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The role of poverty measurements in achieving educational equity through school finance reform

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Abstract

In this paper, we estimate a series of stochastic frontier cost functions for elementary schools, using a short panel of Texas data that allows us to account for student characteristics, input prices, environmental factors and student outcomes. Texas currently uses information about the share of students participating into the Free and Reduced Price Lunch (FRL) program to determine compensatory funding to provide to schools. The FRL measure has been criticized as a relatively poor measure of need. We consider a new, recently developed, measure of poverty, the Spatially Interpolated Demographic and Economic (SIDE) measure, as a possible complement or alternative to the FRL measure. SIDE uses the income of the neighborhood in which the school resides as the basis to assess need and poverty. We find that using both poverty metrics highlights the additional costs associated with serving high poverty populations in high poverty locations, i.e., neighborhood locations matter.

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Notes

  1. See the Education Commission of the States, K-12 Funding: State Profiles. https://www.ecs.org/k-12-funding-state-profiles/.

  2. For example, see Ruggles (1990), Taylor and Dar (2015), Baker, et al. (2013), Renwick (2009, 2011), Short (2011), and Marks et al. (2010).

  3. Recently, the U.S. Census Bureau has calculated a Supplemental Poverty Measure. The new measure uses actual expenditures on food, clothing, shelter and utilities to assess poverty, while also adjusting for geographic variation in housing cost and family size and composition. The measure is updated every 5 years. The supplemental poverty measure estimates the after-tax income and adds back the monetary value of in-kind benefits. While a substantial improvement on the federal poverty level, the supplemental poverty measure is only available at the state and national levels and, therefore, has not been used for poverty measurement in education at the district and/or school level.

  4. Formula-eligible children are determined using “the estimated number of 5–17 year-olds in poverty at the district level, along with counts (determined by administrative records) of Temporary Assistance for Needy Families (TANF) participants, foster children, and neglected and delinquent children” (Snyder et al., 2018, p. 5).

  5. The slight modification is that we have also included the cube of district enrollment as an independent variable.

  6. All variables except those already expressed as percentages or percentage points are in natural logarithms.

  7. Gronberg et al. (2011a) and Gronberg et al. (2015) and Taylor et al. (2014) also use this cubic specification for enrollment. Other researchers have dealt with this issue by excluding the largest Texas districts from the analysis (e.g., Imazeki and Reschovsky 2004).

  8. We exclude from the analysis Alternative Education Accountability (AEA) campuses (e.g., juvenile justice campuses, disciplinary education campuses, residential campuses and all other alternative education campuses), because they are subject to different accountability requirements and may have different cost structures than the other campuses (TEA 2016). Virtual campuses and campuses that lack reliable data on student performance, such as elementary education campuses that serve no students in tested grades, or very small campuses, are also excluded.

  9. Fiscal agents collect funds from member districts in a shared service agreement, and make purchases or pay salaries with those shared funds on behalf of the member districts. As a result, the spending of fiscal agents is artificially inflated, while the spending by member districts is artificially suppressed. However, fiscal agents report annually to TEA the amounts they spent on behalf of their member districts. These data have been used to allocate spending by fiscal agents to their member districts on a proportional basis.

  10. The equation used is: NCEjt = 50 + zjt*21.06, where zjt is the average of the individual standardized gain score for school j in year t.

  11. The wage index is a weighted average of the Comparable Wage Index for Teachers (CWIFT) published by the NCES, the county fair market rents as published by the U.S. Department of Housing and Urban Development, the county unemployment rate as published by the U.S. Bureau of Labor statistics, the percentage of students who have ever been identified as English Language Learners and various measures of geographic isolation, climate, district type, and county type.

  12. During the period under analysis, Texas used the percentage of economically disadvantaged students as state’s official school poverty metric. Texas defined economically disadvantaged students as those eligible for free or reduced-price lunch or eligible for other public assistance. The correlation between the percent economically disadvantaged and the percent free and reduced-price lunch according to the NCES was 0.993.

  13. In Texas (as in other states), a student’s status regarding English proficiency is potentially an endogenous function of his or her academic performance. Therefore, rather than rely on the percentage of students who were currently identified as English Language Learners (ELL), Taylor et al. (2021) constructed a measure of student need that was clearly outside of school district control—the percentage of students in the district who had ever been considered ELL. Using data from the Education Research Center at the University of Texas at Dallas, they traced each student’s academic history to identify those students who had been ELL at some point during their experience in Texas schools. We thank them for sharing these data.

  14. Following Gronberg et al. (2005), high needs special education students are those with a classification other than speech-language difficulties or learning disabilities. Where the share of students with speech-language difficulties or learning disabilities was censored (due to privacy concerns), the researchers presumed that all of the special education students were high needs students.

  15. During the analysis period, Texas had a two-tier funding formula. The first tier is best characterized as a foundation plan, while the second tier is best characterized as a guaranteed yield with recapture. In both tiers, funding levels are weighted by indicators of student need, including the percentage of economically disadvantaged students. The funding formula weight for economically disadvantaged students is 20% higher than the baseline, all other things being equal.

  16. We explored using an estimation strategy from Karakaplan (2022) to test for endogeneity. Unfortunately, that approach required us to reduce in an ad hoc manner the dimensionality of the translog specification used in our model, and ultimately could not be implemented.

  17. We also explored other sets of potential instruments, such as the number of manufacturing establishments in the school’s zip code (from the ZIP Business Patterns produced by the Census Bureau), log square miles in the district, and a 5-year lag of the share of the county population who live in a city, town or other Census-designated place (which is a measure of population dispersion). These alternative instrument sets were also highly correlated with school size and quality in the first stage analysis and also supported the conclusion that we could not reject exogeneity. We chose the most parsimonious set of instruments for this report.

  18. Note that Model 3 nests a SIDE version of Model 1, in which SIDE poverty impacts cost but not inefficiency. As Table 3 illustrates, the hypothesis that SIDE poverty’s impact on inefficiency is negligible is easily rejected.

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Acknowledgements

We thank participants to the Southern Economic Association annual 2021 meeting, the Western Economic Association annual 2022 meeting and to the EWEPA 2022 meetings.

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Authors and Affiliations

  1. Texas A&M University, College Station, TX, USA

    Lori Taylor

  2. Oregon State University, Corvallis, OR, USA

    Shawna Grosskopf

  3. Southern Methodist University, Dallas, TX, USA

    Kathy Hayes

  4. The University of Alabama, Tuscaloosa, AL, USA

    Laura Razzolini

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  1. Lori Taylor
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  2. Shawna Grosskopf
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Correspondence to Kathy Hayes.

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Appendix

Appendix

Tables 7, 8

Table 7 Selected regression coefficients and P values from the IV first stage
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Table 8 Regression Coefficients and P values from Alternative Specifications of the Cost Function Model
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Taylor, L., Grosskopf, S., Hayes, K. et al. The role of poverty measurements in achieving educational equity through school finance reform. J Prod Anal 60, 109–127 (2023). https://doi.org/10.1007/s11123-022-00657-w

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