Will the Tide Lift All Boats? Examining the Equity Effects of Performance Funding Policies in U.S. Higher Education


This study considers whether performance funding policies systematically tend to harm some types of institutions of higher education while helping others. Building on theories of deck stacking and institutional stratification, a formal theoretical model of the effects of performance funding policies on individual institutions is developed and discussed. We find two types of likely policy effects—one which serves to improve overall institutional performance and another which exacerbates unevenness among institutions in terms of quality. We then conduct an initial empirical test of our theory, analyzing a cross-sectional time-series dataset of colleges and universities in the U.S. Our findings are somewhat mixed. The adoption of performance funding policies appears to have the ability to boost overall average levels of degree production in some instances. However, performance funding 2.0 policies are also associated with larger variance in degree production rates. We find some evidence that 2.0 policies also have heterogeneous effects on graduation and retention rates, whereby the benefits of these policies disproportionately accrue to institutions already positioned to perform well.

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Fig. 1


  1. 1.

    Equation (2) implies that all institutions receive an equal amount of resources (funding per student) in the absence of a performance funding policy. Our model can still accommodate variation in funding levels (due to reasons other than performance funding) if we consider such variation to be a part of the \( {x_{i,t}} \) term in Eq. (1) rather than a part of the resources term (\( {r_{i,t}} \)).

  2. 2.

    Some smaller states have very few institutions of higher education, which may lessen concerns that performance funding policies will exacerbate inequality among institutions. Instead, the main sources of inequality will probably be the K-12 education system or uneven distribution of resources within a university or college.

  3. 3.

    Because Eq. (3) assumes that the size of the effect of a performance funding policy on institutional behavior depends (linearly) on the strength of the performance-based component of the allocation formula (or the degree to which funding levels vary based on past performance, as expressed in \( \alpha \)), increasing the intensity of the performance funding policy (\( \alpha \)) will increase both \( \theta \) and \( \tau \). We do not emphasize this aspect of the model, however, and could have easily obtained Eq. (5) under the assumption that \( \tau \) is fixed for all performance funding policies regardless of the magnitude of \( \alpha \); under such an alternative derivation, \( \tau \) would be interpreted as a fixed response to the presence of any performance policy. Under the formulation presented in the manuscript, \( \tau \) represents the response to a one-unit increase in the intensity of a performance funding policy multiplied by the actual intensity of the performance funding policy that is in effect.

  4. 4.

    In year 1, the term \( \theta p_{i,t - 1}^* \) from Eq. (5) is ignored (set to zero) since there is no prior performance for the institutions.

  5. 5.

    Table 1 shows a 0.01 move in the median, but this is likely due to sampling error since the number of simulations is only 500.

  6. 6.

    We can also consider how the existence of fixed institutional characteristics that affect performance might influence the results of our simulations. See the online appendix for additional discussion in which we recognize both fixed and time-varying components of institutions in additional Monte Carlo simulations.

  7. 7.

    Out of 10,704 observations, 182 in our sample belong to schools that report financial data according to the Financial Accounting Standards Board conventions. As a robustness check, we tried dropping these 182 observations and still found substantively similar results to what are reported here.

  8. 8.

    By unbalanced, we mean that some years may include data on all 520 institutions, but many do not. Across all years, 520 institutions appear in the dataset at some point.

  9. 9.

    Specifically, the degree production variable Y was regressed on Yt−1 and then on Yt+1 in two separate regressions. Observations were dropped any time the residuals from both regressions were greater than 10 or both residuals were less than − 10. For the retention rate, which has a larger standard deviation, the same process was followed except that the thresholds were 15 and − 15. Observations were also dropped if the residual from the above regression was greater than 40 or if the reported retention rate was 0.

  10. 10.

    With many of our models, we tried a variety of specifications that included lags of various lengths, but no consistent evidence of lagged effects was obtained.

  11. 11.

    Sources disagree substantially regarding the appropriate coding of the performance funding policy variable for three states: Colorado, North Carolina, and South Dakota. Dropping these three states does not substantially alter the findings presented in Table 2.

  12. 12.

    While there is a theoretical ceiling in performance (a 100% retention rate), even high-performing institutions in our sample rarely come anywhere close to hitting this ceiling (the 99th percentile is 96% retention).

  13. 13.

    As a robustness check, we tried restricting the sample to include only institutions with a Barron’s selectivity rating of 3 or lower since almost all HBCUs have a selectivity rating of 3 or lower. The results with this restricted sample (not shown here but available upon request) are very similar to what we found with the full sample.


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Favero, N., Rutherford, A. Will the Tide Lift All Boats? Examining the Equity Effects of Performance Funding Policies in U.S. Higher Education. Res High Educ 61, 1–25 (2020). https://doi.org/10.1007/s11162-019-09551-1

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  • Performance funding
  • Equity
  • Performance
  • Formal model