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Illinois Higher EducationPerformance Funding Model

Steering Committee PresentationMarch 28, 2012

Dr. Alan Phillips

Purpose

To address the missions, functions, composition,

and responsibilities of the Performance Funding

Refinement Committee and the way ahead for

the refinement effort.

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What We Have Accomplished So Far• Identified the key issues.• Developed performance funding principles.• Identified appropriate performance measures and sub-categories.• Developed performance funding models for both 2-year and 4-year colleges and

universities.• Acquired initial data.• Received input from steering committee members, colleges and universities,

other groups, and individuals.• Finalized the performance funding model for both four-year and two-year

colleges and universities.• Submitted the FY2013 Budget Recommendations, which included a performance

funding component.

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• Performance Metrics Shall:– Reward performance of institutions in advancing the success of

students who are:• Academically or financially at risk.• First generation students.• Low-income students.• Students traditionally underrepresented in higher education.

– Recognize and account for the differentiated missions of institutions of higher education.

– Focus on the fundamental goal of increasing completion.– Recognize the unique and broad mission of public community

colleges.– Maintain the quality of degrees, certificates, courses, and programs.

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Public Act 97-320 (HB 1503)

Refinement Committee Purpose

To address shortcomings and resolve issues regarding

the performance funding model itself and the data

associated with the measures and sub-categories

that were used in the model.

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Intent of the Refinement Effort

• Determine if there are ways we can improve the model (i.e. scale the data).

• Improve the quality of existing data used in the model.• Identify additional sources of quality data.• Refine the existing measures by developing more concise

and clear definitions for what we want to assess.• Expand the scope of the model by identifying and

compiling data for measures and sub-categories that can be added to the model.

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Refinement Committee Composition

• IBHE Deputy Director for Fiscal Affairs (Chair).• Institutional research representatives from each of

the nine universities, as selected by the universities.• ICCB Representative.• IERC Representative.• IBHE Staff.

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Goal

• Develop the best possible model given the constraints of:– Time– Resources– Lack of State funding– Availability of data– The difficulty of capturing the uniqueness of each

individual college or university

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Tasks

• In addition to refining the existing measures, develop a list of additional measures for consideration, to include the definitions and sources of the data.– At a minimum, measures should address, but are not limited to the

following areas;– Efficiency measures (e.g. completions/inputs, unit cost)– Improved research performance measures (e.g. patents, business startups,

and external grants acquired/maintained).– Entry and momentum measures (e.g. retention rates, transfer rates)– Academic quality measures (e.g. full-time vs. part-time faculty)

• Indicate whether these measures are from existing sources or need to be added to current Longitudinal Data System (LDS) data collection efforts.

• Solicit assistance from IERC in the development of definitions and the identification of additional measures that could be added to data collection efforts for use as possible performance indicators.

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Challenges• Ensuring there are enough variables in the model to diversify away

some of the differences in the universities, without making the model too complex.

• Identifying measures that address the entirety of the higher education process.

• Addressing the timeliness and the availability of the data.

• Ensuring we do not create incentives for colleges and universities to do things we do not want them to do.

• The law of unintended consequences.

• Getting to the point where we can lock the model for a period of time so that colleges and universities do not have a continually moving performance target.

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Committee Groundrules

• This is a refinement effort. We are not starting over with a new model.

• This is a partnership.• IBHE has the performance funding responsibility, but we

are looking at this from the perspective of the universities.• The model has to work for all twelve public universities.• The model has to be transparent to the extent possible.• The model has to produce results that are considered to

be equitable.• We are not looking for perfection.

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Performance Funding Model

4-Year Public Universities

Performance Funding Model Steps(4-Year Public University)

• Step 1 – Identify the performance measures or metrics that support the achievement of the state goals.

• Step 2 – Collect the data on the selected performance measures• Step 3 – Award an additional premium (i.e. 40%) for the production of

certain desired outcomes such as completions by underserved or underrepresented populations

• Step 4 – Normalize (scale) the data, if necessary, so it is comparable across variables.

• Step 5 – Weight each of the Performance Measures that reflects the priority of the Measure and the mission of the institution.

• Step 6 – Multiply and sum the Scaled Data times the Weight to produce the Weighted results.

• Step 7 – Use the Weighted results (or Total Performance Value) to distribute performance funding.

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Performance Measures

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Measure Source• Bachelors Degrees (FY07-09) IPEDS• Masters Degrees (FY07-09) IPEDS• Doctoral and Professional Degrees (FY07-09) IPEDS• Undergraduate Degrees per 100 FTE (FY07-09) IPEDS• Education and General Spending per Completion (FY09-11) RAMP• Research and Public Service Expenditures (FY09-11) RAMP

Step 1 – Identify the performance measures or metrics that support the achievement of the state goals.

Step 2 – Collect the data on the selected performance measures (3-year averages)

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Possible Performance Measures

Future Measures*• Retention (By Cohort)• Time to Completion (within 100% or 150%)• Students Accumulation of Credit Hours (24/48/72)• Student Transfers• Remediation• Diversity of Staff and Faculty• Quality

* The data for these measures is either not currently available or is not of sufficient quality to use.

Sub-Categories

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Sub-Category Weight• Low Income (Pell/Map Eligible) 40%• Adult (Age 25 and Older) 40%• Hispanic 40%• Black, non-Hispanic 40%• STEM & Health Care (by CIP Code) 40%

Step 3 – Award an additional premium for the production of certain desired outcomes such as completions by underserved or underrepresented populations

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Future Sub-Categories*• Part-Time• Disabled• Veterans• First Generation• English Language Learners• Residents of Underserved Counties• Additional Ethnic Categories

* The data for these measures is either not currently available or is not of sufficient quality to use.

Possible Sub-Categories

Scaling Factors

• Averaged the measures across all of the institutions.• The average number of bachelors degrees will serve as the base value.• Determine a scaling factor that will normalize the rest of the averages to the average number of bachelors degrees.• Adjust the scaling factors as appropriate (i.e. Masters & Doctorates).• Multiply all of the initial data by the scaling factor to normalize the data.

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Measure Universities 1-12 (Avg) Scaling Factor Adjusted Scaling Factor• Bachelors Degrees (FY07-09) 4,445 1.00 1.00• Masters Degrees (FY07-09) 1,152 3.86 1.00• Doctoral and Professional Degrees (FY07-09) 796 16.25 2.00• Undergraduate Degrees per 100 FTE FY(07-09) 26 173.64 200.00• Education and General Spending per Completion (FY09-11) 4,639 -.96 -1.00• Research and Public Service Expenditures (FY09-11) 10,803,117 .0004115 .0005000

Step 4 – Normalize (scale) the data, if necessary, so it is comparable across variables.

Performance Measure Weights

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Step 5 – Weight each of the Performance Measures that reflects the priority of the Measure and the mission of the institution.

UIUC UIC NIU SIUC ISU22.5% 22.5% 37.5% 37.5% 40.0%15.0% 15.0% 20.0% 20.0% 22.5%15.0% 15.0% 10.0% 10.0% 7.5%

5.0% 5.0% 10.0% 10.0% 12.5%2.5% 0.0% 2.5% 0.0% 2.5%

40.0% 42.5% 20.0% 22.5% 15.0%100.0% 100.0% 100.0% 100.0% 100.0%

Doctoral/ Research-Very High Research-High Research

Masters Colleges & Universities (Large)

SIUE WIU EIU NEIU CSU GSU UIS45.0% 45.0% 45.0% 45.0% 47.5% 50.0% 50.0%25.0% 25.0% 27.5% 27.5% 25.0% 37.5% 37.5%

5.0% 2.5% 0.0% 0.0% 2.5% 0.0% 2.5%15.0% 15.0% 15.0% 15.0% 15.0% 0.0% 0.0%

2.5% 10.0% 10.0% 10.0% 7.5% 10.0% 7.5%7.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Weights Based on Institutional MissionBachelors DegreesMasters Degrees Doctoral and Prof DegreesUndergrad Degrees per 100 FTEEducation Spending/CompletionResearch and Public Service Expenditures

Weights Based on Institutional MissionBachelors DegreesMasters Degrees Doctoral and Prof DegreesUndergrad Degrees per 100 FTEEducation Spending/CompletionResearch and Public Service Expenditures

Performance Value Calculation

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Measure• Bachelors Degrees • Masters Degrees• Doctoral and Professional Degrees• Undergraduate Degrees per 100 FTE• Education and General Spending per Completion• Research and Public Service Expenditures

3,9211,552

209 23.2

3,7885,486,590

6,8131,754

229 23.2

3,7885,486,590

Data Data + Premium Scale112

200-1

.0005

(Data+Premium)x Scale6,8131,754

4584,646

-3,7882,743

xWeight =

35.0%25.0%

5.0%10.0%

5.0%20.0%

100.0%

Total PerformanceValue2385

43823

464-189549

3580

Step 6 – Multiply and Sum the Scaled Data times the Weight to produce the Total Performance Value.

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Percentages for Distribution

Total Performance Value 10,840 4,435 2,027 17,302Percentage of Total 62.7% 25.6% 11.7% 100%

Distribution: Pro Rata $627,000 $256,000 $117,000 $1,000,000

University 1 University 2 University 3 Total

Funding AllocationBased on Performance

Step 7 – Use the Weighted results (or Total Performance Value) to distribute funding based on a Pro Rata Share of the total amount of funds set aside for performance funding.

Performance Funding Model

• All steps are identical at each university• The model accounts for each institution’s unique mission by

adding a weight to each measure.• Each institution’s formula calculation is independent.• The formula calculation for each institution will change each

year based on annually updated data.• The funding allocation is competitive.• Funds are distributed on a pro rata basis according to each

institution’s formula calculation.• The model is not prescriptive in how to achieve excellence

and success (what, not how).

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The Way Ahead

• The next Refinement Committee meeting is scheduled for the 21st of June.

• In preparation for that meeting, committee members will be:– Reviewing the work to date.– Looking at better ways to scale, or normalize, the data.– Working to better define the measures.– Identifying better sources of more current data.– Considering which measures and sub-categories we may want to add

to the performance funding model.

• We will also be working to address data issues in conjunction with the ILDS effort.

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Questions/Comments?

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