Data

The InformEd States Community College Local Funding Dataset includes data on local funding received by community colleges across the United States over five academic years, from Fiscal Years 2017 to 2021. Among 28 states, local funding contributes billions annually to community colleges, yet little is known about the specific sources of local appropriations. This dataset provides more detailed data than the Integrated Postsecondary Education Data System (IPEDS); which has been the only source of data on the amount of local funding colleges receive but does not provide data on the varying types of local funding sources. 

Our research team spent over two years collecting two types of data for each college in each of the panel years: the types of taxes used to allocate local appropriations and the amount of revenues received. The research team primarily collected these data from publicly available financial statements, such as financial statements published on college’s websites, state auditor’s websites, and the Federal Audit Clearinghouse. After gathering data from these sources, we attempted to obtain missing data by searching for colleges’ annual budgets and corresponding directly with the appropriate community college personnel at each institution. 

The dataset includes detailed data elements for each institution-year observation to outline the specific types of local tax sources received. We identified and condensed the large number of terms to 14 different subtypes of local taxes for community colleges, including local sales tax, local gambling tax, local hotel tax, local tobacco or cigarette tax, local alcohol tax, local fuel or gas tax, local entertainment tax, and more.

State Financial Aid Dataset

The InformEd States State Financial Aid Dataset contains detailed information on state financial aid programs based on need, merit, and a combination of need and merit from Fiscal Years 2004 through 2020. The dataset contains the following information for financial aid programs that are available to incoming first-year students in each state (please see additional documentation for inclusion criteria):

• The amount of funds awarded based on need, merit, and a combination of need and merit each year

• The number of students who receive grant aid based on need, merit, and a combination of need and merit each year

• The average amount of grant aid per student based on need, merit, and a combination of need and merit each year (derived from the previous two data elements)

• Name of financial aid programs based on need, merit, and a combination of need and merit each year

• For the most inclusive need-based program in the state in a given year:

  • Indicators for how states define need (e.g., through expected family contribution as determined by FAFSA, income) 

  • Threshold the state uses to determine need (this information is only recorded when it was available)

  • Indicators for the institutions where funds can be used (two-year, four-year, private, proprietary)

• For the most inclusive merit-based program in the state in a given year:

  • Indicators for how states define merit (e.g., college entrance exam, high school GPA) 

  • Threshold the state uses to determine merit (this information is only recorded when it was available)

  • Indicators for the institutions where funds can be used (two-year, four-year, private, proprietary)

• For the most inclusive combination (need and merit) program in the state in a given year: 

  • Indicators for how states define need (e.g., through expected family contribution as determined by FAFSA, income) 

  • Threshold the state uses to determine need (this information is only recorded when it was available)

  • Indicators for how states define merit (e.g., standardized test scores, high school GPA) 

  • Threshold the state uses to determine merit (this information is only recorded when it was available)

  • Indicators for the institutions where funds can be used (two-year, four-year, private, proprietary)

We created this dataset by examining more than 1,200 state financial aid documents, including state budgets and higher education agency and aid commission websites and reports. When historical information was not available on current websites, we used the Internet Archive: Wayback Machine to locate older documents. We also used the Thomson Reuters Westlaw Database to locate historical state administrative code that outlined program requirements. When we were unable to locate information or found inconsistent information, we reached out to state higher education officials to request clarification.

For a more-detailed breakdown of specific data elements, please see the Dataset Documentation.

State Funding Mechanisms Dataset

The InformEd States State Funding Mechanisms Dataset contains detailed information on the methods that states and/or higher education systems use to fund their public colleges and universities from Fiscal Years 2004 through 2020. Most of the existing body of research on state higher education funding has focused on performance-based funding (PBF), but fewer than ten percent of state funding has been allocated via PBF. Our dataset provides the first comprehensive, longitudinal source of funding mechanisms.

Our default unit of data collection was the state-by-sector level (e.g., Ohio public four-year universities), which generally matches how state legislatures or higher education agencies determine how funding flows to individual institutions. In some cases, there were different allocation mechanisms in place for different systems within a sector (e.g., the University of California system compared to the California State University system). We collected data at the system level if that occurred and made clear in the notes section the colleges/systems included in those rows of data.

We created this dataset by examining more than 2,000 policy documents, including state budgets, higher education agency websites, and governing/coordinating board minutes. We resolved remaining questions or inconsistencies through regular research team meetings and by reaching out to state higher education officials. 

This dataset contains information on whether a state or system used one or more of the following three funding mechanisms:

• Base-adjusted funding, in which all institutions within a system or sector received across-the-board or similar percentage increases or decreases in allocations in a given year. We also coded funding formulas that included a base element that was then adjusted based on enrollment, performance, or another mechanism as base-adjusted funding. Finally, funding formulas that included a protective mechanism that guarantees a set portion of the prior year’s allocation were coded as base-adjusted.

• Performance funding, in which funding is tied directly to student outcome measures. This comes from our Performance-Based Funding Policies Dataset.

• Enrollment funding, in which funding is based on the number of students who enroll. We provide details on two different mechanisms: funding based on student headcounts and funding based on full-time equivalent enrollment (typically student credit hours). Finally, we also coded for whether a state or system adjusted funding based on field of study or level of study.

For a more-detailed breakdown of specific data elements, please see the Dataset Documentation.

Performance-Based Funding Policies Dataset

The InformEd States Performance-Based Funding Policies Dataset contains detailed information on performance-based funding (PBF) policies from 1997-2020. The majority of U.S. states use PBF policies to allocate at least a portion of state appropriations to public colleges and universities in an effort to hold colleges more accountable for their outcomes. Although states are taking a wide range of approaches in terms of the percentage of funding tied to outcomes, the types of outcomes being incentivized, and whether (or how) states build equity metrics into PBF systems, prior research on the effects of these approaches relies primarily on binary indicators of PBF. Notably, research is not always clear as to whether PBF means that a state had an approved system on the books that may not have actually received funding based on institutional performance or a system that received funding based on institutional performance.

There have been sporadic efforts to provide snapshots of state PBF policies in certain years, but these datasets are limited in the amount of detail they collect, often do not provide adequate information at the institutional level, and do not provide data to track the trajectory of PBF over time. Other quantitative researchers have developed their own lists of states that have PBF systems, but these lists are not always consistent across researchers. Importantly, researchers have not been able to reach a consensus on which states had PBF or the years in which these policies existed. These data limitations mean that states seeking guidance on how to develop equitable and effective PBF systems do not receive crucial evidence-based information regarding how to design PBF policies to reduce educational inequities and improve student outcomes.

To capture variation in PBF adoption across states and over time, we gathered information on the percent or dosage of PBF for each state in a given year. We gathered information on the specific metrics included in PBF systems, such as credit completion, retention, and the number of postsecondary credentials, as well as the specific subgroups included in any equity-oriented metrics, such as underrepresented minority students and low-income students. Finally, we collected information on the annual dollar amount and percentage of state funding tied to institutional performance. 

The InformEd States PBF Policies Dataset distinguishes policy adoption from actual policy implementation. Not every state with a PBF policy on the books actually tied funding to outcomes in a given year. This scenario can arise due to changes enacted by state legislatures and adjustments to state budgets following unexpected changes in fiscal circumstances. Therefore, information on years of PBF adoption and actual implementation are recorded in separate columns. The following includes a brief description of additional data elements included in the dataset:

• What do we mean by PBF? We define performance funding as a state having a PBF policy on the books under which a public college is eligible to be funded based on student outcomes in a given academic year

• Each observation represents a state in a given fiscal year 

• Years included in the dataset: Fiscal Years 1997-2020

• Key data elements (reported by sector):

  • Annual dollar amount and percentage of state funding budgeted based on institutional performance 

  • Success metrics included in PBF systems, such as credit completion, retention, and the number of credentials 

  • Presence of metrics focusing on certain student subpopulations, such as traditionally underrepresented students and students studying STEM areas

  • Distinguishes year of policy adoption/existence from years when policy was actually funded

For a more-detailed breakdown of specific data elements, please see the Dataset Documentation.