Feb 19, 2006 - the degree of research orientation of a university, the effects of economies of scale (number ..... funding ratio is the University of Florida (UF).
State Financing of Research Universities: The Role of State and University Characteristics
By Sang-Hyop Lee and Carl Bonham Research assistance by Archimedes Gatchalian February 19, 2006
Working Paper No. 2006-2 University of Hawai‘i Economic Research Organization 2424 Maile Way, Room 542 Honolulu, Hawai‘i 96822 www.uhero.hawaii.edu
State Financing of Research Universities: The Role of State and University Characteristics Sang-Hyop Lee and Carl Bonham
∗
February 19, 2006
Abstract This study estimates the effect of underlying determinants on state funding of Doctoral/Research-Extensive Universities (DREU) in the U.S. Using panel data on 98 DREU over the period from 1987 to 2002, we estimate the effect of a variety of DREU and state characteristics while controlling for institutional level unobserved heterogeneity. Unlike previous studies, we focus solely on DREU, so our estimation results are driven by the within variation of DREU, not by the between variation across different types of universities and colleges. We consider determinants not previously studied such as the competitiveness of programs and quality of students, the mix of degree programs and professional schools, the degree of research orientation of a university, the effects of economies of scale (number of students), the cost of providing education services, and other state characteristics. Not surprisingly, we find that these variables are important factors determining state funding of DREU. Finally, we provide four case studies to illustrate the use of our model in evaluating the funding position of various universities. Keywords: State Funding, Doctoral/Resarch-Extensive Universities, Panel, Heterogeneity. JEL: I22, I28, H72
Sang-Hyop Lee is Assistant Professor of Economics at the University of Hawai‘i at M¯anoa, and research associate at UHERO. Carl Bonham is Associate Professor of Economics at the University of Hawai‘i at M¯anoa, and Executive Director of UHERO. Contact Dr. Lee at [email protected] or Dr. Bonham at [email protected] ∗
1. Introduction The composition of funding at U.S. colleges and universities has changed markedly over the past several decades. The share of total funds provided by state governments for public degree granting institutions has declined from a high of more than 30 percent in the late 70s to a low of near 23 percent in the mid 90s. The federal government’s funding share has also declined from nearly 20 percent in fiscal year 1969–70 to 12 percent in late 90s, while the share of funds from tuition has risen steadily from 21 to 28 percent. The declining share of government funding is a source of concern to universities, requiring them to seek an increasing share of funding from both students and private sector sources. There is a growing body of literature on government funding of public universities. Lowry (2001) lists dozens of papers which attempt to identify the factors affecting state appropriations to universities. Most of this research has focused on the effect of state level characteristics, particularly state tax revenues. However, because funding of degree granting institutions varies significantly according to the type of institution and institution characteristics, and because the mix of degree granting institutions varies across states, previous research using state level data has aggregated away much of the cross-sectional variation in the data and may produce misleading results. Surprisingly, only a few studies have measured the impact of university specific characteristics on state funding. Coughlin and Erekson (1986) use cross-sectional data on universities in large football conferences. They find that student and faculty quality, and success in intercollegiate athletics have a significant-positive effect on state funding. Leslie and Ramsey (1986) focus on a single university characteristic—enrollment. Cohen and Noll (1998) study the effects of federal research grants and the existence of an affiliated
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hospital. The above mentioned studies have examined only a small number of potentially important university characteristics. And, they have ignored the effect of university specific unobserved heterogeneity, possibly leading to biased estimates and conclusions. We are aware of only one study which explicitly models unobserved heterogeneity at the university level. Using a two-stage least squares (2SLS) estimation technique to deal with endogeneity problems, Lowry (2001) examines the two-way relationship between state funding and tuition. This paper addresses the need for a comprehensive evaluation of the effect of state and university level characteristics (observed and unobserved) on state appropriations to Doctoral/Research-Extensive Universities (DREU).1 Using nine years of panel data on 98 DREU over the period from 1987-2002, we estimate the effect of a variety of DREU and state characteristics on state funding of DREU. Unlike previous studies such as Lowry (2001), we focus solely on DREU, so our estimation result are driven by the within variation of DREU, not by the between variation across different types of universities and colleges. We consider determinants not previously studied such as the competitiveness of programs and quality of students, the mix of degree programs and professional schools, the degree of research orientation of a university, the effects of economies of scale (number of students), the cost of providing educations services, and other state characteristics. Using a longitudinal econometric method, we are able to control for institutional level unobserved heterogeneity. Finally, we select several universities and evaluate their actual funding relative to the predictions of our model.
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The Carnegie classified Doctoral/Research-Extensive Universities is a classification made by the Carnegie Foundation, signifying those universities that offer a wide range of baccalaureate programs, while simultaneously committed to graduate education. These universities should award 50 or more doctorate degrees per year across 15 disciplines.
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2. Funding Patterns in U.S. Higher Education U.S. Colleges and Universities offer a diverse array of educational experiences. For example, a community college may offer vocational training for the first 2 years of a student’s college experience. A university typically offers a full undergraduate course of study leading to a Bachelor's degree as well as a variety of professional and graduate programs leading to advanced degrees. The composition of an institution’s funding depends heavily on the programs offered and other characteristics. Table 1 and Figure 1 illustrate the mix of funding by type of institution for FY 2000-01. DREU tend to be characterized by a relatively smaller contribution from tuition and state funding and a relatively larger contribution from grants, contracts, private gifts and endowments.
These differences in funding by type of institution have important implications for system wide and state wide funding—variation in the mix of higher education institutions across states may produce significant variation in total state funding. Current-fund revenues of public degree-granting institutions by source of funds and by state for FY 2000-01 is presented in Table 2. The composition of funding varies widely by state. Tuition accounts for 41.2% of total revenue in Vermont, while it accounts for only 8.6% of total revenue in New Mexico. In contrast, the proportion of state support is 34.2% for New Mexico, compared with 14.4% for Vermont. The largest contribution from state funding, 55.5%, occurred in Georgia, where tuition contributed 16.3%. This wide variation in funding by state reflects not only differences in state policy, law, or fiscal situation, but also
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differences in the mix of institutions within a state. Therefore, it is important to consider two sources of variation: one at the state level and the other at the institutional level.
The distribution of funding sources is also highly variable over time. Table 3 and Figure 2 show current fund revenue by source over time. Although state appropriations continue to be one of the largest sources of funds, the state share of total funding has been on a general downward trend since the late 1970s. Tuition’s share of total funding has increased steadily, while the federal share has declined.
3. Funding Research Universities: An Econometric Approach This section presents estimates of the effect on state appropriations of economies of scale, research focus/intensity, competitiveness/quality, mix of professional schools, cost factors, and macroeconomic environment. Below is a brief description of each factor. (See Appendix 1 for a detailed description of data sources.) 1. Economies of Scale: a. The size of each DREU is measured by the number of full time students in natural logarithms (LNSTD).2 2. Research Focus/Intensity a. The ratio of graduate to undergraduate students (RGRAD). b. The ratio of research expenditures to expenditures on instruction (RRSCH) 3. Competitiveness/Quality a. A composite index of competitiveness based on five measures: the ratio of admission to applicants, high school GPA, SAT/ACT scores of freshmen, and high school rank (COMP = 1 for most, very, or highly competitive, 0 otherwise).
2
Our model uses the log of state appropriations as the dependent variable. Alternatively, we used the log of state appropriate per full-time student as the dependent variable. Results from this alternative specification do not change our results and are available from the authors on request.
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4. Mix of Professional Schools a. Binary variables indicating whether a DREU has a Medical or Law School (MED = 1 if medical school, 0 otherwise; LAW =1 if a law school, 0 otherwise). 5. Cost Factors a. ACCRA cost of living index in natural logarithms intended to proxy for differential cost of salaries and other inputs across states (COST). 6. State Tax Revenue a. Per capita state tax collections. According to previous studies, this is one of the most important factors determining the level of state appropriation for higher education (TAX). 7. State Level Macro-Economy a. State median income in natural logarithm (INC) b. Unemployment rate (UNEMP) 8. Other Controls a. A common time dummy to capture economic shocks or national factors which affect all states and DREU in a similar fashion (YEAR). b. Similarity of extracurricular activities of universities (TEAMS = number of varsity teams). Because our data set has a 9-year longitudinal (panel) data structure, we are able to control for some of the unobserved heterogeneity of DREU. Specifically, we model the natural log of state appropriations (LSTAP) using random effects (RE) to control for unobserved institutional characteristics.3 Table 4 provides summary statistics for all variables used in our analysis. Around one-half of the DREU have law schools, and slightly more than one-half have a medical school. Because virtually every DREU has an engineering school it is not necessary to control for this type of professional school. The average DREU has slightly less than 25,000 full time students, of which 22% are graduate students. The average ratio of research to education expenditures is 14%, and 58% of the sample are rated as competitive. The average state median household income is $49,000, while average per capita state tax collections is less than $1,500.
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We report estimation results for a random effects model of the log of state appropriations in Table 5.4 With the exception of the coefficient on RRSCH and COST, all estimated coefficients are statistically significant at the 5% level, and the model fits the data well, explaining 68% of the variation in state appropriations. The estimated coefficient for MED is 0.200, implying that DREU with medical schools receive state appropriations approximately 22.1% higher than DREU without medical schools.5 The impact of a law school is much smaller (11.6%), but is still substantial, roughly $1,300 per student. The more competitive/higher quality a DREU is, the higher its funding, with state appropriations for competitive DREU approximately 13.6% higher than for non-competitive schools. Similarly, the more research oriented, or at least the larger the ratio of graduate to undergraduate students (RGRAD), the greater the state appropriation. The estimated coefficient of the ratio of research to instruction expenditure (RRSCH), which considers the “mission”—research vs. instruction—of a university, is positive but insignificantly different from zero. However, this result should be interpreted with caution, given the high degree of correlation between RRSCH and RGRAD. In fact, a significant result for the coefficient of RGRAD suggests that the more research-oriented universities receive a higher level of state funding, and excluding the RRSCH variable has little effect on the other estimated coefficients. Interestingly, our proxy for economies of scale, the log of the
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For comparison purposes, we also estimated our models using fixed effects which exclude all of the timeinvariant variables. The results do not change any of our qualitative conclusions on the remaining explanatory variables and are available on request. 4 We use a modified zero-order regression method (Greene. 2002. p.60) to handle the missing observations problem and keep the number of observations consistent across models. Because information on a couple of variables are only available since 2000 from the IPEDS, we fill the missing variables with zeros and add variables that take the value one for missing observations and zero for complete ones. 5 The true formula is eβ-1= e0.200-1=22.1%. However with a small β, the two figures, estimated coefficient and calculation based on the formula, are almost identical.
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number of full-time students, has a coefficient of 0.514, implying that a 1% increase in number of students raises the state appropriation by 0.514%. That is, revenue is inelastic with respect to student enrollment.6 The parameter on our proxy for cost (COST) is 0.063, implying that a 10% change in the index (from 100 to 110) increases state funding by 0.63%. However, the variable is insignificant once state characteristics are controlled for suggesting that cost of living and other state level characteristics, such as per capita tax collection and median household income, are highly correlated.
Coefficients on other state level variables have their expected signs and are significantly different from zero. Consistent with previous findings, the coefficient on percapita state tax revenues is positive and highly significant. The parameter value of 0.363 implies that a 1% change in state tax revenues increases state funding of DREU by 0.36%. The positive and significant sign for log of state median household income (INC) could be interpreted in a similar way. The estimated coefficient for unemployment rate is -0.018, implying that a 1 percentage point decrease in the state unemployment rate increases state appropriation by 0.018%. To check the robustness of our results, we conducted several sensitivity tests. We estimate our model excluding the medical (MED) and law school (LAW) dummy variables, the measure of research orientation (RRSCH), using a dummy for a football team by division and the team performance, and using different measures of state tax revenue, such as general fund revenue or total revenue. However, these specification changes do not materially change the estimation results, suggesting that our results are robust. 6
Thus, if we use the amount of state appropriation per FTE student, this coefficient changes to -0.486 (0.514-1), leaving other estimated coefficients unchanged. This is because estimating a model of ln (y/x) =
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4. Case Studies: Under-funded or Over-funded? Our estimated model of state appropriations explains the variation in funding across DREU over time based on both school and state characteristics. Thus, the model can be used to predict or explain the “typical” level of funding for a DREU based on those same characteristics. Here we evaluate the funding of a variety of DREU by comparing their actual state appropriations with the funding predicted by our model. For this purpose, we selected four DREU that fit into the general categories of chronically underfunded, chronically over-funded, exactly-funded, and inconsistently-funded. Our results are summarized in Table 6 (and Figures 3a-3d), and Table 7 reports a ranking of all DREU by the ratio of actual to predicted funding.
Case I — Chronically Under-funded As an example of a substantially under-funded DREU, we consider the University of Colorado-Boulder (UCB). During the entire sample period from 1987 to 2002, UCB received a smaller state appropriation than predicted by our model. In other words, other DREU with similar characteristics to UCB, and located in states with similar levels of median household income, unemployment rates and state tax collections, received substantially higher levels of state funding. In fact, over the 1987-2002 sample, the difference between actual and predicted UCB state funding averaged -$79 million, or almost
b0+b1ln (x) is same as estimating a model of ln (y) = b0 + (b1-1) ln (x).
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-$2800 per full time student.7 By 2002, the extent of under-funding had grown to almost -$107 million. Although other DREU such as the University of Kansas show a similar pattern of under-funding, only the University of Vermont and State Agricultural College is more severely under-funded, with an average funding gap of over -$92 million.
Case II — Chronically Over-funded There are many DREU that receive significantly greater funding than our model predicts based on school and state characteristics. Many of the schools in the University of California System fall into this category.
The DREU with the largest average over-
funding ratio is the University of Florida (UF). In other words, other DREU with similar characteristics to UF, and located in states with similar levels of median household income, unemployment rates and state tax collections, received substantially lower levels of state funding. Over the period from 1987 to 2002, state appropriations to UF averaged 93% greater than the predicted funding level, amounting to an average over-funding of $201 million per year. The University of California-Los Angles (UCLA) has the greatest average absolute excess funding gap of $207 million.8
Case III – Exactly-funded A number of DREU are funded at rates extremely close to those predicted by the model. Specifically, 9 out of 98 DREU received appropriations that averaged 2% more or 7
Note that the difference between actual funding and predicted funding is simply the in-sample prediction error (residual) for UCB and thus represents that portion of UCB state funding that has not been explained by the factors included in our model (including random effects to capture unmeasured heterogeneity). While we have controlled for as many factors as possible, there may be other measured factors that would help to reduce our estimate of UCB under-funding. 8 As discussed in footnote 9, another interpretation is that we have not measured some DREU or state characteristic that can explain the additional funding received by schools like UF and UCLA.
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less than the predicted appropriation, and 15 schools had average state funding within 5% of the predicted funding level. Examples of such school include the University of Michigan or the University of New Mexico.
Case IV – Inconsistently-funded While chronically under-funded universities have had to resort to increased funding through tuition, grants, and other sources, there is something to be said for consistency! A number of universities have experienced significant changes in their state funding during our sample period. Interestingly, the largest single change from exactly or over-funded to under-funded occurred in 2002 for Florida State University (FSU). In 2001 FSU was over-funded by $68.5 million with actual state appropriations of $271.6 million. In 2002, state funding for FSU fell to $246.2 million while predicted funding rose to $262.8 million. Meanwhile, our model suggests that the University of Florida continued to be over-funded by more than $200 million despite a drop in state funding of $44 million in 2002. Another interesting case is the University of Hawaii at Manoa (UHM). During the 1996 fiscal year UHM sustained the second largest change in funding position in our sample. While significant increases in state appropriations and a relatively flat predicted funding level had placed UHM in an over-funded position in the mid-1990s, this all changed starting in FY 1996. In that year, a large budget cut caused UHM to swing from a $45 million over-funding to a $26 million under-funding, a switch in funding position of over
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$70 million. From 1996 on, the gap between actual state appropriations and predicted funding has continued to grow leading to a funding deficit of almost $70 million in 2002.9
5. Conclusion There is a growing body of literature on government funding of public universities, yet most of this research has focused on the effect of state level characteristics, particularly state tax revenues. Because funding of degree granting institutions varies significantly according to the type of institution and institution characteristics, and because the mix of degree granting institutions varies across states, previous research using state level data has aggregated away much of the cross-sectional variation in the data and may produce misleading results. Using panel data on 98 DREU over the period from 1987 to 2002, we estimate the effect of a variety of DREU and state characteristics on state funding of DREU. Using a longitudinal econometric method, we are also able to control for institutional level unobserved heterogeneity. Unlike previous studies, we focus solely on DREU, so our estimation result are driven by the within variation of DREU, not by the between variation across different types of universities and colleges. We consider determinants not previously studied such as the competitiveness of programs and quality of students, the mix of degree programs and professional schools, the degree of research orientation of a university, the effects of economies of scale (number of students), the cost of providing educa9
The use of per capita state tax revenues may overstate the predicted amount of state funding for UHM; unlike other states that use property tax revenues to finance local primary schools, Hawaii finances its pri-
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tions services, and other state characteristics. Not surprisingly, we find that the existence of a medical or law school significantly raises state funding, by 22.0% and 11.6% respectively. Also, highly competitive universities receive 13.6% more state funding that similar non-competitive schools. Similarly, universities with a stronger research focus also receive higher state funding. Interestingly, once we control for state tax revenues and macroeconomic environment, proxies for cost of living are insignificantly different from zero. Finally, we provide four case studies to illustrate the use of our model in evaluating the funding position of various universities. In future research, we will examine the effect of more finely measured indices of university quality and research focus to see whether some of the over- and under-funding we discuss in our case studies is possibly due to insufficient variation in our measure of quality.
mary school system through state tax revenues.
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Table 1: Current-Fund Revenue of Public Degree-Granting Institutions, by Source of Funds and Type of Institution, 2000-01
Type of Institution 1 Total 4-year Doctoral, extensive Doctoral, intensive Master's Baccalaureate Specialized institutions 2-year
Table 2: Current-Fund Revenue of Public Degree-Granting Institutions, by Source of Funds and State, 2000-01 Total
Tuition
Federal
State
Local
Gift
Endowme Auxiliary nt
Hospital
1 US
2 100.0
3 18.1
4 11.2
5 35.6
6 4.0
7 5.1
8 0.8
9 9.3
10 9.5
Educ Activity + Other 11 6.5
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware D.C. Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
Table 4: Mean and Standard Deviation of Variables Variable
Mean/Frequency
Std. Deviation
Institution Characteristics State appropriation (million $) Per FTE student state appropriation ($) % Medical School % Law School % Competitive Schools Number of Atheletic Teams FTE students % of graduate students Research orientation
171.0 6779 55% 50% 58% 19.30 24993 22% 0.143
101.1 2776 50% 50% 49% 4.43 9498 10% 0.315
State Characteristics Cost of living index Per capita state tax collection ($) State unemployment rate Meidan household income ($)
104 1469 5.62% 48957
17 432 1.43% 10923
Number of instituions Number of observations
98 .. 863 ..
Table 5: Random Effects Estimation for Log of State Appropriation Dep=Log (State appropriations) Medical school=1
Coefficient 0.200
Std Error (0.041)
Law school=1
0.116
(0.059)
Competitive university=1
0.136
(0.064)
Number of varsitiy teams
0.020
(0.007)
Log of FTE students
0.514
(0.040)
Ratio of grad to undergrad
0.228
(0.121)
Ratio of expenditure (research/instruction)
0.005
(0.021)
Log of cost
0.063
(0.224)
Log of per capita tax
0.363
(0.060)
Unemployment rate
-0.018
(0.004)
Log of median household income
0.233
(0.112)
Hausman Test (Chi square) Overall R squared Number of obs.
234.4 0.677 863
Other variables include year dummies. Standard errors are in parenthesis. All variables are significant at 5% level with the exception of "ratio of expenditure" and "cost". Hausman test is significant at the 1% level.
Table 7: Ranking of all DREU Based on Ratio of Actual to Predicted State Appropriations Institution Ranking for FY 2001-02 FR Institiution Ranking for FY 1987- FY 2002 UNIVERSITY OF VERMONT AND STATE AGRICULTURAL COLL 0.21 UNIVERSITY OF VERMONT AND STATE AGRICULTURAL COLL UNIVERSITY OF COLORADO AT BOULDER 0.44 UNIVERSITY OF COLORADO AT BOULDER UNIVERSITY OF OREGON 0.47 UNIVERSITY OF OREGON UNIVERSITY OF TOLEDO 0.55 UNIVERSITY OF MARYLAND-BALTIMORE COUNTY UNIVERSITY OF RHODE ISLAND 0.59 UNIVERSITY OF RHODE ISLAND UNIVERSITY OF MARYLAND-BALTIMORE COUNTY 0.59 OLD DOMINION UNIVERSITY COLORADO STATE UNIVERSITY 0.61 UNIVERSITY OF TOLEDO UNIVERSITY OF MISSISSIPPI MAIN CAMPUS 0.64 UNIVERSITY OF VIRGINIA-MAIN CAMPUS UNIVERSITY OF VIRGINIA-MAIN CAMPUS 0.65 COLORADO STATE UNIVERSITY KENT STATE UNIVERSITY-MAIN CAMPUS 0.69 UNIVERSITY OF PITTSBURGH-MAIN CAMPUS OLD DOMINION UNIVERSITY 0.69 UNIVERSITY OF OKLAHOMA NORMAN CAMPUS WESTERN MICHIGAN UNIVERSITY 0.70 TEMPLE UNIVERSITY UNIVERSITY OF OKLAHOMA NORMAN CAMPUS 0.71 WESTERN MICHIGAN UNIVERSITY OHIO UNIVERSITY-MAIN CAMPUS 0.72 UNIVERSITY OF MISSISSIPPI MAIN CAMPUS UNIVERSITY OF HAWAII AT MANOA 0.72 UNIVERSITY OF CALIFORNIA-SANTA CRUZ UNIVERSITY OF LOUISVILLE 0.73 KENT STATE UNIVERSITY-MAIN CAMPUS UNIVERSITY OF CALIFORNIA-SANTA CRUZ 0.74 OHIO UNIVERSITY-MAIN CAMPUS UNIVERSITY OF WISCONSIN-MILWAUKEE 0.77 SUNY AT BINGHAMTON UNIVERSITY OF SOUTHERN MISSISSIPPI 0.78 UNIVERSITY OF LOUISVILLE UNIVERSITY OF KANSAS MAIN CAMPUS 0.80 SUNY AT ALBANY UNIVERSITY OF MEMPHIS 0.83 UNIVERSITY OF KANSAS MAIN CAMPUS INDIANA UNIVERSITY-BLOOMINGTON 0.85 UNIVERSITY OF SOUTH CAROLINA-COLUMBIA UNIVERSITY OF SOUTH CAROLINA-COLUMBIA 0.88 UNIVERSITY OF SOUTHERN MISSISSIPPI FLORIDA INTERNATIONAL UNIVERSITY 0.88 UNIVERSITY OF WISCONSIN-MILWAUKEE OHIO STATE UNIVERSITY-MAIN CAMPUS 0.89 UNIVERSITY OF CONNECTICUT UNIVERSITY OF NORTH TEXAS 0.89 UNIVERSITY OF MEMPHIS TEXAS TECH UNIVERSITY 0.89 NORTHERN ILLINOIS UNIVERSITY SOUTHERN ILLINOIS UNIVERSITY-CARBONDALE 0.91 SOUTHERN ILLINOIS UNIVERSITY-CARBONDALE UNIVERSITY OF ALABAMA 0.92 FLORIDA INTERNATIONAL UNIVERSITY KANSAS STATE UNIVERSITY 0.93 UNIVERSITY OF NEVADA-RENO UNIVERSITY OF WYOMING 0.93 OHIO STATE UNIVERSITY-MAIN CAMPUS THE UNIVERSITY OF TEXAS AT ARLINGTON 0.93 PENNSYLVANIA STATE UNIVERSITY-MAIN CAMPUS FLORIDA STATE UNIVERSITY 0.94 UNIVERSITY OF HAWAII AT MANOA UNIVERSITY OF MISSOURI-COLUMBIA 0.95 UNIVERSITY OF CINCINNATI-MAIN CAMPUS WEST VIRGINIA UNIVERSITY 0.97 THE UNIVERSITY OF TEXAS AT ARLINGTON NORTHERN ILLINOIS UNIVERSITY 0.98 INDIANA UNIVERSITY-BLOOMINGTON UNIVERSITY OF HOUSTON-UNIVERSITY PARK 0.99 UNIVERSITY OF NEW MEXICO-MAIN CAMPUS UNIVERSITY OF WASHINGTON-SEATTLE CAMPUS 0.99 UNIVERSITY OF UTAH UTAH STATE UNIVERSITY 1.00 VIRGINIA COMMONWEALTH UNIVERSITY UNIVERSITY OF MICHIGAN-ANN ARBOR 1.02 KANSAS STATE UNIVERSITY PURDUE UNIVERSITY-MAIN CAMPUS 1.03 UNIVERSITY OF IOWA VIRGINIA COMMONWEALTH UNIVERSITY 1.03 UNIVERSITY OF ALABAMA UNIVERSITY OF NEVADA-RENO 1.04 UTAH STATE UNIVERSITY LOUISIANA STATE UNIV & AG & MECH & HEBERT LAWS CTR 1.05 UNIVERSITY OF MISSOURI-COLUMBIA NORTH CAROLINA STATE UNIVERSITY AT RALEIGH 1.05 UNIVERSITY OF NORTH TEXAS OKLAHOMA STATE UNIVERSITY-MAIN CAMPUS 1.05 UNIVERSITY OF WYOMING UNIVERSITY OF NEW MEXICO-MAIN CAMPUS 1.05 OKLAHOMA STATE UNIVERSITY-MAIN CAMPUS UNIVERSITY OF CONNECTICUT 1.06 TEXAS TECH UNIVERSITY OREGON STATE UNIVERSITY 1.07 UNIVERSITY OF MICHIGAN-ANN ARBOR UNIVERSITY OF ARKANSAS MAIN CAMPUS 1.07 GEORGIA STATE UNIVERSITY WAYNE STATE UNIVERSITY 1.08 WEST VIRGINIA UNIVERSITY AUBURN UNIVERSITY MAIN CAMPUS 1.08 UNIVERSITY OF WASHINGTON-SEATTLE CAMPUS UNIVERSITY OF UTAH 1.09 WAYNE STATE UNIVERSITY UNIVERSITY OF MARYLAND-COLLEGE PARK 1.09 NORTH CAROLINA STATE UNIVERSITY AT RALEIGH UNIVERSITY OF WISCONSIN-MADISON 1.11 UNIVERSITY OF HOUSTON-UNIVERSITY PARK UNIVERSITY OF IOWA 1.12 PURDUE UNIVERSITY-MAIN CAMPUS MISSISSIPPI STATE UNIVERSITY 1.13 NEW MEXICO STATE UNIVERSITY-MAIN CAMPUS MICHIGAN STATE UNIVERSITY 1.15 LOUISIANA STATE UNIV & AG & MECH & HEBERT LAWS CTR UNIVERSITY OF CALIFORNIA-IRVINE 1.16 UNIVERSITY OF CALIFORNIA-IRVINE IOWA STATE UNIVERSITY 1.18 UNIVERSITY OF IDAHO ARIZONA STATE UNIVERSITY-MAIN CAMPUS 1.19 SUNY AT BUFFALO GEORGIA STATE UNIVERSITY 1.20 OREGON STATE UNIVERSITY UNIVERSITY OF MASSACHUSETTS-AMHERST 1.20 FLORIDA STATE UNIVERSITY VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIV 1.21 UNIVERSITY OF CALIFORNIA-SANTA BARBARA NEW MEXICO STATE UNIVERSITY-MAIN CAMPUS 1.22 UNIVERSITY OF ARKANSAS MAIN CAMPUS WASHINGTON STATE UNIVERSITY 1.23 MICHIGAN STATE UNIVERSITY UNIVERSITY OF IDAHO 1.24 UNIVERSITY OF MARYLAND-COLLEGE PARK CLEMSON UNIVERSITY 1.24 VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIV UNIVERSITY OF CALIFORNIA-SANTA BARBARA 1.24 ARIZONA STATE UNIVERSITY-MAIN CAMPUS UNIVERSITY OF NEBRASKA AT LINCOLN 1.26 AUBURN UNIVERSITY MAIN CAMPUS UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN 1.28 UNIVERSITY OF NEBRASKA AT LINCOLN UNIVERSITY OF MINNESOTA-TWIN CITIES 1.32 UNIVERSITY OF MASSACHUSETTS-AMHERST UNIVERSITY OF CALIFORNIA-RIVERSIDE 1.33 SUNY AT STONY BROOK UNIVERSITY OF CALIFORNIA-SAN DIEGO 1.33 MISSISSIPPI STATE UNIVERSITY UNIVERSITY OF SOUTH FLORIDA 1.34 UNIVERSITY OF MINNESOTA-TWIN CITIES UNIVERSITY OF KENTUCKY 1.35 UNIVERSITY OF CALIFORNIA-SAN DIEGO UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL 1.37 IOWA STATE UNIVERSITY THE UNIVERSITY OF TEXAS AT AUSTIN 1.40 WASHINGTON STATE UNIVERSITY UNIVERSITY OF ARIZONA 1.42 UNIVERSITY OF WISCONSIN-MADISON TEXAS A & M UNIVERSITY 1.65 CLEMSON UNIVERSITY UNIVERSITY OF CALIFORNIA-DAVIS 1.68 UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN UNIVERSITY OF FLORIDA 1.71 UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL UNIVERSITY OF ILLINOIS AT CHICAGO 1.77 THE UNIVERSITY OF TENNESSEE GEORGIA INSTITUTE OF TECHNOLOGY-MAIN CAMPUS 1.80 UNIVERSITY OF SOUTH FLORIDA UNIVERSITY OF GEORGIA 1.82 UNIVERSITY OF ARIZONA UNIVERSITY OF ALABAMA AT BIRMINGHAM 1.82 UNIVERSITY OF CALIFORNIA-RIVERSIDE UNIVERSITY OF CALIFORNIA-BERKELEY 1.92 UNIVERSITY OF KENTUCKY UNIVERSITY OF CALIFORNIA-LOS ANGELES 2.04 THE UNIVERSITY OF TEXAS AT AUSTIN UNIVERSITY OF PITTSBURGH-MAIN CAMPUS RUTGERS UNIVERSITY-NEW BRUNSWICK TEMPLE UNIVERSITY GEORGIA INSTITUTE OF TECHNOLOGY-MAIN CAMPUS SUNY AT BINGHAMTON UNIVERSITY OF CALIFORNIA-DAVIS SUNY AT ALBANY UNIVERSITY OF ALABAMA AT BIRMINGHAM PENNSYLVANIA STATE UNIVERSITY-MAIN CAMPUS UNIVERSITY OF CALIFORNIA-BERKELEY UNIVERSITY OF CINCINNATI-MAIN CAMPUS UNIVERSITY OF GEORGIA SUNY AT BUFFALO TEXAS A & M UNIVERSITY SUNY AT STONY BROOK UNIVERSITY OF CALIFORNIA-LOS ANGELES THE UNIVERSITY OF TENNESSEE UNIVERSITY OF ILLINOIS AT CHICAGO RUTGERS UNIVERSITY-NEW BRUNSWICK UNIVERSITY OF FLORIDA Note: FR (funding ratio) = Ratio of Actual State Appropriations to Predicted Appropriations, AFR = average of FR over period
References Cohen, L.R., and R.G. Noll. 1998. Universities, constituencies, and the role of the states, In R.G. Noll, Challenges for Research Universities (pp. 31-62) (Washington D.C.: Brookings Institution Press) Coughlin, C.C., and O.H. Erekson. 1986. Determinants of state aid and voluntary support of higher education. Economics of Education Review, 5: 179-190. Greene, W.H. 2002. Econometric Analysis, 5th ed (Upper Saddle River, NJ: Prentice Hall) Leslie, L.L., and G. Ramey. 1986. State appropriations and enrollments: does enrollment growth still pay? Journal of Higher Education, 57: 1-19. Lowry, Robert C. 2001. The effects of state political interests and campus outputs on public university revenues. Economics of Education Review, 20: 105-119.
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Appendix 1 We make use of several data sets. The primary source of data is nine years of data on public Doctoral/Research-Extensive universities from the U.S. Integrated Postsecondary Education Data System (IPEDS).10 The IPEDS data is then supplemented with information from Barron’s Profiles of American Colleges 2005 (26th Edition), The Council for Community Economic Research (ACCRA), and other publicly available information at the state level. All data sets are merged with IPEDS, creating a single data set that contains all information necessary to complete our regression analysis. Revenue, expenditure, enrollment, and research orientation related variables are obtained from IPEDS data. Information on varsity teams and a proxy for competitiveness of universities are obtained from Barron’s Profiles of American Colleges 2005. This data set contains several pieces of necessary information that are not available from IPEDS. Schools are rated according to Barron’s competitiveness scale, from “Noncompetitive” to “Most Competitive.”11 We construct a single binary variable that equals one if the school is “Most/Highly/Very Competitive” or 0 otherwise. The number of varsity teams is also created from the data set. We also tried to generate a group of sports activity related variables. Information obtained from the National Collegiate Athletic Association (NCAA) website for sports team and their division level is used for sensitivity checks. Information on cost of living is obtained from the Council for Community Economic Research (ACCRA). ACCRA produces the cost of living index (COST) to provide a useful measure 10
The available years for variables include 1987, 1989, 1991, 1993, 1995, 1996, 2000, 2001, and 2002. The following 6 crieteria are used for competitiveness scale. 1) Median entrance examination scores for 2003-2004 freshman class, 2) Percentage of 2003-2004 freshman scoring 500 and above and 600 and above on both the verbal and mathematics reasoning sections of SAT I, 3) Percentage of 2003-2004 freshman scoring 21 and above and 27 and above on the ACT, 4) Percentage of 2003-2004 freshmen who ranked in the upper fifth and upper two-fifths of their high school graduating classes, and 5) Minimum class rank and 11
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of living cost differences among urban areas. Although this may be a noisy and errorridden proxy for living cost, we feel it is important to incorporate some widely used living cost index measure into our estimation. All the state level variables are obtained from either Bureau of Labor Statistics or U.S. Census Bureau.
GPA required for admission (if any), and 6) Percentage of applicants to the 2003-2004 freshman class who were accepted.
