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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

This article has been updated


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.


  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.


  1. Allison, P. D. (1995). Survival analysis using the SAS system: A practical guide. Cary, NC: SAS Institute Inc.

    Google Scholar 

  2. Allison, P. D. (2010). Survival analysis. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 413–424). New York: Routledge.

    Google Scholar 

  3. Alt, J. E., & Lowry, R. C. (2000). A dynamic model of state budget outcomes under divided partisan government. The Journal of Politics, 62(4), 1035–1069.

    Article  Google Scholar 

  4. Andersen, P. K., & Gill, R. D. (1982). Cox’s regression model for counting processes: A large sample study. The Annals of Statistics, 10(4), 1100–1120. doi:10.1214/aos/1176345976.

    Article  Google Scholar 

  5. Barrilleaux, C., & Berkman, M. (2003). Do governors matter? Budgeting rules and the politics of state policymaking. Political Research Quarterly, 56(4), 409–417. doi:10.1177/106591290305600403.

    Article  Google Scholar 

  6. Baybeck, B., Berry, W. D., & Siegel, D. A. (2011). A strategic theory of policy diffusion via intergovernmental competition. The Journal of Politics, 73(1), 232–247. doi:10.1017/S0022381610000988.

    Article  Google Scholar 

  7. Berry, F. S. (1994). Innovation in public management: The adoption of strategic planning. Public Administration Review, 54(4), 322–330.

    Article  Google Scholar 

  8. Berry, F. S., & Berry, W. D. (1990). State lottery adoption as policy innovation: An event history analysis. American Political Science Review, 84(2), 395–415.

    Article  Google Scholar 

  9. Berry, F. S., & Berry, W. D. (2007). Innovation and diffusion models in policy research. In P. A. Sabatier (Ed.), Theories of the policy process (2nd ed., pp. 223–260). Davis, CA: Westview Press.

    Google Scholar 

  10. Berry, W. D., Fording, R. C., Ringquist, E. J., Hanson, R. L., & Klarner, C. E. (2010). Measuring citizen and government in the U.S. states: A re-appraisal. State Politics & Policy Quarterly, 10(2), 117–135. doi:10.1177/153244001001000201.

    Article  Google Scholar 

  11. Berry, W. D., Ringquist, E. J., Fording, R. C., & Hanson, R. L. (1998). Measuring citizen and government ideology in the American states, 1960-93. American Journal of Political Science, 42(1), 327–348.

    Article  Google Scholar 

  12. Bowen, D., & Greene, Z. (2014). Legislative professionalism component scores, 1973–2011. Ewing, NJ: Department of Political Sciences, The College of New Jersey.

  13. Box-Steffensmeier, J. M., & Jones, B. S. (2004). Event history modeling: A guide for social scientists. New York: Cambridge University Press.

    Book  Google Scholar 

  14. Burke, J. C. (2002). Funding public colleges and universities for performance. Albany: Rockefeller Institute Press.

    Google Scholar 

  15. Cleves, M., Gutierrez, R. G., Gould, W., & Marchenko, Y. V. (2010). An introduction to survival analysis using Stata (3rd ed.). College Station, TX: Stata Press.

    Google Scholar 

  16. Cohen-Vogel, L., Ingle, W. K., Levine, A. A., & Spence, M. (2007). The “spread” of merit-based college aid: Politics, policy consortia, and interstate competition. Educational Policy, 22(3), 339–362. doi:10.1177/0895904807307059.

    Article  Google Scholar 

  17. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, 34(2), 187–220.

    Google Scholar 

  18. Diez, D. M. (2013). Survival Analysis in R. OpenIntro. Retrieved from

  19. Dougherty, K. J., Jones, S. M., Lahr, H., Natow, R. S., Pheatt, L., & Reddy, V. (2014a). Performance funding for higher education: Forms, origins, impacts, and futures. The ANNALS of the American Academy of Political and Social Science, 655(1), 163–184. doi:10.1177/0002716214541042.

    Article  Google Scholar 

  20. Dougherty, K. J., & Natow, R. S. (2015). The politics of performance funding for higher education: Origins, discontinuations, and transformations. Baltimore, MD: Johns Hopkins University Press.

    Google Scholar 

  21. Dougherty, K. J., Natow, R., Hare, R., Jones, S., & Vega, B. (2011). The politics of performance funding in eight states: Origins, demise, and change: Final report to Lumina Foundation for Education. New York: Community College Research Center. Retrieved from

  22. Dougherty, K. J., Natow, R. S., Jones, S. M., Lahr, H., Pheatt, L., & Reddy, V. (2014b). The political origins of performance funding 2.0 in Indiana, Ohio, and Tennessee: Theoretical perspectives and comparisons with performance funding 1.0 (CCRC Working Paper No. 68). New York: Community College Research Center. Retrieved from

  23. Dougherty, K. J., Natow, R., & Vega, B. (2012). Popular but unstable: Explaining why state performance funding systems in the United States often do not persist. Teachers College Record, 114(30301), 1–41.

    Google Scholar 

  24. Dougherty, K. J., & Reddy, V. (2013). Performance funding for higher education: What are the mechanisms? What are the impacts? ASHE Higher Education Report, Vol. 39, No. 2. Hoboken, NJ: Wiley.

  25. Doyle, W. R. (2006). Adoption of merit-based student grant programs: An event history analysis. Educational Evaluation and Policy Analysis, 28(3), 259–285.

    Article  Google Scholar 

  26. Eckles, J. E., & Stradley, E. G. (2011). A social network analysis of student retention using archival data. Social Psychology of Education, 15(2), 165–180. doi:10.1007/s11218-011-9173-z.

    Article  Google Scholar 

  27. Ezell, M. E., Land, K. C., & Cohen, L. E. (2003). Modeling multiple failure time data: A survey of variance-corrected proportional hazards models with empirical applications to arrest data. Sociological Methodology, 33, 111–167.

  28. Fox, J. (2002). Cox proportional-hazards regression for survival data: The Cox proportional-hazards model. Toronto: University of Toronto.

    Google Scholar 

  29. Glick, D. M., & Friedland, Z. (2014). How often do states study each other? Evidence of policy knowledge diffusion. American Politics Research, 42(6), 956–985. doi:10.1177/1532673X14521658.

    Article  Google Scholar 

  30. Hillman, N. W., Tandberg, D. A., & Fryar, A. H. (2015). Evaluating the impacts of “new” performance funding in higher education. Educational Evaluation and Policy Analysis, 37(4), 501–519. doi:10.3102/0162373714560224.

    Article  Google Scholar 

  31. Hillman, N. W., Tandberg, D. A., & Gross, J. P. K. (2014). Performance funding in higher education: Do financial incentives impact college completions? The Journal of Higher Education, 85(6), 826–857. doi:10.1353/jhe.2014.0031.

    Article  Google Scholar 

  32. Huber, J. D., Shipan, C. R., & Pfahler, M. (2001). Legislatures and statutory control of bureaucracy. American Journal of Political Science, 45(2), 330–345.

    Article  Google Scholar 

  33. Jenkins, S. P. (2005). Survival Analysis. University of Essex. Retrieved from

  34. Lacy, T. A., & Tandberg, D. A. (2014). Rethinking policy diffusion: The interstate spread of “finance innovations”. Research in Higher Education, 55, 627–649. doi:10.1007/s11162-014-9330-2.

    Article  Google Scholar 

  35. Lahr, H., Pheatt, L., Dougherty, K. J., Jones, S. M., Natow, R. S., & Reddy, V. (2014). Unintended impacts of performance funding on community colleges and universities in three states (CCRC Working Paper No. 78). New York: Community College Research Center. Retrieved from

  36. Li, A. Y. (2014). Performance funding in the states: An increasingly ubiquitous public policy for higher education. Higher Education in Review, 11, 1–29. Retrieved from

  37. Li, A. Y., & Zumeta, W. (2015). State support for higher education. In J. Huisman, H. de Boer, D. D. Dill, & M. Souto-Otero (Eds.), The Palgrave international handbook of higher education policy and governance (pp. 463–482). London, UK: Palgrave/Macmillan.

    Chapter  Google Scholar 

  38. Li, A. Y., & Zumeta, W. (2016). Performance funding on the ground: Campus responses and perspectives in two states. New York, NY: TIAA Institute. Retrieved from

  39. Makse, T., & Volden, C. (2011). The role of policy attributes in the diffusion of innovations. The Journal of Politics, 73(1), 108–124. doi:10.1017/S0022381610000903.

    Article  Google Scholar 

  40. McGuinness, A. C. (2003). Models of postsecondary education coordination and governance in the states. Denver, CO: Education Commission of the States.

    Google Scholar 

  41. 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.

    Article  Google Scholar 

  42. McLendon, M. K., Heller, D. E., & Young, S. P. (2005). State postsecondary policy innovation: Politics, competition, and the interstate migration of policy ideas. The Journal of Higher Education, 76(4), 363–400. doi:10.1353/jhe.2005.0029.

    Article  Google Scholar 

  43. McLendon, M. K., Tandberg, D. A., & Hillman, N. W. (2014). Financing college opportunity: Factors influencing state spending on student financial aid and campus appropriations, 1990 through 2010. The ANNALS of the American Academy of Political and Social Science, 655(1), 143–162. doi:10.1177/0002716214540849.

    Article  Google Scholar 

  44. Mooney, C. Z. (2001). Modeling regional effects on state policy diffusion. Political Research Quarterly, 54(1), 103–124. doi:10.1177/106591290105400106.

    Article  Google Scholar 

  45. National Conference of State Legislatures. (2015). Performance-based funding for higher education. Retrieved March 12, 2016 from

  46. Ohio Board of Regents. (2014). State share of instruction report. Columbus, OH: Ohio Board of Regents. Retrieved from

  47. Reddy, V. T., Lahr, H. E., Dougherty, K. J., Jones, S., Natow, R. S., & Pheatt, L. E. (2014). Policy instruments in service of performance funding: A study of performance funding in three states (CCRC Working Paper No. 75). New York: Community College Research Center. Retrieved from

  48. Rutherford, A., & Rabovsky, T. M. (2014). Evaluating impacts of performance funding policies on student outcomes in higher education. The ANNALS of the American Academy of Political and Social Science, 655(1), 185–208. doi:10.1177/0002716214541048.

    Article  Google Scholar 

  49. Sanford, T., & Hunter, J. (2011). Impact of performance-funding on retention and graduation rates. Education Policy Analysis Archives, 19(33), 1–30.

    Google Scholar 

  50. SHEEO. (2015). Regional interstate compacts for higher education. Retrieved from

  51. Shipan, C. R., & Volden, C. (2008). The mechanisms of policy diffusion. American Journal of Political Science, 52(4), 840–857. doi:10.1111/j.1540-5907.2008.00346.x.

    Article  Google Scholar 

  52. Snyder, M., & Fox, B. (2016). Driving better outcomes: Fiscal year 2016 state status and typology update. Washington, DC: HCM Strategists. Retrieved from

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

    Google Scholar 

  54. Squire, P. (2007). Measuring state legislative professionalism: The Squire index revisted. State Politics and Policy Quarterly, 7(2), 211–227. doi:10.1177/153244000700700208.

    Article  Google Scholar 

  55. Stine, R. A. (1995). Graphical interpretation of variance inflation factors. The American Statistician, 49(1), 53–56. doi:10.1080/00031305.1995.10476113.

    Google Scholar 

  56. Tandberg, D. A. (2010). Politics, interest groups and state funding of public higher education. Research in Higher Education, 51(5), 416–450. doi:10.1007/s11162-010-9164-5.

    Article  Google Scholar 

  57. Tandberg, D. A., & Hillman, N. W. (2014). State higher education performance funding: Data, outcomes, and policy implications. Journal of Education Finance, 39(1), 222–243. doi:10.1353/jef.2014.0007.

    Google Scholar 

  58. Tandberg, D. A., Hillman, N. W., & Barakat, M. (2014). State higher education performance funding for community colleges: Diverse effects and policy implications. Teacher’s College Record, 116(120307), 1–31.

    Google Scholar 

  59. Therneau, T. M., Grambsch, P. M., & Fleming, T. R. (1990). Martingale-based residuals for survival models. Biometrika, 77(1), 147–160.

    Article  Google Scholar 

  60. Volden, C. (2006). States as policy laboratories: Emulating success in the children’s health insurance program. American Journal of Political Science, 50(2), 294–312. doi:10.1111/j.1540-5907.2006.00185.x.

    Article  Google Scholar 

  61. Walker, J. L. (1969). The diffusion of innovations among the American states. American Political Science Review, 63, 880–899.

    Article  Google Scholar 

  62. Zumeta, W. (2009). State support of higher education: The roller coaster plunges downward yet again. Journal of Collective Bargaining in the Academy, 1(1), 1–15.

  63. Zumeta, W., Breneman, D. W., Callan, P. M., & Finney, J. E. (2012). Financing American higher education in the era of globalization. Cambridge, MA: Harvard Education Press.

    Google Scholar 

  64. Zumeta, W., & Li, A. Y. (2016). Assessing the underpinnings of performance funding 2.0: Will this dog hunt? New York, NY: TIAA Institute. Retrieved from

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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.

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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).

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