1
Memorandum
To:
From:
Re:
Date:
Washington Higher Education Coordinating Board
Jason Adams, Ryan Kinsella, Analysts
Comparative Analysis of State Tuition Pricing
December 14, 2009
Executive Summary
The following study considers the correlation between the annual percent increase in state tuition pricing
for undergraduate resident students and a variety of other factors, including the type of tuition policy,
other institutional factors and other economic conditions within a state. We pose this question: what is the
correlation between a state's tuition policy and the price of tuition?
Following the findings of previous research, we control for other economic and institutional factors and
then consider how other explanatory variables correlate with tuition change. Specifically, we consider the
following factors: the previous year’s change in tuition, public and private enrollment, state population,
bachelor degree production, state financial aid, tax revenues and appropriations for higher education.
Comparing these factors of each state across a 10-year period (1998-2008), we aim to identify which of
these factors significantly correlate with changes tuition pricing.
This analysis used a custom data set comprised of education data collected by the National Center for
Education Statistics (administered by the U.S. Department of Education) and the State Higher Education
Executive Officers (SHEEO) and demographic and economic data from the U.S. Census Bureau from
1998 to 2008. In addition to tuition analyzing tuition policy and tuition-setting authorities, we include
other economic and institutional factors that influence tuition prices. To conduct our analysis, we used
five statistical models, including three OLS regressions, a fixed-effects and multilevel model.
Through our descriptive statistic and regression analysis, we came to following key conclusions:




Tuition policies and the type of tuition-setting authorities are not correlated with the change in the
price of tuition
Previous year’s tuition change and appropriation change are inversely correlated with tuition
change
Region and tuition change are not correlated, despite the findings of previous studies
An increase in state tax revenue are positively correlated with tuition increase
Tuition prices continue to rise across all states, and as a result, interest in tuition policy and factors that
influence tuition pricing continue to grow. An understanding of the factors that drive the increases in
tuition, particularly during recessionary periods, will inform state policy-makers of the types of conditions
that likely affect tuition pricing. Moreover, state universities and colleges will benefit from this type of
tuition analysis in terms of future financial planning.
2
Study Background and Purpose
During periods following economic recessions and periods when state support was less than inflation and
enrollment growth, the rate of net tuition increased significantly.1 Essentially, when states reduce funding
due to recessionary impacts, universities and colleges seemingly increase tuition to meet operating costs.2
Regardless of the type of tuition policy, the combination of state support and tuition remains the primary
source of revenue for instructional costs.3 On average, 40% of colleges' and universities' revenue come
from net tuition, with the remainder of revenue coming from the state and other sources.4
Research also indicates that the annual increase in tuition is greater than the annual increase in spending
per student at public institutions across the country.5 Moreover, public universities are using tuition
dollars increasingly more to fund other aspects of their institutions (such as research, student services,
capital projects). For example, tuition increased by an average of 29.8% between 2002 and 2006 at public
research universities across the nation; expenses related to educating students increased by 2.5%.6
Regardless of tuition policy, over the past 20 years, all states continue to appropriate a smaller portion
funding for post-secondary education. As a result, tuition prices continue to increase at rates substantially
greater than inflation and operating costs.
Tuition Policies
Not all states have tuition policies. In general, however, 28 states have either formal or informal tuition
policies that are articulated in state statute, constitution or in the internal policies of an
agency/coordinating board. In contrast, nine states have no single, formal tuition philosophy at the state
level.7 The remaining states have policies that depend upon sector or the individual intuitions.
Moreover, each state’s tuition policy differs, but many policies share similar characteristics in terms of
the authority of who sets tuition, the philosophy behind the policy and the general financing structure for
the state and universities. Groups of states are similar in terms of who oversees the tuition policy (e.g.,
legislature, governing board, etc.), the philosophy behind the policy (low, moderate and high), and state
financing structure. However, no two states are comparable across all three of these aspects. Rather than
policy alone, state appropriations, tax revenues, economic conditions, other characteristics of the state and
a variety of other factors influence what a resident undergraduate pays to attend college.
Initial Literature Review
Previous studies asked questions similar to our research, focusing on different elements that shape tuition
pricing in each state, creating an extensive base of relevant literature on this subject. For our analysis we
used the findings of two studies to shape our research questions and improve upon our method and model.
Specifically, we reviewed research by James Hearn Carolyn Griswold and Ginger Marine in their paper
Region, Resource, and Reason: A Contextual Analysis of State Tuition and Student Aid Policies, and also
1
SHEEO, State Higher Education Finance FY 2008, p. 17.
SHEEO, State Higher Education Finance FY 2008, p. 19.
State Higher Education Executive Officers. State Tuition, Fees and Financial Assistance Policies for Public Colleges and Universities, 2005-06.
November 2006.www.sheeo.org, p. 19.
4
SHEEO, State Higher Education Finance FY 2008, p. 19.
5
Delta Cost Project. Trends in College Spending. 2009, Delta Project on Postsecondary Education Costs, Productivity and Accountability.
www.deltacostproject.org, p. 25
6
Delta, 26.
7
Alabama, Delaware, Michigan, New Jersey, New Mexico, North Dakota, Ohio, Pennsylvania, and Washington.
2
3
3
Jung-cheol Shin and Milton Sande in their paper Rethinking Tuition Effects on Enrollment in Public FourYear Colleges and Universities.
Here are some of the findings of previous researched that shaped our analysis:
Economic Factors
Shin and Sande (1996) find that the state’s economic climate, including job market and government
budgetary restraints, are more likely to affect enrollment, graduation rates and the price of tuition more
than the state’s tuition policy.8 For instance, tuition prices reflect how much students, or the higher
education “market”, is willing to pay for bachelor degree. Additionally, the differences in institutional
operating costs and state appropriations also influence how much universities decide to charge students.
Region
Shin and Sande specifically examined the relationship between tuition (or “financing”) policy and region.
The study found that region and the price of tuition are closely related, where Northeastern and
Midwestern states generally have higher tuition prices and southern states have lower tuition prices.9 This
finding suggests that regions serve as a proxy for other geographic factors, such as social values, norms,
attitudes and expectations. In terms of financial aid, larger states tended to award more aid per capita, and
the northeast region provided more financial aid funding than the southeast and southwest.10
Tuition-Setting Authority
The study also found a strong correlation between tuition prices and the authority that sets tuition prices.
Specifically, states in which multi-university boards or state agencies set tuition generally have higher
tuition prices.11 States with a greater number of private universities also tended to have higher tuition
prices, suggesting that the regional market affects tuition. As a result, we include a variable to account for
the effects of private institutions within the state in our analysis.
Tuition Policy
Both studies indicate that a state’s tuition policy does not have a statistically significant affect on
enrollment, degree production or state appropriations. While tuition policy provides some insight into the
performance of state institutions, a state's tuition policy only partially explains any changes in enrollment,
institutional revenues and net tuition price. These outcomes are also influenced by changes in the local
economies, state and federal appropriations, institutional policy changes, historical costs trends within the
state, and innumerable other social factors.
The two studies support the types of factors included in our study. Our analysis considers the correlation
between the annual percent increase in state tuition pricing and a various other factors, including the type
of tuition policy adopted by the state and other economic conditions within the state. In general, however,
our explanatory variables can be characterized into two types: formal, institutional factors that influence
tuition prices and other market (or economic) factors that influence tuition pricing.
8
Shin, Jung-cheol, Sande Milton, "Rethinking Tuition Effects on Enrollment in Public Four-Year Colleges and Universities," The Review of
Higher Education, 2006, 29, 2, pp.213-237.
9
Hearn, Griswold and Marine, p. 256.
10
Hearn, Griswold and Marine, p. 264.
11
Hearn, Griswold and Marine, p. 262.
4
Project Data
This analysis used a custom data set comprised of education data collected by the National Center for
Education Statistics (administered by the U.S. Department of Education) and the State Higher Education
Executive Officers (SHEEO) and demographic and economic data from the U.S. Census Bureau from
1998 to 2008. Where footnoted, SHEEO adjusts state tuition amounts for the cost of living. To arrive at
constant dollar figures, adjustment factors include Cost of Living Adjustment, Enrollment Mix Index and
Higher Education Cost Adjustment Tuition.
To characterize the types of tuition policy in each state, our analysis uses data compiled by the State
Higher Education Executive Officers (SHEEO) and published in their report, “Survey of State Tuition,
Fees, and Financial Assistance Policies” (2005). The survey asks executive leaders in each state’s higher
education community to characterize their state’s tuition and financial aid policies. In our analysis, we use
the survey to identify the tuition philosophy of each state (low, medium, high, informal or other), in
which a “low” tuition philosophy indicates that the prices are kept low as possible, prioritizing student
access. A “high” tuition philosophy indicates that the state uses a “high tuition, high aid” model where
tuition prices are set high but the state disburses high amounts financial aid, primarily to students with
need. States with “high” tuition philosophy would expectedly have tuition prices greater than other states.
We also consider the tuition-setting authority in each state using the following characterizations as
variables: state statute, constitution, governor, higher education board and individual institutions. The
tuition-setting authority characterizations indentify which governing body/policy is primarily responsible
for setting the price of resident, undergraduate tuition in public 4-year universities. We included this
characterization in our analysis because prior research indicates that some types of governing bodies and
policies traditionally set tuition prices higher than others.
Descriptive Statistics
To begin our analysis we examined descriptive data regarding: annual tuition change at the national and
regional level, and the distribution of tuition policies and tuition setting authorities across states
(Appendix A). From this analysis we found that the national average change in tuition prices varied
significantly between 1998 and 2008 (Figure 1).
5
Tuition-Setting Authority and Policy Type
Another finding of this initial analysis is that formal tuition policies were used by states to set tuition in a
slight majority (52%) of cases (Figure 2). Of the three formal policy setting structures, tuition policies
were most often established by a higher education governing board (28 % of cases), followed by state
statute (18%), and the state’s constitution (6%).
Though many states did not use a formal tuition policy to set prices, a self-described formal or informal
policy was used in 87 percent of cases (Figure 3). Of these cases, a majority (61.4%) used a policy of low
or budgetary need to set tuition prices. This is in contrast to self-described high policies, which were used
to set tuition only 2 percent of the time
Interestingly, the self-described tuition policies and the type of tuition-setting authority vary across states
with the lowest 10-year average percent increase, suggesting that policies have limited effects on pricing.
Tuition Price by Region, Policy Authority, and Type
To continue our analysis we examined how tuition prices varied across region, policy type and authority.
These results, available in Appendices B through D, appear to roughly follow the trend line shown in
Figure 1 with none of three independent variables displaying any obvious divergence from the average.
Average Annual Change – Tuition and Appropriations
The following chart (Figure 4) illustrates the average national change in tuition compared to the average
national change in appropriations to public higher education. As institutions receive less state
appropriations, it would be expected that tuition prices would increase to compensate. Consistent with
this expectation, the chart indicates that this inverse relationship exists. However, in 2006 and 2007 it
appears that tuition rose despite increased appropriations. Unfortunately without measurements from 2008
and 2009 we cannot assess what may have caused this result.
6
Changes in State Fiscal Climate and Net Tuition
The following chart (Figure 5) shows a comparison of the average annual percentage change in tuition
with the average annual percentage change in tax revenues for all states. Initially we believed that when
tax revenues are high, the growth in tuition would be lower than in years when tax revenue growth is low
or negative. This hypothesis is based on the reasoning that public university systems will receive more
state assistance when tax revenues are high, resulting in reduced pressure on tuition prices. However, as
Figure 5 illustrates, this relationship does not appear to exist in our data.
One possible explanation for this finding is that the effect of lower tax revenues on tuition prices is
heavily lagged. Meaning, only after long periods do budgetary pressures cause tuition prices to change. If
this is the case, the declines in tax revenues during the 2001 recession could have some responsibility for
7
increased in tuition prices 2003 through 2006. To test this theory we incorporated a lagged revenue
variable into our research models.
Data Analysis Methods
To analyze tuition increases, we use a linear probability, time-series model, in which the dependent
variable is the percentage change in the price of tuition and fees in each state, relative to the previous
year. The price of tuition is based on the total charge to undergraduate resident students, including all
necessary fees but not reducing the amount for any state appropriations or financial aid. To conduct our
analysis, we compare five models that vary with the types of variables and the type of regression,
including am OLS, fixed effects regression and multilevel regression.
The explanatory variables in our model represent the factors that influence tuition the most, according to
policy-makers and authors of current literature. In general, however, our explanatory variables can be
characterized into two types: formal, institutional factors that influence tuition prices and other market (or
economic) factors that influence tuition pricing.
To gauge the correlation between state government influences on the price of tuition, we will include
three key variables:
 Type of tuition policy (categorical variable)
 Whether the policy is formalized in statute (dummy variable)
 Governing body that sets tuition price (categorical variable)
To estimate the economic factors within the state that influence the price of tuition, we will include the
following variables:










Lagged % annual tuition change
Enrollment for 4-Year public institutions
Enrollment (and lagged enrollment)
Private enrollment
Proportion of private enrollment to undergraduate enrollment
Population
Bachelor degrees production
Amount of state financial aid per student
Percent tax revenue change (and lagged revenue change)
Percent appropriation change (and lagged appropriations change)
Regression Model - Development
To conduct our analysis, we used five statistical models, including three OLS regression, a fixed-effects
and multilevel model. The first model, an OLS regression, serves a control model for which we compare
the findings of our additional models. In the second model, we include the lagged change in tuition,
current enrollment, change in appropriations and change in state revenues, aiming to identify how these
additional factors correlate with tuition. Notably, all these factors are statistically significant in the second
model. The third model introduces the types of tuition policy and tuition setting-authority, all of which we
found to be insignificant.
We then include a fixed-effects model, aiming to isolate the unchanging characteristics of each state from
the factors that we believe correlate with tuition change. With little variation in our policy data, we
removed these variables from our final model, along with the variables for region. The lack of variation in
8
tuition polices across states limited the usefulness of a fixed-effects model, causing these variables to be
insignificant or dropped. However, we believed there would be enough variation between region and
policy type, and so we also analyzed a multi-level model. This fifth model assessed the random effect of
having a formal tuition policy across regions on tuition prices. The results of these five models are
presented below (Table 2).
Table 2: Model Results
Variable Name
Model 1
Coef.
P>t
Model 2
Coef.
P>t
Model 3
Coef.
P>t
Model 4
Coef.
P>t
Model 5
Coef.
P>t
Policy Type:
High
-
-
-
-
0.0408
0.440
(dropped)
-
0.0131
0.807
Moderate
-
-
-
-
0.0064
0.824
(dropped)
-
-0.0120
0.650
Low
-
-
-
-
-0.0201
0.407
(dropped)
-
-0.0322
0.191
Budget Need
-
-
-
-
0.0218
0.380
(dropped)
-
0.0013
0.959
Other
-
-
-
-
-0.0344
0.265
(dropped)
-
-0.0397
0.194
-0.0056
0.703
-0.0065
0.642
-
-
-
-
-0.0178
0.542
Board
-
-
-
-
-0.0342
0.126
(dropped)
-
0.0000
-
Constitution
-
-
-
-
-0.0498
0.129
(dropped)
-
0.0000
-
Statute
-
-
-
-
0.0197
0.353
(dropped)
-
0.0000
-
-0.0023
0.959
-0.0139
0.745
-0.0465
0.335
0.5968
0.158
-0.0044
0.921
Lagged % Tuition Change
-
-
-0.2347***
0.000
-0.2757***
0.000
-0.4146***
0.000
-0.2707***
0.000
Enrollment/1000
-
-
-0.0033***
0.000
-0.0032***
0.000
-0.0026***
0.009
-0.0032***
0.000
Lagged Enrollment/1000
0.0002
0.301
0.0034***
0.000
0.0033***
0.000
0.0030***
0.003
0.0033***
0.000
Private Enrollment/1000
0.0003
0.153
0.0001
0.563
0.0003
0.102
0.0014
0.118
0.0003
0.113
Formal Policy
Tuition Setting Body:
Constant
Proportion Private
0.0321
0.599
0.0288
0.619
0.1137**
0.087
0.0478
0.811
0.0806
0.113
-0.0253***
0.002
-0.0161**
0.052
-0.0204**
0.017
-0.1980**
0.039
-0.0156*
0.051
Bachelor Degrees Produced/1000
0.0052*
0.068
0.0042
0.128
0.0047*
0.099
0.0209*
0.078
0.0025
0.321
Aid Per Enrollment/1000
0.0425
0.196
0.0364
0.244
-0.0125
0.741
-0.0237
0.800
0.0076
0.817
Population/1000000
% Revenue Change
-
-
0.0926**
0.041
0.0880*
0.052
0.0817*
0.072
0.1025**
0.021
0.0761*
0.083
0.1042**
0.017
0.1104**
0.012
0.1140**
0.013
0.1107***
0.010
-
-
-0.2313**
0.013
-0.2361**
0.015
-0.1857*
0.061
-0.2060**
0.029
-0.1280
0.131
-0.1465*
0.077
-0.1410
0.101
-0.0560
0.533
-0.1203
0.151
SE
-0.0003
0.991
0.0020
0.942
-0.0242
0.421
-
-
-
-
NE
-0.0203
0.498
-0.0126
0.656
-0.0074
0.796
-
-
-
-
Plains
-0.0186
0.580
-0.0150
0.638
0.0239
0.500
-
-
-
-
MW
-0.0137
0.660
-0.0069
0.816
-0.0382
0.249
-
-
-
-
SW
0.0444
0.162
0.0606
0.045
0.0491
0.118
-
-
-
-
N=
299
N=
293
N=
293
N=
293
N=
293
R2 =
0.0366
R2 =
0.1086
R2 =
0.1574
R2 =
n/a
R2 =
n/a
Lagged % Revenue Change
% Appropriation Change
Lagged % Appropriation Change
Region:
Other Statistics
*** = p < .01, ** = p < .05, * = p < .1
9
Regression Model - Findings
Analyzing the results of the models, we come to following findings on how these factors correlate with
tuition change:
Tuition Policy and Tuition-Setting Authorities
To examine the effects of policies on tuition pricing, we analyzed the general effects of all types of tuition
policies and tuition-setting authorities. Using a dummy variable to indicate whether the state had any type
of formal tuition policy, we found the effects of a formal tuition policy to be insignificant in the first two
OLS models. This finding is reaffirmed by the results of our multi-level model (Appendix B). This model
found only a small amount of variance (.03 percentage points) in tuition prices in states with formal
policies. Separating the policies into four types (low, medium, high, budget constraints) in the third, OLS
model, the correlation of policies onto tuition change was still insignificant. This finding is consistent
with the previous researched, as discussed in the literature review earlier. However, we also recognize
that the data on tuition policies had little variations within each state over the 10 years, without which the
tuition policies would be insignificant in any of the models.
Lagged Annual Change in Tuition
Across all five models, the previous year’s percent change in tuition was significant, indicating its
importance in affecting tuition change. Our research indicates that an increase in the previous year’s
tuition leads to a percentage point decrease in the current year’s tuition. That is, tuition-setting authorities
may be likely to increase (or even decrease) tuition if tuition increased during the previous year.
Conversely, tuition-setting authorizes might be less inclined to raise to tuition after increasing tuition
during the previous year due to political pressures or uncertainty in how the market for higher education
will react. Regardless, this finding strongly suggests that the previous year’s tuition change largely affects
the current year’s change in tuition.
Bachelor Degree Production
Our research also indicates that bachelor degree production and tuition change are positively correlated.
In four of our models, we found bachelor degree production significant (p<0.10), albeit with a relatively
low coefficients. A potential reason for this result is that bachelor degree production serves as a proxy for
demand for higher education. Consistent with economic theory, when demand is higher prices will rise.
Population
Our analysis also found that population and tuition change were inversely correlated, indicating that an
increase in state’s population leads to a decrease in the percentage point change in tuition. Previous
research explains this correlation by noting that an increase population may lead to more tax revenues
available to appropriate to higher education or that larger states have more infrastructure available to
provide higher education at a lesser cost per student. We believe that both explanations may be true, along
with the possibility that population correlates with other unobserved factors which are also correlated with
tuition change.
Tuition and Revenues
Of these results the most surprising finding is that the relationship of tuition prices and tax revenues is
opposite our initial hypothesis. When we designed our model we anticipated that these variables would
have an inverse relationship; that is, when tax revenues fall, tuition increases (and vice versa). However,
10
our results show that the opposite is true, with a one percentage point increase in the previous year’s tax
revenues resulting in an 11 percentage point increase in tuition prices (Table 1).
We believe this result is caused by two factors. First, that the anticipated effect of revenues on tuition is
being captured by the appropriation variables used in our model which do have an inverse relationship
with tuition prices. Second, that increased tax revenues represent periods of economic growth during
which policy makers are more likely to increase tuition prices than in recessionary periods.
Tuition and Enrollment
Another result of our analysis is that current enrollment and lagged enrollment have opposite effects on
tuition prices. This result means that for every thousand students increase in enrollment, tuition will be
0.32 percentage points lower. By contrast, tuition will be 0.33 percentage points higher for every
thousand students enrolled in the previous year.12 A possible interpretation of this finding is that an
increase in enrollment creates more tuition revenues, lowering the need to increase prices to meet
operating costs.
Insignificant Correlations: Region, Aid per Full-Time Equivalent and Private Enrollment
Following the findings of previous research, we consider region in our analysis, aiming to replicate their
findings. Surprisingly, however, region was not significantly correlated with tuition change in any of our
models, with only one exception.13 The results of our multi-level model reaffirm this finding, as the model
estimated only a 0.03 percentage point variance in annual tuition prices across regions (Appendix B). The
statistical insignificance of region led us to question why our models did not capture the correlation;
however, we did not reach any reasonable explanations.
Also in contrast to our expectations, we found that state need-based financial aid was not correlated with
tuition change. As more states move towards a “high tuition-high aid” tuition policy, we expected that an
increase in tuition would correlate with an increase in financial aid. However, our models suggest that
tuitions increases do not correspond in an increase in financial aid per student.
Lastly, when accounting for policy type and tuition setting authority, private enrollment and proportion of
private enrollment becomes relevant. However, private enrollment is only slightly insignificant across all
models, suggesting the market for private higher education slightly influences the pricing of public
tuition. For instance, tuition-setting authorities may increase tuition if they believe that more students are
willing to attend private universities and pay higher tuition prices. However, another “unobserved”
economic factor might explain this correlation, such as general growth in state’s economy or increase in
inflation. Both these economic factors might lead to an increase in private enrollment and a greater
market tolerance for an increase in tuition.
Discussion and Conclusions
Returning to the focus of our original question of tuition policy, our analysis finds that tuition change is
uncorrelated with state policies or tuition-setting authorities. Though policy and tuition-setting authority
were insignificant, we identified several significant factors, including the change in the previous year’s
tuition and appropriations, and the current year’s change in tax revenue and enrollment.
12
To test the strength of these opposing effects we included multiple lagged enrollment variables to see if the results
would change. These variables were insignificant at the 10 percent level and had no impact on the original variables.
13
The dummy variable for the SW region was statistically significant in one model, and the p-values were lowest of
all regions, (p = 0.162 and p = 0.118).
11
Possible Model Improvements
While providing helpful characterizations, the results of the SHEEO survey demonstrate the complexity
of tuition policies; many states have policies that are composed of elements that fall outside of SHEEO’s
survey characterizations or the type of policy is more complex than a simple characterization while also
varying slightly year to year. Moreover, state tuition policies change incrementally over time, and the
SHEEO characterizations only provide rough depictions of the policies. A more thorough analysis would
identify how key elements of tuition policies change each year within each state; unfortunately, we could
not identify a reliable data source with this information for our analysis.
We believe that identifying longitudinal changes in state tuition policies would allow for a more complete
statistical analysis. As previously mentioned, the lack of variation in our tuition policies data prevented
the use of fixed effects and multi-level models to analyze our key explanatory variables. Improved tuition
policy data would provide the variation necessary to use these statistical methods and allow us to better
discern how tuition prices are established.
Potential for Future Analysis
As Hearn notes, tuition costs tend to drift upward when budgets tighten, as institutions tend to raise
tuition without fear of market retribution. Tuition seems to rise with the constraining of budgets along
with other immediate political, social influences, and the economic context of the time.
Identifying the cause of this discrepancy could be the focus on future research into the relationship
between tax revenues and tuition prices. In our analysis, the tax revenues variable functions as a broad
measure of both the economic and political climate in which tuition prices are set; however, tax revenues
affect state budgeting but also reflect the strength of the state’s economy. Thus, the correlation of tax
revenues and tuition change is complex, making it difficult to identify how policy relates to changes in tax
revenues. Further analysis on how tax revenues correlate with tuition change, particularly during
recessionary periods, will inform state policy-makers on the potential for increases during future years.
Policy Implications
Tuition policies continue to interest policy-makers, especially during recessionary periods. The current
situation in Washington, for example, represents how policy-makers could use our findings to shape
tuition policies. Specifically, Washington state institutions recently experienced one of the largest annual
increases in undergraduate tuition. While legislation caps annual increases at 13% for the coming 3 years,
this increase reflects a distinct change in policy that previously limited undergraduate tuition increases to
7% in state statute. As a result, the Washington legislature recently tasked the HECB with studying
tuition policies and recommending a comprehensive policy for the state. (Currently, Washington has no
formal comprehensive policies, but state statue limits the percent annual increase for each institution.)
Recently, the HECB released a draft of their findings, recommending that the state to fund no less than 55
percent of undergraduate instructional costs at the six baccalaureate institutions. Further, it would limit
the amount of instructional costs provided by tuition to 45 percent.
Though our results show that tuition policies have no discernable effect on tuition prices we believe that
this policy could stabilize tuition prices. A predictable tuition policy would link tuition pricing to tuition
prices, appropriations and tax revenues, because, as our findings demonstrate, these factors are most
likely to change in correlation with pricing.
12
Appendices:
Appendix A: Descriptive Statistics
Variable
Percent Tuition Price Change
N
550
Mean
0.025
Std. Dev.
0.100
Min
-0.380
Max
0.929
Public Enrollment
550
189780.1
242908.5
15324.0
1731754.0
Private Enrollment
450
76885.9
97486.3
950.0
525859.0
Proportion of Total Enrollment Private
450
0.666
0.163
0.000
0.933
Bachelor Degrees Produced
500
17295.6
17248.8
1232.0
112661.0
Percent Revenue Change
500
0.075
0.129
-0.498
0.856
Population
550 5770874.0
Formalized Tuition Policy
Self-Described Tuition Policy:
High
Budgetary Need
Low
Moderate
No Philosophy
Other
Tuition Setting Authority:
Board
Constitution
Informal
Statute
550
0.520
0.500
0
1
529
529
529
529
529
529
0.021
0.270
0.312
0.166
0.125
0.104
0.143
0.445
0.464
0.373
0.331
0.306
0
0
0
0
0
0
1
1
1
1
1
1
551
551
551
551
0.279
0.060
0.479
0.180
0.449
0.238
0.500
0.384
0
0
0
0
1
1
1
1
6343437.0 479602.0
36800000.0
13
14
Appendix E: Data Dictionary
Variable Name
Year
Year
Variable
Type
Time
State
State
Panel
Population
Estimated State
Population per year
SE
Southeast States (AL,
AR, FL, GA, KY, LA,
MS, NC, SC, TN, VA)
Northweast States
(CT, DE, ME, MD,
MA, NH, NJ, NY, PA,
RI, VT, WV)
Plain States (CO, MT,
ND, SD, UT, WY)
Midwest States (IA,
IL, IN, KS, MI, MN,
MO, NE, OH, WI)
Southwest States (AZ,
CA, HI, NV, NM, OK,
TX)
Northwest States (AK,
ID, OR, WA)
Tuition should be high.
Dummy
Tuition should be
moderate.
Tuition should be as
low as possible
Tuition policy is
guided by institutionallevel philosophy or
budgetary needs.
Dummy
NE
Plains
MW
SW
NW
High
Moderate
Low
Budget Need
Description
Continuous
Dummy
Hearn, James C., Griswold, Carolyn P., Marine,
Ginger M. Region, Resource, and Reason: A
Contextual Analysis of State Tuition and Student Aid
Policies. Research in Higher Education, Journal of the
Association for Institutional Research, Vol. 37, 3,
1996.
Dummy
Dummy
Dummy
Dummy
Dummy
Dummy
Dummy
None
Not Formalized
Dummy
Statute
State Statute
Dummy
Board Rule
State higher education
board rule or policy
State Constitution
Dummy
Dummy Variable
indicating whether the
tuition philosophy is
formalized in state
constitution, by
legislative statute, by
state rule, board rule or
Dummy
Formalized
U.S. Census Bureau. (2009). "State Tax Revenues".
Retrieved 25 October 2009 from U.S. Census
http://factfinder.census.gov.
In response to: "Which of the following statements
best describes the overall tuition philosophy or
approach for public colleges and universities in your
state?" State Higher Education Executive Officers.
State Tuition, Fees and Financial Assistance Policies
for Public Colleges and Universities, 2005-06.
November 2006.www.sheeo.org
Dummy
Other
Constitution
Source
Dummy
In response to: "Is this tuition philosophy formalized in
the state constitution, by legislative statute, by state
rule, board rule or policy, or not formalized?" State
Higher Education Executive Officers. State Tuition,
Fees and Financial Assistance Policies for Public
Colleges and Universities, 2005-06. November
2006.www.sheeo.org
15
policy.
Percent Net
Tuition Increase
Annual Percentage
Change in Net Tuition
Continuous
Proportion
National Center for Education Statistics. Digest of
Education Statistics, US Department of Education,
Institute of Education Science,
http://nces.ed.gov/programs/digest/
Total Annual
Revenue
Total State Tax
Revenues
Continuous
U.S. Census Bureau. (2009). "State Tax Revenues".
Retrieved 25 October 2009 from U.S. Census
http://factfinder.census.gov.
Percent Change
Revenues
Annual Percentage
Change in State Tax
Revenues
Continuous
Proportion
U.S. Census Bureau. (2009). "State Tax Revenues".
Retrieved 25 October 2009 from U.S. Census
http://factfinder.census.gov.
Aid/enrollment
Allocated state needbased aid per student
The amount allocated
by the state per fulltime equivalent
Continuous
National Center for Education Statistics
Continuous
The number of
bachelor degree
graduates per state per
year
The total annual
undergraduate
enrollment of all
private, accredited 4year insitutions within
the state.
The proportion of the
state's total enrollment
that are enrolled in
private institutions
The total amount of
resource allocated for
financial aid divided
by the current
enrollment
Total state population
Continuous
Delta Cost Project. Trends in College Spending. 2009,
Delta Project on Postsecondary Education Costs,
Productivity and Accountability.
www.deltacostproject.org
National Center for Education Statistics
Continuous
National Center for Education Statistics
Continuous
Proportion
National Center for Education Statistics
Continuous
National Center for Education Statistics
Continuous
US Census
The percent change in
the amount
appropriated by the
state per FTE
Continuous
Proportion
National Center for Education Statistics
Appropriations
per FTE
Bachelor Degree
Production
Private
Enrollment
Proportion of
Private
Enrollment
Aid per
enrollment
Population
Percent
Appropriations
Change
16
References
Delta Cost Project. Trends in College Spending. 2009, Delta Project on Postsecondary Education Costs,
Productivity and Accountability. www.deltacostproject.org
Hearn, James C., Griswold, Carolyn P., Marine, Ginger M. Region, Resource, and Reason: A Contextual
Analysis of State Tuition and Student Aid Policies. Research in Higher Education, Journal of the
Association for Institutional Research, Vol. 37, 3, 1996.
Shin, Jung-cheol, Sande Milton, "Rethinking Tuition Effects on Enrollment in Public Four-Year Colleges
and Universities," The Review of Higher Education, 2006, 29, 2, pp.213-237.
State Higher Education Executive Officers. State Tuition, Fees and Financial Assistance Policies for
Public Colleges and Universities, 2005-06. November 2006.www.sheeo.org
State Higher Education Executive Officers. State Higher Education Finance, FY 2008 (2009).
www.sheeo.org
Data
National Center for Education Statistics. Digest of Education Statistics, US Department of Education,
Institute of Education Science, http://nces.ed.gov/programs/digest/
National Association of State Student Grant and Aid Programs. Annual Survey Report, 1989-90 through
2004-05. http://www.nassgap.org/
U.S. Census Bureau. (2009). "State Tax Revenues". Retrieved 25 October 2009 from U.S. Census
http://factfinder.census.gov.