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Public Tuition on the Rise: Estimating the Effects of Louisiana’s Performance-Based Funding Policy on Institutional Tuition Levels

Abstract

Louisiana’s performance-based funding (PBF) policy is one of the most recent implementations of performance funding established by a state for accountability purposes. Instead of examining direct academic outcomes, this study focuses on tuition increase as an (un)intended outcome of PBF implementation. We use data from multiple sources to create a panel dataset of public postsecondary institutions across the United States from 2005 to 2013. Applying Difference-in-Differences and propensity score analyses procedures to estimate the causal relationship between PBF implementation and tuition levels at public institutions in Louisiana, our results indicate that treated community colleges responded to PBF by increasing tuition levels statistically significantly above that of their counterfactual institutions. While in-state tuition and fees rose statistically significantly faster at public universities in Louisiana after PBF implementation, out-of-state tuition and fees charged by the treated 4-year institutions did not significantly differ from the increases experienced at counterfactual institutions in non-PBF states. We explore possible explanations for the findings and provide implications for practices and future research.

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Notes

  1. 1.

    The year listed refers to the fall of the academic year (i.e., 2013 for 2013–2014). Following recommendations by Slaughter (2000), we were interested in providing enough years to establish good trends in the unconditional reported tuition level trend figures so we included as many years as we had information on. We used 2005 as the start year for two reasons: we did not have information on all legislation that may have been passed prior to 2005 that could behave as alternative rival explanations and Hurricane Katrina occurred in 2005.

  2. 2.

    While tuition setting authority is an alternative explanatory factor, tuition-setting authority in all non-PBF states remained the same during the time of the study between 2005 and 2013 (Bell et al. 2011; Boatman and L’Orange 2006; Carlson 2013; Kramer et al. 2017). Therefore, this variable is not included in the fixed effects models; we included state fixed effects to control for potential unobserved time-invariant characteristics.

  3. 3.

    Additionally, some of the data is not missing completely at random because some institutions may have been established, merged, or closed operations during the nine-year observation period; therefore, it is inappropriate to impute values for these cases.

  4. 4.

    If there is insufficient evidence of overlap in the treated and untreated groups of institutions, the kernel-based propensity score analysis method is not suitable for causal inference purposes.

  5. 5.

    We employed and reported GLS Regression with random and fixed effects assuming autocorrelated and heteroskedastic errors. We also tested the use of GLS Regression assuming i.i.d. errors as well as GLS Regression with Random Effects and AR(1) Disturbances. Additionally, we tested pooled OLS regression in reduced-form and in diff estimation; we reported these results when used with propensity score matching techniques with fixed effects.

  6. 6.

    To improve the understanding of the statistics reported, we take the inverse function of the log values of dollars (tuition and fees) in this section of descriptive and summary statistics.

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Acknowledgements

The authors would like to thank Laura Perna for her critical feedback and support of our work at the 2017 ASHE Conference as well as the reviewers of Research in Higher Education for their helpful suggestions that have led to the improvement of the final manuscript. The authors also would like to thank Benjamin Todd Gindhart, an undergraduate work-study at the University of Miami, for his assistance on earlier versions of tables. All errors and omissions are our own.

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The researchers received no research funding for purposes of completing this project.

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Correspondence to Xiaodan Hu.

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Appendix

Appendix

See Table 8.

Table 8 Summary statistics: overall distributions and means and standard deviations of variables tested

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Hu, X., Villarreal, P. Public Tuition on the Rise: Estimating the Effects of Louisiana’s Performance-Based Funding Policy on Institutional Tuition Levels. Res High Educ 60, 636–669 (2019). https://doi.org/10.1007/s11162-018-9526-y

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Keywords

  • Performance-based funding
  • Tuition-setting authority
  • Difference-in-differences