Leveraging Data Analytics to Study the Impacts of State Budget Appropriations on Public Higher Education Institutions in the USA

Abstract This research study focuses on the impact of state budget appropriations on the public 4-year higher education institutions in the USA, using full-time equivalent (FTE) as the metric. This is important particularly amid broader economic shifts such as the 2008 financial crisis and the COVID-19 pandemic. A systematic literature review was conducted to identify the research gap and to select variables for investigation. Five independent variables were chosen to capture key student outcomes: state higher education appropriations per FTE (SHEA), average undergraduate charges per FTE (AUGC), student tuition share as a percentage of per capita income (STSPCI), state higher education appropriations as a percentage of GDP (SHEAGDP), and state financial aid (SFA) per FTE. Dependent variables were full-time equivalent enrollment (FTEE) and graduation rate (GR) at 4-year US public higher education institutions. Historical data for 50 years (each year as one dataset, n = 50) between 1971 and 2020 were collected and analyzed. A multiple linear regression model in open-source data analytics and machine learning software was employed to test the hypotheses. For FTEE, three variables, SHEA, AUGC, and SFA, were found to be significant. For GR, two variables, SHEA and STSPCI, were significant. Accordingly, two data analytical models were developed involving these significant independent variables. Results of the first model show that if SHEA funds grow, AUGC goes down, there is more SFA and FTE enrollment increases. The results of the second model revealed that when SHEA funds grew and STSPCI went down, graduation rates rose. These findings underscore how state appropriations and tuition policies shape student outcomes, this is especially relevant in times of economic uncertainty. Policymakers and university administrators can use the results to make informed decisions about budget spending, tuition fees, and financial aid strategies, thereby promoting institutional resilience and student success.

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Publication Details

Journal
Journal of Education Finance and Law
Published
2025-01-01
DOI
https://doi.org/10.5406/30678560.50.1.12
Primary Topic
Public Policy and Administration Research
Type
article
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article

Leveraging Data Analytics to Study the Impacts of State Budget Appropriations on Public Higher Education Institutions in the USA

Praveen Kumar Gurajaa, M. Affan Badarb, Farman A. Moayedc, Christopher J. Klus
Journal of Education Finance and Law
Public Policy and Administration Research
article

Leveraging Data Analytics to Study the Impacts of State Budget Appropriations on Public Higher Education Institutions in the USA

Praveen Kumar Gurajaa, M. Affan Badarb, Farman A. Moayedc, Christopher J. Klus
article en

Abstract

Abstract This research study focuses on the impact of state budget appropriations on the public 4-year higher education institutions in the USA, using full-time equivalent (FTE) as the metric. This is important particularly amid broader economic shifts such as the 2008 financial crisis and the COVID-19 pandemic. A systematic literature review was conducted to identify the research gap and to select variables for investigation. Five independent variables were chosen to capture key student outcomes: state higher education appropriations per FTE (SHEA), average undergraduate charges per FTE (AUGC), student tuition share as a percentage of per capita income (STSPCI), state higher education appropriations as a percentage of GDP (SHEAGDP), and state financial aid (SFA) per FTE. Dependent variables were full-time equivalent enrollment (FTEE) and graduation rate (GR) at 4-year US public higher education institutions. Historical data for 50 years (each year as one dataset, n = 50) between 1971 and 2020 were collected and analyzed. A multiple linear regression model in open-source data analytics and machine learning software was employed to test the hypotheses. For FTEE, three variables, SHEA, AUGC, and SFA, were found to be significant. For GR, two variables, SHEA and STSPCI, were significant. Accordingly, two data analytical models were developed involving these significant independent variables. Results of the first model show that if SHEA funds grow, AUGC goes down, there is more SFA and FTE enrollment increases. The results of the second model revealed that when SHEA funds grew and STSPCI went down, graduation rates rose. These findings underscore how state appropriations and tuition policies shape student outcomes, this is especially relevant in times of economic uncertainty. Policymakers and university administrators can use the results to make informed decisions about budget spending, tuition fees, and financial aid strategies, thereby promoting institutional resilience and student success.

Journal of Education Finance and LawVol. 50(1)
Bowling Green State University (US), University of Pikeville (US), Quality Systems (United States) (US), Indiana State University (US)
Openalex Percentile: Top 81%
Public Policy and Administration Research
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