Like many government-funded entities, public school districts are increasingly asked to “do more with less.” Since the Great Recession of 2007–09, state education budgets have shrunk dramatically; during the 2012/13 school year, 35 states provided less funding to local districts than they did five years earlier.1 At the same time, schools and districts have remained accountable for student achievement.
As a result, districts nationwide are looking for ways to increase their efficiency, which requires maintaining or even improving student outcomes while using fewer resources. One common proxy for district efficiency is the expenditure-to-performance ratio, which is a measure of district spending divided by a student outcome measure.
Districts can choose among different measures of expenditures and student performance to calculate these ratios, and these choices can lead to distinctly different and multiple conclusions about a district’s ability to use resources efficiently. This approach can have real and immediate consequences, particularly when one considers that a state education agency (SEA) might use these ratios to assess accountability or to inform decisions about school district consolidation, an important issue for small and rural districts.
To help state and district leaders better understand how choosing certain expenditure and performance measures over others might influence conclusions about school-district efficiency, particularly the efficiency of districts in different locales (e.g., rural, suburban, urban), REL Northeast & Islands worked with a group of rural educators, policymakers, and researchers to study expenditure-to-performance ratios and how they can be calculated and used.
This work resulted in two reports published by the Institute of Education Sciences (IES) that together offer an example of how descriptive analysis using educational data can identify new patterns and information relevant to a specific research or policy question. The first report provides a guide to calculating six different expenditure-to-performance ratios using publicly available data; the second report explores what happens when you use these six different ratios to rank districts within a sample state. (To learn more about descriptive analysis, download the IES guide, “Descriptive Analysis in Education: A Guide for Researchers.”)
Calculating Six Expenditure-to-Performance Ratios
REL researchers Sarah Ryan and Heather Lavigne and I worked with our partners to create six expenditure-to-performance ratios that illustrate how prioritizing certain measures of expenditures and performance over others can lead to different conclusions about efficiency. The six ratios were calculated using three measures of per-pupil expenditure and two measures of student performance and were based on 2012/13 data from 98 districts serving grades K–12.
The three measures of per-pupil expenditures were calculated using 11 expenditure categories reported by districts in the sample state:
- Total per-pupil expenditures, which includes all 11 categories
- Instructional per-pupil expenditures, which includes only costs directly related to instruction
- Constructed per-pupil expenditures, which excludes costs related to special education instruction, transportation, and debt service
The two measures of student performance were based on district data commonly used for accountability in the Northeast & Islands region:
- Median student growth percentile in math, which compares a district’s median student growth rate for math in grades 4 through 8 with that of all students in the state
- Percentage of students scoring proficient or above in math, which includes students in grades 3 through 8
Using these three expenditure measures and two performance measures, we calculated six different expenditure-to-performance ratios and then ranked the 98 districts using each ratio. We found that these simple measures of efficiency for any one district relative to other districts in the state changed, in some cases considerably, depending on the ratio used.
We also found that, when reviewing the eight—yes, only eight—districts that ranked among the top 25 across all six ratios, half were in rural areas (see Table 1). Although this data point might seem surprising to many, the rural education researchers and rural school leaders with whom we worked found it to be in line with their nuanced understanding of the context and fiscal conditions across various schools and districts in rural locations.
Table 1. Characteristics of districts in the top 25 districts on all six expenditure-to-performance ratios, 2012/13
||Average rank over six ratios
||Poverty status (percentage of students eligible for the federal school lunch program)
Source: Authors’ analysis of 2012/13 data for 98 K–12 districts with complete data. Data on expenditures and performance, enrollment, and eligibility for the federal school lunch program are from the website of the department of education of a state in the Regional Educational Laboratory Northeast & Islands region; data on locale are from the U.S. Department of Education (2012).2
Ratios Are Just One Piece of Data
“This study is helpful and a healthy reminder of the variability across school districts and how it really matters what is measured and what is not,” said John Sipple, associate professor of development sociology at Cornell University and director of the New York State Center for Rural Schools, who served as an advisor on this study. “This is a cautionary tale to not draw sweeping generalizations about rural or urban districts and to pay attention to the specific measures of efficiency available. Both reports will be useful to educational leaders and policymakers.”
It’s important, of course, to remember that expenditure-to-performance ratios are just one descriptive proxy of how efficiently districts use resources. District-level variability in some types of expenditures may reflect differences in district characteristics—such as poverty status, urban/rural status, or student enrollment—rather than in efficiency. Thus, if states are going to use expenditure-to-performance ratios to rank districts, they would be wise to make comparisons among similar districts—for example, the district reference groups established by some SEAs.3 And when states are considering high-stakes decisions—for example, school closure, district consolidation, or continued funding for particular programs—they should include these ratios as just one piece of data alongside other information that may more accurately reflect a district’s efficiency.
Nonetheless, the information in these two reports about calculating and using expenditure-to-performance ratios can help state and district education leaders in vital ways. Rural education stakeholders welcome opportunities to engage in evidence-based discussions about efficiency and performance that reflect the complexities and realities of rural schools and communities. Both of these reports can generate rich conversations among school leaders and policymakers about the usefulness and effectiveness of calculating and comparing district-level expenditure-to-performance ratios across varying locales and conditions.
Browse REL Program work related to rural education.
1 Leachman, M., Albares, N., Masterson, K., & Wallace, M. (2016). Most states have cut school funding, and some continue cutting. Washington, DC: Center on Budget and Policy Priorities; Levin, J., Belfield, C., Hollands, F., Bowden, A. B., Cheng, H., Shand, R., et al. (2012). Cost-effectiveness analysis of interventions that improve high school completion. New York, NY: Center for Benefit-Cost Studies of Education, Teachers College, Columbia University; Oliff, P., Mai, C., & Leachman, M. (2012). New school year brings more cuts in state funding for schools. Washington, DC: Center on Budget and Policy Priorities.
2 Adapted from Lavigne, H. J., Ryan, S., Zweig, J. S., & Buffington, P. J. (2017). Exploring district-level expenditure-to-performance ratios (REL 2017–267). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast & Islands. Retrieved from https://ies.ed.gov/ncee/edlabs/projects/project.asp?projectID=4554.
3 Connecticut School Finance Project. (2016). 10 Years Later: An Updated Look at CT’s District Reference Groups (DRGs). Retrieved from http://ctschoolfinance.org/reports/2016drgs