Policy Adoption,Innovation,and Performance Management: The Case of Performance-funding Policies in State Postsecondary Education

Abstract

Since the 1980s, nearly every state government has implemented some form of performance management. This article turns to the context of public higher education where the use of performance management has been popular but highly controversial and unstable. Using the Cox conditional gap time model for repeating events and state-level panel data, this article investigates the factors associated with the adoption and readoption of performance-based funding policies for public higher education. Results indicate that state higher education governing structures, increases in public tuition, and educational attainment are important predictors of whether a state adopts performance-based funding.

Keywords

performance management accountability higher education state government policy adoption

Performance management continues to dominate many of the scholarly and policy-focused debates over public-sector reform (Moynihan et al. 2010), and the addition of public higher education into this space has created new opportunities for scholarly work. State performance–funding policies for public higher education allocate a portion of an institution’s funding on the basis of student achievement, institution efficiency, and productivity. The policy signals a dramatic shift in the relationship between state governments and public higher education, as the traditional enrollment-based allocation models afforded institutions considerable autonomy (Alexander 2000). By tying funding to specific measures and outcomes, state governments are reducing university autonomy and asserting their desire to shift university priorities away from non-outcome-oriented activities, such as research, toward outcomes relating to undergraduate education (Rabovsky 2014).

While performance funding is relatively popular among the states, with nearly half adopting and implementing the policy, its implementation has been volatile and controversial. A period of rapid adoption in the 1990s was followed by a period of stagnation in the early 2000s as diffusion slowed and several states abandoned their policies (Dougherty, Natow, and Vega 2012). In recent years, however, the policy is making a comeback as several states that once abandoned the policy have readopted it, while others are adopting the policy for the first time or transitioning to more stringent variations (National Conference of State Legislatures [NCSL] 2015).

This turbulent pattern of the adoption of performance funding policies among the states provides an opportunity to explore the factors that lead some states, but not others, to adopt (and readopt) polices. Additionally, the emerging evidence from empirical work suggesting the policy does not improve, and may hurt, institution performance (e.g., Birdsall 2018; Fryar 2011; Rutherford and Rabovsky 2014; Tandberg and Hillman 2014) provides further reason to revisit why states adopt performance-funding policies for public higher education. Thus, this article seeks to answer the following research question: Why do states adopt performance-funding policies for public higher education?

The Empirical Context: State-provided Postsecondary Education

The adoption and implementation of performance management in public higher education has generated considerable interest and controversy among both practitioners and researchers. State governments historically have allocated funding to public colleges and universities based on enrollment. Performance funding directly ties a portion of state funding to institution performance according to a variety of indicators relating to student success and institutional efficiency and productivity (Burke 2002).

While the policy is not new—Tennessee adopted the first policy in 1979 (Burke 2002)—the parallel emergence of the new politics of public higher education and the reinventing government movement in the early 1990s set the conditions for renewed interest in the policy. Across the country, there was a perception that while costs and tuition continued to rise, productivity, administrative efficiency, and quality in public higher education were in decline (Burke 2002; Dougherty et al. 2013). In particular, it was believed that undergraduate education was being neglected, while institutions focused on graduate studies and research (Burke 2002).

As Moynihan (2008) has pointed out, inadequate resources and weak commitment to performance management reforms often lead to their demise. This appears to have been the case with performance funding for public higher education polices, as several states in the early 2000s abandoned their policies due to strong opposition in the academic community, and waning support on the part of governors (Burke and Modarresi 2001; Dougherty, Natow, and Vega 2012). The decline of performance-funding policies, however, was short-lived, as states that once abandoned policies are readopting them, several more states are enacting legislation—or are in formal discussions—to adopt the policies for the first time, and some states with well-established policies are in the process of transitioning to more stringent variations (NCSL 2015). Tennessee, for example, allocates nearly all its state appropriations for public higher education based on performance (Hillman, Fryar, and Crespín-Trujillo 2018).

The Atypical Diffusion of Performance-based Funding among the States

Understanding why public-sector organizations adopt or abandon policy and management innovations, practices, and reforms is essential for explaining large-scale developments in the public sector (Andersen and Jakobsen 2018). State and local governments have adopted and implemented a wide range of policies and reforms in response to a number of factors challenging their survival and legitimacy, such as climate change, economic crises, shrinking budgets, rapid demographic changes, among others (Andersen and Jakobsen 2018; De Vries, Tummers, and Bekkers 2018). Increasingly, public-sector organizations are adopting and implementing these reforms in response to political pressure (Andersen and Jakobsen 2018). In some cases, such as in public higher education, these reforms may come as top-down mandates from political bodies. Public administration and policy researchers seeking to understand why and when governments adopt these policies rely on a set of empirical tools first developed by Walker (1969) and Gray (1973) but institutionalized by Berry and Berry’s (1990) study of state lottery adoptions.

Generally, studies have found that policy adoption by one jurisdiction can influence adoption by other jurisdictions through processes of learning, imitation, normative pressure, competition, and coercion (Berry and Berry 2018). Additionally, scholars find that internal characteristics such as strength of the economy, political ideology, and demographic composition are important predictors of adoption (Berry and Berry 2018).

A frequently demonstrated and discussed feature of the policy adoption and diffusion process is its s-curve pattern over time (Berry and Berry 2018). Diffusion is slow and infrequent in the beginning but subsequently accelerates dramatically over time before tapering off as the pool of potential adopters shrinks (Berry and Berry 2018). An important limitation of the s-curve conception is it assumes the adoption process is over once a jurisdiction adopts a policy, which, as B. S. Jones and Branton (2005) point out, is often an incomplete assumption. After adopting a policy, for example, a jurisdiction may subsequently adopt a series of related policies or abandon the policy and subsequently readopt the policy. The latter is precisely what has made performance funding’s diffusion process so puzzling and interesting. In particular, the case of performance funding is a powerful illustration of the importance of ebbs and flows in both power and interest in the policy process.

Without sustained support and resources, extant policy is at risk of being altered dramatically or abandoned. In the case of performance funding, several of the early adopting states subsequently abandoned policies (Dougherty, Natow, and Vega 2012). The reasons for abandonment vary by state but include loss of support due to retiring political principals, base of support falling largely outside the higher education community, perceived threats to campus autonomy, and cost and budget instability for universities (Burke 2002; Dougherty, Natow, and Vega 2012). While the abandonment of a policy by many states is notable, the more remarkable feature of the policy process is that several of these states subsequently readopted performance funding.

A previous study by McLendon, Hearn, and Deaton (2006) investigated the correlates of adoption for states implementing performance funding through 2002, finding that states with higher education coordinating boards and Republican-controlled legislatures are more likely to adopt the policy. However, much activity has occurred in this space since that paper was published, and it does not account for the phenomenon of abandonment and readoption. More recently, Li (2017) focuses on the adoption of performance funding 2.0, a more stringent variant of the policy, in the period after the McLendon, Hearn, and Deaton’s (2006) study finding that states with Republican-controlled legislatures are more likely to adopt performance funding, while states with bordering neighbors who previously adopted performance funding are less likely to adopt. This article extends the analyses by McLendon, Hearn, and Deaton (2006) and Li (2017) by accounting for both the adoption and readoption of abandoned policies using the Cox conditional gap time model for repeating events.

Why did states that once abandoned performance funding subsequently readopt? The research on readoption, specifically, is still in its early stages, but interviews by S. M. Jones et al. (2015) suggest pressures from the Great Recession (2007–2009) on state budgets led state policy makers to again place greater scrutiny on higher education appropriations. Additionally, increasing the number of state residents with baccalaureate degrees was seen as essential for meeting the needs of the modern economy (S. Jones 2015). Essentially, many of the reasons states adopted—and some readopted—in the late 2000s were the same as the reasons for the initial adoptions in the early 1990s. This pattern of abandonment and readoption illustrates the ebbs and flows of the determinants of adoption. The methodology used in this article allows researchers to incorporate more information about the policy life cycle to better understand the influence of factors associated with adoption.

Methods and Data

Following previous research on state policy adoption, this study uses event history analysis to examine the factors that influence states to adopt performance funding for higher education policies. Specifically, the Cox conditional gap time model is used, which is well suited to examining the adoption process of performance funding, as it accommodates repeat adoptions (Box-Steffensmeier and Zorn 2002; B. S. Jones and Branton 2005). The model stratifies based on event sequence, controlling for the ordering of adoptions and allowing the baseline hazard to vary between adoptions (B. S. Jones and Branton 2005). Allowing the baseline hazard to vary acknowledges that the baseline hazard for adopting the policy is likely to be different if a state adopted it previously.

The data set includes 1,056 observations, with data for forty-eight states over the period of 1990–2011. Nebraska was omitted because its unicameral, nonpartisan legislature precludes measurement of the effect of partisan strength on the likelihood of policy adoption. Tennessee was omitted because it adopted the policy in 1979, which is too early to be included in the study with sufficient data. The paragraphs that follow describe the variables used to test the determinants as well as the theoretical justification for including them. (Detailed descriptions of the variables, their sources, and descriptive statistics are available online in Supplemental Tables 1 and 2.)

Dependent Variable: Adoption of Performance Funding

The dependent variable is a dichotomous indicator coded as 1 if a state adopted performance funding for the first time or readopted the policy after abandoning it. The study primarily relies on data from the NCSL, previous research (Burke and Modarresi 2000, 2001; Burke and Minassians 2003; Dougherty et al. 2013; Rabovsky 2012; Rutherford and Rabovksy 2014; Tandberg and Hillman 2014), and source documents from state governments to identify when a state adopted performance-based funding (a listing of state adoptions by year is available online in Supplemental Table 3).

Higher Education Governing Structures

Each state has a higher education governing board charged with overseeing its public higher education institutions (Lowry 2001). However, the policy-making power and centralization of these governing boards vary considerably, with important implications for the relationship between public higher education institutions and state legislatures, as well as for the direction of state higher education policy (Hicklin and Meier 2008; Knott and Payne 2004; Lowry 2001, 2007; McLendon 2003; McLendon, Hearn, and Deaton 2006; McLendon, Heller, and Young 2005; Nicholson-Crotty and Meier 2003; Tandberg 2013). Higher education researchers generally draw a distinction between consolidated boards and coordinating boards (sometimes also called planning agencies). Consolidated governing boards have centralized decision-making authority, more formal authority, professional staffs, and high autonomy, allowing them to play a central role in developing and implementing public higher education policy (Hicklin and Meier 2008; Lowry 2001). Coordinating boards, on the other hand, are decentralized and have little autonomy, with the scope of their responsibilities essentially limited to acting as an interface between individual public higher education institutions and state government (Nicholson-Crotty and Meier 2003). As Hicklin and Meier (2008) point out, coordinating boards tend to be more likely to implement legislative preferences compared to consolidated boards.

It is useful to think about the relationship between public higher education governing boards and state governments in terms of principal–agent theory (Lowry 2001, 2007; McLendon 2003; Nicholson-Crotty and Meier 2003). Principal–agent theory views the relationship between the principal—who delegates work—and the agent—the party to whom the work is delegated—through the metaphor of a contract (Jensen and Meckling 1976; Eisenhardt 1989). Two primary problems arising from an agency relationship are (1) divergent goals and desires between the principal and agent and (2) the difficulty and expense involved for the principal in verifying that the agent is performing duties as expected and not shirking responsibilities (Eisenhardt 1989). Placing the first agency problem in the context of public higher education, elected officials (principals) may value public higher education in terms of its potential to advance state economic interests through increasing degree production and educational attainment, while higher education institutions (agents) may place higher value on advancing research and graduate studies. In terms of the second problem, a decentralized governing board may introduce significant information asymmetry between elected officials and university administrators. Principal–agent problems like these often create the conditions that make performance accountability systems attractive policy alternatives for policy makers (Heinrich and Marschke 2010).

Following previous research, the variable is coded as 1 if a state has a consolidated governing board and 0 if it has a coordinating board or planning agency (Hicklin and Meier 2008; Lowry 2001; Nicholson-Crotty and Meier 2003; McLendon, Heller, and Young 2005; McLendon, Hearn, and Deaton 2006). It is expected states with consolidated boards to be less likely to adopt performance-funding policies.

Symbolic Benefits: Responding to Increases in Tuition?

As Moynihan (2008) points out, an important symbolic benefit of passing performance management reform is creating a perception that something is being done to control bureaucracies that appear to be spending too much public money without accountability for performance. Tuition increases at public higher education institutions arouse concern among the public and elected officials and invite greater scrutiny of how institutions use their resources (Zumeta 2001). Additionally, previous research has found that state legislators are sensitive to tuition increases and may reduce appropriations to penalize higher education institutions (Tandberg 2010). Thus, spikes in public tuition may increase the likelihood of policy adoption as elected officials seek policy alternatives for responding to public demand for accountability in public higher education. Changes in tuition are measured as the three-year average percent change for each state’s flagship institution, lagged one year.

State Financial Support for Higher Education

Declining state financial support for public higher education is widely regarded as an important factor in sparking the accountability movement in higher education (Alexander 2000; Zumeta 2001; McLendon, Hearn, and Deaton 2006). Additionally, greater state financial support for public higher education may indicate more goal congruence between elected officials and public higher education administrators. Consequently, it is expected states maintaining higher levels of spending on public higher education are less likely to have the conditions that lead state officials to adopt a performance-based funding policy. The study uses state expenditures for higher education as a percentage of gross state product (GSP) as a measure of state financial support.

Past Performance: Degree Attainment

One of the drivers of the performance-funding movement in public higher education is concern over degree attainment (Fryar 2011). Indeed, at least forty-one states have specific goals for increasing educational attainment, making it a critical metric influencing decisions about higher education policy in the states (Lumina Foundation 2018). Officials in states that adopt performance-funding policies may be motivated by perceptions of poor performance by their public higher education institutions, which can be captured by institutions’ historical ability to graduate students.

The study tests two measures of degree attainment that may motivate policy makers to implement performance funding. First, it includes total degrees awarded (undergraduate and graduate) as a percentage of total enrollment across all public higher institutions. Second, it includes the percentage of adults aged twenty-five or older with a bachelor’s degree or higher. While total degrees awarded as a percentage of total enrollment may be a more direct measure of states’ public higher education systems, broader educational attainment goals tend to dominate state-level debates about public higher education policy (Perna and Finney 2014).

Socioeconomic Factors

Public higher education is often considered important to developing the human capital necessary for states to be competitive in an increasingly technology- and information-driven global economy (Alexander 2000). Thus, elected officials may be motivated to adopt performance-based funding policies due to a perceived instrumental benefit of increasing the performance of their public higher education institutions to enhance their states’ economies (Burke 2002; McLendon, Hearn, and Deaton 2006). Officials in states with declining per capita personal income (PCI) and high unemployment in particular may be motivated to adopt a policy with the potential to increase public higher education performance and boost human capital. The paper uses a three-year average percent change to measure changes in PCI and the percentage of unemployed work-seeking residents in a state to measure unemployment.

State Partisan Composition

Recent research has found a preference among Republican-elected officials for performance-funding policies (Dougherty et al. 2013; McLendon, Hearn, and Deaton 2006; Li 2017). In general, studies of policy diffusion and innovation have found partisan control of state government to be an important predictor of policy choices (Berry 1994). Thus, it is expected states with higher percentages of Republicans serving in the legislature and a Republican governor to be more likely to adopt performance funding.

Legislative Professionalism

An additional factor that may be an important predictor of policy adoption is legislative professionalism. States with greater staff capacity, longer legislative sessions, and higher legislator pay may have more educated legislators, a greater capacity to deal with complex policy issues, and are generally thought to be more likely to innovate (Berry 1994; Squire 2007). Legislative professionalism measures the capacity of a legislature to deal with complex policy issues that incorporate factors such as session length, member pay, and staff resources (Squire 2007). States are scored on their similarity with Congress according to these characteristics on a scale where 1 represents perfect resemblance to Congress and 0 indicates no resemblance.

In the context of performance-funding policies, however, two factors suggest more professionalized legislatures may be less likely to adopt performance-funding policies. First, more professionalized legislatures tend to exhibit greater sympathy for public higher education and appropriate greater shares of their states’ budgets to public higher education (Tandberg 2010). Second, legislative professionalism may reduce information asymmetries, as greater staff resources and longer sessions will enhance the ability of legislators to interact with public higher education bureaucracies and become informed about their activities (Nicholson-Crotty and Meier 2003). Thus, it is expected states with more professional legislatures to be less likely to adopt performance funding.

Regional Diffusion

State policy adoption research has long studied the propensity of states to borrow policy ideas from their neighbors (Walker 1969; Berry and Berry 1990; McLendon, Heller, and Young 2005; McLendon, Hearn, and Deaton 2006). It is generally thought that the likelihood of a state adopting a policy is greater when the policy has been adopted by neighboring states (Walker 1969). Previous qualitative research has found that states adopting performance-funding policies often legitimate adoption based on the experiences of other states (Dougherty et al. 2013).

This study loosens the definition of neighbor to include states that share a U.S. Census subregion, in addition to border-sharing states. The advantage of this operationalization of diffusion is that it acknowledges the importance of spatial proximity in policy adoption but also recognizes a meaningful grouping of states beyond shared borders. The measure may help capture the influence of various region-based policy associations such as regional higher education associations. This measure also rescues Alaska and Hawaii—states commonly omitted from state policy adoption studies (Berry and Berry 1990; McLendon, Hearn, and Deaton 2006)—from omission. It is expected states to be more likely to adopt performance funding if a high percentage of their regional neighbors have already adopted the policy.

Findings

The results of the Cox conditional gap time model are presented in Table 1 with exponentiated coefficients. The results show that state higher education governing structure, changes in public tuition rates, educational attainment, legislative professionalism, state support for public higher education, changes in PCI, and the percentage of previously adopting neighbor states are statistically significant predictors of state adoption of performance-funding policies. The results of Grambsch and Therneau tests indicate that the model satisfies the proportional hazards assumption.¹

Table 1.
Results.

Adoption of Performance Funding

β SE

Consolidated governing board 0.255 .153

Growth in tuition 1.075* .029

Completion rate 1.022 .101

Educational attainment 0.929* .041

Republican governor 0.275 .336

Percent republicans in legislature 1.004 .021

Republican Governor × Percent Republican in legislature 1.032 .025

Legislative professionalism 0.112* .143

State support for higher education 0.738* .129

Change in PCI 1.293* .181

Unemployment 1.156 .151

Diffusion 0.978*** .008

Observations 1,056

Log likelihood −149.320

χ² 32.761

Note: Exponentiated coefficients; Efron method for ties. PCI = per capita income; SE = standard error.

p < .10.

p < .05.

p < .01.

First, the results suggest that states with consolidated governing boards are less likely to adopt performance-funding policies than states with coordinating boards. Specifically, states with consolidated governing boards are 75 percent less likely to adopt performance funding than states with coordinating boards, holding other covariates constant.

Research on the politics of state public higher education frequently emphasizes the importance of state higher education governing boards. The characteristics of consolidated boards may help reduce information asymmetries and the likelihood that a performance-funding policy would be considered in the first place. As Moynihan (2008) points out, elected officials often turn to performance accountability in cases where there is uncertainty about bureaucracies’ actual goals and levels of productivity. As boundary-spanning organizations (Tandberg 2013), consolidated governing boards provide a single outlet through which elected officials and university administrators may communicate their goals, values, and activities. The greater analytic capacities of governing boards may also be important in this regard.

The results also show that tuition increases at a state’s flagship university increase the likelihood of policy adoption. The exponentiated coefficient shows that a 1-percentage point increase in tuition is associated with a 7.5 percent increase in the probability of adopting a performance-funding policy, holding the other covariates constant. The result suggests spikes in tuition may increase demand for accountability and greater efficiency and effectiveness on the part of public higher education institutions. As Zumeta (2001) has pointed out, the general perception among elected officials and the public has been that, rather than raise tuition, public higher education institutions ought to find more revenues by improving efficiency and effectiveness, suggesting a lack of fiscal responsibility on the part of public higher education administrators. Thus, imposing stringent performance accountability policies on organizations the public perceives as fiscally irresponsible may be an attractive prospect for elected officials seeking to secure symbolic benefits.

The results show that a 1-percentage point increase in state educational attainment is associated with a 7.1 percent decrease in the likelihood of adopting a performance-funding policy. This result provides some evidence that elected officials are influenced to adopt the potential instrumental benefits of the policy. Most states have goals to increase educational attainment, and state officials may see performance funding as a way to increase educational attainment and enhance their economic position relative to other states (Fryar 2011).

The results suggest that partisan politics do not play an important role in the adoption of performance-funding polices, as neither the percentage of Republicans in a state legislature and the presence of a Republican governor nor their interaction is associated with the adoption of performance funding. While previous research has suggested and empirically demonstrated an association between the percentage of Republicans in the legislature and the adoption of performance-funding policies (McLendon, Hearn, and Deaton 2006; Dougherty et al. 2013; Li 2017), the results of this study suggest that, even if partisan politics are behind the adoption of performance-funding policies in certain cases, there is not a systematic difference between the parties in terms of their preference for the policy in general.

While the partisan composition of the legislature does not appear to be an important factor, the results suggest that more professional legislatures are less likely to adopt performance funding. Specifically, the results suggest that a state with a legislature perfectly resembling Congress in its legislative staff capacity and resources (1 on a scale of 0–1) is 88.8 percent less likely to adopt a policy than a state with a legislature bearing no professional resemblance to Congress.

In addition to professionalism, the results suggest a state’s commitment to funding public higher education is associated with whether a state adopts performance funding. Specifically, the results show that a 1-percentage point increase in public higher education funding as a percentage of GSP is associated with a 27 percent decrease in the likelihood of adopting a performance-funding policy.

Finally, the results indicate that states with greater percentages of neighboring states adopting performance-based funding policies are less likely to adopt the policy, contrary to the hypothesized direction. The results show that a 1-percentage point increase of neighboring states adopting the policy is associated with 2.2 percent decrease in the probability of adopting a policy. This result is consistent with Li’s (2017) finding that, in the case of performance funding 2.0, states with previously adopting bordering neighbors are less likely to adopt the policy. While policy diffusion theory suggests that proximity to previously adopting states generally increases the probability of adoption, Mooney (2001) contends there is no single story explaining diffusion. In particular, if a neighbor’s policy is seen as a failure, a state may be less likely to adopt a similar policy (Mooney 2001). Thus, in the case of performance-based funding, the negative result suggests that some states may be less inclined to adopt the policy in observing the instability of their regional neighbors’ policies.

Discussion and Conclusions

The findings of this article suggest the importance of governing structures to accountability relationships between political principals and public higher education bureaucracies. The findings also provide evidence that officials may be influenced by the prospect of securing symbolic benefits associated with holding institutions accountable for performance during periods of high tuition increases, which may increase public scrutiny of spending on public higher education (Zumeta 2001). The findings also provide some evidence that policy makers see performance funding as a way to increase educational attainment.

The findings have several limitations. First, the findings on state higher education governing boards are limited by the dichotomous nature of the variable used in this study. Factors that may affect interinstitutional relationships—such as board member tenure, composition, and appointment processes—are not directly captured by this measure. These attributes may be important for understanding whether consolidated boards insulate public higher education from political influence or whether they negate the perceived need for performance accountability by reducing information asymmetries. Future research should further investigate the role of higher education governing boards as boundary-spanning organizations that potentially improve the relationship between state government and public higher education institutions.

Why governments impose performance accountability systems is an important question that may have implications for their quality and longevity. Specifically, whether it is in pursuit of symbolic or instrumental benefits may be important. As Moynihan (2004) points out, these are not necessarily mutually exclusive. If, however, symbolic benefits are the overriding goal, it may affect the extent to which policy makers are committed to the policy in the long term as well as how and whether the subjects of the policy cooperate and make changes to increase organization performance.

This article presents evidence that, in the context of public higher education, both considerations may factor into policy makers’ decisions to pursue a policy. Several papers attempt to evaluate the effectiveness of performance funding policies for public higher education, but little consideration is given to the structure of the policies and the circumstances of their adoption. Thus, an important contribution for future research is investigating whether the antecedents of the adoption of performance funding policies are associated with their effectiveness.

Supplemental Material

Supplemental Material, SLGR_17-0078R3,_Table_Supplement - Policy Adoption, Innovation, and Performance Management: The Case of Performance-funding Policies in State Postsecondary Education

Supplemental Material, SLGR_17-0078R3,_Table_Supplement for Policy Adoption, Innovation, and Performance Management: The Case of Performance-funding Policies in State Postsecondary Education by Chris Birdsall in State and Local Government Review

	Adoption of Performance Funding
Consolidated governing board	0.255**	.153
Growth in tuition	1.075***	.029
Completion rate	1.022	.101
Educational attainment	0.929*	.041
Republican governor	0.275	.336
Percent republicans in legislature	1.004	.021
Republican Governor × Percent Republican in legislature	1.032	.025
Legislative professionalism	0.112*	.143
State support for higher education	0.738*	.129
Change in PCI	1.293*	.181
Unemployment	1.156	.151
Diffusion	0.978***	.008
Observations	1,056
Log likelihood	−149.320
χ²	32.761

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Chris Birdsall

Supplemental Material

Supplemental material for this article is available online.

Note

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