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Funding public higher education in Colorado

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Funding public higher education in Colorado how has the college opportunity funding model impacted educational funding and performance?
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How has the college opportunity funding model impacted educational funding and performance?
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Middlemist, George Edward ( author )
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English
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Education, Higher -- Finance -- Colorado ( lcsh )
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During the 2004 legislative session, the Colorado General Assembly enacted Senate Bill 189 (SB189), which established the first system of college vouchers in the United States. The supporters of SB189 hoped that the voucher system, called the College Opportunity Fund (COF), would: 1) stabilize the flow of state funding to higher education; 2) incent institutions to improve performance; and, 3) increase resident and underserved enrollment. This research presents an empirical study of COF to examine the extent to which it has impacted both funding and performance in Colorado’s institutions of higher education. It uses budgetary theory to evaluate the overall success of the COF funding model.
Review:
Through the analysis of funding and performance trends from fiscal year 1999 to 2014, this dissertation focuses on how the COF funding model impacted institutions after the legislation was implemented in fiscal year 2006. The present research effort adds to budgetary literature by 1) evaluating the success of the first ever higher education voucher system in improving performance and 2) developing insights on the nexus of policy design and budgetary theories of incrementalism and performance.
Review:
The study used linear regression, t-tests, and other statistical tools as well as qualitative data to answer four research questions and yielded several findings. First, despite the best efforts to stabilize state funding to higher education, the overall appropriation in real dollars has decreased since the implementation of COF. Second, the Fee for Service funding mechanism that accompanies the COF stipend fails to reward performance as intended, but rather appears to be a mechanism to hold institutional distributions proportionally the same. Finally, although the state has seen increased enrollments both for Colorado resident and Colorado resident underserved students since the implementation of SB189, the COF voucher system seems to have not influenced enrollment at institutions of higher education. A major implication of this research is that SB189 has not been successful in achieving its goals, but instead the budgetary outcomes are best described as incremental. Policy makers must consider that the design of a funding system can bias the budgetary outcomes.
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Thesis (Ph.D.)--University of Colorado Denver
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Includes bibliographical references.
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by George Edward Middlemist.

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Full Text
FUNDING PUBLIC HIGHER EDUCATION IN COLORADO: HOW HAS THE COLLEGE
OPPORTUNITY FUNDING MODEL IMPACTED EDUCATIONAL FUNDING AND
PERFORMANCE?
by
GEORGE EDWARD MIDDLEMIST
B.S., Oklahoma State University, 1986
M.S., Colorado State University, 1995
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Public Affairs
2017


11
2017
GEORGE EDWARD MIDDLEMIST
ALL RIGHTS RESERVED


This thesis for the Doctor of Philosophy degree by George Edward Middlemist has been approved for the Public Affairs by
iii
Christine Martell, Chair Todd Ely Paul Teske Stephen M. Jordan
Date: May 13, 2017


IV
Middlemist, George Edward (Ph.D., Public Affairs)
Funding Public Higher Education in Colorado: How has the College Opportunity Funding Model Impacted Educational Funding and Performance?
Thesis directed by Professor Christine Martell
ABSTRACT
During the 2004 legislative session, the Colorado General Assembly enacted Senate Bill 189 (SB189), which established the first system of college vouchers in the United States. The supporters of SB189 hoped that the voucher system, called the College Opportunity Fund (COF), would: 1) stabilize the flow of state funding to higher education; 2) incent institutions to improve performance; and, 3) increase resident and underserved enrollment. This research presents an empirical study of COF to examine the extent to which it has impacted both funding and performance in Colorados institutions of higher education. It uses budgetary theory to evaluate the overall success of the COF funding model.
Through the analysis of funding and performance trends from fiscal year 1999 to 2014, this dissertation focuses on how the COF funding model impacted institutions after the legislation was implemented in fiscal year 2006. The present research effort adds to budgetary literature by 1) evaluating the success of the first ever higher education voucher system in improving performance and 2) developing insights on the nexus of policy design and budgetary theories of incrementalism and performance.


V
The study used linear regression, t-tests, and other statistical tools as well as qualitative data to answer four research questions and yielded several findings. First, despite the best efforts to stabilize state funding to higher education, the overall appropriation in real dollars has decreased since the implementation of COF. Second, the Fee for Service funding mechanism that accompanies the COF stipend fails to reward performance as intended, but rather appears to be a mechanism to hold institutional distributions proportionally the same. Finally, although the state has seen increased enrollments both for Colorado resident and Colorado resident underserved students since the implementation of SB189, the COF voucher system seems to have not influenced enrollment at institutions of higher education. A major implication of this research is that SB189 has not been successful in achieving its goals, but instead the budgetary outcomes are best described as incremental. Policy makers must consider that the design of a funding system can bias the budgetary outcomes.
The form and content of this abstract are approved. I recommend its publication.
Approved: Christine Martell


I dedicate this thesis to my wife Catherine, children Cassie and Robert, and parents Dennis and Melanie, who have shown great patience and unconditional support of my
pursuit of a PhD.


ACKNOWLEDGEMENTS
I would like to thank everyone who has helped me through the development and writing of this dissertation. I am deeply and forever grateful to my dissertation chair, Christine Martell, for being supportive, responsive, and thoughtful through this process. Without her wisdom and encouragement, I would not have completed this program. I am also grateful to each of my committee members, Stephen Jordan, Paul Teske, and Todd Ely, for contributing their insights and expertise. I am most indebted to Beth Christensen, who was my editor in chief early on in the program. I would also like to thank my family and friends for their unwavering support and belief that I would actually finish the dissertation. Finally, I would like to thank Panera Bread #203077 in the Shops at Walnut Grove for keeping me well fed and alert each weekend as I completed this dissertation.


TABLE OF CONTENTS
viii
CHAPTER
I. SENATE BILL 189 AND THE COF FUNDING MODEL.........................1
Introduction...................................................1
Colorado Higher Education Funding Model........................4
Colorados Higher Education Budget Approval Process.....4
College Opportunity Fund................................6
Fee for Service.........................................8
Funding of Colorado Institutions of Higher Education -
Pre and Post SB189.............................................9
Colorado Tax and Expenditure Limitations, Amendment 23,
Referendum C, and the Higher Education Allocation.............13
Motivation for Study..........................................15
Contributions to Literature...................................17
Scope and Limitations.........................................20
Organization..................................................21
II. LITERATURE REVIEW................................................23
Introduction to Higher Education Funding......................23
Theoretical and Empirical Framework of Budgetary
Decision Making...............................................23
Budgetary Decision-Making.....................................24
Incrementalism................................................28
Real Time Budgeting...........................................33
Performance Budgeting
34


IX
III. RESEARCH QUESTIONS AND METHODOLOGY............................39
Introduction...............................................39
Research Questions.........................................39
Quantitative Methodology...................................42
Research Question 1 Methodology......................42
Research Question 2 Methodology......................45
Research Questions 3 and 4 Methodology...............48
Qualitative Methodology....................................51
IV. THE FUNDING OF HIGHER EDUCATION IN COLORADO...................55
Introduction...............................................55
Developing a Framework to Measure the Stability of
Higher Education Funding...................................55
Hypotheses.................................................56
Equations and Data.........................................58
Results of Equation 1......................................60
Results of Equation 2......................................65
Discussion.................................................67
Equations 1 and 2....................................67
State General Fund Revenues as an Indicator of Stability.69
Enrollment as an Indicator of Stability..............75
Conclusion.................................................80
V. FEE FOR SERVICE AS A MECHANIS TO REWARD PERFORMANCE OR
MAINTAIN STATUS QUO?..........................................83
Introduction...............................................83


X
Hypotheses...................................................84
Equations and Data...........................................85
Results......................................................89
Discussion..................................................108
Conclusion..................................................118
VI. DID THE COF FUNDING MODEL IMPROVE ACCESS TO HIGHER
EDUCATION FOR COLORADO RESIDENTS?..............................121
Introduction................................................121
Hypotheses, Equations, and Data.............................122
Colorado Resident Student Enrollment.................122
Equation 5...........................................123
Colorado Resident Underserved Student Enrollment.....126
Equation 6...........................................127
Results.....................................................128
Discussion..................................................138
Conclusion..................................................145
VII. CONCLUSION.....................................................148
Introduction................................................148
Review of the Research Findings.............................148
Discussion..................................................152
Limitations of Empirical Study..............................155
Contributions to Empirical Research.........................157
Contributions to Theoretical Research.......................159
Future Research
163


XI
REFERENCES............................................................166
APPENDIX
A. Data Sources....................................................186
B. Interview Questions.............................................188
C. Interview Results...............................................189
D. Independently Governed Institutions of Higher Education Funding
Patterns.........................................................192
E. Community College System Funding Patterns.......................196
F. Colorado State University System Funding Patterns...............203
G. University of Colorado System Funding Patterns..................205


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LIST OF FIGURES
FIGURE
1.1 Higher Education Budget Process.............................................5
1.2 General Fund Revenues, University Allocation, and Enrollment Trends.........11
3.1 Research Question Flow Chart................................................41
3.2 Allocation of Higher Education Funding by Political Party...................44
4.1 Average of All Institutions AHEA/ASGF......................................66
4.2 Annual Percentage Change in General Fund Revenues and
Higher Education Allocation..................................................70
4.3 Sources of Appropriated Revenues............................................74
4.4 Aggregate Higher Education Allocation and Enrollment Trends.................77
4.5 Higher Education Allocation Per Student.....................................78
5.1 Colorado Mesa University Funding Patterns....................................99
5.2 Metropolitan State University of Denver Funding Patterns....................100
5.3 MSU Denver Degrees Conferred vs Initial Fee for Service.....................101
5.4 Results of Equation 4......................................................105
5.5 Results of Equation 4 Community Colleges Removed.........................106
5.6 Western State Colorado University Funding Patterns.........................Ill
5.7 Institutional Distribution as a Proportion of the Overall
Higher Education Allocation.................................................114


LIST OF TABLES
xiii
TABLE
1.1 Metropolitan State University of Denver FY 2015 Tuition and Fee Schedule......7
1.2 Colorado General Fund Appropriations, Department of Higher Education
Appropriation, and Resident Headcount for Fiscal Years 2000 through 2014 Adjusted for Inflation......................................................10
2.1 Empirical Municipal Budgeting Studies.........................................32
3.1 List of Variables Used in Research Question 1.................................44
3.2 List of Variables Used in Research Question 2.................................47
3.3 List of Variables Used in Research Question 3.................................50
3.4 List of Variables Used in Research Question 4.................................51
4.1 Descriptive Statistics Equation 1.............................................61
4.2 Correlations for Equation 1...................................................62
4.3 ANOVA Results for Equation 1..................................................63
4.4 Summary of Regression Analysis for Variables Predicting HEA...................64
4.5 Paired Samples Statistics for Equation 2.......................................65
4.6 Paired Samples Correlations for Equation 2.....................................65
5.1 Equation 3 Descriptive Statistics.............................................90
5.2 Equation 3 ANOVA Results......................................................91
5.3 Equation 3 Coefficients.......................................................91
5.4 Modified Equation 3 ANOVA Results.............................................92
5.5 Modified Equation 3 Coefficients..............................................93
5.6 Modified Equation 3 Pearson Correlation.......................................97
5.7 Equation 4 Descriptive Statistics............................................104
5.8 Equation 4 Paired Samples Statistics.........................................107


xiv
5.9 Equation 4 Paired Samples Correlations...................................107
5.10 Equation 4 Paired Samples Test.........................................108
6.1 Colorado Resident Enrollment Pre and Post SB189 Group Statistics.........129
6.2 Colorado Resident Enrollment Pre and Post SB189
Independent Samples Test..................................................130
6.3 Underserved Colorado Resident Enrollment Pre and Post SB189
Group Statistics..........................................................131
6.4 Underserved Colorado Resident Enrollment Pre and Post SB189
Independent Samples Test..................................................131
6.5 Colorado Resident Enrollment Correlations.................................132
6.6 Underserved Colorado Resident Enrollment Correlations....................133
6.7 Colorado Resident Enrollment ANOVA........................................135
6.8 Colorado Resident Enrollment Coefficients.................................136
6.9 Underserved Colorado Resident Enrollment ANOVA...........................137
6.10 Underserved Colorado Resident Enrollment Coefficients...................137
6.11 Colorado and National Underserved Enrollment............................145
7.1 Summary of Key Findings Related to Hypotheses............................152


1
Chapter I
SENATE BILL 189 AND THE COF FUNDING MODEL Introduction
In August 2001, Colorados Governor Bill Owens formed a blue ribbon panel to examine issues facing public higher education in Colorado. The panel was charged with: 1) reviewing the states higher education funding mechanism to ensure adequate funding for Colorados institutions of higher education; 2) considering measures to increase in-state college participation by Colorados citizens; 3) determining the extent to which the state benefits from public funding to institutions of higher education; 4) determining the potential for allocating additional state resources to higher education; 5) joining with members of the Colorado Commission on Higher Education to amend the role and mission of each state institution; and, 6) recommending new initiatives to ensure the availability of resources and authority to enhance the quality and nature of the Colorado higher education system (Blue Ribbon Panel, 2003).
At that time, Colorado had one of the largest populations of individuals over-25 years old holding a bachelors degree, yet was ranked well below the national average in the number of recent high school graduates enrolled in post secondary education. The panel concluded that access for underserved students, as defined by socioeconomic status or by race/ethnicity, was lower than the national average, and that the needs of the state and of this population would be better served if access to postsecondary education within this demographic was expanded. To address these issues, the panel recommended fundamental changes in Colorado's higher education funding mechanisms (Blue Ribbon Panel, 2003).


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The panel proposed increasing the rate of participation in institutions of higher education by underrepresented groups through the creation of educational savings accounts for college students (Blue Ribbon Panel, 2003). The panel (2003) further recommended the creation of a voucher system which would lead not only to greater access to post secondary education, but also to increased efficiency and productivity within Colorados colleges and universities, with the ultimate goal of improving retention and graduation rates for resident students. The panel's recommendations spurred the introduction of Colorado Senate Bill 04-189 (SB189), creating the first voucher system for public institutions of higher education.
Senate Bill 189 was envisioned by the Colorado Commission on Higher Education1 (CCHE) and passed during the 2004 legislative session and enacted the College Opportunity Fund program, which pertains primarily to the funding of undergraduate education. This legislation was passed to implement the recommendations of the Colorado Blue Ribbon Panel by creating a voucher system for higher education. It also created an additional funding mechanism that allowed the state to purchase certain services from higher education institutions and that ensured that no one institution would be significantly harmed or rewarded with regard to the implementation of the bill. Specifically, this bill established the College Opportunity Fund (COF) as the voucher or stipend mechanism, a Fee for Service (FFS) contract to purchase services from the
1 The Department of Higher Education is the executive branch office responsible for overseeing the state institutions of higher education. The Colorado Commission on Higher Education is the statewide governing board responsible for overseeing state institutions of higher education. CCHE is defined as the entity responsible for ultimately overseeing all funding decisions of higher education.


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higher education institutions, and performance contracting in which each institution participating in COF funding was required to enroll by the CCHE (SB189, 2004).
The bill requires each participating institution to enter into performance contracts with the CCHE. Institutions receiving COF and FFS funding are required to negotiate with the CCHE to identify measures of performance and accountability that the institutions will achieve during the coming fiscal year. The performance contracts specify performance measures that address: 1) Student access and success; 2) Quality; and, 3) Institutional efficiency (SB189). As with other performance-based funding programs, these contracts were intended to provide greater flexibility to institutions in exchange for increased accountability (Colorado State Auditors Office, 2012; Hillman, Tandberg & Gross, 2014).
The purpose of this research is to analyze the higher education funding mechanism created with the passage of SB 189 in order to determine if it has had an impact on resource adequacy, institutional access, and performance since its implementation. A further goal is to inform policy makers whether this funding mechanism has led to higher education successfully attaining the stated goals of the bill.
In this analysis, the following questions are addressed: 1) Has the state-wide funding level of Colorados higher education stabilized since the implementation of SB189? 2) Is the Fee for Service allocation to institutions correlated with services, or with maintaining proportional increases to individual institutions? 3) Has the rate of enrollment of Colorado residents to state funded institutions of higher education increased since the implementation of the SB189 funding model? and, 4) Has the share


4
of participation of underrepresented groups increased since the implementation of this
funding model?
Colorado Higher Education Funding Model Colorado's Higher Education Budget Approval Process
Determining the allocation of funds to higher education in Colorado begins with the governor. The Colorado Governors Office, working with the Office of State Planning and Budgeting (OSPB) and the Colorado Department of Higher Education (CDHE), recommends a total appropriation amount for higher education, which includes both COF and FFS, to the Joint Budget Committee (JBC). The JBC then determines the final appropriation amount to be allocated to the states higher education system during the legislative session.
As the final allocation is being determined, the CDHE, in consultation with the higher education governing boards, determines the amounts for COF and FFS, and then recommends these amounts to the JBC. Approximately 80 percent of higher education funding is allocated to either COF or FFS, with the remaining funding allocated to local community colleges and other programs, such as CDHE infrastructure funding, funding for History Colorado, and state financial aid. Figure 1.1 illustrates Colorados higher education state appropriation process.


Higher Education Budget Request Process
Figure 1.1 Higher Education Budget Approval Process


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College Opportunity Fund
In various state prepared documents, depending upon the context, COF can mean the total funding for institutions of higher education, or the stipend provided to individual students. COF is officially defined as those funds appropriated to institutions of higher education for student stipends. CDHE works with the governing boards to: 1) Identify anticipated enrollment change; 2) Identify the portion of total state higher education allocation that will be allocated to COF funding; and, 3) Determine the per student per credit hour COF amount.
To determine the COF funding allocation for colleges and universities, the institutions negotiate amongst themselves formally as a group, and with CDHE, to identify the total amount of COF funding. Concurrently, each institution identifies the anticipated credit hour production (CHP) for the coming fiscal year. The per-student funding is calculated by dividing the total COF funding by the total anticipated CHP. As a part of this negotiation, FFS estimates are also negotiated to balance the individual institutional distributions with the recommended overall higher education allocation. Ultimately, the COF amount per credit hour and the anticipated COF and FFS distribution to each institution is approved by the JBC and legislature.
After the legislature has set the dollar amount of funding per credit hour, a student has access to her COF funding to pay for a portion of her college coursework. At the institutional level, university and colleges set the student tuition charges2 by identifying
2 Higher education institutions need legislative and governing board approval when determining the tuition rates. COF and the student share of tuition should reflect the full cost of providing education.


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a portion that the student must pay, and the portion that can be paid with COF funds3. A student has the choice of authorizing the state to pay her portion of the COF amount, or can choose to pay for her total tuition charges. Given the total tuition, Table 1.1 illustrates how tuition is divided between COF and self-pay portions and is based on fiscal year 20154 tuition charges for Metropolitan State University of Denver (MSU Denver):
Table 1.1
Metropolitan State University of Denver FY 2015 Tuition and Fee Schedule
Credit Hours COF Funding Student Share of Tuition Total Tuition
1 75.00 207.20 $282.20
2 150.00 414.40 $564.40
3 225.00 621.60 $846.60
4 300.00 828.80 $1,128.80
5 375.00 1,036.00 $1,411.00
6 450.00 1,243.20 $1,693.20
7 525.00 1,450.40 $1,975.40
8 600.00 1,657.60 $2,257.60
9 675.00 1,864.80 $2,539.80
10 750.00 2,072.00 $2,822.00
11 825.00 2,279.20 $3,104.20
12 900.00 2,486.40 $3,386.40
13 975.00 2,486.40 $3,461.40
14 1,050.00 2,486.40 $3,536.40
15 1,125.00 2,486.40 $3,611.40
Source: Metropolitan State University of Denver FY 15 Tuition Table
3 In addition to the COF funding available for state institutions of higher education, private institutions of higher education may receive some funding if they enter into performance contracts with CDHE. If they enter into a performance contract, their Pell-eligible students may receive 50 percent of the COF Stipend amount set by the General Assembly (SB189). Currently, Colorado Christian University, Regis University, and the University of Denver have entered into performance contracts with CDHE (Source: CDHEs Website).
4 The Colorado legislature sets its budget annually. Fiscal year 2015 is the Colorado fiscal year ending June 30, 2015.


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Using Table 1 as a reference, a student taking 10 credit hours is charged $2,822 in tuition and is eligible for $750 in COF funding. The student may apply her COF funding towards the total tuition, which means that she is only responsible for the remaining share of tuition, or $2,072. A student may also elect to pay the total tuition amount without receiving COF funds. Students may only receive 145 credit hours of COF funding during their career in higher education. Once a student exceeds 145 credit hours taken at state funded institutions, she is no longer eligible for COF funding and must pay the total tuition charges.5
Additionally, COF funding per credit hour may be changed during the fiscal year based on state revenue levels. For example, in fiscal year 2009, the state experienced a significant general fund revenue shortfall. In response to this shortfall, the legislature reduced the per credit hour stipend from $92 per credit hour to $68 per credit hour (Colorado JBC Appropriations Report, 2010). SB189 prevents institutions of higher education from raising the share of self-pay student tuition to recover lost COF funding. Using the previous example, if the state eliminates the COF funding during fiscal year 2015, MSU Denver would only be allowed to charge the student the self-pay share of tuition, or $2,072 for 10 credit hours of courses.
Fee for Service
In addition to COF funding, SB189 created the Fee for Service (FFS) funding mechanism. SB189 allows CCHE to enter into fee for service contracts with institutions of higher education to purchase certain services from the institutions that are not
5 There is a waiver process where a student may request an exception to exceed the 145 hours. Waivers are granted on a case-by-case basis, and may receive up to an additional 30 credit hours.


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covered by COF funding. CCHE may use fee for service funds to purchase: 1)
Educational services in rural areas; 2) Educational services required to meet reciprocal agreements with other states; 3) graduate school services; 4) Educational services that increase economic development opportunities in the state; and 5) Specialized educational services, professional degrees, and programs that address identified state or national priorities.
Essentially, FFS funding to colleges and universities is a purchase of all other higher education services outside of providing undergraduate education. FFS was also intended to supplement COF funding to ensure that no one institution significantly benefited or was harmed by COF funding in the FY 2006 transition (Protopsaltis, 2008). FFS is the difference between the overall funding allocation to 4-year and 2-year institutions and the COF amount.
Funding of Colorado Institutions of Higher Education Pre and Post SB189
SB 189 fundamentally changed the allocation process for Colorado institutions of higher education. Legislators intended to fund higher education using a stipend model, allowing the funding to follow the student (Protopsaltis, 2008). Table 1.2 identifies the real overall general fund appropriation, the real allocation of funding to higher education, and enrollment for fiscal years 2000 to 2014. In reviewing Table 2, the states general fund appropriation increased slightly more than 19 percent from fiscal year 2000 to 2014. However, the overall higher education appropriation decreased approximately 9 percent, while enrollment increased over 23 percent during this same
time.


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Table 1.2
Colorado General Fund Appropriation, Department of Higher Education Appropriation, and Resident Headcount Enrollment for fiscal years 2000 through 2014 Adjusted for Inflation__________________________________________________________________________
Fiscal Year General Fund Appropriation* Change from Prior Year Higher Ed General Fund** Appropriation* Change from Prior Year Enrollment Change from Prior Year
2000 $7,330,357,319 8.14% $991,547,312 2.82% 148,429 1.99%
2001 $7,587,509,102 3.51% $995,765,613 0.43% 149,724 0.87%
2002 $7,748,417,470 2.12% $973,385,512 -2.25% 145,855 -2.58%
2003 $7,622,533,279 -1.62% $996,423,626 2.37% 154,551 5.96%
2004 $6,899,475,199 -9.49% $730,708,411 -26.67% 161,214 4.31%
2005 $7,208,362,663 4.48% $725,662,161 -0.69% 163,883 1.66%
2006 $7,599,248,127 5.42% $722,866,376 -0.39% 161,603 -1.39%
2007 $7,945,859,188 4.56% $805,111,259 11.38% 158,458 -1.95%
2008 $8,266,166,896 4.03% $852,489,099 5.88% 159,009 0.35%
2009 $8,439,715,713 2.10% $893,835,441 4.85% 162,319 2.08%
2010 $8,281,702,160 -1.87% $731,030,428 -18.21% 177,522 9.37%
2011 $7,568,080,461 -8.62% $700,552,741 -4.17% 189,954 7.00%
2012 $7,348,191,382 -2.91% $653,712,302 -6.69% 189,946 0.00%
2013 $7,779,219,295 5.87% $636,422,264 -2.64% 186,536 -1.80%
2014 $8,094,186,715 4.05% $659,062,854 3.56% 182,596 -2.11%
Source: Colorado Joint Budget Committee Appropriations Reports, Colorado Department of Higher Education Fall Semester Enrollment Database
*Values are constant, base year is 2000 and each year is the final fiscal year end appropriations as reported by the JBC Appropriations Report for the following year.
**The total general fund appropriation for the Department of Higher Education includes the institutional appropriations for the years prior to 2006, COF and FFS for years 2006 and later, financial aid, and appropriations to local area colleges.
An implicit goal of SB189 and the Blue Ribbon Panel was to stabilize higher education funding. From fiscal years 2003 to 2004, state funding to higher education had decreased by 26.7 percent, while enrollment increased by 4.3 percent. CDHE and institutions of higher education crafted SB189 as a means to prevent future reductions of that magnitude. The COF stipend would create an entitlement to students, making it difficult for the JBC and legislature to reduce funding in years where there were revenue shortfalls (Protopsaltis, 2008).
The pre-SB 189 change in the overall state general fund appropriation from fiscal year 2000 to 2005 was an increase of more than 6 percent. Over that same period of


11
time, the appropriation to higher education decreased by nearly 25 percent, while enrollments jumped by over 10 percent. During fiscal years 2006 to 2014, after the implementation of SB189, state funding increased 6.5 percent, while the higher education appropriation decreased approximately 9 percent. Enrollment over this same period increased nearly 13 percent. These figures indicate that SB189 may have partially stabilized higher education funding, decreasing the higher education funding reduction from 25 percent in pre-SB189 years, to only 9 percent after the law was passed.
However, based upon an examination of funding and enrollment trends, it would appear that funding decisions for higher education are made based on incremental changes to state funding. Figure 1.2, using data from Table 1.2, illustrates the appropriation and enrollment patterns in Colorado from fiscal years 2000 through 2014.
Figure 1.2 General Fund Revenues, University Allocation, and Enrollment Trends


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Fiscal years 2000 through 2004 see steady enrollment growth paired with steady declines in higher education appropriations. Post SB189 implementation in fiscal year 2006, one sees steady growth in enrollment from fiscal years 2008 through 2011, but an even sharper decline in appropriations for higher education. In contrast, after fiscal year 2011, a steady decline in enrollment begins, but is now offset with an increase in state funding.
Total values may mask underlying phenomenon and by examining impacts on individual institutions, it becomes unclear if the implementation has achieved its intended goals. For example, the University of Northern Colorados (UNC) enrollment peaked at 9611 students in fiscal year 2005, the year before SB189 was implemented. Since that time, UNC enrollment has declined to 8201 students: a decrease of nearly 15 percent. COF funding to UNC decreased during this time as well, from just over $21 million in fiscal year 2006, the first year of SB189s funding model, to slightly less than $14 million in fiscal year 2014, a decrease of 34.5 percent. Yet during this same time period, UNCs FFS went from $14 million to $19 million, an increase of nearly 41 percent. Also during this span of time, overall higher education funding increased by 2 percent (Colorado Department of Higher Education, Search Data, ND; University of Northern Colorado Budget Data Book, 2006; University of Northern Colorado Budget Data Book, 2014).
Another institution, Colorado Mesa University5 (Mesa) has seen significant enrollment growth after the implementation of SB189 in fiscal year 2006. Mesas enrollment increased nearly 63 percent, the COF funding for the institution increased
5 Colorado Mesa University was formerly known as Mesa State College.


13
by 28.6 percent, while the FFS funding decreased by 27 percent from fiscal years 2006 to 2014 (Colorado Department of Higher Education, Search Data, ND; Mesa State College Budget Data Book, 2006; Colorado Mesa University Budget Data Book, 2014). The COF funding allocations appear to reward Mesa for its ability to recruit and retain students, and penalize UNC for decreasing enrollment. However, FFS for UNC increased significantly during this same time, while Mesas decreased. This may be due to the efforts of UNC to maintain its incremental funding relationship with regards to other institutions of higher education. These examples beg further exploration to determine whether funding mechanisms reward enrollment and performance.
Colorado Tax and Expenditure Limitations, Amendment 23, Referendum C and
the Higher Education Allocation
In order to understand the higher education allocations, it is important to also understand the state adopted tax and expenditure limitations (TELs) that impact budgetary decision making. In 1982, Colorado voters passed the Gallagher Amendment, which capped the portion of property taxes from residential properties and shifted the burden to business (Martell & Teske, 2007). As a result of several constitutional initiatives to limit government spending in Colorado, the legislature passed the Avershaug-Bird Amendment, which limited state general fund spending to th elesser of a six percent increase over prior year spending or, in total, five percent of the state personal income (Martell & Teske, 2007).
Perhaps one of the most restrictive TELs in the country was passed by Colorado voters in 1992 (Martell & Teske, 2007). Article X, Section 20 of the Colorado Constitution, commonly known as the Taxpayer Bill of Rights (TABOR) places


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restriction on the amount of general fund revenues that can be collected and spent by the state (Colorado JBC Appropriations Report, 2006). Under TABOR, total general fund revenues, as well as some cash fund revenues, can only grow based on population growth and the consumer price index, which limits the spending of the state and local governments, as well as restricting the ability to raise taxes without voter approval (Martell & Teske, 2007).
In addition to these TELs, Colorado citizens voted for Amendment 23 in 2000. Amendment 23 required the state to increase base and categorical funding by inflation plus one percent through 2010, and thereafter at the rate of inflation (Martell & Teske, 2007). Amendment 23 had the effect of requiring the state to increase funding to K-12 regardless of the states condition of budget. The combined pressure of the TELs enacted in Colorado and Amendment 23, along with a declining state economy in years 2000 through 2003, led to significant funding cuts in Colorado government. Among the hardest areas hit during these cuts was the allocation to Colorado higher education (Martell & Teske, 2007). During this time, the state legislature projected that there would be zero state funding for higher education by 2009, without some sort of intervention.
In part, due to the significant pressures but on the Colorado budget, the Colorado General Asembly referred Referendum C (Ref C) to the registered electors of Colorado at the November 2005 election. This measure passed and was adopted by the state to retain and spend moneys in excess of the constitutional limits required by TABOR. This measure authorized the state to retain and spend all state revenes in excess of the limitation on state fiscal spending from fiscal year 2006 through 2010 (Colorado JBC


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Appropriations Report, 2006). The passage of Ref C helped to alleviate some of the pressure to continue to reduce higher educations funding in order to comply with the restrictions of TABOR (Colorado Fiscal Institute, 2015). It should be noted that at the same time that Ref C was passed, SB189 exempted higher education from the requirements of TABOR.
Motivation for the Study
The prevailing logic with regard to passing SB189 was that implementation of a market-based voucher system would incent colleges to be more responsive to students, and more accountable for their outcomes (Blue Ribbon Report, 2003; Hillman, et. al., 2014). Stated goals included enhancing institutional performance, as measured using a variety of criteria, including improvements in: 1) Enrollment of underrepresented groups; 2) Enrollment of Colorado residents; 3) Retention rates; and, 4) Graduation rates.
An important contextual factor for consideration is that budgeting in Colorado operates under the Taxpayer Bill of Rights (TABOR), which constitutionally limits the size and growth of the state budget. Higher education took deep cuts in fiscal years 2003 and 2004 to allow the JBC to balance the budget within revenue and spending limits. These cuts impacted educational spending at all levels, but had an undeniably greater impact on higher education, due to its discretionary nature as opposed to elementary and secondary educational funding (Protopsaltis, 2008). SB189 was partially intended to reposition institutions of higher education as enterprises, effectively shielding their revenues from the TABOR calculation that would help remove some of the restrictions on state general fund revenue growth.


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In response to the 2003 and 2004 budget cuts driven by the nexus of the recession and TABOR, an additional objective of SB189 was to help stabilize funding to higher education by creating a new entitlement for Colorado students attending state institutions of higher education (Protopsaltis, 2008). While functionally equivalent, the intent was that COF vouchers, rather than direct appropriations for institutions of higher education, would solidify student expectations of state support and put pressure on the legislature to maintain it, as if the support were mandatory (Protopsaltis, 2008). When SB189 was introduced, it met with resistance from the JBC due to concerns that this entitlement would restrict the legislatures ability to balance the budget, as it would be less able to reduce higher educations share of the budget (Protopsaltis, 2008).
Proponents of SB 189 hoped that it would cause the higher education budget to automatically increase when state revenues grew. The intention was to simultaneously leverage public pressure to insulate it from reductions during times of fiscal retreat or crisis, when legislators would be facing calls to make cuts in discretionary funding in order to "balance the budget."
SB 189 was also intended to incent individual institutions and higher education in general to improve their aggregate performance. The bill created the COF mechanism designed to influence institutional behavior, with the ultimate goal of increasing resident student enrollments through recruitment and retention efforts. It also put FFS and performance contracts in place to ensure that institutions were graduating students as well as providing educational services that benefited the state as a whole. However, with the implementation of FFS funding, SB189 also sought to ensure that no institution was significantly helped or harmed due to fluctuations in enrollment


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(Protopsaltis, 2008). In essence, SB189 put competing budgetary expectations into play. The bill has built-in incentives for institutions to receive additional funding for recruiting and retaining students, as well as the FFS funding mechanism designed to mitigate fluctuations in overall funding to individual higher education institutions.
This research seeks to examine the funding of Colorado institutions of higher education before and after the implementation of COF and FFS funding mechanisms to evaluate whether or not it has achieved its stated goals. It also surveys the budgetary decision making process to determine how it has been influenced by the performance of institutions. It uses budgetary theory including incremental budgeting (Jones, 1999; Wildavsky, 1986), politics of revenues, expenditures and balance (Rubin, 2014), real time budgeting (Rubin, 2014; Thurmaier & Willoughby, 2001), and performance budgeting (Jordan & Hackbart, 1999; Lee & Burns, 2000; Willoughby, 2004) to explain the total funding allocated to Colorado institutions of higher education, as well as the individual distribution of these funds to institutions.
Contributions to Literature
Research to date on the implementation and success of SB189 has been limited in nature. Prior research has largely focused upon the political process of securing its passage (Protopsaltis, 2008), the initial success of the COF funding mechanism (WICHE, 2009), the initially improved access to higher education for underserved Colorado resident students (Ash, 2011), compliance with the bill (Colorado State Auditors Office, 2012), and the overall effectiveness of SB189 and the COF funding model (Hillman, et. al., 2014). These latter studies used data from the first five years of SB189s implementation.


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Protopsaltis (2008) studied the passage of SB189 from the policy process perspective, performing a qualitative study using the multiple streams, and advocacy coalition frameworks, as well as punctuated equilibrium theory. The study was limited to the policy processes that led to the implementation of SB 189, and did not include an assessment of the ultimate success or failure of the COF funding model.
In fiscal year 2009, the Colorado Department of Higher Education (DHE) contracted with the Western Interstate Commission for Higher Education (WICHE) to review the implementation of the COF funding mechanism to determine if it had achieved its stated goals (WICHE, 2009). The WICHE study looked at five years of data covering fiscal years 2004 through 2008. The analysis utilized descriptive statistics to determine whether or not the COF funding model had improved access for Colorado resident students, as well as qualitative analysis to assess the success of the COF and FFS funding mechanisms. The report concluded that after three years of COF, institutions and students were more familiar with the voucher program, but that institutions had not improved their performance regarding access.
In 2011, a more in depth study was conducted on the question of access for Colorado resident students in community colleges (Ash, 2011). The study captured enrollment data from fiscal years 1998 to 2010 and used cross-tabulations and chi-square analyses to examine the impact that the implementation of SB189 had on enrollment trends. It specifically looked at enrollment for underserved students in terms of age, ethnicity, and gender, and found that the funding model had not improved
access for underserved students.


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A fourth study of the COF funding model was conducted by the Colorado State Auditors Office (2012). The study was a performance audit, intended to review CDHEs compliance with the requirements of SB189. The audit found that CDHE should take steps to improve the way in which funding recommendations for COF and FFS were made as regards to keeping pace with inflation and enrollment growth. The study relied largely on descriptive statistics rather than predictive statistics to draw its conclusions.
Finally, Hillman, Tandberg, and Gross (2014) conducted a study of Colorados higher education funding model, employing difference-in-difference analysis, and incorporating data from fiscal year 2000 to 2010. The study used data from Colorado and several similar states in its analysis, evaluating the voucher system to determine if the market-based system improved either institutional efficiency or access. Hillman, Tandberg and Gross, found little evidence that the funding model created either cost efficiencies, or improved access within the 4-year period. They also concluded that among community colleges, the funding model improved cost efficiencies but decreased college access for some underrepresented groups. The study did not look at the FFS funding mechanism.
Building on previous studies regarding Colorado's higher education funding model, the current research delves into the overall impact that SB189 had on institutional performance. This holistic view incorporates both the COF and FFS funding mechanisms to analyze how those distributions are derived, as well as whether or not the funding model has incented institutions to improve performance and increase access to Colorado resident students in general, as well as underserved resident students. Previous research relied on qualitative analyses, and mainly descriptive


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statistics to analyze the effectiveness of COF (Ash, 2011; Colorado State Auditors Office, 2012; WICHE, 2009). Additionally, previous studies did not include in depth analysis of the FFS funding model (Hillman, et. al., 2014). To better understand the effects of SB189 on institutional budget distributions and performance, this study uses a 16-year panel data set. This section adds to the literature by 1) deepening our understanding of Colorados budget allocation responses before and after SB189, 2) using statistical analysis to determine the success of the Colorado funding model in improving institutional performance, 3) conducting qualitative interviews with institutional leadership to support the statistical analysis; and 4) assessing the longer term impacts of SB189 and funding to Colorado institutions of higher education. Results from this research may inform state legislators, policy makers, and institutional leadership regarding the effectiveness of SB189 in delivering its intended goals.
Scope and Limitations
The scope of this dissertation is to construct models capable of measuring the success of SB 189 in achieving the implicit and stated goals for funding institutions of higher education. Institutional budget data books and data reported to the CDHE are used in the models to measure budget distributions, enrollment trends, and performance factors such as numbers of graduates. Regression analysis is used to analyze the effectiveness of SB 189 in improving institutional performance through budget incentives. Descriptive analyses are used to assess patterns of budget allocation, as well as the relationships among the states fiscal health, enrollment trends, and other
institutional characteristics.


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This study is limited by several factors. The period of the study, 16 years, is limited by the implementation date of SB189 in 2006, and the fact that that data related to SB189 began to be collected in that fiscal year. Although SB189 was passed in part to ensure that state funded institutions of higher education were exempted from the requirements of The Tax Payer Bill of Rights (TABOR), TABOR lies outside the scope of the present study. The study is limited to institutions of higher education in Colorado, excluding data from other states, and so will have little applicability to other states budgetary decisions with regards to higher education.
Finally, during the 2014 legislative session, the Colorado General Assembly passed House Bill 1389 (HB1389). HB1389 sought to ensure that distributions at the institutional level were driven more by enrollment and performance. The data for this study ends during fiscal year 2014, before HB1389 was enacted, and so does not evaluate whether this legislation has led to improved institutional performance.
Organization
The rest of the dissertation is organized as follows. Chapter two outlines the theoretical and empirical framework for understanding how budgetary decisions are made, specifically with allocations of state general funds to institutions of higher education. Chapter three presents the research questions and methodology used in the study. Chapter four examines funding stability for institutions of higher education. In chapter five, the research focuses on the FFS mechanism and what factors determine the amount of FFS distributed to each institution, as well as providing the thematic results from the qualitative higher education leadership interviews. Chapter six seeks to answer the question of institutional performance with regard to increasing the number


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of Colorado resident and underserved students entering into higher education. Chapter seven discusses the analytical findings, offers policy implications, and presents considerations for future research.


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CHAPTER II LITERATURE REVIEW Introduction to Higher Education Funding
At the state level, several factors impact higher educations state funding allocations, including the states fiscal health, state demographic indicators, competing state priorities, the political climate, and the overall state culture (Archibald & Feldman,
2006; Rizzo, 2006; Tandberg, 2010a; Weerts & Ronca, 2012). Over the last 30 years, overall public support for higher education has failed to keep pace with the rising costs of educating students. This has led to some negative consequences for students, including rising tuition rates, tightened enrollments, and decreased retention (Weerts & Ronca, 2012). One reason for this decrease in public support is the fact that this funding stream is subject to the discretion of lawmakers, who are themselves under pressure to fulfill a variety of state priorities, of which higher education is only one. Public funding for higher education presents an attractive target for state lawmakers because of their perception that institutions can offset decreases in these appropriations with increases in tuition and fees, as well as institutional initiatives such as the pursuit of private donations, applications for research funding, institutional initiatives, and entrepreneurial activities (Tandberg, 2010a; Tandberg, 2010b).
Theoretical and Empirical Framework of Budgetary Decision Making The public budgeting process is a complicated endeavor. The budget document itself serves a dual purpose in the budgetary process, disclosing the priorities of key institutional actors in the process, and serving as a yardstick by which to measure the relative success of those actors in shaping the final budget that is enacted into law


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(Gosling, 2009). Budgetary literature helps to understand many facets of the state budgetary-making machinery, such as how the decision to implement SB 189 came about, and how funding decisions were made in subsequent years to allocate funds to public higher education and individual institutions. It can also help to better understand how funding relates to institutional performance.
While no single theory explains the intricacies of budgetary decision-making, four areas of budgetary literature are most germane to this study. First, budgetary decisionmaking literature can help illustrate how decision makers balance competing factors when making decisions about how to allocate budgets (Gosling, 2009). Because budgets reflect important spending priorities, a significant amount of literature exists to help policy analysts better understand the decisions made in preparing state budgets. Second, incremental budgeting theory can help to explain the marginal changes to individual institutional allocations. Third, as budgets continually adjust in real-time to account for information from the environment and each decision stream (Rubin, 2014), real-time budgeting information can help to identify how decisions were made to reduce funding in years of so-called economic correction, and conversely, how funding was allocated in years of economic growth. Finally, performance-based budgeting research illuminates how institutional performance may impact the allocation of funds.
Budgetary Decision-Making
Budgetary decision makers face a number of competing factors when making decisions related to how budgets are allocated. These include prioritizing competing normative values, assessing divergent policy priorities, and the difficult but critical task of balancing self-interest against the public interest (Gosling, 2009). Because budgetary


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decision making is complex and difficult, five distinct but interdependent political clusters are necessary to better understand it: 1) Revenues; 2) Process; 3)
Expenditures; 4) Balance; and, 5) Implementation (Rubin, 2014).
The politics of revenues pertains to the amount of funding that will be available for the upcoming year. It specifically examines how much revenue will be available, and poses key questions related to the level of taxes available for funding and their sources. Revenues are particularly important when it comes to budgeting because they are sensitive to the environment. Changes in the economy affect revenue levels, and the perception of public opinion influences political behavior around tax policy (Rubin, 2014). When applied to higher education funding, the revenue cluster can inform the decision making process significantly. For example, when the state economy is weaker and state funding lower, states often cannibalize the higher education budget to balance the revenue shortfall by relying on the ability of colleges and universities to increase their tuition and fees to make up for the funding shortfalls caused by decreases in their state funding (Tandberg, 2010a; Tandberg, 2010b).
When budgetary decision makers are prioritizing or balancing the competing policy issues, the politics of the process sorts out some of the key elements used to allocate the budget. Key questions, such as how much policy focus should the budget have, and how much should it reflect and articulate interest group preferences, are addressed in this cluster (Rubin, 2014).
This process cluster helps policy analysts to understand how actors made the decisions around the passage and implementation of SB189. We know, for example, that when the COF and FFS funding model was being developed, actors negotiated a


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funding model designed to ensure that no institution was either greatly benefited, or harmed by its implementation (Protopsaltis, 2008). This would indicate that although the intention was to have a significant impact on improving access and institutional performance (Colorado Blue Ribbon Panel, 2003), the policy focus was not only on these measures, but also on maintaining the funding status for each institution in Colorado as SB189 was implemented (Protopsaltis, 2008). The political makeup of the legislature is one important factor to consider with regard to the process cluster. Studies have found that Democratically controlled legislatures tend to favor higher funding for higher education, where Republicans have been found to be less supportive of funding for higher education (Archibald & Feldman, 2006; McLendon, Hearn & Mokher, 2009; Rizzo, 2004).
The politics of the expenditures reflects the priorities that governments place on their programs (Gosling, 2009; Rubin, 2014) and involve technical estimates of expenditures (Rubin, 2014). The revenues that a state generates annually are often inadequate to fully fund all programs at levels that would satisfy these programs or their interest groups. Individual state agencies often have tunnel vision when it comes to budgeting, in that they look foremost to the needs and opportunities of their own programs, and are not expected to reconcile these needs with those of other state agencies competing for the same dollars (Gosling, 2009).
When coupled with the fact that the state budget is probably not going to be able to satisfy constituents of all programs, an environment of intense competition can be created that leads to some very lively politics. The expenditures cluster of the budget process emphasizes competition for limited resources (Rubin, 2014). Since higher


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education funding has seen significant decreases over the past 30 years (Weerts and Ronca, 2012), the competition among institutions of higher education to maintain current levels of spending can be significant. Because the competition for these funds is significant, one would expect only incremental changes in final funding decisions, because intense competition among interest groups generally limits any one group's influence over budgetary decisions (Rubin, 2014). The politics of expenditures are heavily impacted by interest groups who have a significant influence on policy outcomes and the setting of priorities with regard to spending (Heinz, Laumann, Nelson & Salisbury, 1993; Tandberg, 2010a). Institutions of higher education are often considered themselves to be a powerful type of interest group (Tandberg, 2010a), with each institution expected to lobby actively to maintain its current funding level, and to seek incremental increases at least whenever possible.
The politics of the balance cluster reflects the simple question of how the budget must be balanced each year, examining questions such as how to increase revenues, and decrease expenditures, or both. Interest groups can play a significant role in these decisions, as citizens and groups make their opinions known regarding the programs that are vulnerable to budget cuts, or sensitive to tax increases (Downs & Rocke, 1986; Rubin, 2014).
When setting funding priorities for higher education in particular, the community served by the individual institutions under scrutiny may be regarded as a powerful interest group. Institutions located in remote or rural locations are often primary economic drivers for that community. Those institutions may come to rely on their communities to apply pressure to budgetary decision makers, with the goal of ensuring


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that their funding continues to track with changes in funding to the overall higher education system. Institutions in larger communities may not be the primary economic driver, and thus those communities may not be able to lobby as effectively on behalf of their local institutions (Heinz, et. al., 1993; Tandberg, 2010a).
The final cluster that can provide insight into the higher education budgetary process is the politics of implementation. This cluster helps provide a context between the goal of maintaining accountability by implementing the passed budget, and the goal of reshaping the budget throughout the fiscal year to accomplish goals upon which there was no initial agreement (Rubin, 2014). The original stated goal of SB189 when enacted was to improve Colorado citizens' access to higher education, and to improve retention and graduation rates. In that context, greater funding for those institutions experiencing enrollment growth and improved performance would have been a reasonable expectation. However, during the struggle for its passage, SB189 was amended to ensure that the new allocation methodology did not greatly harm or benefit any individual institution (Protopsaltis, 2008).
Incrementalism
Generally, two types of policy changes generate budget allocations: 1) Incremental; and 2) Substantial changes to inaugurate new programs (Thurmaier & Willoughby, 2001). While real time budgeting and budgetary decision-making streams can be leveraged to help understand or explain significant changes to a budgetary process (Rubin, 2014), such as the implementation of SB189 or the significant budget reductions of FY 2009 through FY 2011, incrementalism is a powerful tool for understanding changes to existing budgets or programs on an annual basis (Thurmaier


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& Willoughby, 2001). Incrementalism is among the predominant theories of public budgeting articulated by Aaron Wildavsky in the 1960s, and builds on the work of Charles Lindblom (Bailey & OConnor, 1975; Good, 2011; Lindblom, 1959; Wildavsky, 1986; Wildavsky & Hammond, 1965). Lindblom theorized that individuals process information on the margins to make decisions. This is due largely to the inability of individuals to process large amounts of information, causing them to focus on smaller chunks when decision-making time is imminent (Lindblom, 1959; Simon, 1997).
Because of the complexity of public budgeting, Wildavsky theorized that governments set their budgets based on prior years revenues with the expectation that incremental growth would occur in the current year. He concluded that budget managers treat the budgetary base as given, freeing their attention to the budgetary increments of change requests and recommendations (Davis, Dempster, & Wildavsky, 1966; Gosling, 2009; Wildavsky, 1986; Wildavsky & Hammond, 1965). Based on the theory of bounded rationality, wherein individuals are bounded in their ability to deal with complex situations by their cognitive emotional states (Jones, 2001, Simon, 1997), this style of budget-setting is generally a "bottom-up" process. Typically, the governmental agency initiates the process by building the request, which is then forwarded through the approval process (Behn, 1985; Berry, 1990; Wildavsky, 1986).
Decision-makers are generally only cognitively capable of processing incremental changes to a budget and are not able to manage the budget as a whole when making allocation decisions (Baumgartner & Jones, 2009; Jones, 1999; Jones, 2001; Ryu, 2009). Due to his cognitive shortcomings, an individual will sometimes choose alternatives that are not directed toward meeting the organizations goals, but which may instead be


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chosen so as to meet the individuals goals (Baumgartner & Jones, 2009; Simon, 1997). Concurrently, when working to set annual budgets, departments tend to try to protect what they already have, and to increase their share of any incremental change to the appropriation (Schick, 2004; Wildavsky, 1986). Under this model, budgetary changes tend toward the incremental, because it is extremely difficult to upset the current balance of power among the different competing interests (Downs & Rocke, 1984; Schick, 2004).
Finally, Wildavsky (1986) examined budgeting at the state level and found different examples of incremental budgeting. In his analysis, he found the structure of the budget, and the budgeting process to be an obstruction to comprehensive analysis. He concluded that the most important factor for state budget makers is revenue predictability. If revenues are predictable, even if they are low, incremental budgeting is the normal process used by decision makers.
While researchers have not conducted empirical studies of incremental budgeting on state appropriations to higher education, several studies have been conducted at the local government level (Downs & Rocke, 1984; Lewis, 1984, Rickards, 1984). In an analysis of 105 German cities, Rickards (1984) found incrementalism present in budgeting practices in the larger cities. In this study, he looked at the expenditures of eight functions in the cities as a percentage of the overall general fund budget, and the aggregate total budget over a period of ten years. He performed linear regression analysis on changes in the annual expenditure pattern as influenced by the citys population growth rate, per capita tax base, and the percentage of the total budget comprised of categorical assistance. Rickards found incrementalism present due to the


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larger size of the budget and to increased demands of interest groups that result in decision makers relying on proportional changes to individual budgets. He also found that in cities where the financial conditions were constrained, budgeting was essentially a maintenance activity designed to ensure that administrators carry on past policies within the constrained budget. Finally, he concluded that population size and growth rate had a strong correlation with spending patterns (Rickards, 1984).
In a study of U.S. municipalities, researchers operationalized and tested three incremental theories of budgetary decision making: 1) The bureaucratic process theory, wherein budget changes are incremental due to bounded rationality and the inflexibility of organizational responses; 2) Interest group politics theory, wherein budget changes are incremental due to pressures from interest groups; and, 3) Managerial theory, wherein budget changes are incremental due to mandatory or uncontrollable spending requirements (Downs & Rocke, 1984). They analyzed patterns to changes in the budgets of Pittsburgh between the years of 1943 and 1976, and of San Diego from 1949 to 1978.
The study examined the incremental change in the current years departmental budget allocation as a function of the prior years budget allocation, using random effects models to manage the panel data. Random effects models are a type of hierarchical linear model and are used in the analysis of panel data when one assumes no fixed effects (Allison, 2009). They found that in years of economic growth, the three theories easily accounted for changes to departmental budgets. Yet an analysis of periods when revenue declined revealed that only managerial theory explained why budget cuts were incremental in nature in the face of across-the-board cuts.


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The researchers concluded that in periods of revenue decline, the budget changes would be offset through methods such as replacing retiring or resigning employees with entry-level workers. Further, the research suggested that power relationships among interest groups were less stable than previously thought, and that environmental and institutional factors exert greater influence on the budgetary process than these interest groups (Downs & Rocke, 1984).
The findings of Downs & Rocke (1984) were consistent with research conducted by Lewis (1984), where he found evidence of incremental budgeting in 12 cities in the U.S. from 1964 to 1979. The dependent variable in this study was the percentage change in departmental budgets. He assumed that city budget managers would concentrate on dollar amounts rather than programs. The study used regression analysis and found that the prior years allocation was a good predictor of the current years allocation for almost all government functions (Lewis, 1984). Table 2.1 summarizes the three empirical studies.
Table 2.1
Empirical Municipal Budgeting Studies
Author, Year Dependent Variable Methods Results
Rickards, 1984 Functional expenditure patterns of 8 cities Linear regression Population and growth rate have a strong correlation with spending patterns.
Downs & Rocke, 1984 Departmental budget patterns of 2 U.S. cities from the 1940s to the 1970s. Random effects model with panel data In times of economic distress, budget patterns are incremental.
Lewis, 1984 Departmental budget patterns of 12 cities. Linear regression The prior year's allocation is a strong predictor of the current year's allocation.


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Incrementalism may help to explain the process by which budgets for individual institutions in Colorado's higher education system are set, predicting that individual institutions will work to ensure that only incremental changes to their budget are enacted. Specifically that allocation of incremental funds would be proportionally the same as any changes in their total allocation, regardless of actual performance.
Real Time Budgeting
There are times when alternatives to incremental budgeting are necessary, such as when revenues exceed or fall short of normal incremental levels. These conditions make it difficult to sustain incremental budgeting policies (Jordan, 2003; Schick, 2004). Budgets are continually adjusted in real-time to account for information from the environment, and from each discrete decision stream. Budgetary decision making is not linear and sequential, but rather actors tend to jump from one decision cluster into other decision streams, forcing changes in the midst of decision making or implementation (Rubin, 2014). This model of real time budgeting is analogous to the "garbage can," model of policy making. The wide variety of actors with different goals, agendas and resources compounds the need for the budget to be flexible (Thurmaier & Willoughby, 2001).
Real-time budgeting has proven to be a powerful tool of analysis, revealing a strong correlation between changes in funding levels for higher education and deteriorations in a given state's fiscal health. The fact that institutions can generate additional revenues by increasing tuition and creating new revenue streams (Tandberg, 2010b) allows decision makers to shift a disproportionate amount of funds from higher


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education to other governmental functions in order balance the budget when there are significant general fund revenue shortfalls (Hovey, 1999; Humphreys, 2000; Rizzo, 2004; Tandberg, 2010b).
Real-time budgeting offers insights into how different actors with competing goals interacted to forge important budget reduction agreements. In one example, for the FY 2009 state budget, the Colorado Legislature had appropriated nearly $6807 million to higher education for COF and FFS during the 2008 legislative session (Colorado JBC Appropriations Report, 2008). During fiscal year 2009, state revenues declined significantly, requiring the governor and legislature to amend the appropriation to higher education. During the 2009 legislative session, the Joint Budget Committee reduced higher educations COF and FFS appropriations by approximately $147 million (Colorado JBC Appropriations Report, 2009). Then, during the 2010 session, the legislature reduced the COF and FFS budgets by an additional $223 million (Colorado JBC Appropriations Report, 2010). Effectively, during a two-year period, funding dedicated to COF and FFS was reduced from a high of nearly $680 million to approximately $310 million, a decrease of over 50 percent. In comparison, the states general fund revenues decreased from $7.5 billion to approximately $7 billion during these same years, a decrease of approximately 6.9 percent.
Performance Budgeting
Performance budgeting represents another alternative to incremental allocations
and has been a popular tool for state legislatures and governors since the 1990s (Lee &
Burns, 2000; Willoughby, 2004). Performance budgeting requires the measurement of
7 This amount is different from the figure reported in Table 2 because it has not been adjusted for inflation.


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an individual institution's results, outcomes, and impacts, and ties these factors directly to the budget that is allocated to that institution (Melkers & Willoughby, 1998). Institutional budgets, when tied to performance expectations, express the priorities that the state wishes to measure.
These outcomes generally function as statements of state policy, and as a result, institutions where outcomes are greater will receive funding at a higher level than those institutions with less impressive performance outcomes. Essentially, performance budgets set an expectation of a given program's cost and its outcome achievements, as well as criteria for funding those programs (Rubin, 2014). Performance budgeting creates an expectation that performance-related reforms, if applied correctly, will result in management efficiencies, improved program effectiveness, and will lead ultimately to better budget allocation (Willoughby, 2004).
The expectation that performance measures lead to improved program efficiencies is grounded in the idea that improved efficiencies, or the meeting of performance goals, will be rewarded with additional budget (Behn, 2003). Essentially, budget can be allocated to those programs that have improved their performance measures while minimizing the costs that it took to make those improvements, resulting in improved efficiency. In higher education, market-based reforms, like those legislated in SB189, assume that greater competition and consumer choice will induce colleges and universities to be more responsive to students and accountable for their outcome (Hillman, et. al., 2014). If performance-related reforms yield management efficiencies, then this funding method should have effectively incentivized institutions to improve


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their performance and/or efficiencies in meeting resident student recruitment and retention goals.
Performance measurement interacts with the different clusters of budgeting, by 1) supporting and justifying decisions about budgets; 2) influencing decisions about proposed new programs; 3) providing benchmarks useful in decision making; and, 4) justifying the reallocation of funds given performance information. While there is an expectation that performance measures will enhance the effectiveness of the programs and lead to better decision-making, the literature does not definitively indicate that there has in fact been a real-world impact on how the budget is allocated (Jordan & Hackbart, 1999; Melkers & Willoughby, 2005; Rubin, 1990; Willoughby, 2004). Enacting a movement to fund programs based on their performance has been challenging, because it requires all actors to accept an entirely new funding paradigm. Change on this level is fundamentally difficult (Melkers & Willoughby, 2001). Additionally, given the amount of resources required to identify appropriate performance measures, and the tools to collect that data, the link between actual performance and budget allocations has not been clearly established.
For example, some studies have indicated that enacting performance funding for institutions of higher education has not led to improved retention or graduation rates (Sanford & Hunter, 2011; Shin & Milton, 2004). Shin and Milton (2004) studied the impact that performance funding had on 465 institutions of higher education between 1997 and 2001. They specifically studied the impact that funding methodology (performance or other) had on graduation rates for each institution, finding that improvements in those rates were not influenced by the funding method. In Sanford


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and Hunters (2011) study, the researchers examined the impact of performance funding policies on institutional graduation rates. Their study compared graduation rates at institutions in Tennessee, which had performance funding, to graduation rates at peer institutions not engaged in performance funding. Their findings indicate that despite performance incentives, institutions in Tennessee did not improve graduation rates significantly when compared to their peer Institutions.
One issue with performance funding for higher education is the amount of the appropriation available to reward institutions when performance goals are met. Studies have found that without adequate incentives, institutions will not focus on improving performance, but rather on short-term strategies targeting the legislature with the goal of increasing their incremental funding, regardless of actual outcomes (Layzell, 1999; Sanford & Hunter, 2011; Shin, 2010; Shin & Melton, 2004). Additionally, outside interest group pressure has caused some states to completely abandon efforts to switch to performance based funding. (Alexander, 2000; Burke, Modarresi, & Serban, 1999).
However, despite the fact that performance measurement may not lead to different allocation decisions, it can still have a positive impact on program outcomes. In one survey of local and state governments, respondents indicated that their government functioned more effectively once it began using performance measures, because it could use that performance information to make decisions at the program level (Melkers & Willoughby, 2005). This type of information may be the performance measurement's greatest asset. While the inclusion of performance measures in budget documents may not automatically lead to tying budget allocations to performance outcomes, it might function as an important and useful tool in evaluating institutions, playing a significant


38
role in communicating how well a given institution performs in relation to the outcomes anticipated for it (Jordan & Hackbart, 1999; Melkers & Willoughby, 2005).
In the case of Colorados higher education budget setting environment, SB189 was intended to improve the performance of each institution, as well as the level of citizen access to Colorado's higher education system (Protopsaltis, 2008). If so, then it would follow that the institutions with greater enrollments and performance would receive a greater portion of the states higher education allocation, which aligns with the research in performance based budgeting. To determine whether this was the case in practice, all institutions participating in the COF/FFS funding mechanism were required to enter into performance contracts with the DHE. The performance contracts outlined areas, such as enrollment, retention, and graduation, where institutional performance would be measured. However, the performance contracts did not specifically attach funding outcomes to budgetary allocations (Protopsaltis, 2008; SB189).
The literature on incrementalism, real-time budgeting, and performance funding is germane to Colorados higher education budget process due to the need for a mechanism by which public higher education institutions competing for limited funds may be evaluated. The legislature has implemented a budget system designed to incent institutions to increase enrollment, and to improve retention and graduation rates for Colorados citizens. Dramatic changes to the states revenues in 2003 and 2008 required real-time decisions regarding the higher education budget. SB189 intended to reward institutional performance. The overarching inquiry of this paper seeks to answer is this: did SB189 have its intended impact?


39
Chapter III
RESEARCH QUESTIONS AND METHODOLOGY Introduction
This chapter introduces the four research questions posed by the study, as well as the methodology used to assess the success of SB189 in achieving the implied and stated goals over the ten years since its passage. These methodologies include both quantitative and qualitative analysis. The chapter is organized in the following structure: 1) Research questions 1 through 4 are presented; 2) Quantitative research methodologies and data used are proposed; and, 3) Qualitative research is offered.
Research Questions
This research considers the impact of the passage of SB189 on Higher Education funding in Colorado through the lens of four specific research questions, which are laid out below. The unit of analysis is the state allocation of budget to public institutions of higher education in Colorado, and the data were gathered for fiscal years 2000 through 2014. For fiscal years 2000 through 2005, each institution received funding directly appropriated from the state. For fiscal years 2006 through 2014, funding was delivered via the mechanisms established in SB189. The study analyzes the impact of the new funding mechanism on the goals and objectives of SB189, specifically, seeking to answer the following questions:
Research Question 1 (RQ1) Has the state-wide funding level of Colorados higher education stabilized since the implementation of SB189?
Research Question 2 (RQ2) Is the FFS distribution to institutions correlated with services, or with maintaining proportional increases to individual institutions?


40
Research Question 3 (RQ3) Has the rate of enrollment of Colorado residents to state funded institutions of higher education increased since the implementation of the COF/FFS funding model?
Research Question 4 (RQ4) Has the share of participation of underrepresented groups increased since the implementation of this funding model?
Figure 3.1 overlays the research questions with the variables and theoretical frameworks used in the research. Research questions 1 and 2 pertain to funding, while research questions 3 and 4 pertain to performance.


Environmental
Factors
State Fiscal Health -Enrollment
RQ 3 Number of Colorado Residents RQ 4 Number of Underserved
Budgeting
Budgeting
Budgeting
Budgeting
RQ 2 Higher Education Institutional FFS Allocations
RQ 2- Higher Education Institutional FFS Allocations
Performan
Increment
Performar
Incremen
The politics of expenditures
Political Influences
-Executive Branch -Legislature
The politics of balance
Institutional
Performance
-Enrollment Change -Graduation Rates -Retention Rates
Real time budgeting
Total State Higher Education Appropriation
RQ 1 Stabilized Higher Ed Funding
Incremental budgeting
Performance budgeting & Incremental budgeting
Factors influencing the allocation of funds to individual institutions
Figure 3.1 Funding Flow Research Questions


42
The four research questions are analyzed using quantitative and qualitative empirical analysis. First, quantitative data on HE allocation and distributions, FFS distributions, and performance were analyzed to explore each of the research questions on an individual basis. The structure of Chapters 45, and 6 research methods includes a variety of tools to compensate for the small dataset. The chapters are structured with descriptive, correlational, and regression methodologies. A variety of inferential and descriptive tools were used, including general linear regression, proportional change, t-tests, and graphical illustrations of the data. Second, interviews with key stakeholders were conducted to establish a deeper understanding to allow a useful interpretation of the quantitative results. The specific techniques for each research question are introduced in the following sections, and elaborated in Chapters 4, 5, and 6.
Quantitative Methodology Research Question 1 Methodology
The first research question addresses the stabilization of funding to higher education, with a specific focus on the allocation of that funding. Funding trends are examined from two perspectives: performance and incremental budgeting to operationalize stability. The model comprises two equations, Equations 1 and 2. The unit of analysis for this research question is the state budget appropriation to higher education institutions. The population size for the data set is 400, based on 25 state funded 2-year and 4-year institutions. The equations address the question based on data gathered from fiscal years 1999 through 2014. The data have also been adjusted in


43
Research Question 1 to account for inflation. All values have been adjusted for inflation with a base year of 2014.
Where HEA is the higher education allocation for each institution (/) in year t. AHEA is the percentage change in higher education allocation for each institution (/) in year t. SGF is the overall state general fund revenues in year t. ASGF is the percentage change in state general fund from year t-1 to year t. E is the headcount of Colorado resident students in higher education for each institution (/) in year t. PP reflects the political party in power in year t. SB189 reflects the years before and after SB 189 was implemented. The basis for variable selection used in each equation is presented in
(Eql)
A HEAtx_x A SGFtt_i
= 1, then HE funding is stable
(Eq 2)
Chapter 4. Table 3.1 lists all variables used in this analysis.


44
Table 3.1
List of Variables Used in Research Question 1
Variable Variable Name Description N Missing Measure
HEA Higher Education Allocation The general fund allocation to institutions of higher education, in 2014 real values. 399 1 Scale
SGF State General Fund The state general fund revenues earned in year t, in 2014 real values. 400 0 Scale
ASGF Annual Percent Change in State General Fund The percentage change in general fund revenues for year t from year t-1. 375 25 Scale
AHEA Percent change in the Higher Education Allocation The percentage change in general fund allocation to institutions of higher education in year t from t-1. The head count of resident Colorado students in the Fall semester of year t in state funded institutions of higher education. 373 27 Scale
E Enrollment 400 0 Scale
PP Political Party Control variable identifies the political party control of the Colorado legislature. 400 0 Categorical
SB189 Senate Bill 189 Dummy variable identifies years after SB189 was 400 0 Nominal
implemented.
For Equation 1, a general linear regression was performed to identify the levels of significance for each explanatory variable. The goal of the research question is to address higher education funding stability, and to address the small sample size of one state allocation it was necessary to populate the model with institutional data rather than annual statewide allocations.
To test funding stability post-SB189, Equation 2 compares the average share of higher education funding allocated prior to the enactment of the bill to the average


45
share of allocation after implementation. For Equation 2, a paired samples t test was run to determine if SB189 had a stabilizing impact on higher education funding. This test compares two means that are from the same unit, but represent two different times. A pre-test and post-test is conducted to determine whether there is statistical evidence that the mean difference between the paired operations is significantly different from zero (Urdan, 2010). Equation 2 measures the incremental change in higher education's allocation as a ratio of the incremental change in state general fund revenues.
This data set contains political party (PP) identification data, which is categorical. When the categorical variables have three levels, they are converted to two dichotomous variables. Dummy variables were created for split party control and Republican control. Split party control was coded with a 0, and republican control with a 1. The dummy variable, Democratic, was omitted from the regression (Kremelberg, 2011).
Research Question 2 Methodology
Research question 2 analyzes the use of the FFS funding mechanism in relation to institutional performance. Like RQ1, this analysis employs two equations: Equation 3 and Equation 4. The unit of analysis for these equations is the distribution of FFS funding to institutions. The population size for this data set is 225, based on 25 state funded 2-year and 4-year institutions for fiscal years 2006 through 2014.
IFFSlt = f{lCOFlt, HEAtpi, G\, Tl, Ml, L1, El) (Eq 3)
IFFS[ + lCOFlt
----------------r z 1 (Eq 4)
FFFSlt + FCOFl


46
Where IFFS is the initial Fee for Service distribution made prior to the start of each fiscal year to each institution (/) in year t. ICOF is the initial COF distribution prior to the start of the fiscal year to each institution (/) in year t. HEA is the overall higher education allocation in year t. D is the number of undergraduate degrees awarded by each institution (/) in year t. G is the number of graduate level students at each institution (/) in year t. T is the type of institution, M is the mission of the institution, L is the location of the institution, and E is the size of the institution. Chapter 5 presents the justification for the variables selected for Equations 3 and 4.
Equation 3 employed linear regression to identify the level of significance for each of the explanatory variables. The explanatory variables used in the regression are both categorical and continuous. To manage the variables, the first regression analysis used the stepwise linear regression method in SPSS. This method enters each explanatory variable one at a time, where the variable that contributes the most to the predicted equation is entered first, and then the other variables follow in the same order. This methodology clarifies how each explanatory variable contributes to the outcome (McCormick, Salcedo, & Poh, 2015). The research then utilized the Automatic Linear Modeling feature in SPSS to run a linear regression using the categorical variables. This regression also employs the stepwise method to enter the explanatory variables (Yang, 2013). Each regression was run at the institutional level.
Equation 4 was analyzed using a paired samples t-test similar to the one performed on Equation 2. This test is used to help determine the relationship between the initial COF and FFS distributions to the final COF and FFS distributions. The paired samples t-


47
test was performed at the institutional level. Table 3.2 lists the variables that are used in this analysis.
Table 3.2
List of Variables Used in Research Question 2 Equations 3 and 4
Variable Variable Name Description N Missing Measure
IFFS Initial Fee for Service The Fee for Service distribution appropriated to each institution prior to the beginning of the fiscal year. 225 0 Scale
ICOF Initial College Opportunity Fund The initial College Opportunity Fund appropriated to each institution prior to the beginning of the fiscal year. 225 0 Scale
FFFS Final Fee for Service The final institutional Fee for Service appropriated at the end of the fiscal year. 225 0 Scale
FCOF Final College Opportunity Fund The final College Opportunity Fund stipend appropriated to each institution at the end of the fiscal year. 225 0 Scale
D Undergraduate Degrees The number of undergraduate degrees awarded each year. 225 0 Scale
HEA HEA The total of COF and FFS allocated to each institution prior to the beginning of each fiscal year. 225 0 Scale
G Graduate Student Enrollment The head count of graduate students at each institution. 225 0 Scale
T Institutional Type The admission standards of the institutions as defined by their statutory authority. 225 0 Nominal
M Institutional Mission The Carnegie classification of the institution. 225 0 Nominal
L Institutional Location Location of the institution, rural or urban. 225 0 Nominal
E Institutional Size Undergraduate resident enrollment of the institution. 225 0 Scale


48
Research Questions 3 and 4 Methodology
Research questions 3 and 4 analyze the impact that the COF funding model had on improving access for Colorado resident and resident underserved students. The dependent variable for research question 3 is the overall percentage change undergraduate resident enrollment (AfT) in student headcount at each institution from year t-1 to year t. The dependent variable for research question 4 is the overall percentage change in underserved undergraduate resident enrollment (Af/) in student headcount at each institution from year t-1 to year t.
First, independent sampling t-tests to identify potential relationships between the implementation of SB189 and changes in enrollment after the implementation are performed. An independent samples t-test is a tool to assess whether the means for two groups differ on a continuous dependent variable (McCormick, et. al., 2015). The test has four assumptions: 1) it compares only two groups; 2) the dependent variable is continuous; 3) the dependent variable is normally distributed; and, 4) the variation within each category of the explanatory variable SB189 is constant (McCormick, et. al., 2015). This type of analysis is appropriate to attack these questions, because it compares the means for the dependent variable student headcount with the categorical variable, pre- and post-SB 189, to determine if there is a statistically significant difference in the means of the two groups.
To continue the analysis, an equation was created for each research question. Equation 5 analyzed the impact that the funding model had on Colorado resident


49
students, and Equation 6 analyzed underserved students. The units of analysis for each equation comprise the Colorado state funded institutions of higher education in a given year. The sample size for each equation is 375, based on 25 state funded 2-year and 4-year institutions for fiscal years 2000 through 2014.
Mit-1 = f(SB189, ASGFtt_lt UEt,AHStt_1,ANEtt_1,ANHStt_1) (Eq 5)
Wlt-i = /(SB189, ASGFtit_lt UEt, AUHSMJNEAUNHS(Eq 6)
AE is the percentage change in resident undergraduate student headcount at institution (/) in year t. SB189 is a dummy variable to identify pre- and post-SB189 implementation. ASGF is the percentage change in state general fund revenues from year t-1 to year t. UE is the unemployment rate in year t. AHS is the percentage change in Colorado high school graduates and ANHS is the percentage change in national high school graduates from year t-1 to year t. ANE is the percentage change in national enrollment at public institutions of higher education from year t-1 to year t. AUHS is the change in underserved Colorado high school student graduates and AUNHS is the change in national underserved high school graduates from year t-1 to year t. AUNE is the percentage change in national enrollment of underserved undergraduate students at public institutions of higher education.
The analysis uses Pearsons Correlations to determine the strength of the linear relationship between changes in enrollment, and the explanatory variables (Woolridge, 2002). Linear regression was used to identify the levels of significance for each of the explanatory variables. Table 3.4 contains a list of variables used in equation 5, and Table 3.5 comprises a list of variables used in equation 6.


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Table 3.4
List of Variables Used in Research Question 3, Equation 5
Variable Variable Name Description N Missing Measure
AE Percentage Change in Resident Enrollment The annual percentage change in resident head count. 375 0 Scale
SB189 Senate Bill 189 Identifies years that the SB189 Model was used to allocate funds to institutions of higher education. 375 0 Nominal
ASGF Percentage Change in State General Fund Revenues The annual percentage change in Colorado State General Fund revenues. 375 0 Scale
AHS Percentage Change in Colorado High School Graduates The annual percentage change in graduates of Colorado high schools. 375 0 Scale
ANHS Percentage Change in National High School Graduates The annual percentage change in national high school graduates. 325 0 Scale
ANE Percentage Change in National Enrollment The annual percentage change in national enrollment head count at public institutions of higher education. 375 0
UE Unemployment Rates The annual unemployment rate for the state of Colorado. 375 0 Scale


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Table 3.5
List of Variables Used in Research Question 4, Equation 6
Variable Variable Name Description N Missing Measure
AU Percentage Change in Underserved Resident Enrollment The annual percentage change in underserved resident head count. 375 0 Scale
SB189 Senate Bill 189 Identifies years that the SB189 Model was used to allocate funds to institutions of higher education. 375 0 Nominal
ASGF Percentage Change in State General Fund Revenues The annual percentage change in Colorado State General Fund revenues. 375 0 Scale
AUHS Percentage Change in Underserved Colorado High School Graduates The annual percentage change in underserved graduates of Colorado high schools. 375 0 Scale
AUNHS Percentage Change in National Underserved High School Graduates The annual percentage change in national underserved high school graduates. 325 0 Scale
AUNE Percentage Change in National Underserved Enrollment The annual percentage change in national underserved enrollment headcount at public institutions of higher education. 375 0
UE Unemployment Rates The annual unemployment rate for the state of Colorado. 400 0 Scale
Qualitative Methodology
A final qualitative analysis was conducted to supplement the research. A case study analysis of secondary data was performed on five institutions, each selected based on


52
their growth in enrollment and FFS. Five sitting presidents or chief financial officers that have served at their institutions since at least 2008 were interviewed to obtain their insights into, and understanding of how COF and FFS has impacted their institutions. The selection of the five universities was based on the following determinants:
History. The individuals selected have served as leaders at their institutions since the implementation of SB189.
Location. A mix of rural and urban institutions was selected to gain an understanding of SB189s impact on their communities.
Size and Mission. Again, to ensure diverse perspectives, institutions of differing sizes and institutional missions were selected.
Enrollment. Institutions experiencing flat, growing, and declining enrollment were selected.
Funding. Funding trends were analyzed to identify institutions that experienced changes in COF and/or FFS funding between 2006 and 2014, and to determine whether those changes reflected growth in funding, a decline in those funding sources during the same time, or flat funding trends.
The goal was to obtain diverse perspectives of how SB189 impacted the institutions. The interview questions are provided in Appendix B. The research conducted interviews with five institutional leaders in higher education. Leaders were selected based on the factors listed above. To protect anonymity, the individuals interviewed are not listed in this research. The purpose of the interviews was to gain a deeper


53
understanding of the budget process for higher education and the distribution of COF and FFS funding, and to augment the results of the quantitative analysis. The interviews focused on the following themes to determine if the funding model:
Caused institutions to develop strategies to influence either COF or FFS.
Influenced institutional behaviors.
Resulted in unanticipated consequences.
Helped the institutions to meet their mission and to better serve their communities.
These individuals were interviewed using standardized, open-ended interviews (Teddlie & Tashakkori, 2009). All interviewees were asked the same questions in the same order to minimize the introduction of interviewer effects and researcher bias (Johnson & Christensen, 2004). The interview questions were emailed to each participant prior to the interview.
Interview responses were summarized into themes using constant comparative analysis (Glaser, 1965; Leech & Ongwuebuzie, 2007). To perform this analysis, the interviews were reviewed and sorted into smaller, more meaningful parts. The objective of this analysis is to identify the institutional leaders' perspective on how SB 189 impacted the state, their community, and their institution. The themes are matched with the research questions and results of quantitative analysis. Although the respondents are identified, direct quotes are not attributed. The results of the interviews are presented in Appendix C.


54
The following chapters use the research methods presented in Chapter 3 to answer the research questions. The next chapter probes the impact SB 189 had on stabilizing the funding to institutions of higher education.


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CHAPTER IV
THE FUNDING OF HIGHER EDUCATION IN COLORADO Introduction
Chapter 4 addresses Research Question 1: has SB 189 stabilized higher education funding? It introduces two equations used to measure stability. The chapter leverages several tools in the analysis, including linear regression, and a paired sample t-test. Additionally, trend analysis and qualitative interviews are employed to support the results of the statistical analysis. The chapter assesses stability from both performance and incremental perspectives.
Developing a Framework to Measure the Stability of Higher Education Funding
RQ1 focuses specifically on determining whether the new funding model ensures increased stability in higher education funding, provides adequate funding for higher education institutions, and whether or not institutional performance has been impacted.
Addressing the nature of institutional funding stability is challenging, due to how this idea is constructed. For example, SB189 was passed after fiscal years 2003 and 2004, during which state funding to higher education decreased significantly. In relation to this decrease, stability could be defined as institutions experiencing only incremental funding fluctuations, roughly tracking changes in overall state revenues. Referring to Table 2, the states general fund revenues decreased by nearly 9.5 percent, yet Department of Higher Education funding was cut by nearly 27 percent in fiscal year 2004.
One way to operationalize stability is to analyze the incremental change to institutional budgets relative to changes in state funding. As available state revenues


56
increase, it would be expected that the allocation to higher education would increase at about the same rate. Similarly, as state revenues decrease, one would expect the allocation to decrease proportionally as well (Layzell, 2007).
Another measure of stability is adequacy. Perhaps the most accurate way to measure and ensure the adequacy of higher education funding is to compare it to enrollment. The passage of SB189 was an attempt by Colorado higher education institutions to ensure that enrollment was a major basis of funding (Protopsaltis, 2008). Performance funding can be driven by input or output measures (Herbst, 2007), and enrollment is a type of input measure (Herbst, 2007). Under a performance based model, enrollment changes should drive state funding, which would in turn increase or decrease in proportion to fluctuations in enrollment (Layzell, 2007). Adequacy is preserved because the amount of funding is pegged directly to the number of students enrolled in a given year, rather than to the vagaries of the fiscal health of the state, or to the competing priorities among which state legislators must decide, as you would expect based on a performance budgeting model.
RQ1 represents an attempt to operationalize stability from both incremental and performance based perspectives, analyzing factors that influence budgetary decision making, and determining whether those decisions more closely align with incremental budget theory, or performance budget theory.
Hypotheses
One implied goal of SB189 was to stabilize overall higher education funding by creating an implied entitlement (Protopsaltis, 2008). The bills authors felt that resident students would come to depend upon this direct funding of a portion of their education


57
expenses, and would therefore apply pressure to state decision makers to maintain a relatively stable level of state funding (Protopsaltis, 2008). Research question one is designed to establish whether or not SB189 did in fact stabilize funding for institutions of higher education. The following hypotheses and equations are developed:
Hio Higher educations budget post SB189 is driven by proportional changes to the states general fund revenues.
Him Higher educations budget post SB189 decreases are greater than proportional changes to the states general fund.
H1A2 Higher educations budget post SB189 is driven by performance (enrollment). If budgetary incrementalism is supported, then the state general fund, and the year-over-year percentage change to it, should be the primary drivers in the higher education budget allocation process. A further expectation would be that the higher education budget would have stabilized if the changes to its budget mimicked the changes to the states general fund revenues.
If performance theory were supported, it would be expected that enrollment is the primary driver, and that higher educations budget allocation was stabilized based on enrollment trends due to the implementation of SB189. If the allocation to higher education does not follow general state revenue trends or enrollment patterns, SB189 did not stabilize higher education funding.
The remainder of this chapter elaborates on Equations 1 and 2, as introduced in Chapter 3. The equations are defined, the variables are introduced, and analytic results are presented. Moreover, themes arising from the interviews are integrated within the findings.


58
Equations and Data
Equations 1 and 2 are proposed to measure the impact that SB189 has had on the stability of overall higher education appropriation by explaining the appropriation, then examining the relative change of higher education appropriation as a share of the change in state general fund revenues. These two equations are presented as a means to operationalize adequacy. The first-tests the degree to which changes in state general fund revenues and Colorado resident enrollment impact higher educations allocation at the institutional level.
Equation 1 is analyzed using linear regression.
HEA[ = f(SGFt,ASGFtit_1,Eit_1,PPt,SB189t) (1)
where, HEAlt = the higher education institutional allocation for institution / at year t,
SGFt = state general fund revenues at year t,
ASGFt,t-i = the percentage change in state general fund revenues from year t-1 to t,
E \= Colorado resident undergraduate enrollment at institution / in year t,
PPt = political party makeup in the legislature at year t, and SB189t = implementation years of SB189.
For all monetary variables, the values have been adjusted for inflation with a base year of 2014. The dependent variable, the higher education allocation to institution / in year t (HEAlt), represents the general fund distribution to higher education institutions. The data were obtained from the institutional budget data books for fiscal years 1999


59
through 2014 (Institutional Budget Data Books, ND). This amount excludes state financial aid appropriated to higher education, as well as allocations made to local district colleges, specifically Colorado Mountain College and Aims Community College.
The state general fund (SGF) available for allocation is an explanatory variable. The annual percentage change in state general fund [ASGFt is an explanatory variable that demonstrates the incremental change in the states general fund revenues. The data were gathered for fiscal years 1999 to 2014, and were obtained from the Appropriations Report of the Colorado Joint Budget Committee (Colorado Joint Budget Committee Appropriations Report, ND). The final explanatory variable is the percentage change in institutional enrollment (Af^ t_1) in Colorados public higher education system. Enrollment data were gathered from the Colorado Department of Higher Education for academic years 1998 through 2014 and are measured as student headcount (http://highered.colorado.gov/Data/Search.aspx).
The equation also employs an additional control variable: political party control in the legislature in year t (PPt). This variable is included because the makeup of the legislatures political party can have an impact on funding decisions to higher education (Archibald & Feldman, 2006; McLendon, et. al., 2009; Rizzo, 2004). Data on party affiliation in the state legislature were gathered from the National Conference of State Legislatures, and identify the makeup of the Colorado Legislature from 1999 to 2014 ("Partisan composition", 2015). The data were coded to identify either the controlling party, or whether the legislature had split control during that year.


60
Finally, a dummy variable identifies the years that SB189 has been in effect (SB189t). This nominal variable identifies years 1999 to 2005 as "0" and years 2006 to 2014 as "1".
The goal of Equation 2 is to analyze the ratio between the incremental change to the states general fund revenues, and the incremental change to the overall higher education sector allocation:
AHEA
t,t-i
ASGFt£_i
= 1, then HE funding is stable
(2)
This equation compares the percentage change in the overall higher education allocation of funds [AHEAt t_1) with the percentage change in the state general fund [ASGFt t-i). For fiscal years 1999 to 2005, HEA is the state general fund appropriation to higher education, and for fiscal years 2006 to 2014, HEA is the combined initial COF and FFS allocation to higher education. A paired sample t-test was performed to analyze the pre-2006 and post-2006 results of Equation 2.
Results of Equation 1
The results from the descriptive statistics demonstrate a funding range distributed to institutions of higher learning with a minimum distribution of $1,494,416 and a maximum of $142,993,461, with an average institutional distribution of $2,522,443.
The average seems very low, and it is. The explanation for this apparent anomaly is that a large number of community colleges receive a relatively small share of the higher education distribution.
The average annual state general fund revenue from 1999 to 2014 is
$7,656,117,324. The average proportional changes for the state general fund have been


61
positive, with a minimum change of -9.5 percent, a maximum increase of 8.1 percent, and an average change of 1.3 percent. Institutional enrollment has averaged 6,592.82 from 1999 to 2014, with a minimum undergraduate enrollment of 742 and a maximum undergraduate resident enrollment of 22,989 students. Table 4.1 displays descriptive statistics generated by SPSS.
Table 4.1
Descriptive Statistics Equation 1
Variable N Min Max Mean Std. Dev.
HEA* 399 1494416 142993461 25224433 30535349
SGF 400 6778852214 8439715713 7656117324 464087147
ASGF 375 -.0949 .0814 .0132 .0502
E* 400 742 22989 6592.82 5435
* The minimum, maximum, and mean shown are at the institutional level.
Correlation and regression analyses were conducted to examine the relationship between the higher education allocation, and the explanatory variables in Equation 1. The Pearson Correlation presented in Table 4.2 shows enrollment to be moderately to strongly correlated with HEA, with a correlation factor of 0.64 and a significance level of 0.000. The remaining explanatory variables are weakly correlated with HEA.
This analysis finds a moderate correlation between SB189 and the political party distribution, with a Pearsons Correlation of 0.414 and a significance of 0.000. This is probably due to the legislature having a Republican Party majority, or a split legislature from 1999 to 2004, followed by a primarily Democratically controlled legislature since 2005, the year prior to the implementation of SB189. Because the dataset is small, there has not been enough time for significant variation in the political party control factor.
Also of note is the negative correlation between SB 189 and the higher education allocation. This is due to the fact that when adjusted for inflation, the overall higher


62
education allocation amount is lower than the 1999 allocation, and that since 2006, the overall higher education allocation has shown a decrease. Table 4.2 summarizes the correlations derived.
Table 4.2
Correlations for Equation 1
HEA ASGF E PP SB189 SGF
Pearson Correlation HEA 1.000 -.038 .640 -.008 -.045 .016
ASGF .038 1.000 -.038 -.212 .021 .301
E .640 -.038 1.000 .019 .072 .010
PP -.008 -.212 .019 1.000 .414 .110
SB189 -.045 .021 .072 .414 1.000 .616
SGF .016 .301 .010 .110 .616 1.000
Sig. (1-tailed) HEA .232 .000 .438 .194 .381
ASGF .232 .231 .000 .342 .000
E .000 .231 .356 .081 .426
PP .438 .000 .356 .000 .016
SB189 .194 .342 .081 .000 .000
SGF .381 .000 .426 .016 .000
N HEA 375 375 375 375 375 375
ASGF 375 375 375 375 375 375
E 375 375 375 375 375 375
PP 375 375 375 375 375 375
SB189 375 375 375 375 375 375
SGF 375 375 375 375 375 375
A multivariate regression analysis for Equation 1 was generated using SPSS using the simultaneous regression method. The simultaneous regression method enters all variables at the same time when there are few variables and when it is uncertain which will have the strongest significance in the equation (Kremelberg, 2011; McCormick, et. al., 2015). This type of analysis is appropriate when dealing with a small set of variables when it is uncertain which set of explanatory variables creates the most appropriate equation (Kremelberg, 2011; McCormick, et. al., 2015). The Analysis of Variance


63
(ANOVA) presented in Table 4.3 examines the total variability observed within HEA by subdividing the component predictors, and assessing whether the experimental representation of the data influences the results of the regression (Kremelberg, 2011; McCormick, et. al., 2015). The results of the ANOVA for the equation show an F-value of 23.146 and is significant at the p<0.001 level. Thus, the regression line predicted by the explanatory variables explains a statistically significant amount of the variance in HEA.
Table 4.3
ANOVA Results for Equation 1
Sum of Squares df Mean Square F Sig*
Regression 67757260742422832 5 13551452148484566 23.146 .000
Residual 98359629268338224 168 585473983740108
Total 339786746013645950d 173
^Significant at the 0.001 level
The results from the adjusted R squared correlation coefficient (0.390) in Table 4.4 shows the share of variation of the dependent variable that is explained by the explanatory variables. Adjusted R squared is used in the results because R squared tends to be inflated when running regressions with a large number of variables. Adjusted R squared adjusts for the number of variables entered, and if too many unrelated variables are entered the score can be reduced (Kremelberg, 2011; Rumsey, 2011). The adjusted R squared of 0.390 indicates that the five explanatory variables are moderate predictors of HEA. Table 4.4 also provides the beta weights for Equation 1. The beta weight is the average amount that HEA increases when the individual explanatory variable goes up by one standard deviation, holding all other explanatory variables constant. The beta weight helps understand which explanatory variables have the greatest impact on the equation (Kremelberg, 2011; McCormick, et. al., 2015).


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Table 4.4
Summary of Regression Analysis for Variables Predicting HEA
Variable Unstandardized Coefficients B Std. Error Standardized Coefficients Beta t Sig.
Constant 25150695.230 72520847.897 -.347 .729
SGF -.003 .010 -.032 -.289 .773
ASGF 45279631.338 38864847.271 .090 1.165 .246
E 3570.428 216.610 .652 16.483 .000*
PP -2926060.979 5573657.134 -.047 -.525 .600
SB189 -13509318.054 5495495.036 -.198 -2.458 .015**
Adjusted R2 .390
N 173
^Significant at the 0.001 level ^^Significant at the 0.05 level
The results of the regression show that enrollment has the largest influence on HEA. Significant at the p<0.001 level, enrollment at the institutional level exerts the greatest influence on HEA because larger institutions will have a larger portion of the HEA distribution due to the large number of students.
The other statistically significant variable is the control variable SB 189, significant at the p<0.05 level. The beta weight is negatively correlated, indicating that since the implementation of the COF funding model, SB189 has had a negative influence on HEA.
State general fund revenues is the least significant variable in the equation. This would not be expected, as prior research has shown that the availability of state general funds has a direct impact on the amount of funding that is allocated to institutions of higher education (Tandberg, 2010a; Tandberg 2010b). Neither the incremental change in the state general fund, nor the political party in power were statistically significant, and both had minimal impacts on Equation 1. Although prior research has shown that political party control influences the allocation of funding to higher education


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(McClendon, et. al., 2009; Tandberg, 2010a; Tandberg, 2010b), funding decisions have many competing factors and political party was expected to have less of an influence than available state funding, especially in the TABOR environment, where tax and expenditure limitations restrict budgetary discretion (Martell & Teske, 2007).
Results of Equation 2
The goal of the next analysis is to understand how changes in the higher education allocation compare to changes in the states general fund revenues. For this analysis Equation 2 was employed. A paired samples t-test procedure was conducted using SPSS to show if SB189 had a statistically significant relationship before and after its implementation with the ratio of higher education funding, and with state general fund revenues. The paired samples t-test compares the two population means of Equation 2 (AHEA/ASGF) from both pre and post SB189 implementation to determine if SB189 had an impact on the incremental change to the higher education funding allocation. The analysis produced an insignificant t value ((ti36) = .101, p >.05 at 0.241). This analysis of the two means reveals that SB189 did not have a statistically significant impact on stability.
Table 4.5
Paired Samples Statistics for Equation 2_________________________________________
Mean N Std. Deviation Std. Error Mean
Pair 1 NOT_SB189 -5.0604597 136 17.26845174 1.48075752
SB189 3.2142841 136 5.56673476 .47734357
Table 4.6
Paired Samples Correlations for Equation 2
N Correlation Sig.
Pair 1 NOT_SB189 &SB189 136 .101 .241


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Figure 4.1 illustrates the funding trend of the incremental changes in institutional higher educations allocation (AHEA) as a ratio of this incremental change to the states general fund revenues (ASGF).
2000 20012002 2003 20042005 2006 2007 2008 2009 2010 20112012 2013 2014
Fiscal Year
Figure 4.1 Average of All Institutions AHEA/ASGF
Figure 4.1 shows that for the majority of these years, both before and after the implementation, the ratio is significantly greater or lesser than 1, which indicates that the incremental change to the higher education budget was not equal to the incremental change to the states budget. Fiscal years 2001 and 2011 were closest to a factor of 1, with a ratio of 1.12 and 1.3 respectively. It is important to note that the direction of the result, either positive or negative is dependent on both the numerator and denominator in the equation. Years where the change to higher education allocation and the state general fund has positive growth result in a positive factor in the equation. Years where there is a negative change in both variables also result in a positive factor. Finally, years where there is a combination of a negative change in the


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variable and a positive change in the other variable results in a negative factor. In fiscal year 2003, the significant negative result is due to a small positive growth in the change to the states general fund revenues, and a significant negative reduction in the higher education allocation. In fiscal year 2010, the overall positive change in the graph is due to the fact that both the change to the states general fund revenues and the higher education allocation were negative.
Discussion
Equations 1 and 2
In Equation 1, the related variable, state general fund revenues (SGF), was not statistically significant at the 0.01 level. This indicates that the availability of revenues does not play a significant role in determining the level of allocation to higher education. The overall results of the correlations, t-tests, and regression show that the highe education is not tied to either incremental changes in the state general fund revenues nor the performance of institutions that increase student enrollment.
The overall allocation of funding to higher education is not incremental based on changes to the state general fund revenues, and in fact higher education continues to be a tool used by decision makers in balancing the overall budget of the state. One of the queries addressed in this chapter is whether or not the higher education funding allocation was tied to the incremental changes in state general fund revenues. If this null hypothesis were supported, then it would be expected that the percentage change in state general fund revenues would trend with the higher education allocation. However, ASGF was not correlated with the higher education allocation, nor was it statistically significant in Equation 2. This indicates that incremental changes in the


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state general fund is not a primary driver of decisions regarding the allocation to higher education.
Additionally, the allocation to higher education did not stabilize post-SB 189 implementation. When comparing the pre- and post-SB189 shares of incremental change in the higher education allocation to the incremental change in state general fund, SB189 did not have a significant impact on the mean differences between the two variables. These results suggest that the implementation of SB189 did not improve the allocation of funds to higher education.
The variability over time and the insignificant t-tests show that funding levels have not stabilized. The second query addressed whether the higher education allocation has taken a greater proportional reduction than other priorities when compared to the states decline in general fund revenues. Equation 2 points to the variability in higher educations allocation. In years where there was an economic downturn post-SB189 from fiscal year 2009 through 2013, the allocation to higher education falls. This suggests that higher education is used to balance the budget through more significant reductions in higher educations allocation when compared to similar reductions in allocations across departments, when tracked to decreases in overall state revenue.
This is demonstrated by the large fluctuations of the formula, AHEA/ASGF shown in Figure 4.2 during these years. These results suggest that higher educations allocation is greater than the states share of general fund revenue decreases.
Finally, the research explores the question of whether SB189 tied state funding to institutional enrollment. Enrollment was positively correlated with HEA and statistically significant at the 0.001 level with a t value of 16.483. This indicates that


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enrollment is a significant driver of higher educations allocation. When analyzing the impact SB189 had on HEA, it is negatively correlated, which shows that HEA on average has declined since fiscal year 2006. These variables are telling two different stories: 1) the allocation is tied to enrollment that indicates increased funding during enrollment growth, and 2) the allocation has declined since the implementation of SB189.
This disparity may be caused by a limitation of the model. In order to run a stronger regression, institutional distributions, rather than the overall state allocation were used. This increased the sample size from 15 to 375. So while the enrollment variable is significant, it is at the institutional level. The regression views the enrollments as tied to the higher education distribution, and institutions with larger enrollments receive a larger portion of the HEA distribution. Table 1.2 in Chapter 1 illustrates that although enrollment increased from 148,429 students in fiscal year 2000 to 182,596 in 2014, the states higher education allocation decreased from $991.5 million to $659 million in 2014. These results suggest that although SB189 has tied institutional enrollment to funding distributions, overall the state has not rewarded higher education with greater state allocations, in spite of improved performance.
Higher education funding stability has been defined as following one of two paths: state general fund revenue incremental trends or adequacy trends. The discussion that follows mirrors those definitions. First, stability as a factor of state funding is examined, along with the question of whether higher education allocation has stabilized based on available state revenues. Next, stability is analyzed based on performance factors such
as enrollment.


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State General Fund Revenues as an Indicator of Stability
To determine if SB 189 helped to stabilize the higher education allocation, the research examines how changes to state general fund revenues impacts the higher education allocation. SB189s supporters hoped that the passage of SB189 would lead to funding stability for higher education. Incremental budgeting theory predicts that as the states budget changes, the budgets of the states departments and agencies will also change, tracking the increment (Davis, Dempster & Wildavsky, 1966; Gosling, 2009; Wildavsky, 1986; Wildavsky & Hammond, 1965).
In reviewing the results from Equation 1, the variable that is intended to measure percentage changes in state general fund revenues is not statistically significant. This suggests that incremental changes to the states general fund revenues are not a significant driver of higher education funding. Figure 4.2 further highlights the state general fund revenue and higher education funding trends for fiscal years 2000 to 2014.
State General Fund Revenue Change Higher Education Allocation Change
Figure 4.2 Annual Percentage Change in General Fund Revenues and Higher Education Allocation


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The numbers shown represent the percentage change in the state general fund revenues and the higher education allocation at the aggregate level. The incremental change in 2004 and 2005 for state general fund revenues was -8.5 percent and 4.5 percent respectively. During these same years, the higher education allocation change was -26.7 percent and -0.7 percent. The significant reduction to the higher education allocation in these years was a driver in the passage and implementation of SB 189 (Protopsaltis, 2008), which was intended to prevent such significant cuts during economic hardships. However, when revenues significantly decreased in fiscal year 2009 at the statewide level after the implementation as shown in Figure 4.3, SB189 did not prevent the legislature from decreasing the higher education allocation at a level greater than the decreases in state general fund revenues.
In fiscal years 2010 through 2012, the state general fund real revenues changed by factors of -1.9 percent, -8.6 percent, and -2.9 percent respectively. During this time, the higher education allocation adjusted for inflation changed -29.2 percent, 9.8 percent, and -9.6 percent. It should be noted that the higher education allocation actually increased in fiscal year 2011, due to requirements of the American Reinvestment and Recovery Act (ARRA). To qualify for these funds, Colorado was required to fund higher education at levels commensurate to the funding received in fiscal year 2005 (Colorado Joint Budget Committee, 2011; Colorado Joint Budget Committee, 2012). However, when the requirements of this act were not applicable in 2010 and 2012, the state decreased higher educations allocation at a rate much greater than the rate of state revenue reductions. In 2011, although the state restored higher educations allocation to the 2005 funding levels, it cut a significant amount of the ARRA funding to higher


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education that had been available to offset the financial burden that all state entities were facing (Colorado Joint Budget Committee, 2011).
After fiscal year 2006, when changes to state revenues were incrementally larger than the previous year, higher educations budget increased similarly. However, during the economic downturn in the states general fund budget in 2010 through 2012, higher educations budget was impacted at rates significantly greater than the changes to the states budget as a whole.
The disproportionate drop in HEA relative to the decrease in SGF can be understood as a response to the disproportionate reduction in discretionary funds. During fiscal year 2009, the state had a general fund budget of $7,676 billion with approximately $2,295 billion in discretionary spending (Colorado JBC Appropriations Report, 2008). After the budget cuts in fiscal year 2010, the overall state general fund budget was $7,484 billion, or a reduction of 2.5 percent. During this same period, expenditures in mandatory programs increased to $5,505 billion, leaving $1,979 billion less in discretionary spending than in the previous year (Colorado JBC Appropriations Report, 2009). Although the reduction between the prior years discretionary allocations was $315 million, the state decreased higher educations allocation by $476 million.
Literature in budgetary decision making supports these results. In periods of extreme fiscal distress, it becomes increasingly difficult for decision makers to cognitively process the budget as a whole when making allocation decisions. In response, they often choose alternatives that are simpler to process (Baumgartner & Jones, 2009, Jones, 1999; Jones, 2001; Ryu, 2009). This means that decisions will be made that ignore or minimize the complexities of the situation, such as balancing a


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tremendous budget downturn with a mixture of overall revenue increases and eliminating programs (Downs & Rocke, 1986; Gosling, 2009; Rubin, 2014).
When across the nation state general fund revenues decreased significantly in fiscal year 2009, the federal government did make available funding to states to limit the negative fiscal impact that was being felt, especially to higher education. The availablility of funds through the American Recovery and Reinvestment Act (ARRA) helped minimize the higher education reductions by requiring that the state maintain the fiscal year 2006 funding levels (Colorado JBC Appropriations Report, 2009). Although ARRA funds were not included in the regression or data analyses, they did factor into maintaining higher educations funding level in fiscal years 2009, 2010, and 2011. Figure 4.3 illustrates the different types of funds that are appropriated to higher education and include non-resident and resident tuition, ARRA funds, and the state allocation to higher education (Colorado JBC Appropriations Report, 2014, 75). As indicated by Figure 4.3, the share of higher education revenue derived from tuition has increased, while the share of funding from the states general fund has decreased, and the ARRA funds received by higher education helped supplant the loss in state general fund appropriations from fiscal year 2008 to fiscal year 2011.


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Higher Education Institutions Sources of Appropriated Revenues
$2,400 $2,200 $2,000 $1,800 $1,600
Revenue
in $1,400
Millions
$1,200 $1,000 $ 800 $ 600 $ 400 $ 200 $ 0
Fiscal Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 4.3 Sources of Appropriated Revenues
For most states, the budgetary allocations to higher education are the largest discretionary line in the budget, and declines in higher education funding can partially be explained by the need to fund mandatory budget items, such as Medicaid, K-12 education, and corrections, as well as the difficulty involved with cutting smaller discretionary programs (Curs, Bhandari, & Steiger, 2011). Colorado does not classify expenditures as mandatory, but this can be estimated by looking at the budgetary shares devoted to looking at the proportion of funding given to programs such as Health and Human Services, K-12 education, and Corrections (Martell & Teske, 2007). It is important to note, however, that although there are constitutional mandates for K-12 education because of Amendment 23, public education did experience budget cuts during the great recession. The legislature was able to cut state funding to these


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programs by increasing property tax valuations that could be used for school funding when it enacted SB 07-199 (Colorado JBC Appropriations Report, 2014).
In Colorado, over 70 percent of the general fund budget can be considered mandatory, meaning that decision makers were required to offset approximately $315 million in cuts using only 30 percent of the states budget. Because higher education is the largest of the discretionary line items, and because it has a greater ability to absorb cuts due to its ability to increase other sources of revenue, such as tuition and fees (Tandberg, 2010a; Tandberg, 2010b), it comes as no surprise that in spite of the passage of SB189, decision makers elected to cut higher educations budget significantly in order to balance the states budget. The results from this analysis lead to the conclusion that the passage of SB189 did not lead to the stabilization of higher educations allocation as it relates to the incremental change in state general fund revenues, and thus hypothesis Hio that funding is incremental is not supported. However, the research does support Him, that the proportional cuts to higher education are greater than the proportional reductions in state general fund revenues.
Enrollment as an Indicator of Stability
When SB 189 was passed, it was envisioned as a way to improve funding stability by incentivizing institutions to increase undergraduate resident enrollments. Through the significant change in the funding model, where a large portion of an institutions funding is based on institutional enrollment, institutions were expected to compete with each other, improve operational efficiencies, and to increase undergraduate resident student populations (Protopsaltis, 2008).


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During fiscal years 2003 and 2004, Colorados higher education enrollments increased by nearly 6 percent and by over 4 percent respectively. During these same years, the state actually reduced funding to Colorado institutions by 18 percent in 2003, and by nearly 26 percent in 2004. This 26 percent cut to the overall Colorado higher education budget8 was in response to shortfalls in the state's general fund revenues.
The following discussion analyzes higher education funding stability as a function of enrollment after the implementation of SB189.
Enrollment was a statistically significant predictor of HEA at the 0.001 levels. To further assess whether institutional allocations on increasing enrollments helped to stabilize funding, the study employs two descriptive methods. First, it assesses the overall higher education allocation as compared to overall enrollment trends. Figure 4.4 compares the relative levels of aggregate HEA and undergraduate enrollment. This graphic clearly demonstrates that the funding for HEA does not track with changes in enrollment. While enrollment increased in most years, aggregate HEA experienced decreases from fiscal year 2009 through 2013, post-SB189 implementation. Overall, from 2000 to 2014, enrollment increased by 34,167 students and the HEA decreased by over $332 million.
8 This includes the allocation to institutions of higher education, the central department budget, allocations to local area colleges, financial aid, and the Colorado History Museum. The budget change to individual institutions was an increase of approximately 5 percent in 2004, while the other functions saw greater decreases in 2004.


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Figure 4.4 Aggregate Higher Education Allocation and Enrollment Trends
To further illustrate this trend, Figure 4.5 shows real per student funding trending down from fiscal year 2000 to 2014. Per student funding decreased from fiscal year 2000, when the state funded higher education at $6,680 per student to $3,609 per
student in fiscal year 2014.


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200020012002200320042005200620072008200920102011201220132014
Fiscal Year
^Aggregate Higher Education Allocation Per Student ---Linear (Aggregate Higher Education Allocation Per Student)
Figure 4.5 Higher Education Allocation Per Student
The graphical representations shown in Figure 4.4 and 4.5 support the conclusion that enrollment is actually not a significant factor in funding decisions. Language in SB 189 states in part, "While it is the general assemblys intent to change the process of funding for postsecondary education, funding for postsecondary education is not an entitlement. During periods of revenue shortfalls, the general assembly may use a variety of mechanisms to balance the states budget, including reducing appropriations to institutions of higher education, decreasing the value of the stipend, or placing a limit on the number of stipends funded under this act based upon the overall budgetary needs of the state," (SB189, 7). It further states that, "An institution shall not increase the students share of in-state tuition to make up for an actual or effective reduction in


79
the stipend amount from which the total in-state tuition amount was calculated or for issues relating to the timing of stipend payments" (17).
This language has had two likely impacts on the stability of higher education funding as it relates to enrollment. First, it has prevented the funding of enrollment growth during periods of revenue shortfall. Second it has minimized the ability to incentivize interest groups to put pressure on the states decision makers with the goal of keeping full funding for higher education intact during times of fiscal stress (Tandberg, 2010a).
The results from the qualitative interviews support the notion that changes in enrollment has not put pressure on decision makers to maintain higher educations funding levels. Amongst the leaders, there was consensus that in the early stages of SB189s implementation, there was optimism regarding the funding allocations to higher education. However, during the great recession beginning in fiscal year 2009, the sense of optimism faded as the leadership realized that the state, to balance the budget, was cutting COF funding and there was no pressure from students or their families to maintain the stipend. Because SB 189 prevents institutions from increasing tuition in years where the COF stipend is cut, leadership believes that COF was not designed correctly. Although, institutional leaders indicated that an original intent of the COF mechanism was to empower students to 1) better understand the states contribution to their post-secondary education and 2) to put pressure on decision makers to not cut funding to these students, the implementation of SB 189 did not achieve these goals. Because language in the bill specifically prevents institutions from passing along to students tuition increases if the state cuts the COF funding, students do not truly


80
understand how COF funding ties to the states support of their education. By adding this language to SB 189, policy makers essentially removed the entitlement funding which eliminated the incentive for students to put pressure on the legislature to keep their funding whole. The analysis does not support hypothesis H1A2, that SB189 stabilized higher educations allocation based on its performance with regard to enrollment.
Conclusion
The goal of this chapter was to examine whether SB189 helped to stabilize funding from the state. Empirical results related to this question indicate that funding was not stabilized. In looking at stabilization from the perspective of state general fund revenue changes, the results found that SB189 did not tether higher educations allocation to the state general fund revenues. Rather, the results identified that in years when the state general fund revenues declined significantly, higher educations allocation takes a larger share of the reduction than other state programs.
These results are consistent with what other states are experiencing with regards to state allocations to higher education. Prior to the great recession from 1986 to 2008, state and local government funding to higher education increased, growing from $31.4 billion to $88.8 billion (SHEEO, 2012; SHEEO, 2014). However, during the great recession beginning in 2008, dramatically reduced state revenue led to the end of the growth in state support to higher education. Federal funding provided to states from the American Recovery and Reinvestment Act (ARRA) helped minimize state budget cuts to higher education. In 2011, 31 states provided $2.8 billion in ARRA funding to higher education (SHEEO, 2012; SHEEO, 2014).


81
Across the nation, state programs in higher education continue to receive large cuts during economic downturns, which was demonstrated after the great recession in 2008. During fiscal years 2009 to 2013, the national per student state support to public institutions of higher education decreased by an average of 13 percent. During this same time, the support per student decreased in 47 of the 50 states. Six states,
Louisiana (-38.4 percent), Pennsylvania (-35.3 percent), Arizona (-33.2 percent), Colorado (-33.0 percent), New Hampshire (-32.3 percent), and Idaho (-31.8 percent) saw decreases of over 30 percent during this time.
National support for higher education experienced some growth in fiscal year 2014, when higher education funding grew from 50.4 percent of the total support in 2013 to 51.1 percent in 2014 (SHEEO, 2014). State support increased by 5.4 percent on a per student basis. This reflects a growing positive financial trend as states recover from the great recession. However, although the funding increased in 2014, it continues to remain significantly lower than pre-recession levels (SHEEO, 2014).
While the bills authors may have intended to create another mandatory line item in the states budget by tying funding to the changes in enrollment, a strategy that has been successfully pursued with regard to maintaining or increasing other state budget line items in Colorado (Martell & Teske, 2007), the mandate was effectively nullified in the final draft of the bill (Protopsaltis, 2009). Because SB189 did not require COF to increase or maintain per student funding, decision makers were empowered to continue to use the higher education allocation as a discretionary pool from which to balance the state's budget during times of fiscal crisis. For example, during fiscal year 2009, when the state began to experience revenue shortfalls, the legislature cut COF


82
funding from the original per credit hour stipend of $92 to $68. During fiscal year 2010, the state further reduced the stipend from $68 to $44 (Colorado State Auditors Office, 2012).
The results of the regression and other analyses illustrate that SB 189 did not stabilize the allocation of funding to higher education. Higher education funding remains a mechanism to allow the legislature to balance budgetary shortfalls during times of fiscal distress. The implication to performance budgeting theory is that funding tied to university enrollments is unlikely to be successful when there is a legal escape clause, as it is predicted that states will continue to use higher education to balance the overall states budget during times of recession. The design of SB189, reflecting the political will, allows for the decoupling of performance and budget allocations. For Colorado, the allocation has not kept pace with the enrollment growth at institutions, and from the perspective of adequacy, represents a decrease in funding on a per student basis. This was shown in the years of the great recession, when higher educations budget was cut at levels greater than state revenue shortfalls. Additionally, despite enrollment growth since 2000, higher educations allocation has decreased consistently since the implementation of SB189. Thus we accept the alternative hypothesis, Him, in which decreases to the states general fund revenues lead to disproportionate decreases to higher educations budget allocation.


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CHAPTER V
FEE FOR SERVICE AS A MECHANISM TO REWARD PERFORMANCE OR MAINTAIN
STATUS QUO?
Introduction
Since the early 2000s, funding for state institutions has been under competing pressure from spending requirements for state health care, state retirement systems, and K-12 education, as well as overall structural deficits. Because of these factors, state support for higher education has ebbed and flowed over these years (Layzell, 2007; Tandberg, 2010a; Tandberg, 2010b). While SB189 had an implied goal of stabilizing the higher education budget, it had an implicit goal of incenting institutional performance around enrollment growth and other factors, such as graduating students. To meet this stated goal, SB189 created two funding mechanisms: 1) the College Opportunity Fund (COF) and 2) Fee for Service (FFS).
COF is a performance funding mechanism tied directly to a given institutions ability to attract and retain Colorado resident students. It is allocated to institutions based on their resident credit hour production. FFS was created as an additional funding mechanism to allow the state to purchase specific services from institutions (Protopsaltis, 2008) and is a performance contracting mechanism, providing for a certain level of funding to a specific institution for specified services, or levels of performance (Layzell, 2007). This chapter examines institutional distributions of FFS.
Despite the aims of SB 189 to improve institutional performance through new funding mechanisms, it also includes language intended to level out revenue volatility for poorly performing institutions. Other factors may put pressure on the decision makers to ensure that funding changes are proportionally the same from institution to


84
institution. For example, higher education interest groups play an important role in how funds are distributed9 among institutions. Governing boards and local communities put pressure on decisions makers to maintain the funding balance of lower enrollment institutions (Tandberg, 2010a; Tandberg, 2010b; Tandberg, 2013). When there is intense competition for limited resources, institutions will attempt to protect their existing distribution in order to support their current level of spending, regardless of the needs or performance of other institutions (Rubin, 2014). The analysis in Chapter 5 teases out whether FFS distributions function as purchases of services and performance rewards as intended, or if FFS is merely used to maintain the status quo funding for each institution.
Hypotheses
The supporters of SB189 insisted that institutions needed to be incentivized to improve their institutional performance around recruiting, retaining, and graduating Colorado residents. They believed that SB189 would provide the funding mechanisms and incentives to accomplish this goal. The following hypotheses and equation seek to determine if FFS is a mechanism to ensure that the overall funding for each institution is increased incrementally based on the overall funding available for higher education, or if it is a mechanism to purchase services and reward institutions:
H20 FFS is adjusted to ensure that the overall budget distribution remains proportionally similar for each institution.
H2A -FFS rewards institutional performance.
9 For the purposes of differentiation, the states budgeted amount given to higher education in aggregate is referred to as an allocation. Appropriations in the form of COF and FFS to individual institutional state are referred to as distributions.


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Equations and Data
The distribution of state funding to institutions of higher education takes place twice annually. At the start of the fiscal year, an initial distribution is made to each institution for COF and FFS. At the end of the fiscal year, the distribution is trued up to reflect actual resident enrollment at each institution. Equation 3 is used to analyze the factors that impact the institutions initial FFS distribution:
IFFS[ = f(lCOFi, HEAtpl Gj, T\M\ L1, El) (3)
where, IFFSlt = the initial fee for service distribution for institution / in year t, ICOFl= the initial COF distribution for institution / in year t,
HEAt = the aggregate higher education allocation in year t,
Dj = Number of undergraduate degrees awarded for institution / in year t,
Gf = Number of graduate students for instiutiton / in year t,
Tl= Type of institution /,
Ml= Mission of institution /,
L1 = Location of institution /, and El = Size of institution /.
The dependent variable is the initial FFS distribution for each institution / in year t (IFFSl). Assuming that the initial FFS distribution functions as a reward for certain types of performance, it should be influenced by performance factors. The initial distribution is obtained from institutional budget data books for fiscal years 2006 through 2014 (Institutional Budget Data Books, ND).
The initial COF distribution for each institution / in year t (ICOF^) is an explanatory variable. COF funding is driven strictly by resident undergraduate enrollment. The data


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were gathered from institutional budget data books for fiscal years 2006 through 2014 (Institutional Budget Data Books, ND). Under the null hypothesis, the expected relationship between COF and FFS is inverted, in that more COF funding will lead to less FFS funding so that the distribution to individual institutions remains incrementally the same as to other institutions. Another explanatory variable is the initial aggregate allocation of higher education funding for COF and FFS (HEAt). The data were gathered from institutional budget data books for fiscal years 2006 through 2014 (Institutional Budget Data Books, ND).
Several other explanatory variables are used to determine the impact of performance on FFS. The number of undergraduate degrees awarded (D£) includes the number of either associates or bachelors degrees awarded by institutions / in year t and is a measure of institutional performance. Data are gathered for fiscal years 2006 through 2014 from the CDHE searchable database (Colorado Department of Higher Education Data Website, ND). The size of this sample impacts decisions to run model that lags the data. Higher education reports enrollment information in the Fall semester, graduation information in the Spring semester, and it is not continuously reported throughout the year, or even each semester. This is one reason that the data are relatively limited for a 15-year study. The sample size for this equation is 225, which means the degrees of freedom is relatively small. The variable, number of degrees, is reported after decision makers have made funding decisions. In Colorado, funding decisions are made two years after degrees have been earned. For example, if a student graduates during the 2010 academic year, this is learned when funding decisions are made for fiscal year 2012. In models that employ data lagging, the


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DISS_title FUNDING PUBLIC HIGHER EDUCATION IN COLORADO: HOW HAS THE COLLEGE OPPORTUNITY FUNDING MODEL IMPACTED EDUCATIONAL FUNDING AND PERFORMANCE?
DISS_dates
DISS_comp_date 2017
DISS_accept_date 01/01/2017
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DISS_para During the 2004 legislative session, the Colorado General Assembly enacted Senate Bill 189 (SB189), which established the first system of college vouchers in the United States. The supporters of SB189 hoped that the voucher system, called the College Opportunity Fund (COF), would: 1) stabilize the flow of state funding to higher education; 2) incent institutions to improve performance; and, 3) increase resident and underserved enrollment. This research presents an empirical study of COF to examine the extent to which it has impacted both funding and performance in Colorado’s institutions of higher education. It uses budgetary theory to evaluate the overall success of the COF funding model.
Through the analysis of funding and performance trends from fiscal year 1999 to 2014, this dissertation focuses on how the COF funding model impacted institutions after the legislation was implemented in fiscal year 2006. The present research effort adds to budgetary literature by 1) evaluating the success of the first ever higher education voucher system in improving performance and 2) developing insights on the nexus of policy design and budgetary theories of incrementalism and performance.
The study used linear regression, t-tests, and other statistical tools as well as qualitative data to answer four research questions and yielded several findings. First, despite the best efforts to stabilize state funding to higher education, the overall appropriation in real dollars has decreased since the implementation of COF. Second, the Fee for Service funding mechanism that accompanies the COF stipend fails to reward performance as intended, but rather appears to be a mechanism to hold institutional distributions proportionally the same. Finally, although the state has seen increased enrollments both for Colorado resident and Colorado resident underserved students since the implementation of SB189, the COF voucher system seems to have not influenced enrollment at institutions of higher education. A major implication of this research is that SB189 has not been successful in achieving its goals, but instead the budgetary outcomes are best described as incremental. Policy makers must consider that the design of a funding system can bias the budgetary outcomes.
DISS_supp_abstract
DISS_binary PDF Middlemist_ucdenver_0765D_10819.pdf
DISS_attachment
DISS_file_name Database_10_23.xlsx
DISS_file_category spreadsheet
DISS_file_descr Excel Spreadsheet of Data and Figures
DISS_restriction
DISS_repository
DISS_version 2011-11-08 15:37:33
DISS_agreement_decision_date 2017-04-04 16:51:52
DISS_acceptance 1
DISS_delayed_release
DISS_access_option



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PAGE 26

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PAGE 27

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PAGE 32

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PAGE 33

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