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Rethinking Policy Diffusion: The Interstate Spread of “Finance Innovations”

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Abstract

Recently a number of studies have focused on states' adoptions of postsecondary-specific policies. Cutting across much of this research is the presence and influence of interstate diffusion of policy adoptions, a phenomenon for which support is scant. This paper seeks to address this through broadening the categorization of policies beyond the discrete form traditionally used to one that encompasses a larger conception of "finance policy." Our sample uses 131 finance innovations for 47 states over a 29 year period, finding that upon broadening our definition, we can detect the process of diffusion. However, the findings are striking, showing that while states do learn from one another, the process is dynamic and shifts across time.

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Notes

  1. This is perhaps best described in the final report of the 1947 President’s Commission on Higher Education.

  2. As noted by Boehmke and Skinner (2012), the tendency to focus on discrete, single policies is also a phenomenon in the political science field.

  3. See McLendon et al. (2006) for Performance Funding; Doyle et al. (2010) for 529 Savings Plans and Prepaid tuition; Deaton (2006) and Warne (2008) for Tuition Decentralization; and Doyle (2006) for Merit Aid.

  4. In the case of prepaid tuition, this is limited to publics.

  5. Following 1999, the federal government hastened the adoption of 529 plans and by 2002 every state but Washington had adopted a plan.

  6. While the first performance funding policy was implemented in the late 1970s (Tennessee) it was not until the 1990s that widespread adoption occurred. Prior to 1990, only Tennessee and Connecticut had a performance funding policy in place, a number that would expand to 26 by 2003.

  7. Since the 1990s increases in the amount of money devoted to merit aid programs have far outpaced any increases in other state financial aid programs (National Association of State Student Grant and Aid Programs 2010).

  8. We also owe a debt to the work of Michael Mintrom who examined the broad category of “school choice” in K-12 education. See Mintrom and Vergari (1998), Mintrom (2000).

  9. We include state flagship as they are typically the most visible of all state institutions and, practically, Wyoming’s only 4-year institution is its state flagship.

  10. A cataloging of these sources can be found in Appendix 1.

  11. This and Nebraska’s two innovations do not affect our measure of total adoptions by neighboring states.

  12. Every state except Washington adopted a saving plan.

  13. This information is presented in tabular form in Appendix 3.

  14. Recall that these values are adjusted to the consumer price index, which partially explains these early declines.

  15. For example, income and the frequently used variable measuring the percentage over adults over the age of 25 with a Bachelor’s Degree have a correlation of 0.79.

  16. Other higher education researchers have used this broad family of estimators to study student outcomes, typically in the form of persistence/completion/departure. See, for example, Chen and DesJardins (2008); DesJardins et al. (2002); Ishitani (2006).

  17. For example, one could conceive of this process as a very slow poisson process or competing, alternative events. As we conceptually argue that these policies are from a broader family, we consider them similar events. Further, we prefer the duration model as it enables us to gain leverage over the temporal nature of this process.

  18. The Cox Proportional Hazards model has several important attributes that make it attractive for repeating events structures. For a detailed discussion for social scientists, see Jones and Branton (2005) and Box-Steffensmeier and Zorn (2002).

  19. Researchers can address this violation in a number of ways. However, because time has conceptual importance to our study we explicitly incorporate in our model.

  20. When interpreting covariates across models , it is of particular importance to take care in the interaction term. This is due to our need to interpret the interaction term and its 95 % CIs as combined effects.

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  1. The University of North Carolina System Office, Chapel Hill, NC, USA

    T. Austin Lacy

  2. Florida State University, Tallahassee, FL, USA

    David A. Tandberg

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Correspondence to T. Austin Lacy.

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An earlier version of this paper was presented at the annual meeting of the Association for the Study of Higher Education, Charlotte, NC. We thank Jim Hearn and Michael McLendon for their comments on an early draft and continued support. All errors contained are our own.

Appendices

Appendix 1

See Table 4

Table 4 Policy sources table
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Appendix 2

See Table 5.

Table 5 State policy adoptions
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Appendix 3

See Table 6.

Table 6 Independent variable descriptions and sources
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Lacy, T.A., Tandberg, D.A. Rethinking Policy Diffusion: The Interstate Spread of “Finance Innovations”. Res High Educ 55, 627–649 (2014). https://doi.org/10.1007/s11162-014-9330-2

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