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Covet Thy Neighbor or “Reverse Policy Diffusion”? State Adoption of Performance Funding 2.0

An Erratum to this article was published on 12 July 2017

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Abstract

Performance funding has become an increasingly prevalent state policy to incentivize student retention and degree completion at public colleges. Using a Cox proportional hazards model on state-level data from years 2000 to 2013, this study analyzes the latest wave of policies that embed base appropriations into the state budget to fund student outcomes. Results indicate that having a greater proportion of bordering performance funding states diminishes the likelihood of policy adoption, capturing a “reverse policy diffusion” effect. States with Republican-controlled legislatures, more professionalized legislatures, and rapid growth in unemployment rates are more likely to adopt the policy, while those with higher educational attainment levels and more bachelor’s degrees awarded per student are less likely. Implications include the surprising finding of reverse policy diffusion, which suggests that states are delaying adoption until after they can observe the political consequences and impacts of the policy in neighboring states. Findings point to a policy learning effect—by observing other state’s experiences, policymakers can make more informed decisions about whether to pursue performance funding as an accountability tool.

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Change history

  • 12 July 2017

    An erratum to this article has been published.

Notes

  1. 1.

    Performance funding policy adoptions can occur through a legislative statute by the state, or sometimes within a public university system such as in the case of the Pennsylvania State System of Higher Education.

  2. 2.

    These operationalization choices were made for statistical reasons and to preserve power.

  3. 3.

    States with an independent governor (no party affiliation) were also coded as divided. Because of its unicameral legislature, Nebraska was excluded. Excluding Nebraska reduced the total number of policy adoptions in the dataset from 21 to 20.

  4. 4.

    Using the “distribution of votes in congressional races and ADA/COPE scores for members of Congress”, the citizen ideology measure captures the “ideological position of the electorate” (Berry et al. 1998, p. 118).

  5. 5.

    There were no instances of states that adopted, discontinued, and then re-adopted performance funding 2.0 between 2000 and 2013.

  6. 6.

    Performance funding 2.0 adoptions occurred more often in the later years of the dataset. Because the number of state adoptions was skewed towards these later years, I examined the severity of bias introduced in the models. I ran analyses using the Breslow’s method and the Efron’s method for handling ties (Cleves et al. 2010; Fox 2002). The directions of covariate effects were identical, and the sizes of covariate effects were substantively comparable.

  7. 7.

    Model fit was assessed using the Bayesian Information Criteria (BIC) (Allison 2010). Nested models were compared using the likelihood ratio test (Diez 2013; Jenkins 2005). Collinearity was assessed using the Variance Inflation Factor (VIF) by modeling time as the outcome in a regression framework (Stine 1995). In the reported models, the mean VIF value was 1.75, and ranged from 1.06 to 2.47, well below the problematic value of 10 or above (Eckles and Stradley 2011). In any case, multicollinearity is a minor concern in survival analysis because each unit change in a predictor has a multiplicative effect on the hazard rate, not a linear effect (Allison 2010).

  8. 8.

    If a covariate violates tests of the proportional hazards assumption, interacting the covariate with ln(time) can correct for non-proportionality. Earlier studies in the higher education policy literature that use the Cox model may have placed more weight on the proportionality assumption than necessary. “Although many researchers get very concerned about whether their data satisfy this assumption [of proportional hazards in the Cox model], I believe that these concerns are often unwarranted. If the assumption is violated for a particular predictor variable, it simply means that the coefficient for this variable represents a kind of “average” effect over the period of observation. For many applications, this may be sufficient.” (Allison 2010, p. 421). Applied to the study of higher education finance policies, this average effect is ample for extrapolating policy implications and substantive conclusions.

  9. 9.

    The Nelson-Aalen estimator generates the true cumulative hazard function, here the cumulative number of performance funding 2.0 adoptions over time, and for this purpose is preferred over the Kaplan–Meier estimator on small sample sizes (Jenkins 2005). Martingale residuals estimate the “difference between the observed event count over time [applies to the single-failure data setup in this study] and the model-based expected number of events” (Ezell et al. 2001, pp. 120–121). Residuals help assess model accuracy and are equivalent to random errors in a regression model (Therneau et al. 1990).

  10. 10.

    In alternative analyses examining raw levels of income and unemployment, a one percentage point increase in the unemployment rate led to a 57% higher likelihood of policy adoption (results not reported). This larger effect is attributed to the range of the unemployment rate variable, from a minimum of 2.3% to a maximum of 13.8%. The rate of change in unemployment ranged from −45 to 136%, and a one percentage point change is much smaller in relative terms.

  11. 11.

    This study examined whether regional higher education compacts were related to the adoption of performance funding 2.0. Interstate compacts hold meetings and discuss ways to improve quality, efficiency, and access to higher education (SHEEO 2015). The four compacts are: New England Board of Higher Education; Southern Regional Education Board; Midwestern Higher Education Compact; and Western Interstate Commission for Higher Education. New York, New Jersey, and Pennsylvania were grouped with the New England states. Results pointed to no regional effects.

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Acknowledgement

This research was supported by a Dissertation Grant from the American Educational Research Association which receives funds for its “AERA Grants Program” from the National Science Foundation under Grant #DRL-0941014. Opinions reflect those of the author and do not necessarily reflect those of the granting agencies. This research was supported in part by a research grant from the TIAA Institute, Grant #63-0151 and by the Department of Education, Institute of Education Sciences, Grant #R-305-B-090012. This study is the first of three dissertation papers on performance funding. An earlier version of this paper was presented at the 2015 conference of the Association for the Study of Higher Education in Denver, CO. The author thanks William Zumeta, Chris Adolph, Nick Hillman, Maresi Nerad, and Justin Marlowe for comments on earlier versions of this paper. The author also thanks discussant Jennifer Delaney for her comments at ASHE, as well as the feedback of two anonymous reviewers.

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Correspondence to Amy Y. Li.

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The original version of this article was revised: The mid initial of the author has been abbreviated.

An erratum to this article is available at https://doi.org/10.1007/s11162-017-9465-z.

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Li, A.Y. Covet Thy Neighbor or “Reverse Policy Diffusion”? State Adoption of Performance Funding 2.0. Res High Educ 58, 746–771 (2017). https://doi.org/10.1007/s11162-016-9444-9

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Keywords

  • Performance funding
  • Higher education finance
  • Policy diffusion
  • Policy learning
  • State policy adoption
  • Survival analysis