For-Profit Schools
Large Schools and Schools that Specialize in Healthcare Are More Likely to Rely Heavily on Federal Student Aid Gao ID: GAO-11-4 October 4, 2010In the 2008-2009 school year, about 2,000 for-profit schools received almost $24 billion in grants and loans provided to students under federal student aid programs. In the early 1990s, Congress was concerned that some for-profit schools receiving federal student aid were recruiting students who were not ready for higher education. Many of these students left school with no new job skills and few employment prospects in their fields of study and many defaulted on their federal student loans. In response, Congress enacted the 85/15 rule in 1992, which required for-profit schools to obtain at least 15 percent of their revenues from sources other than federal student aid. Proponents of the rule believed that for-profit schools offering a quality education should be able to earn a minimum percentage of their revenue from sources other than federal student aid. In 1998, Congress amended this law to create the 90/10 rule, which reduced to 10 percent the proportion of revenues schools must obtain from sources other than federal student aid. These revenues can include cash payments from students, private student loans, state educational grants, and federal education assistance payments for veterans. For-profit schools are required to report in their annual financial statements the percentage of their total revenues obtained from federal student aid funds. This calculation is called a school's "90/10 rate" and it is verified each year by an independent auditor. Schools that do not comply with the 90/10 rule risk losing their eligibility to participate in federal student aid programs. As required by the Higher Education Opportunity Act, we are providing information on for-profit schools' compliance with the 90/10 rule. Specifically, this report addresses the following questions: 1. What percentage of for-profit schools is in compliance with the 90/10 rule and to what extent do schools derive their revenues from federal student aid funds? 2. What school characteristics are associated with higher average 90/10 rates? 3. What school characteristics are associated with an increased likelihood of having a very high 90/10 rate?
Between 2003 and 2008, almost 100 percent of for-profit schools reported complying with the 90/10 rule. During this period, the average percent of revenue received from federal student aid (the average 90/10 rate) for all for-profit schools increased slightly from 62 to 66 percent. In 2008, schools with the following characteristics had significantly higher average 90/10 rates than schools without these characteristics. Specifically, these schools: (1) Had high proportions of low-income students; (2) Granted degrees no higher than associate's; (3) Specialized in healthcare; (4) Offered distance education; (5) Were large (with 2,000 students or more); (6) Had a publicly traded parent company; (7) Were part of a corporate chain. We found that in 2008, schools that (1) were large, (2) specialized in healthcare, or (3) did not grant academic degrees were more likely than others to have very high 90/10 rates (above 85 percent), when controlling for the effects of other characteristics. Large schools and schools that specialized in healthcare both had higher average 90/10 rates and were much more likely to have very high 90/10 rates than other schools.
GAO-11-4, For-Profit Schools: Large Schools and Schools that Specialize in Healthcare Are More Likely to Rely Heavily on Federal Student Aid
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Report to Congressional Committees:
United States Government Accountability Office:
GAO:
October 2010:
For-Profit Schools:
Large Schools and Schools that Specialize in Healthcare Are More
Likely to Rely Heavily on Federal Student Aid:
GAO-11-4:
Contents:
Letter:
Appendix I: Briefing Slides:
Appendix II: Scope and Methodology:
Appendix III: Tables Comparing Schools with Different Characteristics:
Appendix IV: Technical Appendix on Results from Regression Model:
Appendix V: GAO Contact and Staff Acknowledgments:
Tables:
Table 1: 90/10-Rate Categories:
Table 2: Percent of Schools, Students Attending, and Federal Student
Aid Dollars Earned by For-Profit Schools with Specific Characteristics:
Table 3: Percent of For-Profit Schools with Specific Characteristics
by Ownership Status:
Table 4: Odds and Odds Ratios for Very High 90/10 Rates by School
Enrollment:
Figures:
Figure 1: Distribution of For-Profit Schools by 90/10 Rate Category,
2003-2008:
Figure 2: Distribution of 2008 90/10 Rates:
Abbreviations:
FTE: full-time equivalent:
IPEDS: Integrated Postsecondary Education Data System:
OIG: Office of Inspector General:
OPEID: Office of Postsecondary Education Identification Number:
PEPS: Postsecondary Education Participants System:
[End of section]
United States Government Accountability Office:
Washington, DC 20548:
October 4, 2010:
The Honorable Tom Harkin:
Chairman:
The Honorable Michael B. Enzi:
Ranking Member:
Committee on Health, Education,
Labor, and Pensions:
United States Senate:
The Honorable George Miller:
Chairman:
The Honorable John P. Kline:
Ranking Member:
Committee on Education and Labor:
House of Representatives:
In the 2008-2009 school year, about 2,000 for-profit schools received
almost $24 billion in grants and loans provided to students under
federal student aid programs.[Footnote 1] In the early 1990s, Congress
was concerned that some for-profit schools receiving federal student
aid were recruiting students who were not ready for higher education.
Many of these students left school with no new job skills and few
employment prospects in their fields of study and many defaulted on
their federal student loans.
In response, Congress enacted the 85/15 rule in 1992, which required
for-profit schools to obtain at least 15 percent of their revenues
from sources other than federal student aid.[Footnote 2] Proponents of
the rule believed that for-profit schools offering a quality education
should be able to earn a minimum percentage of their revenue from
sources other than federal student aid. In 1998, Congress amended this
law to create the 90/10 rule, which reduced to 10 percent the
proportion of revenues schools must obtain from sources other than
federal student aid.[Footnote 3] These revenues can include cash
payments from students, private student loans, state educational
grants, and federal education assistance payments for veterans. For-
profit schools are required to report in their annual financial
statements the percentage of their total revenues obtained from
federal student aid funds.[Footnote 4] This calculation is called a
school's "90/10 rate" and it is verified each year by an independent
auditor. Schools that do not comply with the 90/10 rule risk losing
their eligibility to participate in federal student aid programs.
As required by the Higher Education Opportunity Act, we are providing
information on for-profit schools' compliance with the 90/10 rule.
[Footnote 5] Specifically, this report addresses the following
questions:
1. What percentage of for-profit schools is in compliance with the 90/
10 rule and to what extent do schools derive their revenues from
federal student aid funds?
2. What school characteristics are associated with higher average
90/10 rates?
3. What school characteristics are associated with an increased
likelihood of having a very high 90/10 rate?
On August 12 and 20, 2010, we briefed cognizant congressional staff on
the results of this study, and this report formally conveys the
information provided during these briefings. (See appendix I for the
briefing slides.) In general, we found that:
* Between 2003 and 2008, almost 100 percent of for-profit schools
reported complying with the 90/10 rule. During this period, the
average percent of revenue received from federal student aid (the
average 90/10 rate) for all for-profit schools increased slightly from
62 to 66 percent.
* In 2008, schools with the following characteristics had
significantly higher average 90/10 rates than schools without these
characteristics. Specifically, these schools:
* Had high proportions of low-income students:
* Granted degrees no higher than associate's:
* Specialized in healthcare:
* Offered distance education:
* Were large (with 2,000 students or more):
* Had a publicly traded parent company:
* Were part of a corporate chain:
* We found that in 2008, schools that (1) were large, (2) specialized
in healthcare, or (3) did not grant academic degrees were more likely
than others to have very high 90/10 rates (above 85 percent), when
controlling for the effects of other characteristics. Large schools
and schools that specialized in healthcare both had higher average
90/10 rates and were much more likely to have very high 90/10 rates
than other schools.
We used the following methodology to develop our findings. To
understand the 90/10 rule and factors that influence schools' 90/10
rates, we reviewed relevant federal laws, regulations, and program
guidance. We also interviewed officials from the Department of
Education (Education), Education's Office of Inspector General (OIG),
six for-profit schools that represent both large and small schools
with a mix of ownership types and academic programs, seven independent
auditors with varying levels of experience in verifying 90/10 rates at
for-profit schools, and associations focusing on higher education. To
determine the percentage of schools in compliance with the 90/10 rule,
we analyzed 90/10 rates from Education's eZ-Audit system for fiscal
years 2003 through 2008 and augmented this information with data from
Education and Education's OIG.[Footnote 6] Finally, to learn about
what school characteristics are associated with higher average 90/10
rates and with an increased likelihood of having a 90/10 rate above 85
percent, we conducted descriptive statistical analyses and a
multivariate regression analysis using 90/10 rates for fiscal year
2008 reported in eZ-Audit and school year 2008-2009 school
characteristic data reported in Education's Integrated Postsecondary
Education Data System (IPEDS). For the purposes of our analyses, we
considered a for-profit school to be an entity with a unique
identification number assigned by Education (known as an OPEID)
because Education requires each for-profit school with an OPEID to
submit an annual 90/10 rate, and monitors compliance with the 90/10
rule on an OPEID basis. However, depending on how schools are
organized, an OPEID may correspond with one or multiple campuses. For
instance, in one case, five campuses may be organized under one OPEID,
and in another, five related campuses may all have their own OPEIDs.
In the first scenario, the five campuses would be part of the same
school and would collectively report one 90/10 rate, while in the
second scenario, each campus would report its own 90/10 rate and count
as a separate school in our analysis. We conducted statistical testing
and found that this difference did not materially affect the results
of our regression analysis. We assessed the reliability of each
Education dataset we used by interviewing agency officials
knowledgeable about the data, reviewing relevant documentation, and
conducting additional analyses. We determined that the data from each
dataset were sufficiently reliable for the purposes of this report.
For additional information on our scope and methodology, see
appendixes II, III, and IV.
We conducted our work from October 2009 to October 2010 in accordance
with generally accepted government auditing standards. Those standards
require that we plan and perform the audit to obtain sufficient,
appropriate evidence to provide a reasonable basis for our findings
based on our audit objectives. We believe that the evidence obtained
provides a reasonable basis for our findings.
We provided a draft copy of this report to Education for review and
comment. Education did not have any comments on the report.
We are sending copies of this report to relevant congressional
committees, the Secretary of Education, and other interested parties.
In addition, this report will be available at no charge on GAO's Web
site at [hyperlink, http://www.gao.gov].
If you or your staffs have any questions about this report, please
contact me at (202) 512-7215 or scottg@gao.gov. Contact points for our
Offices of Congressional Relations and Public Affairs may be found on
the last page of this report. GAO staff who made key contributions to
this report are listed in appendix V.
Signed by:
George A. Scott:
Director, Education, Workforce, and Income Security Issues:
[End of section]
Appendix I: Briefing Slides:
For-Profit Schools: Large Schools and Schools that Specialize in
Healthcare Are More Likely to Rely Heavily on Federal Student Aid:
Briefing to Congressional Committee Staff:
Education and Labor:
House of Representatives:
Health, Education, Labor, and Pensions:
United States Senate:
Overview:
* Introduction:
* Research Objectives:
* Scope and Methodology:
* Summary of Findings:
* Background:
* Findings:
Introduction:
For-Profit Schools Receive Billions of Dollars in Federal Student Aid,
but Face a Limit on How Much They Can Receive:
During the 2008-09 school year, about 2,000 for-profit schools
received almost $24 billion in federal student aid.[Footnote 7]
In the early 1990s, Congress was concerned that some for-profit
schools that received federal student aid funds were recruiting
students who were not ready for higher education. Many of these
students:
* left school with no new job skills and few employment prospects in
their fields of study, and;
* defaulted on their federal student loans.
In 1992, Congress enacted the 85/15 rule, which required for-profit
schools to obtain at least 15 percent of their revenues from sources
other than federal student aid.[Footnote 8]
Proponents of the rule believed that for-profit schools offering a
quality education should be able to earn a minimum percentage of
revenue from sources other than federal student aid.
In 1998, Congress amended this law to create the 90/10 rule, which
reduced to 10 percent the proportion of revenues schools must obtain
from sources other than federal student aid. Schools may now receive
up to 90 percent of revenues from federal student aid.[Footnote 9]
For-Profit Schools that Violate the 90/10 Rule Risk Losing Eligibility
to Receive Federal Student Aid Funds:
For-profit schools are required to report in their annual financial
statements the percentage of their total revenues obtained from
federal student aid funds.
* This calculation is called a school's "90/10 rate."
* The 90/10 rate is verified each year by an independent auditor
according to guidelines issued by Education's Office of Inspector
General (01G).
* Schools report their 90/10 rate by their Education identification
number (known as their OPEID). Schools with multiple locations may
have one or many OPEIDs, depending on how they are organized.[Footnote
10]
If a for-profit school does not comply with the 90/10 rule for:
* one fiscal year: the school's eligibility to participate in the
federal student aid program becomes provisional for the next two
years.[Footnote 11]
* two consecutive fiscal years: the school becomes ineligible to
participate in the federal student aid program for at least two fiscal
years.
Research Objectives:
1. What percentage of for-profit schools is in compliance with the
90/10 rule and to what extent do schools derive their revenues from
federal student aid funds?
2. What school characteristics are associated with higher average
90/10 rates?
3. What school characteristics are associated with an increased
likelihood of having a very high 90/10 rate?
Scope and Methodology:
To address our objectives, we:
* reviewed relevant federal laws, regulations, and program guidance;
* analyzed the Department of Education's data for fiscal years 2003
through 2008 on for-profit schools' reliance on federal student aid;
[Footnote 12]
* interviewed officials from Education, Education's Office of
Inspector General, six for-profit schools representing both large and
small schools with a mix of ownership types and academic programs,
seven independent auditors with varying levels of experience in
verifying 90/10 rates, and associations focusing on higher education;
and;
* conducted additional analyses using:[Footnote 13]
- 90/10 rates for fiscal year 2008 reported in eZ-Audit, and;
- school year 2008-2009 school characteristic data reported in
Education's Integrated Postsecondary Education Data System (IPEDS).
We determined that these data were sufficiently reliable for our
purposes.
Using descriptive statistics, we calculated average 90/10 rates of
schools with different characteristics to assess, for example, whether
large schools had a significantly different average 90/10 rate than
very small schools.[Footnote 14]
Multivariate regression analysis enabled us to identify
characteristics that indicated an increased likelihood of having a
very high 90/10 rate (above 85 percent) while controlling for the
effects of other characteristics. Through this analysis, we could
determine whether large schools, for instance, were significantly more
likely to have very high 90/10 rates than very small schools, when
controlling for other factors.[Footnote 15]
We conducted our review between October 2009 and October 2010 in
accordance with generally accepted government auditing standards.
Summary of Findings:
Large Schools and Schools that Specialize in Healthcare Are More
Likely to Rely Heavily on Federal Student Aid:
Between 2003 and 2008, almost 100 percent of for-profit schools
reported being in compliance with the 90/10 rule. During this period,
the average percent of revenue received from federal student aid (the
average 90/10 rate) for all for-profit schools increased slightly from
62 to 66 percent.
In 2008, schools with the following characteristics had significantly
higher average 90/10 rates than schools without these characteristics.
Specifically, these schools:
* Had high proportions of low-income students;
* Were large (with 2,000 students or more):
* Granted degrees no higher than associate's;
* Had a publicly-traded parent company;
* Specialized in healthcare[Footnote 16];
* Were part of a corporate chain[Footnote 17];
* Offered distance education.
We found that in 2008, schools that (1) were large, (2) specialized in
healthcare, or (3) did not grant academic degrees were more likely
than others to have very high 90/10 rates (above 85 percent), when
controlling for the effects of other school characteristics.
Background: For-Profit Schools: Characteristics of For-Profit Schools
Have Changed in Recent Years:
Traditionally, for-profit schools:
* Were owned by local, sole proprietors; and;
* Offered certificate and associate's degree programs ranging from
cosmetology to medical assistance and business administration.
More recently, for-profit schools:
* Range from small, privately-held schools to large, publicly traded
companies;[Footnote 18]
* Have expanded their offerings to also include bachelor's, master's,
and doctoral level programs; and;
* Provide course offerings through distance education.[Footnote 19]
Background: 90/10 Calculation: No More than 90 Percent of a For-Profit
School's Total Revenues May Be Obtained from Federal Student Aid:
Only revenues received for a school's educational and institutional
charges, such as tuition, fees, and certain required course materials,
are included in its 90/10 calculation.[Footnote 20]
* Other revenues, such as those from vending machines and parking
lots, are excluded from the calculation.[Footnote 21]
The 90/10 rate must be calculated using a cash basis of accounting.
[Footnote 22]
No more than 90 percent:
Only revenues from federal student aid programs authorized by Title IV
of the Higher Education Act are included, such as:
* Pell Grants,
* Stafford Loans, and,
* Federal Work Study funds.
At least 10 percent:
Revenues counted include:
* cash payments from students,
* private student loans (including payments for loans made by schools),
* state educational grants,
* federal education assistance payments for military personnel and
veterans, and,
* federal and state job training grants.
Background: Enrollment: Enrollment at For-Profit Schools Has
Increased Substantially in Recent Years:
Between the fall of 2003 and 2008, student enrollment increased from
about 1 million to about 1.8 million at for-profit schools and from
about 16 million to about 18 million at all other schools.[Footnote 23]
Percentage Growth in Enrollment from Fall 2003 to 2008:
For-profit schools: 83%;
All other schools: 9%.
Source: GAO analysis of IPEDS data on fall enrollments.
Background: Funding: Schools Have More Than Tripled in Recent Years:
Between the 2002-03 and 2008-09 school years, federal student aid
revenues increased from about $8 billion to about $24 billion at for-
profit schools and from about $48 billion to about $82 billion at all
other schools.
For-profit schools' share of fall 2008 total enrollment was about 9
percent, while their share of school year 2008-2009 total federal
student aid revenue was about 23 percent.[Footnote 24]
Percentage Growth in Federal Student Aid from School Year 2002-03 to
2008-09:
For-profit schools: 210%;
All other schools: 69%.
Source: GAO analysis of Education's annual federal student aid funding
report data.
Finding 1: 90/10 Compliance--Overview: Almost All For-Profit Schools
Report Being in Compliance with the 90/10 Rule:
Between 2003 and 2008, almost 100 percent of for-profit schools
reported being in compliance with the 90/10 rule.[Footnote 25]
The average percent of revenue for-profit schools received from
federal student aid (their average 90/10 rate) increased slightly
between 2003 and 2008, from 62 to 66 percent.
Finding 1: 90/10 Compliance: Very Few For-Profit Schools Report Not
Complying with the 90/10 Rule:
Between 2003 and 2008, an average of 99.8 percent of for-profit
schools reported being in compliance with the 90/10 rule.[Footnote 26]
During this period, seven schools lost eligibility for federal student
aid due to non-compliance with the 90/10 rule.[Footnote 27]
Table: Percent and Number of For-Profit Schools Reporting Non-
Compliance with the 90/10 Rule, 2003-2008:
Percent out of compliance:
2003: 0.1%;
2004: 0.1%;
2005: 0.3%;
2006: 0.2%;
2007: 0%;
2008: 0.4%;
2003-2008: 0.2%.
Number out of compliance:
2003: 2;
2004: 1;
2005: 6;
2006: 3;
2007: 0;
2008: 7;
2003-2008: 18[Footnote 28].
Source: GAO analysis of Education's eZ-Audit data.
Table Note: We excluded from our 2005 list one school that did not
have an auditor-verified 2005 90/10 rate and was non-compliant in
2006, because Education did not calculate its actual 2005 rate. We
included one school with a rate below 90 percent in our non-compliant
list because it received more than 90 percent of its revenue from
federal student aid during the only part of the year in which it was
eligible for that aid.
[End of table]
Finding 1: 90/10 Compliance: For-Profit Schools' Average 90/10 Rates
Increased Slightly Between 2003 and 2008:
Between 2003 and 2008, the average 90/10 rate of for-profit schools
increased from 62 to 66 percent.[Footnote 29]
The proportion of schools with 90/10 rates above 85 percent rose from
about 10 percent in 2007 to about 15 percent in 2008.[Footnote 30]
* During the same period, the proportion of students at for-profit
schools with 90/10 rates above 85 percent rose from about 8 percent to
about 19 percent.
For-profit school representatives stated that increases in 90/10 rates
may be in part due to increased maximum federal student aid award
amounts, as well as the effects of the recent recession that are
making private student loans and state student aid grants less
available.
Finding 2: Average Rates”Overview: For-Profit Schools with Certain
Characteristics Had Higher Average 90/10 Rates than Others:
For-profit schools with the following characteristics had higher
average 90/10 rates than schools without these characteristics.
Specifically, these schools:[Footnote 31]
Student Population:
Had high proportions of low-income students.
Educational Focus:
Granted degrees no higher than the associate's level;
Specialized in healthcare;
Offered distance education.
School Size, Control, and Management:
Were large (with 2,000 students or more)[Footnote 32];
Had a publicly-traded parent company;
Were part of a corporate chain.
Finding 2: Average Rates”Student Population: For-Profit Schools with
High Proportions of Low-Income Students Tended to Have Higher Average
90/10 Rates:
Officials from for-profit school groups have indicated that having a
low-income student population makes it more challenging to comply with
the 90/10 rule, because such students utilize more federal student aid
than others.
For-profit schools with high proportions of students receiving Pell
Grants (which are targeted at low-income students) had higher 90/10
rates than other schools.[Footnote 33]
* Schools with at least 75 percent of students receiving Pell Grants
had an average 90/10 rate of 77 percent vs. 64 percent for all other
schools.
Finding 2: Average Rates”Educational Focus: For-Profit Schools
Offering Associate's Degrees Had a Higher Average 90/10 Rate:
Schools offering degrees no higher than the associate's level (about
18 percent of all for-profit schools) had a significantly higher
average 90/10 rate than other schools (75 vs. 65 percent).[Footnote 34]
An Education official suggested these schools may have higher 90/10
rates because their students are less likely to receive employer
funding for continuing education than students working on a bachelor's
degree or higher.
Finding 2: Average Rates”Educational Focus: For-Profit Schools
Specializing in Healthcare Had a Higher Average 90/10 Rate:
For-profit schools that specialized in healthcare had a significantly
higher average 90/10 rate than other schools (75 vs. 63 percent).
[Footnote 35] These schools represented nearly one-third of all for-
profit schools.
A higher education association official we spoke with suggested that
some for-profit schools specializing in healthcare tend to attract
lower-income students that rely more heavily on federal student aid,
leading these schools to have higher 90/10 rates.
* We found that schools specializing in healthcare had higher
proportions of students receiving Pell Grants (which are targeted at
low-income students) than other schools, at 60 vs. 52 percent.
Finding 2: Average Rates”Educational Focus: For-Profit Schools
Specializing in Cosmetology Had a Lower Average 90/10 Rate:
For-profit schools that specialized in cosmetology had a significantly
lower average 90/10 rate than other for-profit schools (62 percent vs.
71 percent).[Footnote 36]
* While 45 percent of schools specialized in cosmetology, they only
enrolled 9 percent of students and received 4 percent of federal
student aid dollars earned by for-profit schools.[Footnote 37]
According to Education officials, independent auditors, and school
representatives we spoke with, cosmetology schools tend to rely less
on federal student aid than other schools because they earn additional
revenue from student-run clinics.
Finding 2: Average Rates”Educational Focus: For-Profit Schools that
Offered Distance Education Had a Higher Average 90/10 Rate:
Schools that offered distance education had a significantly higher
average 90/10 rate than other schools, at 72 percent vs. 66 percent.
Schools offering distance education accounted for about 12 percent of
all for-profit schools, but they enrolled 55 percent of students and
received 70 percent of federal student aid dollars earned by for-
profit schools.
The majority of these schools were part of a corporate chain and many
were owned by a publicly-traded company.[Footnote 38]
Finding 2: Average Rates”School Size and Control: Large Schools Had a
Higher Average 90/10 Rate than Very Small Schools:
Large schools (with 2,000 students or more) had a significantly higher
average 90/10 rate than very small schools (with fewer than 100
students), at 74 percent vs. 60 percent.
* Only about 9 percent of all schools were large, but they enrolled 69
percent of students at for-profit schools.
* While 30 percent of schools were very small, they only enrolled 2
percent of students at for-profit schools.[Footnote 39]
Schools with 500 students or more also had a significantly higher
average 90/10 rate than schools with under 100 students.
Finding 2: Average Rates”School Size and Control: Chain Schools Had
Higher Average 90/10 Rates than Other Schools:
Schools owned by publicly-traded companies had a significantly higher
average 90/10 rate than other schools, at 72 vs. 66 percent.
* These schools represented about 14 percent of all for-profit schools
in 2008, enrolled 55 percent of students, and received about 65
percent of federal student aid dollars earned by for-profit schools.
[Footnote 40]
Chain schools (about half of all for-profit schools) had a
significantly higher average 90/10 rate than other schools, at 70 vs.
63 percent.
Finding 3: Very High Rates”Overview: A Few School Characteristics Were
Associated with an Increased Likelihood of Very High 90/10 Rates:
In 2008, schools that:
(1) were large (with 2,000 students or more),[Footnote 41]
(2) specialized in healthcare, or,
(3) did not grant academic degrees;
were more likely than others to have very high 90/10 rates (above 85
percent”close to the 90 percent limit).
Unlike with our average 90/10 rate analysis, schools with these
characteristics had increased likelihoods of very high rates after
controlling for the effects of other factors.[Footnote 42]
Finding 3: Very High Rates: Large For-Profit Schools Were Much More
Likely to Have Very High 90/10 Rates (Above 85 Percent) than Very
Small Schools:
Large for-profit schools (with 2,000 students or more) were much more
likely than very small schools (with less than 100 students) to have
very high 90/10 rates when controlling for other characteristics.
[Footnote 43]
* Large schools had more than three times greater odds of very high
90/10 rates than very small schools.
* Schools with 500 students or more had at least two times higher odds
of very high 90/10 rates than schools with less than 100 students.
Finding 3: Very High Rates: For-Profit Schools Specializing in
Healthcare and Schools Not Offering Degrees Were More Likely to
Have Very High 90/10 Rates (Above 85 Percent):
Schools specializing in healthcare were much more likely to have very
high 90/10 rates than other schools when controlling for other
characteristics.
For-profit schools that did not offer academic degrees were
significantly more likely to have very high 90/10 rates than schools
offering at least bachelor's degrees when controlling for other
characteristics.[[Footnote 44]
Finding 3: Very High Rates: Schools Owned by Publicly-Traded Companies
Were Less Likely to Have Very High 90/10 Rates (Above 85 Percent):
Schools owned by publicly-traded companies were significantly less
likely to have very high 90/10 rates than other schools when
controlling for the effects of other characteristics.[Footnote 45]
* However, they actually had higher average 90/10 rates than other
schools without controlling for other characteristics.
Publicly-traded schools may have a lower likelihood of very high 90/10
rates, but a higher average rate than other schools, because they
manage their rates more carefully to avoid risking non-compliance.
School officials we spoke with described strategies to manage their
90/10 rates, such as:
* expanding programs that attract students with funding from sources
other than federal student aid, and;
* encouraging students to contribute cash payments when possible.
Finding 3: Very High Rates: A School's Tuition Rate Was Not Associated
with Its Likelihood of Having Very High 90/10 Rates:
School officials and auditors told us that schools with low tuition
rates tend to have more trouble complying with the 90/10 rule.
* Federal student aid is more likely to cover the cost of tuition and
fees at these schools than at schools with high tuition rates.
However, we did not find any relationship between a school's tuition
rate and its likelihood of having a very high 90/10 rate.[Footnote 46]
* Additionally, we found no correlation between a school's tuition
rate and its average 90/10 rate.
* In one exception, schools with tuition rates that did not exceed
2008-2009 Pell Grant and Stafford Loan award limits did have slightly
higher average 90/10 rates than other schools, at 68 vs. 66 percent.
[Footnote 47]
[End of section]
Appendix II: Scope and Methodology:
To address our objectives, we used 90/10 compliance data from the
Department of Education's (Education) eZ-Audit system to describe
schools' reliance on federal student aid and analyze trends over time.
We also reviewed relevant federal laws, regulations, and program
guidance. Lastly, we interviewed officials from Education, Education's
Office of Inspector General (OIG), and other stakeholder groups. These
groups included six for-profit schools that represent both large and
small schools with a mix of ownership types and academic programs,
seven independent auditors with varying levels of experience in
verifying 90/10 rates at for-profit schools, and associations focusing
on higher education. Additionally, we reviewed Education and OIG
examinations of schools' compliance with the 90/10 rule. Finally, we
conducted descriptive and multivariate statistical analyses using
90/10 rates recorded in eZ-Audit and school characteristic data
reported by schools through Education's Integrated Postsecondary
Education Data System (IPEDS) to examine what school characteristics
are associated with higher average 90/10 rates and a greater
likelihood of having a very high 90/10 rate. We conducted our review
between October 2009 and October 2010 in accordance with generally
accepted government auditing standards. Those standards require that
we plan and perform the audit to obtain sufficient, appropriate
evidence to provide a reasonable basis for our findings and
conclusions based on our audit objectives. We believe that the
evidence obtained provides a reasonable basis for our findings based
on our audit objectives.
Data Sources:
eZ-Audit:
Since June of 2003, Education has required for-profit schools to
submit financial audit data, including their reported 90/10 rates, to
the eZ-Audit data system. Because a complete set of 90/10 rates for
fiscal year 2009 was not available at the time of our study, we
limited the scope of our analysis to fiscal years 2003 to 2008. These
study years included data on 1,554 schools in 2003 and up to 1,962
schools in 2008. We assessed the reliability of the eZ-Audit data
elements we used in our study and determined them to be reliable for
the purpose of describing 90/10 rates and doing statistical analyses
on these rates. (See below for a detailed account of our assessment of
90/10 rate reliability.)
IPEDS:
The Integrated Postsecondary Education Data System (IPEDS) is a system
of interrelated surveys conducted annually, and gathers information
from every college, university, and technical and vocational
institution that participates in federal student financial aid
programs. The Higher Education Act of 1965, as amended, requires that
institutions participating in federal student aid programs report data
on enrollments, program completions, graduation rates, institutional
prices, and student financial aid. Through electronic testing,
discussions of variables with IPEDS officials, and reviewing past GAO
uses of IPEDS data, we assessed the reliability of IPEDS data elements
used in our study and concluded that IPEDS data were sufficiently
reliable for reporting on and analyzing characteristics of schools and
their possible associations with 90/10 rates.
Other Data Sources:
We used a variety of other Education data sources to supplement our
data analysis. We used data from the Postsecondary Education
Participants System (PEPS) to obtain data on schools' changes in
ownership. PEPS is Education's primary system for tracking the
eligibility status of schools to receive federal student aid funds.
Based on discussions with Education officials and our verification of
date information for a random sample of schools that experienced a
change in ownership, we concluded that these data were reliable for
identifying changes of ownership that occurred during the period in
question. We also used data provided by Education on federal student
aid funding and schools owned by publicly traded companies. After
discussions with Education officials about how these data are compiled
and used, we determined that they were sufficiently reliable for
reporting aggregate federal student aid amounts for different
categories of schools and for identifying schools owned by publicly
traded companies.
Assessing Reliability of 90/10 Rates Reported in eZ-Audit:
We undertook extensive efforts to assess the reliability of for-profit
schools' reported 90/10 rates, which are verified by independent
auditors and then self-reported to Education. In particular, we
reviewed OIG evaluations of independent audits of for-profit schools
and spoke with seven independent auditors of varying experience levels
about the steps they take to verify their clients' 90/10 rates. We
also interviewed Education officials about their procedures for
assessing the accuracy of 90/10 rates reported to be above a certain
90/10 rate threshold and reviewed all OIG audits and Education
examinations of schools' compliance with the 90/10 rule to determine
how frequently they found errors in schools' reported 90/10 rates and
how large these errors tended to be.
Because of the importance of independent auditors' role in verifying
schools' 90/10 rates, we reviewed OIG evaluations of their work to
determine how commonly the OIG had identified weaknesses in their
verification practices. Despite the fact that the OIG selects
independent auditors for review using risk-based selection criteria,
the OIG found errors in the 90/10 rates verified by selected auditors
only in a small minority of cases. The OIG found the audit
documentation did not provide sufficient evidence that the auditor
appropriately verified a school's calculation in a few additional
cases.
We also interviewed independent auditors at seven accounting firms
with different levels of for-profit school experience. We spoke with
auditors from three experienced firms, one moderately experienced
firm, and three less experienced firms.[Footnote 48] All of the
auditors at the firms with extensive for-profit school experience
described thorough 90/10 verification practices, while auditors with
moderate and limited experience had more varied practices. It is
possible that weaknesses in the practices of less experienced auditors
could result in inadequate 90/10 rate verification and ultimately in
reporting errors. We chose to group schools into 90/10 rate categories
for the purposes of our statistical analyses, instead of using
schools' actual reported 90/10 rates as a continuous measure, to
minimize the effect of these errors on our data analysis.[Footnote 49]
We reviewed a combined total of 42 OIG audits and Education program
reviews and final audit determinations of for-profit schools with
90/10 related findings. The OIG and Education selected schools for
review based on a variety of risk factors. In about a quarter of
cases, OIG and Education found that a school reporting a compliant
90/10 rate was actually out of compliance. In about half of cases, no
calculation errors were found, and in two cases, reviewers had
insufficient information to recalculate the school's 90/10 rate. In
other cases, errors were identified that either did not change a
school's compliance status or resulted in an out-of-compliance school
being reclassified as in compliance. For all reviews in which the OIG
or Education did recalculate a school's 90/10 rate, the median
difference between the school's reported and recalculated rates was
3.3 percentage points. This level of error would be relatively
unlikely to result in our miscategorizing a school within our 90/10
rate categories.
Lastly, we reviewed the outcomes of Education examinations of all
schools that reported 90/10 rates above a certain threshold. Officials
we spoke with described a thorough review process, including using
auditor workpapers to assist them in their verification efforts.
Education found errors in the reported 90/10 rates of less than one
quarter of the cases we reviewed. In less than half of these cases,
Education found that the school's rate was higher than reported, while
in the rest, it found that the school's rate was lower than reported.
Revising the eZ-Audit Data:
After receiving the original eZ-Audit data file from Education, we
conducted manual and electronic data testing to identify records that
were duplicated, were missing from the data set, or contained a likely
inaccurate 90/10 rate based on our analysis of OIG audits and
Education's reviews. We then worked with Education to resolve these
issues. To ensure that the data set was sufficiently reliable for the
purposes of our analyses, we performed the following tasks.
Because Education officials do not consistently update the eZ-Audit
data set when they determine that a school reported an incorrect 90/10
rate, we manually updated all 90/10 rates for which we had evidence of
an error that had been identified and corrected elsewhere by
Education. We reviewed all OIG audits and Education reviews that
examined for-profit schools' compliance with the 90/10 rule and
updated the 90/10 rates in our eZ-Audit data file for the schools
where the OIG or Education found a 90/10 rate error and calculated a
new rate. Through electronic data testing, we also found a large
number of schools in the data set reporting 90/10 rates of zero. In
discussions with Education, we determined that many of these records
with 90/10 rates of zero were for schools that were not eligible for
or certified to receive federal student aid for the record year. After
consulting with Education, we created a set of rules to identify and
remove records of schools that were likely to fall into these groups.
Through electronic data testing, we also identified a relatively small
percentage of records that were duplicated due to a data entry error
on the part of Education. After receiving clarification from Education
on all records, we removed the duplicated records. We also identified
cases in which schools had more than one eZ-Audit record in a single
calendar year. According to Education, schools changing their fiscal
year end date are required to submit a financial audit for both their
old and new fiscal years, which results in two eZ-Audit submissions
for the calendar year in which the change took place. To limit our
data to one record per school per year, we retained only the record
with the fiscal year end date that matched the date in the subsequent
year.[Footnote 50]
Unit of Analysis:
Each for-profit school that disburses federal student aid must report
a 90/10 rate to Education annually.[Footnote 51] For the purposes of
the 90/10 requirement and our analysis, a school is an entity that
corresponds to a unique six-digit Office of Post-Secondary Education
Identification (OPEID) number. Because schools with multiple locations
may have one or many OPEIDs, depending on how they are organized, a
90/10 rate may correspond to a single campus or many campuses. Due to
this variation, five campuses with only one OPEID will count as one
school in our study, whereas five related campuses, each with its own
OPEID, will count as five schools.
Creating Our Data Set for 2008 Statistical Analyses:
To prepare the data for our statistical analyses, we merged an eZ-
Audit data set containing schools' fiscal year 2008 90/10 rates with
various school year 2008-2009 IPEDS survey files, using the OPEID as
our merger variable.[Footnote 52] In eZ-Audit, each school with a six-
digit OPEID number reports one 90/10 rate per fiscal year. However, in
IPEDS, a school with a six-digit OPEID number may choose to report
more than one set of data by using multiple eight-digit subreporter
OPEIDs. For instance, a school with five campuses could establish five
separate eight-digit subreporter OPEIDs and break out information on a
campus-by-campus basis, reporting data on elements such as total
enrollment, degrees offered, and average tuition rates separately for
each campus.
To merge IPEDS data with eZ-Audit data along the OPEID and create
variables needed for our statistical analyses, we collapsed data from
IPEDS records with multiple eight-digit subreporters into a single
record corresponding to each six-digit OPEID. We made decision rules
to create collapsed variables for schools with multiple subreporters.
For instance, to measure a school's total enrollment, we simply summed
enrollment across all subreporters. To identify the highest level of
education offered by a school, we used the highest level reported by
any subreporter. We coded a school as offering distance education if
at least one of its subreporters offered distance education. To create
school-wide averages (such as average tuition rates), we took into
account the number of students corresponding to each subreporter.
While many variables were straightforward to create, we took extra
steps when creating a few variables. First, we assigned each school a
specialty based on which of its programs enrolled or graduated the
highest number of students, depending on the school's reporting
method. We defined specialties using the Classification of
Instructional Program codes, which schools use to report their
programs' educational focuses to Education. Second, due to variation
in how schools report their tuition rates, we tested three different
methods for measuring schools' average tuition rates.[Footnote 53] As
noted in appendix IV, each method we tested produced similar results
when included in our regression models. Therefore, in our final
regression model, we chose to use an academic-year equivalent tuition
measure for all schools. Lastly, we created school size categories to
reflect the non-linear relationship between school enrollment and
90/10 rates. We defined large schools as those with 2,000 students or
more, mid-sized schools as those with 500 to under 2,000 students,
small schools as those with 100 to under 500 students, and very small
schools as those with less than 100 students.
For 31 schools in the eZ-Audit data set, we could not find
corresponding IPEDS data. Therefore, these 31 schools are excluded
from the descriptive and multivariate analyses of school
characteristics.
Dividing Schools into Categories Based on 90/10 Rates:
For the purposes of our study, we divided schools into categories
based on their relative rates of reliance on federal student aid.
Grouping schools into categories of reliance on federal student aid
rather than reporting specific 90/10 rates allowed us to mitigate the
impact of relatively small errors in schools' 90/10 rate calculations
on our results. In particular, auditors suggested that schools with
relatively low reported 90/10 rates might be more error-prone. Due to
limited risk of unidentified non-compliance resulting from errors in
low reported 90/10 rates, auditors may not undertake extensive efforts
to resolve small calculation errors in these rates. By grouping
schools into categories of reliance, if a school's actual 90/10 rate
should have been 50 percent, but the school mistakenly calculated its
rate to be 45 percent, the school would still correctly fall into our
low 90/10 rate category laid out below.
For the purposes of analysis, we categorized schools into the groups
listed in table 1.
Table 1: 90/10-Rate Categories:
Category: Low 90/10 rate;
Percent of revenues from federal student aid: 60% or less.
Category: Medium 90/10 rate;
Percent of revenues from federal student aid: >60% to 75%.
Category: High 90/10 rate;
Percent of revenues from federal student aid: >75% to 85%.
Category: Very high 90/10 rate;
Percent of revenues from federal student aid: >85%.
[End of table]
Source: GAO.
The percentages of for-profit schools with 90/10 rates from 2003 to
2008 in each of these categories are presented in figure 1.
Figure 1: Distribution of For-Profit Schools by 90/10 Rate Category,
2003-2008:
[Refer to PDF for image: vertical bar graph]
Year: 2003;
Low (60% or less): 39.6%;
Medium (>60% to 75%): 25.8%;
High (>75% to 85%): 22.6%;
Very high (more than 85%): 12%.
Year: 2004;
Low (60% or less): 37.2%;
Medium (>60% to 75%): 27.2%;
High (>75% to 85%): 23.2%;
Very high (more than 85%): 12.5%.
Year: 2005;
Low (60% or less): 35.4%;
Medium (>60% to 75%): 26.6%;
High (>75% to 85%): 26.4%;
Very high (more than 85%): 11.6%.
Year: 2006;
Low (60% or less): 37.9%;
Medium (>60% to 75%): 27.6%;
High (>75% to 85%): 23.8%;
Very high (more than 85%): 10.9%.
Year: 2007;
Low (60% or less): 37.3%;
Medium (>60% to 75%): 27.8%;
High (>75% to 85%): 25%;
Very high (more than 85%): 9.8%.
Year: 2008
Low (60% or less): 31.7%;
Medium (>60% to 75%): 27.6%;
High (>75% to 85%): 25.8%;
Very high (more than 85%): 14.9%.
Source: GAO analysis of 2003 to 2008 eZ-Audit data.
[End of figure]
Descriptive Statistics:
We conducted various descriptive statistical analyses to determine
whether specific kinds of schools had higher 90/10 rates than others.
First, to assess the statistical significance of the relationship
between school characteristics and the 90/10 rate category associated
with schools possessing these characteristics, we conducted chi-
squared tests of association. For example, the chi-squared test
assessed the distributions of 90/10 rate categories among schools that
did and did not offer distance education, and how these distributions
differed from what would be expected if no relationship existed. For
dichotomous variables, such as whether a school was owned by a
publicly traded company, we also conducted t-tests to assess the
statistical significance of differences in mean 90/10 rates. For
continuous variables, such as the proportion of students that were
adult learners over the age of 25, we examined the correlation between
each variable and the 90/10 rate[Footnote 54]. All differences
reported in the second findings section of this report are significant
at the 95 percent level unless otherwise noted. This indicates that
there is less than a 5 percent probability that we would have gotten
such a result by chance if there were really no difference in average
rates or in association between variables. Note that a correlation
between two variables does not indicate that one "causes" the other;
rather, it shows that the two appear to be associated with each other,
without controlling for the effects of other variables.
Multiple Regression Analysis:
Multiple regression analysis is a method for exploring how a dependent
variable is related to a number of independent variables, while
controlling for other factors that could have an impact on the value
of the dependent variable. Because the distribution of 90/10 rates was
not normal, with more than half of schools reporting 90/10 rates above
70 percent but an extremely low percentage of schools reporting 90/10
rates above 90 percent, it was not appropriate to use ordinary least
squares regression modeling to assess the average change in 90/10
rates associated with specific variables. Instead, we used logistic
regression analysis, a regression method for assessing dichotomous
outcomes, to estimate the influence of various predictor variables on
whether or not a school had a very high 90/10 rate of above 85
percent.[Footnote 55] We tested multiple model specifications and
definitions of "very high 90/10 rates" to ensure that our estimates
were stable across different models, and used robust standard errors
to control for the potential effect of clustering of schools within
the same corporate entity on our variance estimates. See appendix IV
for a detailed discussion of our modeling procedures and results.
Limitations of the Analysis:
One limitation of our study relates to inconsistency in our unit of
analysis, which is due to the variation in how schools are organized
and report their 90/10 rates. As described earlier, in one case, five
campuses may be organized under one OPEID, and in another case, five
related campuses may each have their own OPEIDs. In the first
scenario, the five campuses would count as one unit of analysis (or
school) in our study, while in the second case, the five campuses
would count as five separate units of analysis with five 90/10 rates.
We explored whether we could resolve this inconsistency by combining
the 90/10 rates of related OPEIDs, such as the five campuses each with
its own OPEID, into one unit of analysis with an aggregate 90/10 rate.
However, we determined that doing so was impossible because eZ-Audit
does not contain sufficient information to allow us to weight
accurately each 90/10 rate when creating an aggregate rate. We also
explored whether we could increase the impact of schools with multiple
campuses, but only one OPEID in our model; it was not possible,
however, because such schools do not report data that would allow us
to identify the 90/10 rates for each of their campuses. This
limitation reduced the influence of these schools in our model.
However, to determine whether inconsistencies in our unit of analysis
substantively affected our results, we ran separate models for related
OPEIDs and stand-alone OPEIDs and compared their results with those of
our other models. Our results were consistent across each type of
model, which showed that the limitations of our unit of analysis did
not materially affect our results. Consequently, we are confident in
our treatment of the unit of analysis and in our study's results.
While we are confident that we have adequately handled the limitations
in our unit of analysis, care should be taken in interpreting some of
our results. Particularly, differences in how schools are organized
and report 90/10 rates may affect their size categories. For example,
a school (or OPEID) with five campuses and 500 students at each campus
(for a total of 2,500 students) would be considered large because it
had at least 2,000 students in total. However, five equally sized
campuses (of 500 students each) with a common owner, but each with its
own OPEID, would not be considered large, because no one campus had
2,000 students or more. Additionally, because there are a large number
of very small for-profit schools in our universe and a relatively
small number of large schools, it may be difficult to determine the
prominence of each type of school in the for-profit industry. For
example, while schools owned by publicly traded companies only
accounted for 14 percent of all schools in our universe, they enrolled
55 percent of students and received 65 percent of federal student aid
that went to for-profit schools. Appendix III includes a table that
lists school characteristics we examined, and compares the percent of
schools with each characteristic to the percent of students those
schools enrolled and the percent of federal student aid they received.
This table also lists the average 90/10 rate of schools with each
characteristic. Appendix III also includes a table listing the
characteristics we examined and showing the percent of schools owned
by a publicly traded company and the percent of schools not owned by a
publicly traded company with each characteristic.
In addition, we could not fully assess the relationship between
certain school and student characteristics and a school's likelihood
of having a very high 90/10 rate in our multivariate regression
analysis because of data limitations. Specifically, we could not
include student income in our model because the only proxy variable
available to us through IPEDS--the percent of students receiving Pell
Grants--was very closely related to our dependent variable, reliance
on federal student aid.[Footnote 56] Therefore, we decided to leave
this measure of student income out of our model. However, because our
model did include other variables that were correlated with a school's
percent of students receiving Pell Grants, the relationships found
between these variables and a school's likelihood of having a very
high 90/10 rate may be overstated. In particular, a school's percent
of students receiving Pell Grants and percent of minority students
were correlated (0.39). Thus, our model may have overstated the effect
of a school's percent of minority students on its likelihood of having
a very high 90/10 rate. However, variables uncorrelated with student
income (as indicated by a school's percent of students receiving Pell
Grants) should not be affected by our inability to directly or
indirectly control for student income in our model.
[End of section]
Appendix III: Tables Comparing Schools with Different Characteristics:
The tables below provide various comparisons of characteristics
examined in our study. Table 2 shows the average 2008 90/10 rate of
schools with each characteristic. It also lists the percent of for-
profit schools with each characteristic, and compares that number to
the percent of students attending, and federal student aid funds
received by for-profit schools with that characteristic. Table 3 lists
selected characteristics and compares the percent of schools owned by
a publicly traded company with that characteristic to the percent of
schools that were not owned by a publicly traded company and had the
same characteristic.
Table 2: Percent of Schools, Students Attending, and Federal Student
Aid Dollars Earned by For-Profit Schools with Specific Characteristics:
Student population:
Characteristic: Schools with a high proportion of students receiving
Pell Grants (75 percent or more);
Average 90/10 rate (%): 77;
95 Percent confidence interval (%): Lower bound: 75.2;
95 Percent confidence interval (%): Upper bound: 77.9;
Percent of schools (%): 22;
Percent of students attending (%): 13;
Percent of federal student aid dollars (%): 10.
Characteristic: Schools with less than 75 percent of students
receiving Pell Grants;
Average 90/10 rate (%): 64;
95 Percent confidence interval (%): Lower bound: 62.9;
95 Percent confidence interval (%): Upper bound: 64.8;
Percent of schools (%): 78;
Percent of students attending (%): 87;
Percent of federal student aid dollars (%): 90.
Educational focus:
Characteristic: Schools that did not grant academic degrees;
Average 90/10 rate (%): 65;
95 Percent confidence interval (%): Lower bound: 63.8;
95 Percent confidence interval (%): Upper bound: 65.7;
Percent of schools (%): 70;
Percent of students attending (%): 24;
Percent of federal student aid dollars (%): 15.
Characteristic: Schools that granted degrees up to the associate's
level;
Average 90/10 rate (%): 75;
95 Percent confidence interval (%): Lower bound: 73.5;
95 Percent confidence interval (%): Upper bound: 76.3;
Percent of schools (%): 18;
Percent of students attending (%): 23;
Percent of federal student aid dollars (%): 16.
Characteristic: Schools that granted bachelor's degrees or higher;
Average 90/10 rate (%): 67;
95 Percent confidence interval (%): Lower bound: 64.9;
95 Percent confidence interval (%): Upper bound: 69.2;
Percent of schools (%): 12;
Percent of students attending (%): 53;
Percent of federal student aid dollars (%): 69.
Characteristic: Schools that offered distance education;
Average 90/10 rate (%): 72;
95 Percent confidence interval (%): Lower bound: 69.6;
95 Percent confidence interval (%): Upper bound: 73.8;
Percent of schools (%): 12;
Percent of students attending (%): 55;
Percent of federal student aid dollars (%): 70.
Characteristic: Schools that did not offer distance education;
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 65.0;
95 Percent confidence interval (%): Upper bound: 66.9;
Percent of schools (%): 88;
Percent of students attending (%): 45;
Percent of federal student aid dollars (%): 30.
Characteristic: Schools that specialized in cosmetology;
Average 90/10 rate (%): 62;
95 Percent confidence interval (%): Lower bound: 60.6;
95 Percent confidence interval (%): Upper bound: 63.1;
Percent of schools (%): 45;
Percent of students attending (%): 9;
Percent of federal student aid dollars (%): 4.
Characteristic: Schools that specialized in construction, mechanics,
and manufacturing;
Average 90/10 rate (%): 71;
95 Percent confidence interval (%): Lower bound: 67.8;
95 Percent confidence interval (%): Upper bound: 74.2;
Percent of schools (%): 5;
Percent of students attending (%): 6;
Percent of federal student aid dollars (%): 5.
Characteristic: Schools that specialized in business;
Average 90/10 rate (%): 64;
95 Percent confidence interval (%): Lower bound: 59.3;
95 Percent confidence interval (%): Upper bound: 69.2;
Percent of schools (%): 4;
Percent of students attending (%): 32;
Percent of federal student aid dollars (%): 41.
Characteristic: Schools that specialized in healthcare;
Average 90/10 rate (%): 75;
95 Percent confidence interval (%): Lower bound: 73.4;
95 Percent confidence interval (%): Upper bound: 75.9;
Percent of schools (%): 30;
Percent of students attending (%): 32;
Percent of federal student aid dollars (%): 27.
Characteristic: Schools that specialized in computer technology;
Average 90/10 rate (%): 68;
95 Percent confidence interval (%): Lower bound: 65.5;
95 Percent confidence interval (%): Upper bound: 72.9;
Percent of schools (%): 2;
Percent of students attending (%): 3;
Percent of federal student aid dollars (%): 3.
Characteristic: Schools that specialized in culinary arts;
Average 90/10 rate (%): 55;
95 Percent confidence interval (%): Lower bound: 49.1;
95 Percent confidence interval (%): Upper bound: 60.2;
Percent of schools (%): 2;
Percent of students attending (%): 3;
Percent of federal student aid dollars (%): 3.
Characteristic: Schools that specialized in visual and performing arts;
Average 90/10 rate (%): 55;
95 Percent confidence interval (%): Lower bound: 49.6;
95 Percent confidence interval (%): Upper bound: 59.5;
Percent of schools (%): 3;
Percent of students attending (%): 5;
Percent of federal student aid dollars (%): 3.
School size, control, and management:
Characteristic: Schools that were large (2,000 FTE students or
more)[A];
Average 90/10 rate (%): 74;
95 Percent confidence interval (%): Lower bound: 71.1;
95 Percent confidence interval (%): Upper bound: 76.0;
Percent of schools (%): 9;
Percent of students attending (%): 69;
Percent of federal student aid dollars (%): 71.
Characteristic: Schools that were mid-sized (500 to 1999 FTE students);
Average 90/10 rate (%): 75;
95 Percent confidence interval (%): Lower bound: 73.3;
95 Percent confidence interval (%): Upper bound: 76.3;
Percent of schools (%): 20;
Percent of students attending (%): 20;
Percent of federal student aid dollars (%): 20.
Characteristic: Schools that were small (100 to 499 FTE students);
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 64.5;
95 Percent confidence interval (%): Upper bound: 67.1;
Percent of schools (%): 42;
Percent of students attending (%): 10;
Percent of federal student aid dollars (%): 7.
Characteristic: Schools that were very small (less than 100 FTE
students);
Average 90/10 rate (%): 60;
95 Percent confidence interval (%): Lower bound: 58.8;
95 Percent confidence interval (%): Upper bound: 62.0;
Percent of schools (%): 30;
Percent of students attending (%): 2;
Percent of federal student aid dollars (%): 1.
Characteristic: Schools owned by a publicly traded company;
Average 90/10 rate (%): 72;
95 Percent confidence interval (%): Lower bound: 70.0;
95 Percent confidence interval (%): Upper bound: 73.1;
Percent of schools (%): 14;
Percent of students attending (%): 55;
Percent of federal student aid dollars (%): 65.
Characteristic: Schools not owned by a publicly traded company;
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 64.8;
95 Percent confidence interval (%): Upper bound: 66.7;
Percent of schools (%): 86;
Percent of students attending (%): 45;
Percent of federal student aid dollars (%): 35.
Characteristic: Schools that were part of a corporate chain;
Average 90/10 rate (%): 70;
95 Percent confidence interval (%): Lower bound: 68.9;
95 Percent confidence interval (%): Upper bound: 71.2;
Percent of schools (%): 48;
Percent of students attending (%): 79;
Percent of federal student aid dollars (%): 87.
Characteristic: Schools that were not part of a corporate chain;
Average 90/10 rate (%): 63;
95 Percent confidence interval (%): Lower bound: 62.2;
95 Percent confidence interval (%): Upper bound: 64.6;
Percent of schools (%): 52;
Percent of students attending (%): 21;
Percent of federal student aid dollars (%): 13.
Characteristic: Schools purchased by another school in the 3 years
prior to 2008;
Average 90/10 rate (%): 69;
95 Percent confidence interval (%): Lower bound: 66.4;
95 Percent confidence interval (%): Upper bound: 71.1;
Percent of schools (%): 9;
Percent of students attending (%): 11;
Percent of federal student aid dollars (%): 13.
Characteristic: Schools not purchased by another school in the 3 years
prior to 2008;
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 65.5;
95 Percent confidence interval (%): Upper bound: 67.3;
Percent of schools (%): 91;
Percent of students attending (%): 89;
Percent of federal student aid dollars (%): 87.
Characteristic: Schools with low tuition rates (at or below
$10,231)[B];
Average 90/10 rate (%): 68;
95 Percent confidence interval (%): Lower bound: 66.3;
95 Percent confidence interval (%): Upper bound: 70.2;
Percent of schools (%): 23;
Percent of students attending (%): 13;
Percent of federal student aid dollars (%): 9.
Characteristic: Schools with tuition rates above $10,231;
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 65.2;
95 Percent confidence interval (%): Upper bound: 67.0;
Percent of schools (%): 77;
Percent of students attending (%): 87;
Percent of federal student aid dollars (%): 91.
Characteristic: Schools with a failing composite score[C];
Average 90/10 rate (%): 66;
95 Percent confidence interval (%): Lower bound: 64.0;
95 Percent confidence interval (%): Upper bound: 68.8;
Percent of schools (%): 13;
Percent of students attending (%): 13;
Percent of federal student aid dollars (%): 14.
Source: GAO analysis of 2008 eZ-Audit data and 2008-2009 IPEDS data.
[A] A school's full-time equivalent (FTE) enrollment was determined
using both undergraduate and graduate enrollment totals for the 2007-
2008 school year.
[B] $10,231 is the 2008-2009 Pell Grant and Stafford Loan award limit
for dependent first-year undergraduates.
[C] Composite scores are calculated by Education using various
financial ratios and estimate a school's level of financial
responsibility.
[End of table]
Table 3: Percent of For-Profit Schools with Specific Characteristics
by Ownership Status:
Student population:
Characteristic: Schools with a high proportion of students receiving
Pell Grants (75 percent or more);
All schools (%): 22;
Percent of non-publicly traded schools with characteristic (%): 24;
Percent of publicly traded schools with characteristic (%): 9.
Characteristic: Schools with less than 75 percent of students
receiving Pell Grants;
All schools (%): 78;
Percent of non-publicly traded schools with characteristic (%): 76;
Percent of publicly traded schools with characteristic (%): 91.
Educational focus:
Characteristic: Schools that did not grant academic degrees;
All schools (%): 70;
Percent of non-publicly traded schools with characteristic (%): 75;
Percent of publicly traded schools with characteristic (%): 40.
Characteristic: Schools that granted degrees up to the associate's
level;
All schools (%): 18;
Percent of non-publicly traded schools with characteristic (%): 17;
Percent of publicly traded schools with characteristic (%): 27.
Characteristic: Schools that granted bachelor's degrees or higher;
All schools (%): 12;
Percent of non-publicly traded schools with characteristic (%): 9;
Percent of publicly traded schools with characteristic (%): 33.
Characteristic: Schools that offered distance education;
All schools (%): 12;
Percent of non-publicly traded schools with characteristic (%): 9;
Percent of publicly traded schools with characteristic (%): 33.
Characteristic: Schools that did not offer distance education;
All schools (%): 88;
Percent of non-publicly traded schools with characteristic (%): 91;
Percent of publicly traded schools with characteristic (%): 67.
Characteristic: Schools that specialized in cosmetology;
All schools (%): 45;
Percent of non-publicly traded schools with characteristic (%): 49;
Percent of publicly traded schools with characteristic (%): 20.
Characteristic: Schools that specialized in construction, mechanics,
and manufacturing;
All schools (%): 5;
Percent of non-publicly traded schools with characteristic (%): 5;
Percent of publicly traded schools with characteristic (%): 7.
Characteristic: Schools that specialized in business;
All schools (%): 4;
Percent of non-publicly traded schools with characteristic (%): 4;
Percent of publicly traded schools with characteristic (%): 6.
Characteristic: Schools that specialized in healthcare;
All schools (%): 30;
Percent of non-publicly traded schools with characteristic (%): 29;
Percent of publicly traded schools with characteristic (%): 37.
Characteristic: Schools that specialized in computer technology;
All schools (%): 2;
Percent of non-publicly traded schools with characteristic (%): 2;
Percent of publicly traded schools with characteristic (%): 7.
Characteristic: Schools that specialized in culinary arts;
All schools (%): 2;
Percent of non-publicly traded schools with characteristic (%): 1;
Percent of publicly traded schools with characteristic (%): 6.
Characteristic: Schools that specialized in visual and performing arts;
All schools (%): 3;
Percent of non-publicly traded schools with characteristic (%): 3;
Percent of publicly traded schools with characteristic (%): 5.
School size, control, and management:
Characteristic: Schools that were large (2,000 FTE students or more);
All schools (%): 9;
Percent of non-publicly traded schools with characteristic (%): 4;
Percent of publicly traded schools with characteristic (%): 36.
Characteristic: Schools that were mid-sized (500 to 1999 FTE students);
All schools (%): 20;
Percent of non-publicly traded schools with characteristic (%): 17;
Percent of publicly traded schools with characteristic (%): 36.
Characteristic: Schools that were small (100 to 499 FTE students);
All schools (%): 42;
Percent of non-publicly traded schools with characteristic (%): 45;
Percent of publicly traded schools with characteristic (%): 23.
Characteristic: Schools that were very small (less than 100 FTE
students);
All schools (%): 30;
Percent of non-publicly traded schools with characteristic (%): 34;
Percent of publicly traded schools with characteristic (%): 5.
Characteristic: Schools that were part of a corporate chain;
All schools (%): 48;
Percent of non-publicly traded schools with characteristic (%): 40;
Percent of publicly traded schools with characteristic (%): 98.
Characteristic: Schools that were not part of a corporate chain;
All schools (%): 52;
Percent of non-publicly traded schools with characteristic (%): 60;
Percent of publicly traded schools with characteristic (%): 2.
Characteristic: Schools purchased by another school in the 3 years
prior to 2008;
All schools (%): 9;
Percent of non-publicly traded schools with characteristic (%): 7;
Percent of publicly traded schools with characteristic (%): 19.
Characteristic: Schools not purchased by another school in the 3 years
prior to 2008;
All schools (%): 91;
Percent of non-publicly traded schools with characteristic (%): 93;
Percent of publicly traded schools with characteristic (%): 81.
Characteristic: Schools with low tuition rates (at or below $10,231);
All schools (%): 23;
Percent of non-publicly traded schools with characteristic (%): 24;
Percent of publicly traded schools with characteristic (%): 15.
Characteristic: Schools with tuition rates above $10,231;
All schools (%): 77;
Percent of non-publicly traded schools with characteristic (%): 76;
Percent of publicly traded schools with characteristic (%): 85.
Characteristic: Schools with a failing composite score;
All schools (%): 13;
Percent of non-publicly traded schools with characteristic (%): 13;
Percent of publicly traded schools with characteristic (%): 15.
Source: GAO analysis of 2008 eZ-Audit data and 2008-2009 IPEDS data.
Note: The percent of schools in each column represents the percent of
schools with that ownership status that had the characteristic being
measured. Values across columns do not add up to 100 percent.
[End of table]
[End of section]
Appendix IV: Technical Appendix on Results from Regression Model:
To explore how 90/10 rates varied among schools with different
characteristics, we conducted descriptive analysis. Additionally, we
conducted a series of multiple regression analyses to address the
question of which school characteristics are associated with an
increased likelihood of having a very high 90/10 rate, controlling for
other factors.
Model Selection:
Multiple regression analysis is a method for exploring how a dependent
or "outcome" variable (such as a 90/10 rate) is related to a number of
independent or "explanatory" variables, controlling for other factors
that may have an impact on the value of the dependent variable. The
primary dependent variable of interest in this report is a school's
90/10 rate, which summarizes a for-profit school's reliance on federal
student aid as a proportion of its total revenue. The independent
variables we tested were related to school characteristics, such as
size, academic focus, ownership structure, and other factors discussed
below.
The distribution of 90/10 rates is not uniform across the range of
theoretical values from zero to 100. Among for-profit schools with
corresponding IPEDS data in 2008, fewer than 20 percent of schools had
90/10 rates in 2008 of 50 points or below, while more than 50 percent
of schools had rates above 70 up to 94 points, the maximum value
observed in 2008 (see figure 2).
Figure 2: Distribution of 2008 90/10 Rates:
[Refer to PDF for image: vertical bar graph]
Percentage of For-Profit Schools:
2008 90/10 Rates: 0-5: 0.73%;
2008 90/10 Rates: 5-10: 0.36%;
2008 90/10 Rates: 10-15: 0.93%;
2008 90/10 Rates: 15-20: 1.19%;
2008 90/10 Rates: 20-25: 0.57%;
2008 90/10 Rates: 25-30: 1.55%;
2008 90/10 Rates: 30-35: 2.07%;
2008 90/10 Rates: 35-40: 3.06%;
2008 90/10 Rates: 40-45: 3.73%;
2008 90/10 Rates: 45-50: 4.4%;
2008 90/10 Rates: 50-55: 5.39%;
2008 90/10 Rates: 55-60: 6.89%;
2008 90/10 Rates: 60-65: 6.99%;
2008 90/10 Rates: 65-70: 9.79%;
2008 90/10 Rates: 70-75: 11.13%;
2008 90/10 Rates: 75-80: 11.76%;
2008 90/10 Rates: 80-85: 14.4%;
2008 90/10 Rates: 85-90: 14.76%;
2008 90/10 Rates: 90-95: 0.31%.
Source: GAO analysis of Education‘s eZ-Audit data.
Note: Figure excludes schools without corresponding data in IPEDS.
[End of figure]
This non-normal distribution renders a common multiple regression
technique, ordinary least squares analysis, inappropriate as the
primary methodology for regression analysis.[Footnote 57] Instead, we
chose to use logistic regression analysis with a dichotomous dependent
variable that categorizes schools as having a very high 90/10 rate or
not having a very high rate. We defined schools with "very high" 90/10
rates as those 15 percent of schools with 90/10 rates that exceeded 85
percent. Because this definition was not based on a natural break in
the distribution of 90/10 rates, we tested alternative definitions to
confirm that the results described in our report are robust to
alternative definitions of "very high" rates; see below for further
discussion.
Logistic regression analysis is a technique used in evaluating
dichotomous, or binary, outcomes. Unlike ordinary least squares
regression analysis, which provides an estimate of the absolute change
in the dependent variable associated with each unit change in an
independent variable, logistic regression estimates the changes in the
likelihood of having a specific outcome such as a very high 90/10
rate. Because the specific probability of having an outcome depends on
the full set of characteristics associated with a school, we often use
a transformation of logistic regression estimates, or odds ratios, as
a means of interpreting the results. Odds ratios compare the odds that
a school with one particular characteristic has the specific outcome
(here, a very high 90/10 rate), compared to a school that lacks that
specific characteristic.
Table 4 illustrates how to construct odds ratios from raw data for one
specific characteristic, school size. In our data, 167 of the for-
profit schools in our data set were large, with 2,000 full-time
equivalent (FTE) or more students, and 579 were very small with fewer
than 100 FTE students. Of the large schools, 42 schools had very high
90/10 rates exceeding 85 points, and 125 did not have very high 90/10
rates. The odds that a large school had a very high 90/10 rate, or the
"odds on" a very high 90/10 rate, were thus 42 to 125, or .336. In
contrast, among very small schools, 46 schools had very high 90/10
rates, and 533 did not. The odds on very high rates for these schools
were 46 to 533, or .086. The odds ratio compares the odds on very high
90/10 rates among schools in the two size categories. The odds ratio
for large schools having very high 90/10 rates compared to very small
schools is .336 to .086, or approximately 3.9. The odds ratio shows us
that the odds of having a very high 90/10 rate were nearly 4 times
higher for large schools than for very small schools.
Table 4: Odds and Odds Ratios for Very High 90/10 Rates by School
Enrollment:
Characteristic: Large schools (2,000 or more FTE students);
Very high 90/10 rate: 42;
Not very high 90/10 rate: 125;
Odds on very high: 42:125;
Odds on very high 90/10 rate and derived odds ratio: .336.
Characteristic: Very small schools (less than 100 FTE students);
Very high 90/10 rate: 46;
Not very high 90/10 rate: 533;
Odds on very high: 46:533;
Odds on very high 90/10 rate and derived odds ratio: .086.
Characteristic: Odds ratio, large to very small schools;
Very high 90/10 rate: [Empty];
Not very high 90/10 rate: [Empty];
Odds on very high: [Empty];
Odds on very high 90/10 rate and derived odds ratio: .336/.086, or
3.91.
Source: GAO example using 2008 eZ-Audit data and 2008-2009 IPEDS data.
[End of table]
Odds ratios of one indicate that the sets of schools within comparison
categories had similar odds of having the outcome variable. Logistic
regression analysis enables us to construct odds ratios for individual
characteristics that "control for," or hold constant, the effect of
other independent variables on the outcome.
Model Specification and Estimation:
Model specification refers to the choice of independent variables to
include in a model, as well as methodological decisions such as the
mathematical form of these variables and type of method used to
calculate standard errors. Based on research, interviews and previous
GAO reports, we identified a set of school characteristics to use as
independent variables in our model predicting the likelihood that a
school would have a very high 90/10 rate exceeding 85 percent. These
included:
* Whether a school offered distance education.
* A school's total FTE enrollment.
* Whether a school was bought by another school in the three years
prior to 2008.
* Whether a school was owned by a publicly traded company.
* Whether a school was part of a corporate chain.
* The school's specialty, as designated by the Classification of
Industrial Programs code associated with the school's largest program.
* A school's highest level of educational offering. (We divided
schools into three categories: those that did not grant academic
degrees, schools that offered degrees up to the associate's level, and
those that offered bachelor's degrees or above.)
* A school's number of eight-digit IPEDS subreporters. (This variable
measured whether the reported 90/10 rate applied to one or more
locations or campuses.)
* A school's proportion of students under/over the age of 25.
* The proportion of students that were minorities, and the proportion
of "unknowns," or students that did not self-identify as white or
minority.
* The proportion of students that were female.
* A school's average tuition.[Footnote 58]
* A school's composite score, a numerical score calculated by
Education using various financial ratios that estimates a school's
level of financial responsibility.
Because of the possibility that schools owned by the same corporate
entity may have systematic similarities in administration, mission,
and other factors, we accounted for potential non-independence in
observations by using robust standard errors that allowed for
clustering within corporate entities.
We tested a variety of model specifications before selecting a model
to use for additional sensitivity testing (see below). For example,
after initial testing revealed high colinearity between a school's
highest offering and whether a school was categorized as being a
degree granting institution, we selected the more detailed variable
listing the specific highest degree for use in the model. However,
because relatively few schools reported offering masters' degrees, we
combined those schools into one category with schools that offered
bachelor's degrees.
For continuous variables, such as average tuition, total enrollment,
or the proportion of female students, we tested both continuous and
categorical versions. In some models we tested distributionally-based
categories, such as by quartile, in order to identify relative
outliers (such as in the top quartile only). In others, we used a
combination of distributional information and substantively based
categories. For example, we tested our indicator of school size, total
enrollment, directly as a logged variable to mitigate the effect of
large outliers on our estimates. After finding that the logged
enrollment variable had strong predictive power, we generated unequal
size categories that adequately captured the relationship between the
log of enrollment and likelihood of having a very high 90/10 rate,
which also enabled us to illustrate the effect of size in terms of
size category rather than a log transformation.
In addition to ensuring that our results were robust across different
specifications and definitions of the independent variables, we
examined a likelihood fit statistic, the Quasi-likelihood Information
Criteria, to check whether inclusion/exclusion of variables or
inclusion of different versions of the same variables results in an
increase or decrease in model fit. For sensitivity testing, we
selected a final model that performed well in terms of model fit
relative to other models.
Results:
Our logistic regression analyses revealed several statistically
significant predictors of whether a school had a very high 90/10 rate,
controlling for other characteristics. As discussed below, these
results were robust to different definitions of the dependent
variable, different specifications of the model and of independent
effects, and different subpopulations. Point estimates for odds ratios
presented here are illustrative of typical results across a variety of
models and are significant at the 95 percent confidence level unless
otherwise indicated.
* Compared to schools that did not specialize in healthcare, schools
that specialized in healthcare were more likely to have very high
90/10 rates (estimated odds ratio approximately 2.2 to 2.9). Aside
from healthcare, schools that specialized in other areas such as
cosmetology; business; construction, mechanics, and building trades;
culinary arts; engineering; law enforcement; communications; visual
and performing arts; or computers and information technology were not
significant predictors of having a high 90/10 rate when compared to
schools without these specific specialties.
* Compared to schools that offered bachelor's degrees or higher,
schools that did not grant academic degrees were more likely to have a
very high 90/10 rate, with estimated odds of having a high 90/10 rate
approximately twice as high as schools that did not offer degrees.
This result was consistently significant at the 90 percent level and
frequently significant at the 95 percent level.
* The odds of having a very high 90/10 rate increased with school size
as indicated by total enrollment. In categorical terms, large schools
with 2,000 students and above had much higher odds of having a very
high 90/10 rate than very small schools with less than 100 students,
with estimated odds more than 3 times higher. Similarly, mid-sized
schools with between 500 and 1,999 students had estimated odds of
having a very high 90/10 rate more than two times higher than very
small schools.
* Publicly traded schools had significantly lower odds of having a
very high 90/10 rate than non-publicly traded schools. The odds that a
publicly traded school had a very high 90/10 rate were approximately
40 percent lower than those for a non-publicly traded school
(estimated odds ratios of approximately 0.5 to 0.6).
* Several variables appeared to have a statistically significant
relationship with a school's 90/10 rate in some models, but were not
consistently significant or were only significant in some models at
the 90 percent level. Notably, schools that offered associate's
degrees as their highest level of educational offering appeared to
have a somewhat higher 90/10 rate than schools offering bachelor's
degrees or higher in some models, but this result was not consistently
significant. Similarly, schools that had been purchased by another
school appeared to have significantly lower odds of having a very high
90/10 rate than schools not having been purchased by another school in
some models, but not consistently across most or all models. In some
specifications, small schools of 100 to less than 500 students had
somewhat higher odds of having a very high 90/10 rate than very small
schools (estimated odds ratio approximately 1.5) at the 90 percent
confidence level.
* Several student demographic factors were associated with the
likelihood of having a very high 90/10 rate. For example, the odds of
having a very high 90/10 rate also increased with the proportion of
adult learners over the age of 25 (estimated odds ratio approximately
1.01).[Footnote 59] The odds of having a very high 90/10 rate
increased with the proportion of minority students (odds ratio of
approximately 1.03). The odds of having a very high 90/10 rate also
increased with the proportion of students failing to self-identify for
race (estimated odds ratio of 1.02). However, as discussed below, our
inability to control for a key demographic factor, student income, may
have an impact on the magnitude of our estimates for factors
correlated with student income, including student demographic factors.
* There was no apparent relationship between a school's 90/10 rate and
the following variables across most or all models tested in our
regression analyses: having a very high proportion of female students,
offering distance education, being part of a corporate chain, a
school's number of OPEID subreporters, or being designated as not
financially responsible under Education's composite score.
Additionally, there was no apparent relationship between average
tuition measured in quartiles and the odds of having a very high 90/10
rate across any of three different tuition measures, including a
school's tuition as reported, an annualized measure of tuition, and a
credit-hour measure of tuition. Further, when we tested the model with
an intercept for having annualized low tuition below the 2008-2009
Pell Grant and Stafford Loan maximum award amount of $10,231 for
dependent first-year undergraduates, we found that for-profit schools
with low tuition rates did not have significantly higher or lower odds
of having a very high 90/10 rate than schools with higher tuition
rates, controlling for other factors.[Footnote 60]
Sensitivity Testing:
After selecting a final model, we conducted a series of sensitivity
tests to ensure the stability and robustness of our results. For
example, based on concerns that chain schools may vary systematically
from independent schools, we tested a model among separate populations
of chain schools versus all other schools. Similarly, we also tested
our model separately among schools reporting multiple subreporters
under one OPEID. The results of our models were substantively similar
across these different population categories.
To ensure the results of the model were not sensitive to the specific
cut-point for "very high" 90/10 rates, we conducted sensitivity tests
with a dependent variable consisting of whether a school's 90/10 rate
was 75 percent or higher, 82 percent or higher, and 88 percent or
higher. Again, these models produced results that were consistent with
models that used a cut-off point of above 85 points as our definition
of "very high."
Limitations:
Despite tests to ensure the stability and robustness of our results
across different model specifications, alternative definitions of the
dependent variable, and different subpopulations, the results of our
model are subject to several limitations. Most critically, we were
unable to include a control for the proportion of a school's students
with low incomes because our proxy of student income, the proportion
of students receiving Pell Grants, factors into the calculation of the
dependent variable.[Footnote 61] As such, odds ratio estimates for
other variables correlated with student income, such as the proportion
of adult learners ages 25 and over or the proportion of students that
self-identified as minorities, may partly reflect the effect of
student income on a school's 90/10 rates.[Footnote 62] Additionally,
these models were not designed to explore outcome variables associated
with the quality of a school's educational offerings, such as job
placement rates or student loan defaults, and therefore do not provide
information about the relationship between very high 90/10 rates and
these factors.
[End of section]
Appendix V: GAO Contact and Staff Acknowledgments:
GAO Contact:
George A. Scott, (202) 512-7215 or scottg@gao.gov.
Staff Acknowledgments:
The following staff members made key contributions to this report,
Melissa Emrey-Arras, Assistant Director; Michelle St. Pierre, Analyst-
in-Charge; Ellen Phelps Ranen; Carl Barden; James Bennett; Jessica
Botsford; Russell Burnett; Susannah Compton; John Mingus; Reina Nunez;
Anna Maria Ortiz; Sara Pelton; Sal Sorbello; and Shana Wallace.
[End of section]
Footnotes:
[1] These programs are authorized under Title IV of the Higher
Education Act of 1965, as amended.
[2] See Pub. L. No. 102-325. For the purposes of this report, by
federal student aid revenues, we mean revenues from financial aid
programs authorized under Title IV of the Higher Education Act of
1965, as amended.
[3] See Pub. L. No. 105-244. The 90/10 rule is found at 20 U.S.C. §
1094(a)(24).
[4] See 34 C.F.R § 668.23(d)(4).
[5] See Pub. L. No. 110-315, § 1105.
[4] For the purposes of our analyses, a school corresponds with a
unique OPEID. Therefore, we consider an OPEID with five campuses to be
one school. Conversely, we consider five campuses with a common owner,
but each with its own OPEID, to be five separate schools.
[5] Education officials said they can more quickly revoke these schools‘
eligibility if they do not meet program responsibilities.Prior to
changes enacted in August 2008 (Pub. L. No. 110-315), a for-profit
school lost federal student aid eligibility after one year of non-
compliance.
[6] Through its eZ-Audit system, Education collects information from
the annual audited financial statements of all schools receiving
federal student aid funds.
[7] We define federal student aid as financial aid programs authorized
by Title IV of the Higher Education Act of 1965, as amended.
[8] See Pub. L. No. 102-325.
[9] See Pub. L. No. 105-244. The 90/10 rule is found at 20 U.S.C. §
1094(a)(24).
[10] For the purposes of our analyses, a school corresponds with a
unique OPEID. Therefore, we consider an OPEID with five campuses to be
one school. Conversely, we consider five campuses with a common owner,
but each with its own OPEID, to be five separate schools.
[11] Education officials said they can more quickly revoke these
schools' eligibility if they do not meet program responsibilities.
Prior to changes enacted in August 2008 (Pub. L. No. 110-315), a for-
profit school lost federal student aid eligibility after one year of
non-compliance.
[12] Through its eZ-Audit system, Education collects information from
the annual audited financial statements of all schools receiving
federal student-aid funds. The data for this time period represent the
most recent data available at the time of our study.
[13] Education's eZ-Audit data are based on a school's fiscal year.
IPEDS data capture school characteristics at points during its school
year and Education funding data are based on a school year.
[14] All differences described in this report are statistically
significant at the 95 percent level, unless otherwise noted.
[15] A key limitation in our regression model is that it does not
account for student income. Specifically, we could not include a
school's proportion of students receiving Pell Grants (the only proxy
for low-income students available to us) in our model because Pell
Grants are included in schools' 90/10 rates.
[16] All schools were assigned a specialty according to which of their
programs had either more enrolled students in the fall of 2008 or more
graduates in the 2008-2009 school year (depending on the school's
reporting method) than any of its other programs.
[17] If a school's parent company owned at least one other for-profit
school, we considered the school to be part of a corporate chain.
[18] A publicly-traded company is authorized to offer its securities
(e.g., stocks and bonds) for sale to the general public, typically
through a stock exchange. The securities of a publicly-traded company
are typically owned by many investors.
[19] Distance education is an option for earning course credit at off-
campus locations via the Internet or other means.
[20] Federal student aid revenues in excess of a school's educational
and institutional charges must be excluded from a school's calculation.
[21] For more information, see 20 U.S.C. § 1094(d) and 34 C.F.R. §
668.28.
[22] On a cash basis of accounting, revenues are recorded when they
are received regardless of when they are earned. Outside of the 90/10
calculation, schools generally track revenues on an accrual basis of
accounting, where revenues are recorded when they are earned.
[23] All other schools include public and non-profit schools.
[24] For-profit schools' share of revenues may be greater than their
share of enrollment because they generally have higher tuition rates
than public schools and enroll a larger percentage of low-income
students, who are eligible for more federal aid than other students.
For more 12 information on the characteristics of students at for-
profit schools, see GAO, Proprietary Schools: Stronger Department of
Education Oversight Needed to Help Ensure Only Eligible Students
Receive Federal Aid, [hyperlink,
http://www.gao.gov/products/GAO-09-600] (Washington, D.C., Aug. 17,
2009).
[25] Data on 90/10 rates are based on a school's fiscal year. As noted
earlier, schools' 90/10 rates are verified by independent auditors.
[26] For each year between 2003 and 2008, the number of schools
subject to the 90/10 rule was 1,554; 1,873; 1,917; 1,941; 1,950; and
1,962; respectively. In 2008 (the most recent year for which we had
data), 1,955 schools were in compliance with the 90/10 rule.
[27] In some other cases, schools that did not comply either closed or
lost eligibility for other reasons.
[28] This number represents unique schools and does not equal the sum
of all cases because one school was non-compliant in two years.
[29] Schools' average 90/10 rates for each intervening year were 64,
64, 63, and 64 percent, respectively.
[30] The number of schools that received at least 85 percent of their
revenues from federal student aid in 2008 was 292. After removing the
7 schools that had 90/10 rates above 90 percent and were therefore out
of compliance with the 90/10 rule, 285 had rates of 85 percent or
higher.
[31] We used a descriptive statistical test, which did not control for
the effects of additional school characteristics, to analyze whether
certain schools' average 90/10 rates were significantly different than
those of other schools.
[32] Large schools had higher average 90/10 rates than very small
schools (with under 100 students). Schools with 500 to under 2,000
students also had higher average 90/10 rates than very small schools.
[33] This result was expected because Pell Grants are included in
schools' 90/10 rates. It is logical to assume that a school's
proportion of students receiving Pell Grants would be correlated with
its 90/10 rate. A school's proportion of students receiving Pell
Grants was the only proxy for student income available to us.
[34] Schools offering bachelor's degrees or higher had an average
90/10 rate of 67 percent and schools that did not offer academic
degrees had an average 90/10 rate of 64 percent.
[35] Additionally, schools specializing in construction, mechanics, or
manufacturing had a significantly higher average 90/10 rate than their
counterparts: 71 vs. 66 percent. However, only about 5 percent of
schools were in this category.
[36] Schools that specialized in culinary arts, about 2 percent of all
for-profit schools, also had a lower average 90/10 rate than other for-
profit schools, at 55 vs. 67 percent. The 3 percent of for-profit
schools that specialized in visual and performing arts also had a
lower average 90/10 rate than other schools, at 55 vs. 67 percent.
[37] Enrollment data are based on 2007-2008, while funding data are
based on the 2008-2009 school year. See appendix II for more details.
[38] Some chain schools were owned by publicly-traded companies.
[39] For the purposes of our analysis, a school is an entity with a
unique OPEID that reports an annual 90/10 rate to Education. Schools may
be organized in different ways, and an OPEID may correspond with one
or many campuses. Thus, an OPEID with five campuses and 500 22
students at each campus (for a total of 2,500 students) would be
considered large. However, five equally-sized campuses (of 500
students each) with a common owner, but each with its own OPEID, would
not be considered large because no OPEID has 2,000 students.
[40] A total of 23 publicly-traded companies owned 278 for-profit
schools.
[41] These schools were more likely to have very high 90/10 rates
compared to very small schools (under 100 students).
[42] We could not include a measure of student income in our model.
Some characteristics that are correlated with student income,
specifically, a school's proportion of minority students and students
over the age of 25, appear significant in our model. Due to concerns
that these characteristics may partially reflect the effect of student
income on school 90/10 rates, we do not focus on them in this report.
[43] In conducting this analysis, we used statistical techniques to
mitigate the effect of large outliers on our model. We determined a
school's total number of students on a full-time-equivalent basis.
[44] In a few of our models, this finding was significant only at the
90 percent confidence level.
[45] Schools that had experienced a change in ownership in the three
years prior to 2008 had a significantly higher average 90/10 rate than
other schools (69 vs. 66 percent) only at the 90 percent confidence
level. In some models, they appeared to be less likely to have very
high 90/10 rates, but this result was not consistent across all models.
[46] We tested multiple measures of tuition to account for differences
in how schools report their tuition rates and found no relationship in
any measure. We did not attempt to assess whether rising federal
student aid limits had any impact on schools' tuition rates or 90/10
rates.
[47] These award amounts were for first-year dependent undergraduates.
[48] The experienced firms each conducted over 50 audits and
collectively conducted 22 percent of all fiscal year 2008 audits of
for-profit schools for which Education had a record of the audit
firm's name. The moderately experienced firm conducted 10 audits in
2008, and the least experienced firms conducted less than 5 audits
each.
[49] We did, however, use for-profit schools' reported 90/10 rates to
calculate each year's average 90/10 rate.
[50] In the few cases where one reported 90/10 rate was zero and
another rate was greater than zero, we retained the non-zero rate
regardless of its fiscal year end date.
[51] Schools that disburse less than $200,000 a year in federal
student aid funds may request a waiver of this requirement. However,
once the waiver period is over, schools must report, and the auditor
must verify, their 90/10 rates for the years in which the waiver was
in effect. See 34 C.F.R § 668.27.
[52] In the few cases in which 2008-2009 school year data were
unavailable, such as for our total enrollment variable, we used IPEDS
data from the 2007-2008 school year.
[53] Some schools report their tuition rates on an academic-year
basis, while others report their tuition rates on a credit-hour basis
or contact-hour basis, based on the length of their largest program.
[54] The correlation coefficient measures the direction of and
strength of association between two variables, where the strength of
association refers to how the scores on one variable are distributed
with respect to the scores on other variables. The statistic ranges
between -1 and 1; with the strength of association between two
variables increasing the further the statistic is from zero.
[55] In a 1997 study, GAO conducted several regression analyses to
determine the impact of a school's level of reliance on federal
student aid on its completion rate, placement rate, and default rate,
while holding a variety of other school characteristics constant. That
study found that schools that relied more on federal student aid
tended to have worse student outcomes. Our regression analyses, on the
other hand, measured whether certain school characteristics predicted
an increased likelihood of having a very high rate of reliance on
federal student aid (a 90/10 rate of above 85 percent). See GAO,
Proprietary Schools: Poorer Student Outcomes at Schools that Rely More
on Federal Student Aid, [hyperlink,
http://www.gao.gov/products/GAO/HEH-97-103] (Washington, D.C.: June
13, 1997).
[56] Our dependent variable was generated using schools' 90/10 rates,
which are calculated using the dollar value of a school's federal
student aid disbursements, including Pell Grants. We tested the
relationship between a school's percent of students receiving Pell
Grants and its 90/10 rate, and found about a 0.5 correlation.
[57] Ordinary least squares regression analysis can illustrate the
specific change in a dependent variable associated with a unit change
in an independent variable; for example, the predicted increase in 90/
10 rates associated with a percentage point increase in the proportion
of adult learners ages 25 and over.
[58] We tested three separate measures of tuition; these measures are
described in appendix II.
[59] The odds ratio for a continuous variable reflects the change in
odds associated with a one-unit increase in the independent variable,
such as a percentage point increase in the proportion of adult
learners ages 25 and over.
[60] Schools with tuition rates that did not exceed 2008-2009 Pell
Grant and Stafford Loan award limits did have slightly higher average
90/10 rates than other schools, at 68 vs. 66 percent.
[61] The 90/10 rate is calculated as the proportion of school revenue
received from federal student aid sources, including Pell Grants. The
proportion of students receiving Pell Grants, the only proxy for
student income available to us, was significantly correlated with the
90/10 rate at r=0.48.
[62] The correlations between the proportion of students receiving
Pell Grants and the proportion of adult learners over the age of 25,
and with the proportion of minority students, were modest but
statistically significant at r=.11 and .39 respectively.
[End of section]
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