outlined herein, postsecondary policy innovation scholarship should consider several new directions for research. The following four areas offer approaches to identifying diffusion processes that would extend the focus of research in this domain. These suggestions may be used individually or in combination, and are provided to encourage conversations among scholars interested in the adoption and spread of postsecondary policy ideas. Pairing Causal Mechanisms of Diffusion with SpeciÞc Policy Types Each of the four mechanisms discussed previously provides theoretical groundings for investigations into the diffusion of postsecondary policy ideas. Each mechanism, however, may be more likely to explain diffusion processes when matched appropriately to a particular type of policy. Research has identiÞed several distinct and discrete policy types, including distributive, regulative, redistributive, moralistic, and administrative, among others (Lowi, 1964; Mooney, 2001; Peterson, 1981; see also Anderson, 1997; Roberts & Dean, 1994; and Tolbert, 2002). Each policy type is likely to diffuse differently in response to unique conditions. Regulatory policy, for example, typically has low salience with the public, is technical in nature, and evolves in an absence of controversy. Given such characteristics, regulatory policy may be more likely to diffuse as a result of a socialization process then as a result of a competitive interstate environment. On the other hand, morality policy may be more likely to diffuse as a result of an imitative learning process, where decision-makers look to other states with similar political conditions in order to gauge the potential consequences of action or inaction. Along this line, studies have suggested that morality policies experience adoption processes driven more by values than socioeconomic conditions (Mooney & Lee, 1995). Studies should make efforts to identify the type of postsecondary policy

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innovation under investigation, and explicitly identify a mechanism of diffusion mostly likely to impact policy spread of the identiÞed policy type. Measures of Ideological Distance and Diffusion Pressures Postsecondary policy innovation research has assumed both explicitly and implicitly that the distance between two states (or some other network of states) impacts the likelihood a policy will diffuse. Those states that are closest—most often deÞned as sharing a physical border—are presumed to inßuence the policy actions of each other. This conceptualization, although methodologically convenient, unnecessarily limits the impact mechanisms of diffusion may have on policy spread. Rethinking distance in terms of state political ideology offers an alternative to geography-based measurements (Grossback, Nicholson-Crotty, & Peterson, 2004). State ideology may inßuence the spread of postsecondary policy. As policymakers consider new policy ideas, accurately positioning the idea in a state’s issue space is important. One way policymakers may assess the appropriate placement of a policy idea in their states’ issue space is by considering the ideological make-up of previously adopting states. When a state adopts a policy, it provides informational cues to other states that the policy is close to the adopting state’s ideological ideal point (or at least closer to this point than the status quo) (Grossback et al., 2004). Therefore, as states ideologically similar to potential adopter states enact the policy, the likelihood of adoption should increase because the considering state has received cues that the policy is likely to be a good ideological Þt within the state. Conversely, if ideologically dissimilar states adopt the policy, the cue reverses: policy adoption is less likely since it is assumed the policy is not a good ideological Þt for the state (Grossback et al., 2004). The consideration of ideology in previously adopting states serves as a proxy for gauging the political implications of adopting new policy, and moreover, does not limit policy learning (or other mechanisms of diffusion) to geographic contiguity—state governments can learn from any other state in the network being studied.1 Thinking in terms of ideological distance also builds on prior research Þndings in interesting ways. Extant postsecondary policy innovation scholarship has found that intrastate political conditions play a signiÞcant role in postsecondary policy adoptions. Investigating a diffusion process that relies on ideological distance attempts to reßect the interstate role of politics in the adoption and spread of postsecondary 1

Using ideological distance also has the advantage of allowing investigations into diffusion to retain Alaska and Hawaii in risk sets; these states are often removed given their lack of boarding states.

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policy. Determining how the exchange of ideological cues inßuences policy spread would add to the growing body of literature on the politics of higher education (Doyle, 2007) and is an additional response to calls for exploration into the inßuence of politics on postsecondary policymaking (McLendon, 2003; McLendon & Hearn, 2007). The Role of National and Regional Organizations in the Diffusion of Policy Notably absent from postsecondary policy innovation research is consideration of the role that national and regional organizations may play in the diffusion of policy. Organizations such as the National Governors Association (NGA), the National Council of State Legislators (NCSL), and the four regional higher education compacts exist in part to facilitate the distribution of policy ideas through advocacy and the education of policymakers. In addition to organizations, individual policy entrepreneurs have the capacity to operate in multiple jurisdictions and facilitate the spread of policy ideas (Mintrom, 1997). Capturing the inßuence of these political and advocacy organizations, and individuals, operating in multiple states offers a fertile area for study. Outside the domain of postsecondary policy, research has suggested interest group campaigns impact the diffusion of same-sex marriage bans (Haider-Markel, 2001) and have pointed to a signiÞcant impact of national associations on healthcare policy (Balla, 2001) and living-will laws (Hays & Glick, 1997) spread. Capturing the impact of national and regional organizations on policy spread in event history analysis models requires certain conditions exist. For quantitative approaches to analysis to be effective, consistent interaction points across the network of states being studied are needed. When data limitations call into question the appropriateness of quantitative methods, qualitative methodologies may be better suited to uncovering the inßuence of national/regional organizations and policy entrepreneurs on the spread of policies. Scholars should consider adapting their approach to research as necessary to capture the inßuence of multi-state actors on policy spread. Studying Diffusion Throughout the Policymaking Process There is ample reason to assume that policy diffusion is not solely evident at the adoption stage of policymaking. Policy ideas may diffuse in other stages of the policymaking process (Karch, 2004; Kingdon, 2003). A process-oriented framework for studying postsecondary policy diffusion could examine varying stages, including agenda setting, information generation, customization, and enactment or adoption (Karch, 2004). Distinguishing among these four stages could highlight how different

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mechanisms contribute to policy diffusion at differing points of the policymaking process. Interstate inßuences, for instance, may be more likely to elevate postsecondary policy ideas onto agendas, whereas the impact of intrastate inßuences may increase during later stages of the process as localized conditions become more valued in policy contests. Extending diffusion research beyond the adoption stage of the postsecondary policymaking process holds the promise of uncovering inßuences on the spread of policy ideas overlooked to this point. It may very well be that consideration of a new state-based Þnancial aid policy is driven by an emulative process of policy learning, and that this process is appropriately captured by taking into account neighboring state policy actions. However, questions of this nature have not been investigated in the domain of postsecondary education policy. Uncovering the conditions that lead to policy ideas being placed on state agendas would provide solid evidence on which to build future adoption and diffusion studies. Moreover, such research has the potential to explain whether the conditions that impact policy adoptions are the same as those that cause policy to appear on states agendas – an implicit yet untested assumption in published postsecondary policy innovation literature. Conclusion The study of policy innovations in the domain of postsecondary education policy has increased in recent years. Scholars from within the sphere of higher education are critically assessing the ability of traditional policy innovation theory to explain the passage and spread of new policy ideas. This article discussed four causal mechanisms of policy diffusion that can add to this emergent line of research. By precisely articulating why policy diffusion is likely to occur, scholarship may be elevated beyond loose notions of policy learning and corresponding assumptions that geographic-proximity is the most appropriate way to capture process of policy spread. Diffusion may occur because policymakers decide to imitate the policy choices of other states that share policy-relevant characteristics; because they decide to emulate a successful version of a policy from another jurisdiction; because they perceive interstate competition which requires enactment of a new policy; because of the coercive actions of other actors; or because of socialization pressures. Distinguishing among these processes is a task for postsecondary policy innovation research and is a critical element of efforts to describe postsecondary policymaking appropriately.

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References Anderson, J. L. (1997). Governmental suasion: Refocusing the Lowi policy typology. Policy Studies Journal, 25, 266-282. Bailey, M. A. (2005). Welfare and the multifaceted decision to move. The American Political Science Review, 99(1), 125-135. Balla, S. J. (2001). Interstate professional associations and the diffusion of policy innovations. American Politics Research, 29, 221-245. doi:10.1177/1532673X01293001 Berry, F. S. (1994). Sizing up state policy innovation research. Policy Studies Journal, 22(3), 442-456. Berry, F. S., & Berry, W. D. (1990). State lottery adoptions as policy innovations: An event history analysis. The American Political Science Review, 84(2), 395-415. Berry, F. S., & Berry, W. D. (1992). Tax innovation in states: Capitalizing on political opportunity. American Journal of Political Science, 36(3), 715742. Berry, F. S., & Berry, W. D. (2007). Innovation and diffusion models in policy research. In, P. Sabatier (Ed.), Theories of the Policy Process. (pp. 223-260). Nashville, TN: Westview. Berry, W. D., & Baybeck, B. (2005). Using geographic information systems to study interstate competition. The American Political Science Review, 99(4), 505-519. Boehmke, F. J., & Witmer, R. (2004). Disentangling diffusion: The effects of social learning and economic competition on state policy innovation and expansion. Political Research Quarterly, 57(1), 39-51. Box-Steffensmeir, J., & Jones, B. (2004). Event history modeling. New York, NY: Cambridge University Press. Brandeis, L. D. (1932). Dissenting opinion. New State Ice Co. v. Liebmann, 285 U.S. 262, 311. Crain, L. (1966). Fluoridation: Diffusion of an innovation among cities. Social Forces, 44(4), 467-476. Doyle, W. R. (2006). Adoption of merit-based student grant programs: An event history analysis. Educational Evaluation and Policy Analysis, 28(3), 259285. Doyle, W. R. (2007). Public opinion, partisan identiÞcation, and higher education policy. The Journal of Higher Education, 78(4), 369-401. Doyle, W. R., McLendon, M. K., & Hearn, J. C. (2005, November). The adoption of prepaid tuition and savings plans in the American states: An event history analysis. Paper presented at the annual meeting of the Association for the Study of Higher Education, Philadelphia, PA. Dye, T. R. (1990). Competition among governments. Lexington, MA: Lexington Books. Foster, J. L. (1978). Regionalism and innovation in the American states. Journal of Politics, 40, 179-187.

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Fowles, J. (2010). Modeling state decentralization of higher education governance: A hazard model approach. Unpublished doctoral dissertation, The University of Kentucky. Gray, V. (1973). Innovation in the states: A diffusion study. The American Political Science Review, 67(4), 1174-1185. Gray, V. (1994). Competition, emulation, and policy innovation. In L. C. Dodd & C. Jillson (Eds.), New Perspectives on American Politics. Washington, DC: Congressional Quarterly. Graham, E., Shipan, C. R., & Volden, C. (2008). The diffusion of policy diffusion research. Retrieved from http://psweb.sbs.ohio-state.edu/faculty/cvolden/ research.htm Grossback, L. J., Nicholas-Crotty, S., & Peterson, D. A. M. (2004). Ideology and learning in policy diffusion. American Politics Research, 32(5), 521-545. doi: 10.1177/1532673X04263801 Haider-Markel, D. P. (2001). Policy diffusion as a geographic expansion of the scope of political conßict: Same sex-marriage bans in the 1990’s. State Politics and Policy Quarterly, 1, 5-26. Hays, S. P., & Glick, H. R. (1997). The role of agenda setting in policy innovation: An event history analysis of living-will laws. American Politics Quarterly, 25, 497-516. Hearn, J. C., McLendon, M. K., & Mokher, C. G. (2008). Accounting for student success: An empirical analysis of the origins and spread of state student unitrecord systems. Research in Higher Education, 49, 665-683. Karch, A. (2004, September). Democratic laboratories: The politics of innovation in the American states. Paper presented at the Annual Meeting of the American Political Science Association, Chicago, IL. Karch, A. (2006). Democratic laboratories: Policy diffusion among the American states. Ann Arbor: University of Michigan Press. Karch, A. (2007). Emerging issues and future directions in state policy diffusion research. State Politics and Policy Quarterly, 7(1), 54-80. Kingdon, J. W. (2003). Agendas, alternatives, and public policies (2nd ed.). New York: Longman. Lieberman, R. C., & Shaw, G. M. (2000). Looking outward, looking inward: The politics of state welfare innovation under devolution. Political Research Quarterly, 53, 215-240. doi: 10.1177/106591290005300201 Lowi, T. J. (1964). American Business, Public Policy, Case Studies, and Political Theory. World Politics, 16, 677-715. McLendon, M. K. (2003). The politics of higher education: Toward an expanded research agenda. Education Policy, 17(1), 165-191. doi:10.1177/0895904802239291 McLendon, M.K., & Hearn, J.C. (2007). Incorporating political indicators into comparative-state research on postsecondary policy: Opportunities and limitations of space and time. In K. Shaw & D. E. Heller (Eds.), The challenges of comparative state-level higher education policy research. Sterling, VA: Stylus.

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McLendon, M. K., Heller, D. E., & Young, S. (2005). State postsecondary education policy innovation: Politics, competition, and the interstate migration of policy ideas. The Journal of Higher Education, 76(4), 363-400. McLendon, M. K., Hearn, J. C., & Deaton, R. (2006). Called to account: Analyzing the origins and spread of state performance-accountability policies for higher education. Educational Evaluation and Policy Analysis, 28(1), 1-24. doi:10.3102/01623737028001001 McLendon, M. K., Deaton, R., & Hearn, J. C. (2007). The enactment of state-level governance reforms for higher education: A test of the political-instability hypothesis. The Journal of Higher Education, 78(6), 645-675. McVoy, E. C. (1940). Patterns of diffusion in the United States. American Sociological Review, 5, 219-227. Mintrom, M. (1997). Policy entrepreneurs and the diffusion of innovations. American Journal of Political Science, 41(3), 738-770. Mokher, C. G., & McLendon, M. K. (2009). Uniting secondary and postsecondary education: An event history analysis of state adoption of dual enrollment policies. American Journal of Education, 115, 249-277. doi: 10.1086/595668 Mooney, C. Z. (2001). The public clash of private values: The politics of morality policy. In C. Z. Mooney (Ed.), The Public Clash of Private Values: The Politics of Morality Policy (pp. 3-20). Chatham, NJ: Chatham House. Mooney, C. Z., & Lee, M. (1995). Legislating morality in the American states: The case of pre-Roe abortion regulation reform. American Journal of Political Science, 39, 599-627. Roberts, R., & Dean, L. E. R. (1994). An inquiry into Lowi’s policy typology: The conservation coalition and the 1985 and 1990 Farm Bills. Environment and Planning C: Government and Policy, 12, 71-86. Peterson, P. E. (1981). City limits. Chicago, IL: University of Chicago Press. Peterson, P. E., & Rom, M. C. (1990). Welfare magnets: A case for a national standard. Washington, DC: Brookings Institution. Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis. Location: Oxford University Press. Shipan, C. R., & Volden, G. (2008). The mechanisms of policy diffusion. American Journal of Political Science, 52(4), 840-857. Sponsler, B. A. (2010). State adoption of undocumented student tuition policy: An Event History Analysis. Unpublished doctoral dissertation, The George Washington University. Tolbert, C. J. (2002). Rethinking Lowi’s constituent policy: Governance policy and direct democracy. Environment and Planning C: Government and Policy, 20, 75-93. Volden, G. (2002). The politics of competitive federalism: A race to the bottom in welfare beneÞts? American Journal of Political Science, 50(2), 352-363. Volden, G. (2006). States as policy laboratories: Emulating success in the Children’s Health Insurance Program. American Journal of Political Science, 50(2), 294-312.

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Volden, G., Ting, M., & Carpenter, D. P. (2008). A formal model of learning and policy diffusion. The American Political Science Review, 102(3), 319-332. Walker, J. L. (1969). Diffusion of innovations among the American states. The American Political Science Review, 63(3), 880-899. Winder, D. W., & LaPlant, J. L. (2000). State lawsuits against “Big Tobacco”: A test of diffusion theory. State and Local Government Review, 32, 132-141. Winter, J. P., & Eyal, C. H. (1981). Agenda setting for the Civil Rights issue. Public Opinion, 45(3), 376-383.

Brian Sponsler is a doctoral candidate at The George Washington University. He can be reached at [email protected]

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