United States Government Accountability Office

GAO Report to Congressional Committees

HIGHER EDUCATION

Schools’ Use of the 

Antitrust Exemption 

Has Not Significantly 

Affected College 

Affordability or 

Likelihood of Student 

Enrollment to Date 

September 2006 

 

GAO-06-963 
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

September 2006

HIGHER EDUCATION

Schools' Use of the Antitrust Exemption 

Has Not Significantly Affected College 

Affordability or Likelihood of Student 

Enrollment to Date 

Highlights of GAO-06-963, a report to 

congressional committees 

In 1991 the U.S. Department of

Justice sued nine colleges and 

universities, alleging that they had 

restrained competition by making

collective financial aid 

determinations for students 

accepted to more than one of these 

schools. Against the backdrop of 

this litigation, Congress enacted a 

temporary exemption from

antitrust laws for higher education 

institutions in 1992. The exemption 

allows limited collaboration 

regarding financial aid practices 

with the goal of promoting equal 

access to education. The

exemption applies only to 

institutional financial aid and can 

only be used by schools that admit 

students without regard to ability 

to pay.

In passing an extension to the 

exemption in 2001, Congress

directed GAO to study the effects 

of the exemption. GAO examined

(1) how many schools used the 

exemption and what joint practices 

they implemented, (2) trends in 

costs and institutional grant aid at 

schools using the exemption, 

(3) how expected family

contributions at schools using the 

exemption compare to those at

similar schools not using the 

exemption, and (4) the effects of 

the exemption on affordability and 

enrollment. GAO surveyed schools, 

analyzed school and student-level

data, and developed econometric

models. GAO used extensive peer 

review to obtain comments from

outside experts and made changes 

as appropriate. 

Twenty-eight schools—all highly selective, private 4-year institutions—

formed a group to use the antitrust exemption and developed a common 

methodology for assessing financial need, which the group called the 

consensus approach. The methodology used elements already a part of 

another need analysis methodology; schools modified this methodology and 

reached agreement on how to define those elements. By the 2004-2005 

school year, 25 of 28 schools in the group were using the consensus 

approach. Schools’ implementation of the approach varied, however, with 

officials from 12 of the 25 schools reporting that they partially implemented 

it, in part because they believed it would be costly to do so. 

Over the last 5 years, tuition, room, and board costs among schools using the 

antitrust exemption increased by 13 percent compared to 7 percent at all

other private 4-year schools not using the exemption. While the amount of 

institutional aid at schools using the exemption also increased—it did so at a 

slower rate. The average institutional grant aid award per student increased 

by 7 percent from $18,675 in 2000-2001 to $19,901 in 2005-2006. 

There was virtually no difference in the amount students and their families 

were expected to pay between schools using the exemption and similar 

schools not using the exemption. While officials from schools using the 

exemption expected that students accepted to several of their schools would 

experience less variation in the amount they were expected to pay, GAO 

found that students accepted to schools using the exemption and 

comparable schools not using the exemption experienced similar variation 

in the amount they were expected to pay. Not all schools using the 

consensus approach chose to adopt all the elements of the methodology, a 

factor that may account for the lack of consistency in expected family 

contributions among schools using the exemption.

Based on GAO’s analysis, schools’ use of the consensus approach did not 

have a significant impact on affordability—the amount students and families 

paid for college—or affect the likelihood of enrollment at those schools to 

date. While GAO found that the use of the consensus approach resulted in 

higher amounts of need-based grant aid awarded to some student groups 

compared to their counterparts at schools not using the consensus 

approach, the total amount of grant aid awarded was not significantly 

affected. It was likely that grant aid awards shifted from non-need-based aid, 

such as academic and athletic scholarships, to aid based on a student’s 

financial need. Finally, implementing the consensus approach did not 

increase the likelihood of low-income or minority students enrolling at 

schools using the consensus approach compared to schools that did not. 

The group of schools using the exemption reviewed this report and stated it 

was a careful and objective report. However, they had concerns about the 

data used in GAO’s econometric analysis, which GAO believes were reliable.

www.gao.gov/cgi-bin/getrpt?GAO-06-963.

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact Cornelia Ashby 

at (202) 512-7215 or ashbyc@gao.gov. 
Contents 

Letter 1

Results in Brief 4

Background 6

Twenty-Eight Schools Used the Antitrust Exemption to Develop a 

Common Methodology for Assessing a Family’s Financial Need 9 

As the Cost of Attendance at Schools Using the Exemption Rose, 

the Amount of Institutional Grant Aid They Provided to Students 

Increased at a Slower Rate 15 

Students Accepted to Both Schools Using the Exemption and 

Comparable Schools Had No Appreciable Difference in the 

Amount They Would Be Expected to Contribute Towards 

College 19

Implementation of a Common Methodology Has Not Significantly 

Affected Affordability or Enrollment at Schools Using the 

Exemption 20

Concluding Observations 23 

Agency Comments 23 

Appendix I Statistical Analysis of Expected Family 

Contributions at Schools Using the Exemption 

and Comparable Schools 27 

Appendix II Econometric Analysis of Effects of the Higher 

Education Antitrust Exemption on College 

Affordability and Enrollment 32 

Theories of the Effects of the Consensus Approach on Financial 

Aid 32

Sources of Data for the Model 33 

Selection of Control Schools 34 

Specifications of Econometric Models and Estimation 

Methodology 38

Estimation Results of the Effects of Attending Meetings and 

Implementing the Consensus Approach 62 

Limitations of the Study 70 

Page i GAO-06-963 Higher Education
Appendix III Classification of 1999-2000 Academic Year and 

Schools Only Attending the 568 Group Meetings 74 

Does Academic Year 1999-2000 belong to the Pre– or Post– 

Consensus Approach Implementation Period? 74 

Do the Schools That Only Attended the 568 group Meetings belong 

to the Control or Treatment Group? 75 

Appendix IV Comments from 568 Presidents’ Group 77 

Appendix V Consultants and Peer Reviewers 87 

Appendix VI GAO Contact and Staff Acknowledgments 88 

Bibliography 89 

Tables 

Table 1: Comparison of the Federal Methodology and the College 

Board’s Base Institutional Methodology for Need Analysis 9 

Table 2: Schools Using the Antitrust Exemption, as of May 2006 10 

Table 3: Comparison of Consensus Approach Developed by 

Schools Using the Antitrust Exemption Compared to the 

College Board’s Institutional Methodology 13 

Table 4: Number of Schools That Did Not Implement Certain 

Consensus Approach Options in School Year 2005-2006 14 

Table 5: Estimated Changes in Amount Paid, Financial Aid, and 

Enrollment at Schools Using the Consensus Approach 

Compared to Schools Not Using the Exemption 21 

Table 6: Schools Included in Analysis of Effects of Exemption 23 

Table 7: Summary Statistics of Expected Family Contributions 29 

Table 8: Tests of Variations in Expected Family Contributions 31 

Table 9: Control and Treatment Schools for Analyzing Effects of 

the Consensus Approach Implementation 37 

Table 10: Summary Statistics of Variables Used in Regression 

Analysis, 1995-1996 and 2003-2004: CA Schools 45 

Page ii GAO-06-963 Higher Education
Table 11: Summary Statistics of Variables Used in Regression 

Analysis, 1995-1996 and 2003-2004: Non-CA Schools 46 

Table 12: CA and Non-CA Schools: Price and Financial Aid 48 

Table 13: CA and Non-CA Schools—Financial Aid Applicants Only: 

Price and Financial Aid 49 

Table 14: Regression Estimates of Effects of Consensus Approach 

Implementation on Price, Tuition, and Enrollment 59 

Table 15: Regression Estimates of Effects of Consensus Approach 

Implementation on Financial Aid 61 

Table 16: Estimates of Effects of Consensus Approach 

Implementation on Affordability and Enrollment in CA 

Schools Relative to Non-CA Schools 65 

Table 17: Estimates of Affordability and Enrollment before the 

Consensus Approach Implementation for Particular 

Groups of Students in Both CA and Non-CA Schools 68 

Table 18: Differential Effects of Consensus Approach 

Implementation on Affordability and Enrollment in CA 

Schools for Particular Groups of Students 69 

Table 19: Comparison of Observed and Predicted Price and 

Financial Aid Variables in CA and Non-CA Schools: Pre— 

and Post—Consensus Approach Implementation Period 72 

Figures 

Figure 1: Determining a Student’s Financial Need 8 

Figure 2: Average Tuition, Fees, and Room and Board at Schools 

Using the Antitrust Exemption Compared to All Other 

Private 4-Year Not-For-Profit Schools and Comparable 

Schools, School Years 2000 to 2005 16 

Figure 3: Percentage of Students at Schools Using the Antitrust 

Exemption Receiving Various Types of Institutional Grant 

Aid from 2000 to 2006 17 

Figure 4: Average Amount of Various Institutional Grant Aid 

Awards at Schools Using the Antitrust Exemption from 

2000 to 2006 18 

Page iii GAO-06-963 Higher Education
Abbreviations 

CA Consensus Approach 

EFC expected family contribution 

IPEDS Integrated Postsecondary Education Data System 

MIT Massachusetts Institute of Technology 

NPSAS National Postsecondary Student Aid Study 

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Page iv GAO-06-963 Higher Education
United States Government Accountability Office

Washington, DC 20548 

September 21, 2006 

Congressional Committees 

In 1991, the U.S. Department of Justice sued nine colleges and universities, 

alleging that by collectively making financial aid determinations for 

students accepted to more than one of these schools, the schools had 

unlawfully conspired to restrain trade in violation of the Sherman Act. 

Specifically, Justice argued that by agreeing upon the amount of money 

that the families of admitted students would be expected to pay towards 

their student’s education, these schools were engaging in price fixing. 

Justice and the schools ultimately reached settlements that ended these 

activities. These schools, which are among the nation’s most prestigious 

private universities, had engaged in these activities for more than 30 years. 

Against the backdrop of this litigation, in 1992 Congress enacted a 

temporary exemption from the antitrust laws for higher education 

institutions that has been renewed several times and is set to expire in 

2008. Under the exemption, schools are allowed a limited degree of 

collaboration on financial aid practices in the hope that it would further 

the government’s goal of promoting equal access to educational 

opportunities for students, including low income and minority students. 

Under the exemption, schools that admit students without regard to ability 

to pay would be able to develop and use common principles of financial

aid policies and make changes to formulas used to calculate financial aid 

awards, but not discuss specific students’ awards. Specifically, such 

schools would be allowed to engage in the following joint practices: 

1. agreeing to award financial aid only on the basis of demonstrated 

financial need; 

2. using common principles of analysis for determining financial need; 

3. using a common aid application form; and 

4. exchanging, through an independent third party, financial information 

submitted by students and their families. 

The exemption only applies to an institution’s own aid. Federal aid, which

is allocated based on a statutory formula, was not targeted by the 

exemption. Proponents of the exemption believe that common principles 

Page 1 GAO-06-963 Higher Education
could lead to a more equitable allocation of aid, make attendance at 

schools using the exemption more affordable, and, in turn, increase 

enrollment of low income students at these schools. Moreover, proponents 

believe that allowing schools to use common principles for determining 

financial need should reduce variation among schools in what a family is 

expected to pay and enable students to choose a school without making 

cost the defining factor. On the other hand, some are concerned that 

exempting schools from antitrust laws would reduce competition. 

Specifically, with less competition, some students would pay more for 

college because their opportunities to consider price differences when 

choosing schools would be diminished. 

In passing the 2001 extension to the exemption, Congress directed GAO to 

study whether the exemption resulted in changes in the amount students 

and their families would pay for college. In response to this mandate, we 

determined: (1) how many schools used the exemption and what joint 

practices these schools implemented, (2) trends in cost of attendance and

institutional grant aid at schools using the exemption, (3) how expected 

family contributions at schools using the exemption compare to those at 

similar schools that did not use the exemption, and (4) the effects of the 

exemption on affordability and enrollment. 

To determine the number of schools that made use of the exemption since 

1992, we reviewed literature and studies on the exemption, interviewed 

higher education associations, and reviewed documents that identified a

group of schools that were using the exemption. We interviewed officials 

of these schools, reviewed reports of their activities, and collected 

information on their financial aid policies. To determine if other schools 

might have formed groups to participate in activities allowed under the 

exemption, we also surveyed selected similar schools and found no such 

other groups. 

To determine trends in cost of attendance—tuition, room, and board—and

institutional grant aid at the schools using the exemption, we collected 

data from them and supplemented it with information available from the 

U.S. Department of Education’s Integrated Postsecondary Education Data

System (IPEDS) for school years 2000-2001 through 2005-2006. We 

received data from 26 of the 28 schools using the exemption. We 

determined that the IPEDS and institutional data were sufficiently reliable 

and valid for purposes of our review. 

To determine how expected family contributions (EFC) at schools using 

the exemption compared to those at similar schools not using the 

Page 2 GAO-06-963 Higher Education
exemption, we collected and compared student-level EFC data from both 

sets of schools as of April 1, 2006. To assess the extent of variation in EFC 

across multiple schools, we isolated the EFCs of individual students 

accepted at (1) multiple schools using the exemption, (2) multiple schools 

not using the exemption, and (3) both schools using the exemption and 

schools that did not. While EFC determinations of students accepted at 

both schools using the exemption and those that did not best show the 

extent of variation because it allows us to control for differences in 

student characteristics, this group of students was small. Thus, we 

supplemented our analysis with data from the other two groups listed. 

Based on our discussions with school officials on the steps taken to 

ensure reliability of the EFC data, we determined that the data were 

sufficiently reliable and valid for purposes of our review. See appendix I 

for further details of our statistical analysis. 

To assess the effects of the exemption on affordability and enrollment, we 

developed econometric models to examine the effects of the exemption on 

tuition, financial aid (including grants and loans), amount paid for college 

(measured by the total cost of attendance less total grant aid), and student

enrollment at schools using the exemption. Determining “effect” requires 

both a treatment group (those schools using the exemption) and a control 

group (a comparable set of schools that did not use the exemption) as well 

as controlling for variations in the actions of the schools over time that are 

independent of the exemption. Differences found between the two groups 

in terms of affordability and enrollment (effects) can then be attributed to 

the exemption (treatment). GAO’s econometric analysis was focused on 

the mandate from Congress that requires us to examine the effects of the 

exemption. It is different from a market-specific analysis conducted in an

antitrust investigation and is not intended to address whether or not 

conduct may be taking place that might violate the antitrust laws in the 

absence of the exemption. In order to find a comparative set of schools, 

we used the U.S. News and World Report annual rankings of the “best 

colleges.” We obtained school-level data from IPEDS and student-level 

data from the National Postsecondary Student Aid Study for academic 

years 1995-1996, 1999-2000, and 2003-2004. We also collected data from 

other sources, including a GAO survey of the schools using the exemption

and the comparable schools. We analyzed whether there were any effects 

on affordability and enrollment at schools using the exemption for all 

students and whether there were differences by family income or race. We

also controlled for other factors that could cause changes in affordability 

and enrollment, such as school or student characteristics. Because of data 

limitations, we were not able to include all schools using the exemption in

the treatment group. Nevertheless, there were sufficient similarities 

Page 3 GAO-06-963 Higher Education
between the excluded schools and the schools we included in our model 

to allow for a meaningful analysis. In developing the models, we reviewed 

several studies on the economics of higher education. We provided a 

detailed draft outline of our econometric methodology, including a 

description of the types and sources of data we used, to outside experts 

with whom we consulted on the design and analysis because of their indepth knowledge of antitrust law and the economics of higher education.

We also provided a draft of our report to peer reviewers in academia and 

incorporated their comments when appropriate. See appendixes II and III

for a detailed explanation of our econometric analysis. We conducted our 

work in accordance with generally accepted government auditing 

standards between May 2005 and September 2006. 

Twenty-eight schools—all highly selective, private 4-year institutions—

formed a group to use the antitrust exemption, and of the four 

collaborative activities allowed, the group has engaged in only one—

development of a common methodology for assessing financial need, 

which the group called the “consensus approach.” With respect to the 

other three activities allowed under the exemption, the schools either 

chose not to engage in the activities or piloted them on a limited basis. For 

example, three schools in the group attempted to share student-level 

financial aid data through a third party. However, the schools reported 

that because the effort was too burdensome and yielded little useful 

information, they chose not to continue. The consensus approach to need 

analysis developed by the group is based on elements already a part of 

another need analysis methodology that considers a family’s income and 

assets to determine a student’s ability to pay for college. Schools modified 

some elements of that methodology and reached agreement on how to 

define those elements. Although schools in the group agreed to the 

concept of the consensus approach, the schools varied in their 

implementation of the methodology. Schools that partially implemented or

did not implement the consensus approach often cited concerns about 

potential increased costs associated with implementing the methodology. 

Twenty five of the 28 schools implemented the consensus approach 

methodology; three did not. Schools that chose to use part or all of the 

elements of the consensus approach did so between 2002 and 2005. 

Results in Brief 

Over the last 5 years, tuition, room, and board costs at the group of 

schools using the exemption increased, and while the amount of grant aid 

these schools provided to students also increased, it did so at a slower 

rate. Between school years 2000-2001 and 2004-2005, tuition, room, and 

board increased by 13 percent, from $38,319 to $43,164, compared to a 7 

Page 4 GAO-06-963 Higher Education
percent increase at other private 4-year not-for-profit schools. Average 

institutional grant aid awards increased by 7 percent from $18,675 to

$19,901 at schools using the exemption, and the percentage of students 

receiving such aid increased from 37 to 40 percent, from school years 

2000-2001 to 2005-2006. Among students receiving institutional grant aid

awards, the percent of students who received need-based institutional 

grant aid at schools using the exemption increased from 34 to 36 percent,

and the percent of students receiving non-need-based institutional grant 

aid awards (i.e., academic or athletic scholarships) also increased slightly 

from 2 to 4 percent. 

We found virtually no difference in the amounts students and their families 

were expected to pay at schools using the exemption compared to similar 

schools not using the exemption. Average expected family contribution 

(EFC) for students accepted at schools using the exemption was $27,166 

and for those accepted at comparable schools not using the exemption 

was $27,395 in school year 2005-2006. While officials from schools using 

the exemption expected that students accepted to several of their schools 

would experience less variation in their EFC, we found that the variation

in the EFC for a student who was accepted to several schools using the 

exemption was similar to the variation in EFC that same student received

from schools not using the exemption. The variation in EFCs for these 

students was about $6,000 at both sets of schools. Not all schools using the 

consensus approach chose to adopt all the elements of the methodology, a 

factor that may account for the lack of consistency in EFCs among schools

using the exemption. For example, seven schools chose not to use the 

consensus method for considering home equity that could have 

contributed to the variation in EFCs at schools using the exemption. 

Based on our analysis, schools’ use of the consensus approach did not 

have a significant impact on affordability—the amount students and 

families paid for college, which is measured by the total cost of attendance 

less total grant aid—or affect the likelihood of enrollment at schools using 

the exemption. While we found that the use of the consensus approach 

resulted in higher amounts of need-based grant aid awarded to some 

student groups (middle income, Asian students, and Hispanic students) 

compared to their counterparts at schools not using the consensus 

approach, the total amount of grant aid awarded did not significantly 

change. It is likely that because the change in total grant aid was similar 

compared to the change at schools not using the consensus approach, the 

increase in need-based grant aid was offset by a decrease in non-needbased aid, such as academic scholarships. We also found that low income 

students at schools using the consensus approach, compared to those at 

Page 5 GAO-06-963 Higher Education
schools not using the consensus approach, received a significantly higher 

amount of total aid, which includes both grants and loans. However, the 

amount of grant aid that these students received did not significantly 

change, which suggest they likely received more aid in the form of loans, 

which they would need to repay. Additionally, implementing the 

consensus approach did not affect the likelihood of low-income or 

minority students enrolling at schools using the consensus approach 

compared to schools that did not. Because we have data for only one year 

after implementation, it is possible that some eventual effects of the 

consensus approach may not be captured. The effects of using the 

consensus approach could be gradual, rather than immediate, and 

therefore may not be captured until later years. 

We provided the group of schools using the antitrust exemption, Secretary 

of Education, and Attorney General with a copy of our draft report for 

review and comments. The group of schools using the exemption 

reviewed a draft of this report and stated it was a careful and objective 

report, but raised concerns about the data used in our econometric 

analysis and the report’s tone and premise. We believe that the data we 

used were reliable to support our conclusions. The group of schools using 

the exemption also provided technical comments, which we incorporated

where appropriate. The group’s written comments appear in appendix IV. 

The Department of Education reviewed the report and did not have any 

comments. The Department of Justice provided technical comments, 

which we incorporated where appropriate. 

In the early 1990’s the U.S. Department of Justice (Justice) sued nine 

universities and colleges, alleging that their practice of collectively making

financial aid decisions for students accepted to more than one of their 

schools restrained trade in violation of the Sherman Act.1

 By consulting 

about aid policies and aid decisions, through what was known as the 

Overlap group, the schools made certain that students who were accepted 

to more than one Overlap school would be expected to contribute the 

same towards their education. Thus, according to Justice, “fixing the 

Background 

Legal History of Antitrust 

Exemption for Higher 

Education Institutions 

 1

The schools sued were: Brown University, Columbia University, Cornell University, 

Dartmouth College, Harvard College, Massachusetts Institute of Technology, Princeton 

University, University of Pennsylvania, and Yale University. 

Page 6 GAO-06-963 Higher Education
prices” students would be expected to pay. All but one school, 

Massachusetts Institute of Technology (MIT), settled with Justice out of 

court, ending the activities of the Overlap group. The District Court ruled 

that MIT’s joint student aid decisions in the Overlap group violated the 

Sherman Act. On appeal, the Third Circuit Court of Appeals agreed with 

the District Court that the challenged practices were commercial activity

subject to the antitrust laws. However, it reversed the judgment and 

directed the District Court to more fully consider the procompetitive and 

noneconomic justifications advanced by MIT during the court proceedings

and whether social benefits attributable to the practices could have been 

achieved by means less restrictive of competition.2

 In recognition of the 

importance of financial aid in achieving the government’s goal of 

educational access, but also mindful of the importance of antitrust laws in 

ensuring the benefits of competition, the Congress passed a temporary 

antitrust exemption.3

 In 1994, Congress extended the exemption and 

specified the four collective activities in which schools that admit students 

on a need-blind basis could engage.4

 The exemption was extended most 

recently in 2001, and is set to expire in 2008.5

Determining a Student’s

Financial Need 

For many students, financial aid is necessary in order to enroll in and 

complete a postsecondary education. In school year 2004-2005, about 

$113 billion in grant, loan, and work-study aid was awarded to students 

from a variety of federal, state, and institutional sources.6

 Need analysis 

methodologies are used to determine the amount of money a family is 

expected to contribute toward the cost of college and schools use this 

information in determining how much need-based financial aid they will 

award. For the purposes of awarding federal aid, expected family 

contribution (EFC) is defined in the Higher Education Act of 1965, as 

amended, as the household financial resources reported on the Free 

Application for Federal Student Aid, minus certain expenses and 

 2

U.S. v. Brown, 5 F.3d 658 (3rd Cir. 1993). The Department of Justice and MIT subsequently

entered into a settlement agreement in which MIT agreed to certain “Standards of

Conduct.” 

3

Pub. L. No. 103-325 (1992). 

4

Pub. L. No. 103-382 (1994). 

5

Pub. L. No. 107-72 (2001). 

6

Some financial aid is awarded to students based on merit rather than financial need. 

Page 7 GAO-06-963 Higher Education
allowances. The student’s EFC is then compared to the cost of attendance

to determine if the student has financial need. (see fig. 1) 

Figure 1: Determining a Student’s Financial Need

While the federal methodology is used to determine a student’s eligibility 

for federal aid, some institutions use this methodology to award their own 

institutional aid. Others prefer a methodology developed by the College 

Board (called the institutional methodology) or their own methodology.7

Schools that use the institutional methodology require students to 

complete the College Scholarship Service/Financial Aid PROFILE 

application and the College Board calculates how much they and their 

families will be expected to contribute toward their education. Schools 

that use these alternative methodologies feel they better reflect a family’s 

ability to pay for college because they consider many more factors of each 

family’s financial situation than the federal methodology. For example, the 

institutional methodology includes home and farm equity when calculating 

a family’s ability to pay for college, while the federal methodology 

excludes them. See table 1 below for a comparison of the federal 

methodology to the institutional methodology. 

 7

The College Board is a not-for-profit membership association composed of more than 

5,000 schools, colleges, universities, and other educational organizations. In conjunction 

with financial aid professionals and economists, the College Board developed its own

methodology to measure a family’s ability to pay for college. 

Page 8 GAO-06-963 Higher Education
Table 1: Comparison of the Federal Methodology and the College Board’s Base Institutional Methodology for Need Analysis 

Federal methodology Institutional methodology 

Home equity Not included. Included. 

Family farm equity Not included. Included. 

Student assets Included, 35 percent of student’s net worth 

expected to be used for college costs. 

Minimum contribution from student expected.

Included, 25 percent of student’s net worth 

expected to be used for college costs. Minimum 

contribution from student expected. 

Family assets Excluded the assets of families whose 

income fell below $50,000 and who filed a 

simple tax return. 

12 percent of assets expected to be used 

towards college. 

Included a fuller range of family assets, such as 

home equity, other real estate, and business 

and farm assets. 

5 percent of assets expected to be used towards 

college. 

Divorced and separated families 

(Noncustodial parent contribution) 

Excluded noncustodial parent income and 

assets. 

Included noncustodial parent income and 

assets. 

Total income Included only the adjusted gross income 

reported on federal tax returns, plus various 

categories of untaxed income.

Included in total income any untaxed income 

and any paper depreciation and business, 

rental, or capital losses that artificially reduced 

adjusted gross income. 

Medical/elementary and secondary 

school expenses 

Not included. Included.a

Cost of living variance Not included. Not included.b

Number of siblings in college Included—divides the parental contribution 

by the number of siblings enrolled in college. 

Included—instead of dividing by the number in 

college, parental contribution per student 

reduced by 40 percent for 2 in college and by 

55 percent for 3. 

Source: GAO analysis.

Notes: The institutional methodology is the base one provided by the College Board to schools. A 

school may select other options available in the institutional methodology when assessing a student’s 

financial need. 

a

Elementary and secondary school expenses are an option that could be added by a school. 

b

Cost of living variance is an option that could be used by a school. 

Twenty-eight schools formed a group under the antitrust exemption and 

engaged in one of the four activities allowable under the exemption. 

School officials believed that the one activity—development of a common 

methodology for assessing financial need—would help reduce variation in

amounts students were expected to pay when accepted to multiple 

schools and allow students to base their decision on which school to 

attend on factors other than cost. In developing the common methodology, 

called the consensus approach, schools modified an existing need analysis

methodology and reached agreement on how to treat each element of the 

methodology. While the schools reached agreement on a methodology, 

implementation of the methodology among the schools varied. 

Twenty-Eight Schools 

Used the Antitrust 

Exemption to 

Develop a Common 

Methodology for 

Assessing a Family’s

Financial Need 

Page 9 GAO-06-963 Higher Education
Highly Selective Private4-

Year Colleges and 

Universities Formed a 

Group to Participate in 

Activities Allowable under 

the Exemption 

Twenty-eight schools, all of which have need-blind admission policies as 

required under the law, formed the 568 Presidents’ Group in 1998 with the 

intent to engage in activities allowed by the antitrust exemption.8

 Members 

of the group are all private 4-year schools that have highly selective

admissions policies. One member school dropped out of the group 

because the school no longer admitted students on a need-blind basis. 

(See table 2 below for a list of current and former member schools.) 

Table 2: Schools Using the Antitrust Exemption, as of May 2006 

Amherst College Middlebury College

Boston College Northwestern University 

Brown University Pomona College 

Claremont McKenna College Rice University

Columbia University Swarthmore College 

Cornell University University of Chicago 

Dartmouth College University of Notre Dame 

Davidson College University of Pennsylvania 

Duke University Vanderbilt University 

Emory University Wake Forest University 

Georgetown University Wellesley College 

Grinnell College Wesleyan University 

Haverford College Williams College 

Massachusetts Institute of Technology Yale University

Source: GAO analysis.

Note: Bowdoin College and Macalester College were once members of the group. 

Membership is open to colleges and universities that have need blind 

admissions policies in accordance with the law. Member schools must 

(1) sign a certificate of compliance confirming the institution’s need-blind 

admissions policy and (2) submit a signed memorandum of understanding 

that indicates willingness to participate in the group and adhere to its 

guidelines. Additionally, members share in paying the group’s expenses. 

In addition to the group’s 28 members, 6 schools attended meetings of the 

group to observe and listen to discussions, but have not become 

 8

568 refers to the section in the Improving America’s Schools Act of 1994 where the

exemption is contained.

Page 10 GAO-06-963 Higher Education
members.9

 In order to attend meetings, observer schools were required to 

provide a certificate of compliance stating that they had a need-blind 

admission policy. Observer schools explained that their participation was 

based on a desire to be aware of what similar schools were thinking in 

terms of need analysis methodology, as well as have an opportunity to 

participate in these discussions. Despite these benefits, observer schools 

said they preferred not to join as members because they did not wish to 

agree to a common approach to need analysis or they did not want to lose 

institutional independence. 

Other institutions with need-blind admissions reported that, although 

eligible to participate in activities allowed by the exemption, they were not 

interested or not aware of the group formed to use the antitrust 

exemption. Some told us that they did not understand how students would 

benefit from the schools’ participation in such activities. Others cited 

limited funding to make changes to their need analysis methodology and 

concerns that they would lose the ability to award merit aid to students.10

Participating Schools 

Agreed to a Common 

Methodology for Assessing 

Financial Need, but 

Schools Varied in Their 

Implementation of the 

Methodology 

Of the four activities allowed under the antitrust exemption, the 28 schools 

engaged in only one—development of the consensus approach for need 

analysis. With respect to the other three activities allowed under the 

exemption, the schools either chose to not engage in the activities or 

piloted them on a limited basis. For example, three schools in the group 

attempted to share student-level financial aid data through a third party. 

However, they reported that because the effort was too burdensome and 

yielded little useful information, they chose not to continue. The group 

also expressed little need or interest in creating another common aid 

application form as such a form already existed. Schools also decided to

leave open the option to award aid on a non-need basis. 

According to the officials representing the 28 schools, the main purpose of 

the group was to discuss ways to make the financial aid system more 

understandable to students and their families and commit to developing a 

common methodology for assessing a family’s ability to pay for college, 

which they called the consensus approach. Developing an agreed upon 

 9

These schools were: California Institute of Technology, Case Western University, Harvard 

University, Stanford University, Syracuse University, and University of Southern California. 

10Participation in the 568 Presidents’ Group, however, does not prohibit members from 

awarding merit aid. 

Page 11 GAO-06-963 Higher Education
common approach to need analysis, according to school officials, might 

help decrease variation in what families were expected to pay when 

accepted to multiple schools, allowing students to base their decision on 

what school to attend on factors other than cost. School officials also 

believed that agreeing to a common need analysis methodology would 

produce expected family contributions that were reasonable and fair for 

families and allow schools to better target need-based aid. The group did 

not address the composition of a student’s financial aid package; 

specifically, what combination of grants, loans, or work-study a student 

would receive. 

In developing the consensus approach for need analysis, the schools 

modified elements already in the College Board’s institutional 

methodology, but member schools agreed to treat these elements the same 

when calculating a student’s EFC. Some of the modifications that the 

group made to College Board’s institutional methodology were later 

incorporated into the institutional methodology. The consensus approach

and the institutional methodology similarly treat income from the noncustodial parent, and both account for the number of siblings in college in

the same manner when calculating a student’s expected family 

contribution. However, there are differences in how each methodology 

treats a family’s home equity and a student’s assets. For example, the 

institutional methodology uses a family’s entire home equity in its 

assessment of assets available to pay for college, while the consensus 

approach limits the amount of home equity that can be included. 

According to one financial aid officer at a member school, including the 

full amount of a family’s home equity was unfair to many parents because 

in some areas of the country the real estate market had risen so rapidly 

that equity gains inflated a family’s assets. Officials representing some 

member schools stated that adjustments to home equity would likely 

affect middle and upper income families more than lower income families 

who are less likely to own a home. Table 3 below further illustrates the 

differences and similarities between the consensus approach and the 

institutional methodology. 

Page 12 GAO-06-963 Higher Education
Table 3: Comparison of Consensus Approach Developed by Schools Using the Antitrust Exemption Compared to the College 

Board’s Institutional Methodology 

Institutional methodology Consensus approach

Home equity Included. No limit on amount considered asset 

available to pay for college. 

Included. Home value is capped at 2.4 times

income minus mortgage debt. 

Family farm equity Included. Included. 

Student and family assets Included, but assets counted separately. 

25 percent of student’s net worth expected to be 

used for college costs. 

5 percent of parent’s assets expected to be used 

for college costs. 

Included. In general student assets—such as

prepaid and college savings plans are 

combined with family assets. 5 percent of 

family assets expected to be used for college. 

Trust funds will be considered on a case by 

case basis. 

Divorced and separated families 

(Noncustodial parent) 

Included. Expects noncustodial parent to contribute 

towards college costs. 

Same as IM. 

Total income/adjusted gross 

income 

Included in total income any untaxed income and 

any paper depreciation and business, rental, or 

capital losses which artificially reduced adjusted 

gross income. 

Excluded business and rental losses from 

calculation of income. 

Medical/elementary and 

secondary school expenses 

Included.a Included. 

Cost of living variance Excluded.bAdjusted living expenses based on 

geographic location. Takes into consideration

that it is more costly to live in some areas of 

the country. 

Number of siblings in college Included—considers number of children enrolled in 

college, but instead of dividing by the number in 

college, it reduced the parental contribution for 

each student by 40 percent if 2 in college and by 55 

percent if 3. 

Same as IM. 

One-time income adjustment Not included.cExcluded income that was not received on an

annual basis, such as unemployment income

or capital gains. 

Family debt Not included. Made allowance for debt payments on loans

incurred by parents for student’s education. 

Source: GAO analysis.

Note: The consensus approach is being compared to the base institutional methodology. Schools 

may choose to implement other options available under the institutional methodology when assessing

a student’s financial need. 

a

Private elementary and secondary school tuition allowed at the option of the institution. 

b

As an option schools can adjust living expenses based on geographic locations. 

c

This is not in the base IM; however, a financial aid officer can adjust for this on a case-by-case basis, 

consistent with professional judgment. 

Page 13 GAO-06-963 Higher Education
In addition, under the consensus approach schools agreed to a common 

calendar for collecting data from families. Members continue to maintain

the ability to exercise professional judgment in assessing a family’s ability 

to pay when there are unique or extenuating financial circumstances. 

Twenty-five of 28 schools implemented the consensus approach; 3 did not. 

While 13 schools implemented all the elements of the consensus approach, 

the remaining schools varied in how they implemented the methodology. 

As shown in table 4 below, seven schools chose not to use the consensus 

approach method for accounting for family loan debt, home equity, and 

family and student assets. 

Table 4: Number of Schools That Did Not Implement Certain Consensus Approach

Options in School Year 2005-2006 

Options in the consensus approach

Number of schools that

did not implement optiona

Number of siblings in college 1

One-time income adjustments 2

Elementary and secondary school tuition 

expenses 3

Medical expenses 3

Cost of living variances 5

Divorced and separated families 6

Family and student assets 7

Home equity 7

Family loan debt 7

Source: GAO analysis of schools’ survey responses. 

a

A total of 25 member schools used part or all of the consensus approach. 

The 25 schools that implemented the consensus approach did so between 

2002 and 2005. Member schools reported that they preferred to use the 

consensus approach as opposed to other available need analysis 

methodologies because it was more consistent and fairer than alternative 

methodologies. Moreover, according to institution officials, they believed

the new methodology had not reduced price competition and had resulted

in the average student receiving more financial aid. In some cases, if using 

the consensus approach lowered a student’s EFC, the institution would 

then allocate more money for financial aid than it would have if it had 

used a different need analysis methodology. For some schools the 

consensus approach was not that different from the methodology their 

institution already had in place, but other schools said that fully 

Page 14 GAO-06-963 Higher Education
implementing the consensus approach cost their school more money. 

Among schools that partially implemented the consensus approach, many 

explained they did not fully implement the new methodology because it 

would have been too costly. 

The cost to attend the schools participating under the exemption rose over 

the past 5 years by over 10 percent while cost increases at all other private 

schools rose at about half that rate. At the same time, the percentage of 

students receiving institutional aid increased and institutions increased 

the amount of such aid they provided students, although at a slower rate 

than cost increases. 

During the past 5 years, the cost of attendance—tuition, fees, room, and 

board—at schools using the exemption increased by approximately 

13 percent from $38,319 in school year 2000-2001 to $43,164 in school year 

2004-2005, a faster rate than other schools.11 For example, at other private 

4-year schools there was a 7 percent increase in these costs, from $25, 204

to $27, 071.12 Additionally, as figure 2 illustrates, among a set of schools 

that were comparable to the schools using the exemption, costs increased 

by 9 percent from $40,238 to $43,939 over that same time period.13

As the Cost of 

Attendance at Schools 

Using the Exemption 

Rose, the Amount of 

Institutional Grant 

Aid They Provided to 

Students Increased at 

a Slower Rate 

Cost of Attendance 

Increased at Schools Using 

the Exemption 

Corresponding to 

Increases at Other Private 

Schools 

 11All dollar amounts are in 2005 dollars. Data presented for schools using the exemption 

was collected from 26 of the 28 schools using the exemption.

12Other private 4-year schools include not-for-profit institutions and do not include forprofit institutions. This set of schools includes schools that do not have need-blind 

admission policies and therefore would not be able to participate in activities allowed 

under the exemption. 

13Comparable schools include the seven schools selected as control schools for our

econometric analysis. 

Page 15 GAO-06-963 Higher Education
Figure 2: Average Tuition, Fees, and Room and Board at Schools Using the 

Antitrust Exemption Compared to All Other Private 4-Year Not-For-Profit Schools 

and Comparable Schools, School Years 2000 to 2005 

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005

Cost of attendance

Source: GAO analysis of IPEDS data.

School year

Comparable schools

Schools using the exemption

All Other private 4 year not-for-profit

Note: Comparable schools include the seven schools selected as control schools for our econometric 

analysis. 

Percentage of Students 

Receiving Institutional 

Grant Aid and the Amount 

Schools Provided Them 

Increased 

Over the same time period, the percentage of students who received any

form of institutional grant aid at schools using the exemption increased by

3 percentage points, from 37 to 40 percent, as illustrated by figure 3. 

Page 16 GAO-06-963 Higher Education
Figure 3: Percentage of Students at Schools Using the Antitrust Exemption

Receiving Various Types of Institutional Grant Aid from 2000 to 2006 

0

10

20

30

40

50

200

2004-2005

2005-2006

3-2004

2002-2003

2000-2001

2001-2002

Percent of students recieving aid

School year

Need-based institutional grant aid

Non-need-based institutional grant aid

Source: GAO analysis of institutional data.

Note: Data collected from 26 of the 28 schools using the antitrust exemption. 

Among students receiving institutional grant aid, the percentage of 

students receiving need-based grant aid increased from 34 to 36 percent 

from 2000 to 2006. The percentage of students receiving non-need-based 

grant aid also increased slightly, from 2 to 4 percent. Non-need-based aid

is awarded based on a student’s academic or athletic achievement and 

includes fellowships, stipends, or scholarships. The majority of schools 

using the exemption did not offer any non-need-based institutional grant 

aid in school year 2005-2006. However, in 2005-2006 some schools did, 

allocating non-need-based grant aid to between 16 to 54 percent of their

students. 

As the cost of attendance and percentage of students receiving 

institutional aid rose, participating institutions increased the amount of 

such aid they provided students, although the percentage increases in aid 

were smaller. As shown in figure 4, the average need-based grant aid 

Page 17 GAO-06-963 Higher Education
award across the schools using the exemption increased from $18,925 to

$20,059, or 6 percent. The average amount of non-need-based grant aid 

awards dropped slightly from $12,760 in 2000-01 to $12,520 in 2005-06, or 

2 percent. Overall, the average total institutional grant aid awarded to 

students, which included both need and non-need-based aid, increased 

from $18,675 in 2000-01 to $19,901 in 2005-06, or 7 percent. 

Figure 4: Average Amount of Various Institutional Grant Aid Awards at Schools

Using the Antitrust Exemption from 2000 to 2006 

Note: Data collected from 26 of the 28 schools using the exemption.

Page 18 GAO-06-963 Higher Education
There was virtually no difference in the amounts students and their 

families were expected to pay between schools using the exemption and 

similar schools not using the exemption. Average EFC was $27,166 for 

students accepted at schools using the exemption, and $27,395 for those 

accepted at comparable schools not using the exemption in school year 

2005-2006. Moreover, the variation in the EFC for a student who was 

accepted to several schools using the exemption was similar to the 

variation in EFC that same student received from schools not using the 

exemption. The variation in EFCs for these students was about $6,000 at

both sets of schools.14 Because the number of such students was small, we 

also analyzed variation in EFCs for students who were accepted only at 

schools using the exemption and compared it to the variation for students 

who were only accepted at comparable schools not using the exemption.

15

We found slightly greater variation among EFCs for students who were 

accepted at schools using the exemption; however, because we could not 

control for student characteristics, factors external to the exemption could 

explain this result, such as differences in a family’s income or assets. 

Students Accepted to 

Both Schools Using 

the Exemption and 

Comparable Schools 

Had No Appreciable 

Difference in the 

Amount They Would 

Be Expected to 

Contribute Towards 

College 

Although officials from schools using the exemption expected that 

students accepted at several of those schools would experience less 

variation in the amounts they were expected to pay, none of our analyses

confirmed this. The lack of consistency in EFCs among schools using the 

exemption may be explained by the varied implementation of the 

consensus approach. As previously mentioned, not all schools using the 

consensus approach chose to adopt all the elements of the methodology. 

For example, seven schools chose not to use the consensus approach to 

home equity, which uses a percentage of the home equity in calculating the 

EFC. Using another method for assessing a family’s home equity could 

significantly affect a student’s EFC. For instance, we estimated that a

family residing in Maryland with an income of $120,000 and $350,000 in

home equity would have an EFC of $58,243 if a school chose not to 

implement the home equity option in the consensus approach. Under the 

consensus approach, the amount of home equity included in asset 

calculations would be capped and only $38,000 of the home’s equity would 

be included in the calculation of EFC. The same family would then have an 

EFC of $42,449 if the school chose to implement the option. 

 14Variation was measured by the standard deviation of the EFCs. 

15For a more detailed discussion of our analysis see appendix I. 

Page 19 GAO-06-963 Higher Education
Based on our econometric analysis, schools’ use of the consensus 

approach did not have a significant impact on affordability, nor did it 

cause significant changes in the likelihood of student enrollment at 

schools using the consensus approach compared to schools that were not

using the consensus approach.16 As shown in table 5, while we found that 

the consensus approach resulted in higher need-based grant aid awards 

for some student groups (middle income, Asian students, and Hispanic 

students) compared to similar students at schools that were not using the 

consensus approach, this increase was likely offset by decreases in nonneed-based grant aid, such as academic or athletic scholarships.17 Thus, 

total grant aid awarded was not affected by the consensus approach 

because the increase in need-based aid was likely offset by decreases in 

non-need-based grant aid.18

Implementation of a 

Common 

Methodology Has Not 

Significantly Affected 

Affordability or 

Enrollment at Schools 

Using the Exemption 

 16GAO’s econometric analysis was focused on the mandate from Congress that requires us 

to examine the effects of the exemption. It is different from a market-specific analysis 

conducted in an antitrust investigation, and is not intended to address whether or not

conduct may be taking place that might violate the antitrust laws in the absence of the

exemption. 

17The results were similar for need-based institutional grant aid. 

18The discussed effects of the consensus approach are statistically significant (i.e., different

from zero) at the 5 percent significance level or less. 

Page 20 GAO-06-963 Higher Education
Table 5: Estimated Changes in Amount Paid, Financial Aid, and Enrollment at Schools Using the Consensus Approach

Compared to Schools Not Using the Exemption 

Estimated changes of using the consensus approach on: 

Student group

Amount 

students paid

Total

grant aid

Need-based total 

grant aid

Total aid (grant, 

loans, work-study)

Probability of 

enrollment

All students 

$3,021 -$749

$6,125b

[$239, $12,011] -$2,886 38%

Financial-aid applicants 2,177 n/a n/a n/a 22

Low-income 

-4,061 3,688 1,956

12,121b

[1,837, 22,404] 59

Lower-middle income 8,089 c-3,671 6,556 -7,776 95

Middle income

2,320 1,618

20,221a

[6,718, 33,724] 1,178 26

Upper-middle income -1,048 -973 2,769 -3,054 18

High income 3,699 -714 4,687c-3,856 31

Asian students

-376 5,726

14,628a

[5,051, 24,206] 3,694 1

Black students 4,468 -1,227 4,332 -6,542 -26

Hispanic students 

1,168 1,520

9,532b

[1,006, 18,059] 3,648 108

White students

2,588 -491

6,017b

[178, 11,856] -2,879 19

Source: GAO analysis (see table 16 in app. II).

a

Result is statistically significant at the 1 percent level or lower. 

b

Result is statistically significant at the 5 percent level or lower. 

c

Result is statistically significant at the 10 percent level or lower. 

Notes: The estimates in brackets are the confidence levels of the estimates that are significant at the 

5 percent or lower level. 

n/a means not applicable because of data limitations. 

All the monetary values are in 2005 dollars. 

Amount students paid is defined as tuition, room, board, fees, and other expenses minus grant aid. 

Total grant aid includes both need- and non-need-based aid from federal, state, institutional and other 

sources. 

Total aid includes grants, loans, and work-study aid from federal, state, institutional, and other 

sources. 

The effect of the consensus approach on need-based institutional grant aid was $6,020, significant at 

the 5 percent level, with confidence interval between $512 and $11,528. 

The value of the effect of the consensus approach on institutional grant aid was $1,331, but not 

statistically significant. 

Page 21 GAO-06-963 Higher Education
A different effect was found when low-income students at schools using 

the consensus approach were compared to their counterparts at schools 

not using the consensus approach. As shown in table 5, low income 

students at schools using the consensus approach received, on average, a 

significantly higher amount of total aid—about $12,121, which includes 

both grants and loans. However, the amount of grant aid that these 

students received did not significantly change, suggesting that that they

likely received more aid in the form of loans, which would need to be

repaid, or work-study. Our analysis of the effects of the consensus 

approach on various racial groups showed no effect on affordability for 

these groups compared to their counterparts at schools not using the 

consensus approach. While Asian, white, and Hispanic students received 

more need-based grant aid compared to their counterparts at schools not 

using the consensus approach, their overall grant aid awards did not 

change. 

Finally, as shown in table 5, there were no statistically significant effects 

of the consensus approach on student enrollment compared to the 

enrollment of students at schools not using the consensus approach. In 

particular, the consensus approach did not significantly increase the 

likelihood of enrollment of low-income or minority students or any 

student group. 

Our econometric analysis has some limitations that could have affected 

our findings.19 For example, we could not include all the schools using the 

consensus approach in our analysis because there were no data available

for some of them. However, there were enough similarities (in terms of 

“best college” ranking, endowment, tuition and fees, and percentage of 

tenured faculty) between the included and excluded participating schools 

that allowed for a meaningful analysis. (See table 6 for a list of schools 

included in our analysis). 

 19For a more detailed discussion on our econometric models and the limitations of our 

analysis see appendix II. 

Page 22 GAO-06-963 Higher Education
Table 6: Schools Included in Analysis of Effects of Exemption

Schools using the consensus approach

Comparable schools not using the 

consensus approach

Cornell University 

Duke University

Georgetown University 

University of Notre Dame 

Vanderbilt University 

Wake Forest University 

Yale University

Brandeis University 

Bryn Mawr College 

New York University 

Princeton University 

Tulane University 

University of Rochester 

Washington University at St. Louis 

Source: GAO analysis.

Moreover, the data for our post-consensus approach period was collected 

in 2003-2004—the first or second year that some schools were using the 

consensus approach. Because we have data for only one year after 

implementation, it is possible that some eventual effects of the consensus 

approach may not be captured. The effects of using the consensus 

approach could be gradual, rather than immediate, and therefore may not 

be captured until later years. 

By providing an exemption to antitrust laws enabling schools to 

collaborate on financial aid policies, the Congress hoped that schools 

would better target aid, making college more affordable for low income 

and other underrepresented groups. The exemption has not yet yielded 

these outcomes. Nor did our analysis find an increase in prices that some 

feared would result from increased collaboration among schools. Initial 

implementation of the approach has been varied; some schools have not 

fully implemented the need analysis methodology, and many schools are 

still in the initial years of implementation. As is often the case with new 

approaches, it may be too soon to fully assess the outcomes from this 

collaboration. 

We provided the group of schools using the antitrust exemption, the 

Secretary of Education, and the Attorney General with a copy of our draft 

report for review and comments. The group of schools using the 

exemption provided written comments, which appear in appendix IV. In 

general, the group stated that our study was a careful and objective report, 

but raised some concerns about the data used in our econometric analysis 

and the report’s tone and premise. Specifically, they raised concerns 

about the selection of treatment and control schools for our econometric 

analysis. As we noted in the report, we selected schools for selection in

treatment and control groups based, in part, on the availability of studentConcluding 

Observations 

Agency Comments 

Page 23 GAO-06-963 Higher Education
level data in the NPSAS. Some schools that used the consensus approach 

were not included because there were no data available for them. 

However, we believe there were enough similarities between the included

and excluded schools to allow for a meaningful analysis. The group also

stated that a number of conclusions were based on a very small number of 

observations. In appendix II, we acknowledge the small sample size of the 

data could make the estimates less precise, especially for some of the 

subgroups of students we considered. However, we performed checks to

ensure that our estimates were reliable and believe that we can draw 

conclusions from our analysis. With respect to the tone and premise of the 

report, the group raised concerns about using low income students as “a 

yardstick for judging the success of the Consensus Approach.” When 

passing the exemption, Congress hoped that it would further the 

government’s goal of promoting equal access to educational opportunities 

for students. Need-based grant aid is one way to make college more 

affordable for the neediest students to help them access a post-secondary 

education. The group also highlighted several positive outcomes from 

their collaboration, including a more transparent aid system and more 

engagement by college presidents in aid-related discussions, topics which 

our study was not designed to address. The group provided technical 

comments, which we incorporated where appropriate. Education 

reviewed the report and did not have any comments. The Department of 

Justice provided technical comments, which we incorporated where 

appropriate.

We are sending copies of this report to the Secretary of Education, 

Attorney General, appropriate congressional committees, and other 

interested parties. In addition, the report will be available at no charge on

GAO’s Web site at http://www.gao.gov. 

Page 24 GAO-06-963 Higher Education
If you or your staff have any questions please call me on (202) 512-7215. 

Contact points for our Offices of Congressional Relations and Public 

Affairs may be found on the last page of this report. Other contacts and 

staff acknowledgments are listed in appendix VI.

Cornelia M. Ashby, Director 

Education, Workforce and 

 Income Security Issues 

Page 25 GAO-06-963 Higher Education
List of Congressional Committees

The Honorable Arlen Specter 

Chairman 

The Honorable Patrick J. Leahy 

Ranking Minority Member 

Committee on the Judiciary 

United States Senate 

The Honorable F. James Sensenbrenner, Jr. 

Chairman 

The Honorable John Conyers, Jr. 

Ranking Minority Member 

Committee on the Judiciary 

House of Representatives 

Page 26 GAO-06-963 Higher Education
Appendix I: Statistical Analysis of Expected 

Family Contributions at Schools Using the Appendix I: Statistical Analysis of Expected Exemption and Comparable Schools 

Family Contributions at Schools Using the 

Exemption and Comparable Schools 

We compared variation in expected family contributions (EFCs) between 

students who were admitted to both schools using the exemption and 

comparable schools that did not. We collected data on student EFCs from

27 of the 28 schools using the exemption and 55 schools that had similar 

selectivity and rankings as schools using the exemption. The data included 

the student’s EFC calculated by the schools as of April 1, 2006, based on 

their need analysis methodology. We determined that these data would 

most likely reflect the school’s first EFC determination for a student and 

thus would be best for comparison purposes. We then matched students 

across both sets of schools to identify students accepted to more than one 

school (which we call cross-admits). 

Our sample consisted of data for the following three types of cross-admit 

students: 

1. Students accepted to several schools using the exemption and several 

schools that were not (type 1 students); 

2. Students accepted to only schools using the exemption (type 2 

students); and 

3. Students accepted to only schools not using the exemption (type 3 

students). 

Data from the type 1 sample provided the most suitable data for our 

analysis because it controlled for student characteristics. However, 

because this sample was relatively small, we used the other samples to 

supplement the analysis. 

Once the cross-admits were identified, the EFCs for each student were 

used to evaluate the mean and median as measures of location and the 

standard deviation and range as measures of variation. Given the potential

scale factor, the variation measures were standardized. The standard 

deviation was standardized by dividing it by the mean, and the range was 

standardized by dividing it by the median. The two resulting variation 

measures were the coefficient of variation (V1) and its robust counterpart

(V2), respectively. 

These two measures of variation were estimated for each and every 

student. The estimates were grouped for both sets of schools. We labeled 

schools using the exemption as “568 schools” and comparable schools that 

were not as “non-568 schools.” 

Page 27 GAO-06-963 Higher Education
Appendix I: Statistical Analysis of Expected 

Family Contributions at Schools Using the

Exemption and Comparable Schools 

Table 7 reports various estimates averaged over students in each group. 

The table generally shows similar group averages for the mean, standard

deviation, median, and range that were used to compute V1 and V2. The 

values reported are the averages for all the students in each group. There 

are fewer observations for the 568 schools than for the non-568 school, 

except for type 1 students where the number of observations were equal 

because the students were in both groups of colleges. In addition, we 

imposed the following three conditions: 

• First, for the coefficient of variation V1, we excluded all observations 

where the standard deviations were zero. The zero standard deviations are 

excluded because some of the non-568 schools that use only the federal 

methodology to calculate EFCs report the same EFCs for a student and 

are likely to bias the results. None of the observations with zero standard 

deviations that we excluded involved a 568 school. 

• Second, for the coefficient of variation V2, we excluded all observations 

where the medians were zero because we could not construct this 

measure that was obtained by dividing the range by the median. 

• And, third, for the coefficient of variation V2, we excluded observations 

where the standardized variation exceeded 3 based on the observed 

distributions of the data.

The test results were similar when none of those conditions were imposed. 

Page 28 GAO-06-963 Higher Education
Appendix I: Statistical Analysis of Expected 

Family Contributions at Schools Using the

Exemption and Comparable Schools 

Table 7: Summary Statistics of Expected Family Contributions

Schools using the exemption 

(568 schools) 

Comparable schools 

(Non-568 schools) 

Students Type 1 students All students Type 1 students All students

Statistic 

Standard deviation $6,188 $6,447 $6,190 $7,035

Mean $27,166 $31,640 $27,395 $28,747

[$22,576, $31,757] [$30,380, $32,900] [$22,293, $32,497] [$27,924, $29,571]

Coefficient of 

variation 1 (V1) 0.27 0.24 0.36 0.35

Range $12,200 $12,886 $9,671 $8,813

Median $30,374 $31,677 $29,225 $31,075

[$25,380, $35,367] [$30,394, $32,961] [$23,858, $34,593] [$30,314, $31,836]

Coefficient of 

variation 2 (V2) 0.47 0.49 0.52 0.37

Number of students N1=79 N1=1,158 N1=79 N1=2,866

N2=76 N2=1,150 N2=76 N2=3,653

Source: GAO analysis.

Notes: Coefficient of variation 1 (V1) equals standard deviation divided by mean. 

Coefficient of variation 2 (V2) equals range divided by median. 

Type 1 consists of students with multiple offers from 568 colleges as well as offers from non-568 

colleges. For the 568 colleges, all students consist of type 1 and type 2—students with multiple offers 

from only 568 colleges. And for the non568 colleges, all students consist of type 1 and type 3—

students with multiple offers from only non-568 colleges. 

The values in brackets are the 95 percent lower and upper bounds (confidence intervals). 

N1 is the sample size for coefficient of variation 1 (V1) and N2 is sample size for coefficient of 

variation 2 (V2).

Denoting the estimates of V1 and V2 for the two groups by and 

, and and , the empirical distribution of was 

then compared with the empirical distribution of to examine

whether and had identical distributions (that is EFCs for 

568 schools were similar in variations to those for non-568 schools). A 

similar comparison was made using the robust measures , 

1568 Vˆ

non568 V1ˆ 2568 Vˆ 2 568 ˆV non 1568 Vˆ

1 568 ˆV non

1568 Vˆ 1 568 ˆV non

2568 Vˆ

Page 29 GAO-06-963 Higher Education
Appendix I: Statistical Analysis of Expected 

Family Contributions at Schools Using the

Exemption and Comparable Schools 

and . 2 568 ˆV non

1

 To more closely examine the difference between the 

variations in EFCs of cross-admit students for 568 and non-568 schools, we 

performed the Kolmogorov-Smirnov test. The test examines whether the 

distributions of the variation measures and were the same. 

The same analysis was done for the V2 measures. The test was reported 

for both samples, consisting of type 1 students and all students. The 

results reported in table 8 suggest that there was no difference in EFC 

variations across the two groups, using type 1 students. The results using 

all students, however, are inconclusive for the V1 estimate, but suggest 

that non-568 schools have smaller EFC variation for the V2 estimate. The 

results based on the type 1 sample are more useful as a stand-alone 

descriptive finding, because this sample controls for student 

characteristics. The finding based on the combined data requires further 

analysis to control for student characteristics that we were unable to 

perform due to data limitations. 

1568 Vˆ 1 568 ˆV non

 1

We used the KSMIRNOV command in Stata to perform the tests.

Page 30 GAO-06-963 Higher Education
Appendix I: Statistical Analysis of Expected 

Family Contributions at Schools Using the

Exemption and Comparable Schools 

Table 8: Tests of Variations in Expected Family Contributions

Variable 

Student 

Data 

Alternative 

hypothesis

Teststatistic, 

D p-value Conclusion 

Coefficient of 

variation 1 (V1)

Type 1 

N1=79 

N2=79 

Non-568 EFCs

are smaller 

Non-568 EFCs

are larger 

0.1013 

-0.1519 

0.445 

0.162 

EFCs are similar

EFCs are similar 

Overall—EFCs

are similar 

Coefficient of 

variation 2 (V2)

Type 1 

N1=76 

N2=76 

Non-568 EFCs

are smaller 

Non-568 EFCs

are larger 

0.1447 

-0.1053 

0.203 

0.431 

EFCs are similar

EFCs are similar 

Overall—EFCs

are similar 

Coefficient of 

variation 1 (V1)

All 

N1=1,158

N2=2,866

Non-568 EFCs

are smaller 

Non-568 EFCs

are larger 

0.1724 

-0.1788 

0.000 

0.000 

Non-568 EFCs

are smaller 

Non-568 EFCs

are larger 

Overall—

Inconclusive 

Coefficient of 

variation 2 (V2)

All 

N1=1,150

N2=3,653

Non-568 EFCs

are smaller 

Non-568 EFCs

are larger 

0.3970 

-0.0399 

0.000 

0.061 

Non-568 EFCs

are smaller 

EFCs are similar 

Overall—Non568 EFCs are 

smaller 

Source: GAO analysis.

Notes: Coefficient of variation 1 (V1) equals standard deviation divided by mean. 

Coefficient of variation 2 (V2) equals range divided by median. 

All means students with multiple offers from 568 schools as well as offers from non-568 schools (type 

1), students with multiple offers from only 568 schools (type 2), and students with multiple offers from 

only non-568 schools (type 3). 

The p-values are for the Kolmogorov-Smirnov tests of equality of distribution functions. All tests are 

interpreted using the 5 percent or lower level of significance. 

N1 is the sample size for coefficient of variation 1 (V1) and N2 is sample size for coefficient of 

variation 2 (V2). 

Page 31 GAO-06-963 Higher Education
Appendix II: E

of the Hig

on C

conometric Analysis of Effects

her Education Antitrust Exemption 

ollege Affordability and Enrollment 

Page 32 GAO-06-963 

Appendix II: Econometric Analysis of Effects 

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

To estimate the effects of schools’ implementation of the consensus 

approach to need analysis on affordability (measured by price) and 

enrollment of freshmen students, we developed econometric models. This 

appendix provides information on theories of the exemption effects on 

student financial aid, the data sources for our analyses and selection of

control schools, specifications of econometric models and estimation 

methodology, our econometric results, and limitations of our analysis. 

Two theories exist about the effects the consensus approach on student 

financial aid. It is important to note that the award of grant aid represents 

a discount from the nominal "list price", which lowers the price students 

actually pay for college. So, any decision to limit grant aid would be an

agreement to limit discounts to the list price, and thus may raise the price 

some students would pay. It is also important to note that schools admit 

only a limited number of students. One of the theories suggests that 

allowing schools a limited degree of collaboration could reduce the 

variation in financial need determination for an individual student and 

reduce price competition among colleges vying for the same students. 

While the reduced competition would imply lower financial aid (hence 

higher prices) for some students, schools could thus devote more financial 

aid resources to providing access to other students, especially 

disadvantaged students. This “social benefit theory” assumes that under 

these conditions disadvantaged students would receive more grant aid and 

as a result, pay less for school. Also, an implicit assumption of this theory 

is that the exemption would essentially result in redistribution of financial 

aid without necessarily changing the amount of financial aid resources 

available. Moreover, because costs to students and their families would 

change for some students, enrollment of such students would be affected. 

Theories of the 

Effects of the 

Consensus Approach 

on Financial Aid 

An opposing theory is that the exemption will allow schools to coordinate 

on prices and reduce competition. This “anti-competitive theory” 

essentially views coordination by the group as restraining competition. 

Specifically, under this theory, allowing an exemption would result in less 

grant aid and higher prices on average, especially for students that schools 

competed over by offering discounts on the list price. As a result, the 

amount of financial aid available to some students would likely decrease. 

If prices are higher on average, it could cause a decrease in enrollment, 

particularly of disadvantaged students since they would be less able to 

Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

afford the higher prices.1 Our analyses allowed us to test these two 

theories with the data available. 

To construct our model, we used data from: 

• National Postsecondary Student Aid Study (NPSAS): These data, available 

at the student-level, served as the primary source for our study because we 

were interested in student outcomes of the exemption. Data were 

published every 4 years during the period relevant to our study; hence, we

have data for academic years 1995-1996, 1999-2000, and 2003-2004. The 

data contained student-level information for all freshmen enrollees in the 

database, including enrollment in school, cost of attendance, financial aid, 

Scholastic Aptitude Test (SAT) scores, household income, and race. The 

number of freshmen in the database for our study was 1,626 in 1995-1996,

272 in 1999-2000, and 842 in 2003-2004. 

Sources of Data for 

the Model 

• Integrated Postsecondary Education Data System (IPEDS): These data,

available at the school level, included tuition and fees, faculty 

characteristics, and student enrollment for 1995-1996 and 2003-2004, there

were no data published for 1999-2000. However, some of the data for 1999-

2000 were reported in the subsequent publications. We were able to 

construct some data for 1999-2000 through linear interpolation of the data

for 1998-1999 and 2000-2001 or using the data for either year depending on

availability; we believed this was reasonable because data for these 

institutions did not vary much over time.2

• National Association of College and University Business Officers 

(NACUBO): This source provided data on school endowment from 1992 

through 2004.3

 1

This theory is consistent with the idea that non-profit organizations have an incentive to

exercise market power despite not directly capturing profits, because the extra resources

from exercising market power allow them to invest in other areas they deem important; 

e.g., schools may charge high prices to students because it could enable them to offer 

higher salaries to attract high-caliber faculty. 

2

Student enrollment data was obtained through linear interpolation, and faculty data was 

based on 1998-1999 data. 

3

Where necessary, the data were supplemented by data from IPEDS. 

Page 33 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

• GAO Survey: The survey collected data on the activities of the schools 

using the higher education antitrust exemption, including when schools 

implemented the consensus approach methodology. 

Determining the effects of the exemption required both a treatment group 

(schools using the exemption) and a control group (a comparable set of 

schools that did not use the exemption). To find a comparable set of 

schools we used data on school rankings based on their selectivity from 

years 1994 to 2004 from the U.S. News and World Report (USNWR). We 

selected control schools similar to schools using the antitrust exemption 

that had comparable student selectivity and quality of education using the 

“best schools” rankings information in the USNWR.

4

 The combined control 

and treatment schools were matched to school-level data from IPEDS, and 

student-level data from NPSAS. We selected the control schools based on

their ranks in the years prior to the implementation of the consensus 

approach—1995-1996 and 1999-2000—and after the implementation of the 

consensus approach—2003-2004. The USNWR published its “best schools” 

rankings annually in August or September. Thus, the 2004 publication 

reflected the selectivity of the schools during 2003-2004. However, because 

publications in prior years—2002 and 2003—provided relevant information 

to students who enrolled in 2003-2004, we considered the rankings 

published from 2002 through 2004 as important input into decisions made 

by students and the schools for 2003-2004. Similarly, the publications from 

1994 through 1996 were used to determine the selectivity of the schools in

1995-1996, and the publications from 1998 to 2000 were used to determine 

school selectivity for 1999-2000. 

The USNWR published separate rankings for liberal arts schools and 

national universities. The schools using or affiliated with the exemption 

consisted of 28 current members, two former members, and six observers.5

These 36 schools comprised the treatment schools used initially to select

Selection of Control 

Schools 

 4

Schools were ranked annually based on various criteria (including selectivity, faculty and 

financial resources, graduation rate, and alumni satisfaction) in various publications—

particularly in the USNWR, the Peterson’s Four-Year Schools, and the Barron’s Profiles of 

American Schools. The rankings of the schools by the different publishers were generally 

similar, but since the data were readily available in the USNWR we chose its rankings.

Using the published rankings helped avoid a possible bias from arbitrarily picking the

schools. Furthermore, these rankings were widely used and generally stable over time. 

5

Although the observers were not members they attended the group’s meetings. The former

members were Bowdoin College and Macalester College. 

Page 34 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

the comparable control schools. All 36 treatment schools were private; 13 

were liberal arts schools and 23 were national universities.6

 To ensure 

there were enough control schools for the treatment schools, we initially 

selected all the schools ranked in tier 1 (and tier 2 when available) in the 

USNWR rankings for each of the two types of institutions—liberal arts 

schools and national universities.7

 This resulted in 250 schools, including 

all 36 treatment schools, for nine selected years (1994 to 1996, 1998 to 

2000, and 2002 to 2004). All the treatment schools were ranked in each of

the nine years (except for one school that was not ranked in 2002). The 

initial list of 250 schools was refined further to ensure a proper match in 

selectivity between the treatments and controls. 

Although we were interested in obtaining an adequate number of control 

schools to match the treatment schools, we refined the selection process 

to ensure they were comparable using the following conditions. First, we 

limited the selection of all the schools (controls and treatments) to those 

that were ranked in tier 1. This reduced the sample of schools from 250 to 

106 schools, comprising all 36 treatment schools and 70 control schools. 

Second, the list of 106 schools was used to match school-level data from 

the IPEDS in each of the three academic years.8

 Third, these data were 

then matched with the IPEDS data for each of the three academic years to

student-level data from NPSAS. From the NPSAS, we selected data for 

cohorts who entered their freshmen year in each of the three academic 

years.9

 Fourth, since we used a difference-in-difference methodology for 

the analysis, we wanted data for each school in at least two of the three 

academic years—one in the pre-treatment and one in the post-treatment 

period. We therefore initially constructed four samples of schools, 

depending on whether there were matches between all three academic 

years, or between any two of the three academic years. This resulted in 30

schools with data in all three academic years 1995-1996, 1999-2000, and 

 6

Liberal arts schools emphasize undergraduate education and award at least half of their 

degrees in the liberal arts discipline, and most are private. National universities offer a 

wide range of undergraduate majors as well as master’s and doctoral degrees, and many

emphasize research. 

7

The number of schools in the two tiers for each type of school was between 50 and 90 for 

each year. 

8

We also used endowment data from NACUBO, and school-level data from GAO’s survey of 

the schools. 

9

We used data for students who were enrolled as freshmen, as of October of the academic 

year, in the NPSAS database. 

Page 35 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

2003-2004 (referred to as sample 1). There were 34 schools with data in 

1995-1996 and 2003-2004 (sample 2); 35 schools with data in 1999-2000 and 

2003-2004 (sample 3); and 37 schools matched between 1995-1996 and 

1999-2000 (sample 4).10 Finally, we limited the selection to private schools 

because all of the treatment schools are private. We did this because the 

governance of private schools generally differed from state-controlled 

public schools and these differences were likely to affect affordability and 

enrollment at a school. 

We also determined the academic year(s) data that would be used to 

represent the period before and the period after the implementation of the 

consensus approach. Since we had data for only1995-1996, 1999-2000 and 

2003-2004, and given that the consensus approach was implemented in 

2003-2004 (or in the prior year by some schools) we selected 1995-1996 as 

the pre-consensus approach period and 2003-2004 as the post-consensus 

approach period. Although the 1999-2000 data were relatively current for

the pre-consensus approach period, it is possible that the 1999-2000 data

may offer neither strong pre- nor post-consensus approach information 

since the period was very close to the formation of the 568 President’s 

Group in 1998. Furthermore, the institutional methodology, which is a 

foundation for the consensus approach and used by some of the control 

schools in 2003-2004, was revised in 1999. We therefore investigated 

whether it was appropriate to include 1999-2000 in the pre-consensus 

approach period or in the post-consensus approach period. We also 

investigated in which group (control or treatment) the schools that only 

attended the 568 President’s Group meetings, but had not become 

members of the group or implemented the consensus approach, belonged.

Using the Chow test for pooling data, we determined that 1999-2000 

should be excluded from the pre-consensus approach period as well as 

from the post-consensus approach period. We also determined that 

schools that only attended the 568 President’s Group meetings could not 

be regarded as control schools or treatment schools in analyzing the 

effects of the consensus approach.11 Therefore, the treatment schools 

Determination of the 

Appropriate Time Periods 

for Assessing Effects and 

Classification of Schools 

that Only Attended the 

Meetings

 10Although the sample periods used by Hoxby (2000) and Netz (2000) are much earlier than

what we used, our list of schools is reasonably consistent with theirs. Similarly, our list of 

schools was consistent with the schools in the Consortium for Financing Higher Education 

(COFHE), which are some of the most selective private schools in the U.S. 

11See appendix III for details of the tests. 

Page 36 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

consisted of the group members that implemented the consensus 

approach, and the control schools consisted of the schools that were not

members of the 568 Group and did not attend their meetings. Based on the 

analysis above, we used the data in sample 2, which excluded data 

collected in 1999-2000, for our baseline model analysis; the period before

the consensus approach is 1995-1996 and the period after is 2003-2004; the 

control schools that did not use the consensus approach (non-CA schools) 

are Brandeis University, Bryn Mawr College, New York University, 

Princeton University, Tulane University, University of Rochester, and 

Washington University at St. Louis, and the treatment schools that used 

the consensus approach (CA schools) are Cornell University, Duke 

University, Georgetown University, University of Notre Dame, Vanderbilt

University, Wake Forest University, and Yale University. The complete list 

of the schools is in table 9. 

Table 9: Control and Treatment Schools for Analyzing Effects of the Consensus Approach Implementation 

Academic years Control school (Non-CA) Treatment school (CA) 

1995-1996 

1999-2000 & 

2003-2004 

Sample 1: 

Brandeis University 

New York University 

Princeton Universityb 

Tufts Universitya,b 

Tulane University 

University of Rochester 

Washington University at St. Louis 

1995-1996 & 

 2003-2004 

Sample 2—All of Sample 1 Plus: 

Bryn Mawr Collegeb 

Yeshiva Universitya

1999-2000 & 

 2003-2004 

Sample 3—All of Sample 1 Plus: 

Colgate University 

Lehigh University 

Whitman College 

Samples 1, 2, or 3: 

Boston Collegea 

Cornell Universityb 

Duke University

Georgetown University 

Massachusetts Institute of Technologya,b 

University of Notre Dame 

University of Pennsylvaniaa,b 

Vanderbilt University 

Wake Forest University 

Yale Universityb

1995-1996 & 

1999-2000 

Sample 4—All of Sample 1 Plus: 

Carnegie Mellon University 

Johns Hopkins University 

Sample 4—All of Above Plus:

Columbia Universityb

Source: GAO analysis.

a

Schools were excluded because there were no data for SAT scores for 2003-2004. 

b

Member of the former Overlap group. 

c

Members of the 568 Group that had not implemented the consensus approach. 

d

Were not members of the 568 Group but attended meetings. 

e

Former member of the 568 Group. 

Notes: Schools that Only Attended 568 Group Meetings: Sample 2: Stanford Universityd, University of

Southern Californiaa,d and Sample 4: Case Western Reserve University

e 

. 

Page 37 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

Member schools that had not implemented the consensus approach: Sample 2: Brown University,

b,c

Sample 3: Dartmouth Collegeb,c

. 

Other 568-Affiliated Schools: Amherst College,b

 Bowdoin College,b,e California Institute of 

Technology,d Claremont McKenna College, Davidson College, Emory University, Grinnell College, 

Harvard University,b,d

 Haverford College, Macalester College,e Middlebury College,b Northwestern 

University, Pomona College, Rice University, Swarthmore College, Syracuse University,d

 University of

Chicago, Wellesley College,b

 Wesleyan University,b Williams Collegeb

We developed models for analyzing the effects of the implementation of 

the consensus approach (CA) on affordability and enrollment of incoming 

freshman using the consensus approach.12 We used a difference-indifference approach to identify the effects of implementation of the 

consensus approach. This approach controlled for two potential sources 

of changes in school practices that were independent of the consensus 

approach. First, this approach enabled us to control for variation in the 

actions of schools over time that were independent of the consensus 

approach. Having control schools that never implemented the consensus 

approach allowed us to isolate the effects of the exemption and permitted 

us to estimate changes over time that were independent of the consensus 

approach implementation. Second, while we had a control group of 

schools that did not use the consensus approach, but were otherwise very 

similar to treatment schools, it is possible that schools using the 

consensus approach differed in ways that would make them more likely to

implement practices that are different from those of other schools.13 The 

difference-in-difference approach controlled for this possibility by 

including data on schools using the consensus approach both before and 

after its adoption. Controlling then for time effects independent of the 

consensus approach as well as practices by these schools before adoption, 

the effect of the use of the consensus approach could be estimated. 

Specifications of 

Econometric Models 

and Estimation 

Methodology 

 12 We did not separate the effects of the CA into the effects of only attending the 568 Group 

meetings and the effects of only implementing the CA, although some schools only 

attended meetings and had not implemented the CA, because in table 9 there are only three

schools in sample 2 that would serve as treatments or serve as controls if we investigate 

the effects of only attending meetings or the effects of only implementing the CA, 

respectively. 

13In addition to having the control schools, we also controlled for a number of school 

characteristics that are discussed below. It is only the possibility of changes in differences 

between treatment and control schools that were not measurable or not observable that

might lead to bias in estimating the effects of the consensus approach implementation. For 

example, schools adopting the consensus approach might differ in their objectives 

concerning their preferred student body. As discussed next in the text, the difference-indifference approach provided controls for such possibilities. 

Page 38 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

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on College Affordability and Enrollment 

Compared to the schools that did not use the consensus approach, we 

expected that the implementation of the consensus approach would have 

a significantly greater impact on the schools using the consensus approach 

because its use has potential implications for affordability and enrollment 

of students in these schools. 

Modeling the Effects of the 

Consensus Approach 

Methodology for Financial 

Need on Affordability and 

Enrollment 

The basic tenets of financial need analysis are that parents and students 

should contribute to the student’s education according to their ability to 

pay. The CA schools used the consensus approach for its need analysis 

methodology and to determine the expected family contribution (EFC) for 

each student based on that methodology. Conversely, the non-CA schools 

primarily used a need analysis methodology called the institutional 

methodology (IM). The difference between the cost of attendance (COA) 

and the EFC determines whether a student has financial need. If so, the 

school then develops a financial aid package of grants, loans, and work 

study from various sources. The actual amount that students and families 

pay depends on how much of the aid received is grant aid. Therefore, the 

implementation of the consensus approach was expected to affect the 

price paid and the financial aid received by students, and by implication, 

their enrollment into schools. 

Dependent variables: 

The study examined the effects of the implementation of the consensus 

approach on two key variables: affordability (measured by price) and 

enrollment of freshman. We also estimated other equations to provide 

further insights on affordability— tuition, total grant aid, need-based grant 

aid, and total aid. All the dependent variables were measured at the 

student level, except tuition. Also, all monetary values were adjusted for 

inflation using the consumer price index (CPI) in 2005 prices.14 The 

dependent variables were defined as follows: 

• Price (PRICEijt): Price, in dollars, actually paid by freshman i who enrolled 

in school j in an academic year t. The variable was measured as the cost of 

attendance less total grant aid. The cost of attendance consisted of tuition

 14All the dependent variables were from NPSAS, except tuition, which was from IPEDS. We

used the general price level instead of the price index for higher education to adjust the 

monetary values because the former better reflected potential substitution effects between 

college education and other expenditures by households. Furthermore, sector-specific 

price indexes generally tend to be more volatile. 

Page 39 GAO-06-963 Higher Education
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on College Affordability and Enrollment 

and fees, on-campus room and board, books and supplies, and other 

expenses such as transportation. Total grant aid consisted of institutional 

and non-institutional grant aid; it excluded self-help aid (loans and work 

study). 

The other dependent variables that we estimated to help provide more 

insights into the results for affordability were: 

• Tuition (TUITIONijt): The amount of tuition and fees in dollars charged 

by school j to freshman i who enrolled in an academic year t.

15

• Total grant aid (AIDTGRTijt): The amount of total grant aid received, in 

dollars, by a freshman i who enrolled in school j in an academic year t. 

The counterpart to grant aid was self-help aid.16

• Need-based grant aid (AIDNDTGRTijt): The amount of need-based grant 

aid received, in dollars, by freshman i who enrolled in school j in an 

academic year t. The counterpart to need-based aid was non-needbased aid, which consisted mainly of merit aid.17

• Total aid package (AIDTOTAMTijt): The amount of total aid received, in 

dollars, by freshman i who enrolled in school j in an academic year t. 

The total aid consisted of total grants (from the school, the various 

levels of government—federal, state—and other sources) and self-help 

(includes loans and work-study). 

• Student enrollment (ENRCAijt): An indicator variable for student 

enrollment into a CA school (ENRCAijt). It equals one if a freshman i

enrolled in an academic year t in school j that was a school using or later 

the consensus approach, and zero otherwise. Thus, at t=0 (1995-1996), a 

school was designated as a CA school if it implemented the consensus 

approach in period t=1 (2003-2004). Students who enrolled in a non-CA 

school were assigned a value of zero. In other words, ENRCA takes a value 

of one for every student enrolled in a CA school in any time period (1995-

1996 or 2003-2004), and zero otherwise. 

 15The tuition amount was the same for all freshmen in a private school. 

16We also estimated an equation for institutional grant aid (AIDINSTGRT) and self-help aid

(AIDSELFPLUS). 

17We also estimated an equation for need-based institutional aid (AIDNDINST) and nonneed-based grant aid (AIDNONDTGRT), which was the difference between total grant aid 

and need-based aid. However, we did not have enough data to estimate merit-only aid. 

Page 40 GAO-06-963 Higher Education
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on College Affordability and Enrollment 

Explanatory variables: 

Several variables could potentially affect each of the dependent variables

identified above. The explanatory variables we used were based on 

economic reasoning, previous studies, and data availability.18 All the 

equations used were in quasi reduced-form specifications. The key 

explanatory variable of interest was the exercise of the exemption through 

the implementation of the consensus approach by the 568 Group of 

schools. We were also interested in the effects of the implementation of 

the consensus approach on affordability and enrollment of disadvantaged 

students. In order to isolate the relationships between the consensus 

approach implementation and each of the dependent variables, we 

controlled for the potential effects of other explanatory variables. The 

following is a complete list of all the explanatory variables we used: 

• Exemption indicator: EMCAjt

19

The exemption was captured by the implementation of the consensus 

approach by a school.20 EMCA equals one if school j has implemented CA 

by academic year t, where t is 2003-2004 and zero otherwise. 

We used other explanatory variables in our equations, in addition to the 

exemption indicator for the implementation of the consensus approach. 

These variables included school-level characteristics, school specific 

fixed-effects, time specific fixed-effects, and student-level characteristics. 

• School-level characteristics:21

The school variables or attributes varied across the schools (j) and over 

time (t), but did not vary across the students (i). The school 

characteristics may capture the quality of the schools, expenditures by the 

schools that may compete with financial aid for funding, revenue sources 

 18We relied on several previous studies, including Avery and Hoxby (2003), Carlton et al. 

(1995), Bamberger and Carlton (1993), Epple et al. (2005), Hill et al. (2005), Hoxby (2000), 

Kim (2005), Netz (1999, 2000), Hill and Winston (2001), Morrison (1992), Salop and White 

(1991), Shepherd (1995), and Winston and Hill (2005). 

19This variable was from the GAO survey of the CA and non-CA schools.

20The CA schools are the 568 schools that have either implemented the consensus approach

fully or in part by implementing some of the options under that need analysis methodology 

for financial aid. Of the seven CA schools in sample 2 in table 9, only three had not fully 

implemented the consensus approach (Georgetown, Vanderbilt, and Wake Forest). 

21All the school-level variables are from IPEDS. 

Page 41 GAO-06-963 Higher Education
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on College Affordability and Enrollment 

for financial aid, or the preferences of the students.22 The variables used 

were: 

• ENDOWSTUjt: The interaction between the 3-year average endowment 

per student and the 3-year average percentage rate of return on 

endowment per student at school j for an academic year t. The 

inclusion of the rate of returns from endowments helped minimize the 

possibility that developments in financial markets could bias the 

results especially if the average endowment per student differed across 

the two groups of schools. 

• RANKAVGjt: The average “best schools” rank of school j for an 

academic year t. Although we used this variable to select the control 

schools that were comparable in selectivity to the treatment schools 

before matching the data to the NPSAS data, this variable was 

included, due to data limitations, to control for the possibility that the 

two groups of schools used in the sample may differ in selectivity. 

• ENROLUGjt: The 3-year average growth rate (in decimals) of 

undergraduate enrollment at school j for an academic year t. 

• TENUREDjt: The percentage (in decimals) of total faculty at school j 

that was tenured in an academic year t. 

• Time specific fixed-effects: 

These variables captured differences over time that did not vary across the 

schools, such as increases in national income that could increase 

affordability of schools. This was an indicator variable for the academic 

years (time): 

AY1995t

: Equals one for the academic year 1995-1996, and zero otherwise

AY2003t

: Equals one for the academic year 2003-2004, and zero otherwise.

 22The school specific fixed-effects were estimated using the fixed-effects estimator, where

feasible. This effect captured differences among the schools that did not vary over time, 

such as location, memberships in athletic conferences and other organizations such as the 

former Overlap group. Also, several school-level variables could not be used in the models 

because the variables did not vary over time, and were therefore expected to be captured 

by the school specific fixed-effects. 

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• Student characteristics:23

All the student-level variables or attributes generally varied across 

students (i), across schools (j), and across time (t). The student 

characteristics indicated the preferences of the students for a school as 

well as the decisions of the schools regarding the students they admitted.

The variables used were: 

• FINAIDijt: Equals one if a freshman i who enrolled in school j in an 

academic year t applied for financial aid, and zero otherwise. 

• RACE: Equals one if a freshman i who enrolled in school j in an 

academic year t is: 

Asian—ASIANijt, and zero otherwise. 

Black—BLACKijt, and zero otherwise. 

Hispanic—HISPANICijt, and zero otherwise. 

White—WHITEijt, and zero otherwise. 

Foreigner—FOREIGNijt, and zero otherwise. 

None of the above—OTHERijt, and zero otherwise.24

• INCOME: Equals one for a freshman i who enrolled in school j in an 

academic year t has household income in the following quintiles: 

INCLOijt: Below or equal to the 20th percentile, and zero otherwise. 

These were low-income students, and the median income for the group 

was $13,731 in 2005 dollars. 

INCLOMDijt: Above the 20th and below or equal to the 40th percentile, 

and zero otherwise. These were lower-middle income students, and the 

median income for the group was $40,498 in 2005 dollars. 

INCMDijt: Above the 40th and below or equal to the 60th percentile, and 

zero otherwise. These were middle-income students, and the median 

income for the group was $59,739 in 2005 dollars.

 23All the student-level variables were from NPSAS. 

24We included Native Americans in OTHER because of their relatively small numbers. 

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INCUPMDijt: Above the 60th and below or equal to the 80th percentile, 

and zero otherwise. These were upper-middle income students, and the 

median income for the group was $88,090 in 2005 dollars. 

INCHIijt: Above the 80th percentile, and zero otherwise. These were 

high-income students, and the median income for the group was 

$145,912 in 2005 dollars.

Since we included minority students (Asian, black, and Hispanic students) 

as well as lower income groups (low income and lower-middle income 

students) to measure needy students, the minority variables likely 

captured nonincome effects.25

EFCijt: Expected family contribution for a freshman i who enrolled 

in school j in an academic year t. Although this variable captured 

the income of the students, it also reflected other factors that 

affect financial aid, such as the number of siblings in college.26

SCORESATijt: The combined scholastic aptitude test (SAT) scores 

for math and verbal of freshman i who enrolled in school j in an 

academic year t. 

Tables 10 and 11 show summary statistics for the variables listed above for 

treatment and control schools in sample 2 (as listed in table 9).27 In 

general, the values of the variables were similar between the two groups of 

schools. 

 25To avoid the dummy-variable trap in the estimation, we excluded white students from the 

racial groups, and high-income students from the income groups. 

26The EFC is the federal calculation, which differs significantly from the EFC calculated by

the CA schools, and to some extent from the EFC calculated by the non-CA schools. We

found a negative relationship between the number of siblings and EFC using the limited 

data on siblings, although the link was not strong. 

27The reported values are probability-weighted.

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Table 10: Summary Statistics of Variables Used in Regression Analysis, 1995-1996 and 2003-2004: CA Schools 

Variable Mean Std Min Max

School-level

TUITION $26,245 $3,557 $18,910 $31,152

ENDOWSTU $227,213 $230,768 $44,061 $1,146,129

RANKAVG 16 9 2 27

ENROLUG 2% 6% -1% 21%

TENURED 56% 13% 25% 75%

Student-level 

PRICE $30,792 $11,144 $1,065 $52,354

AIDTGRT $7,133 $9,866 $0 $40,658

AIDNDTGRT $5,526 $8,722 $0 $35,321

AIDNONDTGRT $1,607 $4,360 $0 $30,403

AIDTOTAMT $12,465 $13,566 $0 $43,195

AIDSELFPLUS $4,794 $8,155 $0 $36,730

EFC $24,486 $22,268 $0 $115,090

SCORESAT 1301 144 790 1600

FINAID 76% n/a n/a n/a

ASIAN 9% n/a n/a n/a

BLACK 5% n/a n/a n/a

HISPANIC 7% n/a n/a n/a

FOREIGN 2% n/a n/a n/a

OTHER 5% n/a n/a n/a

WHITE 71% n/a n/a n/a

INCLO 5% n/a n/a n/a

INCLOMD 11% n/a n/a n/a

INCMD 13% n/a n/a n/a

INCUPMD 17% n/a n/a n/a

INCHI 54% n/a n/a n/a

Schools Cornell University, Duke University, Georgetown University, University of Notre Dame, Vanderbilt 

University, Wake Forest University, Yale University 

Number of observations 241

Source: GAO analysis.

Note: All values are (probability) weighted averages, and the monetary values are in 2005 dollars. 

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Table 11: Summary Statistics of Variables Used in Regression Analysis, 1995-1996 and 2003-2004: Non-CA Schools 

Variable Mean Std Min Max

School-level

TUITION $27,031 $2,259 $24,571 $31,714

ENDOWSTU $256,147 $329,513 $27,909 $1,504,930

RANKAVG 23 12 1 43

ENROLUG 1% 1% -2% 4%

TENURED 56% 12% 26% 72%

Student-level 

PRICE $28,815 $10,305 $4,569 $50,726

AIDTGRT $10,869 $9,792 $0 $32,803

AIDNDTGRT $8,573 $9,132 $0 $31,487

AIDNDTGRT $2,296 $5,419 $0 $27,919

AIDTOTAMT $16,487 $13,875 $0 $48,572

AIDSELFPLUS $5,293 $7,686 $0 $48,041

EFC $21,717 $21,724 $0 $105,095

SCORESAT 1268 151 740 1590

FINAID 80% n/a n/a n/a

ASIAN 12% n/a n/a n/a

BLACK 5% n/a n/a n/a

HISPANIC 4% n/a n/a n/a

FOREIGN 3% n/a n/a n/a

OTHER 3% n/a n/a n/a

WHITE 74% n/a n/a n/a

INCLO 10% n/a n/a n/a

INCLOMD 9% n/a n/a n/a

INCMD 12% n/a n/a n/a

INCUPMD 21% n/a n/a n/a

INCHI 48% n/a n/a n/a

Schools Brandeis University, Bryn Mawr College, New York University, Princeton University, Tulane

University, University of Rochester, Washington University at St. Louis 

Number of Observations 277

Source: GAO analysis.

Note: All values are (probability) weighted averages, and the monetary values are in 2005 dollars. 

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Table 12 shows summary statistics on price and financial aid before and 

after the implementation of the consensus approach in 2003-04 at the CA

and non-CA schools in sample 2. Similarly, table 13 shows the summary

statistics by income and racial groups.28 It is important to note that the 

summary information on the observed differences before and after the 

implementation of the consensus approach for the CA and non-CA schools 

are heuristic and do not conclusively determine the potential effects of the 

implementation of the consensus approach. It is also important to note 

that, for any given variable, it is possible that there are other factors than 

implementing the consensus approach that are responsible for the 

observed differences, including differences between CA and non-CA 

schools’ student populations or differences in the characteristics of the 

schools, or both. For instance, the price paid by middle-income students 

increased more in CA than in non-CA schools. While this may reflect the 

effect of consensus approach, it is possible that other factors are 

responsible for the differences. For example, the racial composition of 

middle-income students might also be different between the two groups, 

or there may be systematic differences in endowment growth between the 

CA and non-CA schools that affect financial aid to middle-income 

students. Thus, to assess the effect of consensus approach, it is necessary 

to study the effects of consensus approach while controlling 

simultaneously for all factors that influence price and aid policies. 

Comparison of Prices and 

Financial Aid in CA and 

Non-CA Schools 

 28The reported values are probability-weighted.

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Table 12: CA and Non-CA Schools: Price and Financial Aid 

CA Schools Non-CA Schools

All students 1995-1996 2003-2004

Percentage

difference 1995-1996 2003-2004

Percentage

difference

Pricea $28,039 $35,488 27% $28,068 $30,838 10%

Tuition & fees 24,062 29,967 25 25,770 30,447 18

Total

observations 150 91 198 79

Financial-Aid Applicants Only

Student Applied 

for Financial Aid 

 Pricea $25,845 $32,897 27% $24,960 $29,705 19%

Total grant aid 9,142 9,775 7 13,391 13,960 4

Need-based total 

grant 7,771 6,439 -17 11,863 8,122 -32

Institutional grant 

aid 7,073 6,529 -8 11,297 11,116 -2

Total aid 16,604 16,046 -3 19,827 22,255 12

Loans (incl. 

PLUS) 5,954 4,849 -19 5,271 6,669 27

Work study 710 866 22 986 715 -27

Number of 

observations 112 72 152 73

Student Did Not 

Apply for 

Financial Aidb

 Pricea 34,645 44,504 28 37,714 44,292 17

Number of 

observations 38 19 46 6

Total

observations 150 91 198 79

Source: GAO analysis.

a

Price equals cost of attendance less total grant aid. Cost of attendance equals tuition and fees, plus 

expenses (including room and board, and books).

b

Financial aid data were not available for students who did not apply for financial aid. 

Notes: All values are (probability) weighted averages, and the monetary values are in 2005 dollars. 

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Table 13: CA and Non-CA Schools—Financial Aid Applicants Only: Price and Financial Aid 

CA schools Non-CA schools

1995-1996 2003-2004

Percentage

difference 1995-1996 2003-2004

Percentage

difference

Income level 

Low income

 Pricea $12,566 $10,095 -20 $18,950 $21,886 15

Total grant aid $23,429 $27,020 15 $19,093 $21,861 14

Need-based total grant $21,422 $21,191 -1 $16,849 $17,756 5

Institutional grant aid $17,278 $12,597 -27 $14,060 $17,447 24

Total aid $27,385 $35,956 31 $24,772 $26,523 7

Number of observations 9 3 26 8

Lower-middle income 

 Pricea $17,613 $30,437 73 $17,623 $20,546 17

Total grant aid $17,531 $14,793 -16 $20,598 $23,014 12

Need-based total grant $15,762 $12,949 -18 $20,417 $15,456 -24

Institutional grant aid $11,735 $9,667 -18 $15,742 $19,386 23

Total aid $24,025 $18,409 -23 $28,462 $30,509 7

Number of observations 13 12 22 5

Middle income

 Pricea $22,146 $30,156 36 $21,240 $25,279 19

Total grant aid $12,277 $12,076 -2 $17,173 $17,336 1

Need-based total grant $8,293 $10,767 30 $16,053 $9,048 -44

Institutional grant aid $11,096 $9,936 -10 $16,000 $13,854 -13

Total aid $18,811 $20,743 10 $24,261 $25,176 4

Number of observations 16 10 20 12

Upper-middle income 

 Pricea $23,759 $31,631 33 $26,905 $29,524 10

Total grant aid $10,410 $10,374 -0.3 $12,030 $13,478 12

Need-based total grant $9,732 $5,864 -40 $10,289 $6,593 -36

Institutional grant aid $8,694 $7,719 -11 $10,900 $7,198 -34

Total aid $16,926 $19,277 14 $19,425 $20,769 7

Number of observations 21 13 32 11

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CA schools Non-CA schools

1995-1996 2003-2004

Percentage

difference 1995-1996 2003-2004

Percentage

difference

High income

 Pricea $31,776 $37,127 17 $30,184 $33,806 12

Total grant aid $3,493 $5,468 57 $7,941 $10,331 30

Need-based total grant $2,810 $1,694 -40 $6,185 $5,377 -13

Institutional grant aid $2,532 $3,430 35 $7,062 $8,884 26

Total aid $12,393 $10,850 -12 $13,300 $19,797 49

Number of observations 53 34 52 37

Total observations 112 72 152 73

Raceb

Asian

Pricea $28,082 $28,756 2 $25,642 $27,624 8

Total grant aid $8,371 $16,265 94 $10,827 $17,834 65

Need-based total grant $7,675 $13,129 71 $9,900 $14,646 48

Institutional grant aid $6,906 $11,607 68 $7,771 $13,376 72

Total aid $14,343 $23,037 61 $15,513 $27,425 77

Number of observations 11 5 23 13

Black

 Pricea $12,702 $22,935 81 $13,530 $17,375 28

Total grant aid $21,360 $19,958 -7 $23,296 $25,010 7

Need-based total grant $19,836 $8,932 -55 $18,517 $15,631 -16

Institutional grant aid $15,046 $17,404 16 $18,582 $21,231 14

Total aid $29,572 $24,950 -16 $29,121 $26,707 -8

Number of observations 10 3 8 4

Hispanic

Pricea$21,177 $21,529 2 $20,282 $16,694 -18

Total grant aid $15,432 $18,586 20 $17,028 $25,993 53

Need-based total grant $13,514 $13567 0.4 $14,684 $18,611 27

Institutional grant aid $11,960 $13,813 15 $13,187 $17,998 36

Total aid $20,110 $25,576 27 $22,446 $32,732 46

Number of observations 7 8 11 2

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CA schools Non-CA schools

1995-1996 2003-2004

Percentage

difference 1995-1996 2003-2004

Percentage

difference

White

Pricea $27,832 $35,099 26 $25,736 $30,952 20

Total grant aid $6,711 $7,382 10 $13,028 $12,512 -4

Need-based total grant $5,271 $4,284 -19 $11,645 $6,249 -46

Institutional grant aid $5,105 $4,240 -17 $11,378 $10,187 -10

Total aid $14,635 $14,130 -3 $20,042 $21,005 5

Number of observations 81 48 103 49

Total observations 112 72 152 73

Source: GAO analysis.

a

Price equals cost of attendance less total grant aid. Cost of attendance equals tuition and fees, plus 

expenses (including room and board, and books).

b

Data for other race, including Native American, unidentified race, and foreign students were too few 

to report. 

Notes: All values are (probability) weighted averages, and the monetary values are in 2005 dollars. 

Model Specifications and 

Estimation Methodology 

Our econometric analysis is based on panel data, which pooled crosssectional and time series data. The cross-sectional data were based on 

freshmen who enrolled in CA schools and non-CA schools, and the time 

series data were for academic years 1995-1996 and 2003-2004. Where 

feasible, we used panel-data estimation appropriate for cross-sectional and 

time series data. Also, we used fixed-effects estimation instead of randomeffects estimation because the observations were not randomly chosen 

and there were likely to be unobserved school-specific effects.

29 The 

reported estimates were based on the fixed-effects estimators, using 

probability weights, and the standard errors were robust.30

 29The panel data were unbalanced because there were different observations on the

freshmen for each school in each academic year. An important purpose in combining crosssectional and time series data was to control for individual school-specific unobservable 

effects, which may be correlated with the covariates in the models. An advantage of using

the fixed-effects estimator was that there was no need to assume that the unobserved 

school-specific effects were independent of the covariates. However, unlike the randomeffects estimator, the fixed-effects estimator did not allow the inclusion of time-invariant 

variables, such as the former Overlap group and membership in sports associations, as 

covariates. 

30The weights are the probability weights from the number of students in the sample for 

each school, and the robust estimates of the standard errors are based on the Huber/White

sandwich estimator. All estimates were obtained using Stata. 

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Price, Tuition, and Financial Aid Equations: 

Let Yijt be the dependent variable for freshman i’s outcomes at the chosen

school j in academic year t, where the main outcome variable studied is 

affordability represented by price (PRICEijt).31 The regression equations 

were specified generally as follows: 

jt ijt ijt

jt ijt jt jt jt

J

j 2 j

T

t 2 ijt t

 EMCA S

Y I S EMCA EMCA I

γ ε

α ψ θ β ρ δ η

+ +

= + ∑ + ∑ + + + + = = (1) 

where I and S are vectors of school (institution)-level and student-level 

variables, and EMCA represents the consensus approach implementation; 

ψ (time specific fixed-effects) and θ (school specific fixed-effects) are 

scalar parameters, and α and ε are the constant and the random error 

terms, respectively. There are interactions between EMCA and the schoollevel variables and between EMCA and the student-level variables.32

We were primarily interested in the total effects of the implementation of 

the consensus approach on affordability, as well as the effects that were 

specific to particular groups of students, such as low-income and minority 

students, and students who applied for financial aid. 

Using equation 1, the total effect of the CA implementation on price was 

estimated by I ˆ S ˆ , ˆ

δ + η + γ where I and S are averages of I and S taken 

over the observations for the CA schools during the period of the 

consensus approach implementation (2003-2004).33 This measures the 

effect of the consensus approach implementation on CA schools, relative 

to non-CA schools, controlling for time invariant differences in schools 

and other variations over time that are common to both groups. The 

 31The same model specification is used to estimate the financial aid equations, and tuition 

equation, which excludes the student-level variables. 

32In the estimated equations, the interaction terms between EMCA and other variables have

the suffixes “*;” for example ENDOWSTU* is the interaction term between ENDOWSTU 

and EMCA. 

33This effect can be tested as a linear restriction if the joint test of significance of EMCA 

and the terms involving EMCA is significant.

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coefficient measures the unconditional effect of the consensus 

approach implementation on price, while 

δ

ˆ

ηˆ and γˆ measure the conditional 

effects of the consensus approach implementation on price through the 

school-level variables and student-level variables, respectively. 

The expression for the total effects of the consensus approach 

implementation can be evaluated for particular groups of students by 

averaging I and S over that particular subset of students. For example, the 

effects of the consensus approach implementation on prices paid by lowincome (INCLO) students can be estimated by 

I ˆ S ˆ , ˆ INCLO INCLO δ + η + γ where the school-level and student-level 

variables are averaged over the low-income students. More specifically, 

the second term is the coefficient estimates of each school-level variable 

multiplied by the school-level variable averaged over the subset of lowincome (INCLO) students attending CA schools after the consensus 

approach implementation; similarly the average is taken for the third term, 

which is for the student-level variables. 

Alternatively, we can use equation 1 to illustrate the effects of the 

consensus approach implementation for particular groups. Consider a 

simple example in which there are two student characteristics, Fijt and Aijt , 

where Fijt is an indicator variable equal to one if the student is a financial 

aid applicant and zero otherwise, and Aijt is an indicator equal to one if the 

student is black, and zero otherwise. Then, using equation 1, the equation 

for this example is: 

jt jt jt ijt F jt ijt A ijt

jt ijt F ijt A jt

J

j 2 j

T

t 2 ijt t

 EMCA I EMCA F EMCA A

Y I F A EMCA

η γ γ ε

α ψ θ β ρ ρ δ

+ + + +

= + ∑ + ∑ + + + + = = (1.1) 

Now consider a white student who is a financial aid applicant in school j

at time t.

34 The predicted price for a white student if j is a CA school is: 

 34White students are excluded from the race groups in the estimation to avoid the dummy 

trap. 

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t j jt F jt F

I ˆ ˆ ˆ ˆ ˆ I ˆ αˆ +ψˆ + θ + β + ρ + δ + η + γ (1.2) 

and the predicted price if j is not a CA school is: 

t j jt F

ˆ ˆ I ˆ αˆ +ψˆ + θ + β + ρ (1.3)

The effect of the consensus approach implementation for a financial aid 

applicant at school j is then the difference between equations 1.2 and 1.3, 

which is: 

jt F

I ˆ ˆ ˆ

δ + η + γ (1.4)

The coefficient measures the effect of adopting the consensus approach 

that is invariant across school and student type, the term 

δ

ˆ

Iηˆ captures the 

differential effect of adopting the consensus approach for a school with 

characteristics Ijt, and the third term, γˆ , captures the differential effect of 

adopting the consensus approach for a white student who is a financial aid

applicant. Repeating the exercise above for a black student who is a 

financial aid applicant, the predicted effect of adopting the consensus 

approach would be: 

jt F A

I ˆ ˆ ˆ ˆ

δ + η + γ + γ (1.5)

The first three terms in equation 1.5 are the same as equation 1.4, while the 

fourth term captures the differential effect of the consensus approach 

implementation for a black student. In this example, then, the estimated 

effect of the consensus approach implementation on financial aid students 

would be the weighted average of the terms in equations 1.4 or 1.5, with 

weights corresponding to the proportions of white and black financial-aid

students across all schools j that adopted the consensus approach at time 

t, respectively. 

Another estimate of the consensus approach’s effect on a particular group 

is the estimated differential effect on a group, given byγˆ , holding 

everything else constant. For example, one can ask how a low-income 

student as compared to a high-income student would be affected by the 

consensus approach implementation, assuming all other characteristics of 

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the student and the student’s school are held constant. This estimated 

effect is simply given by the element of the vector in γˆ that corresponds to 

INCLO. This differs from the total effect of the consensus approach 

implementation discussed above by taking as given the consensus 

approach implementation, and by abstracting from the likelihood that lowincome students will have other characteristics and attend different 

schools than non low-income students. We will also discuss the 

coefficient ρˆ , which captures the value of the dependent variable for the 

particular group in both CA and non-CA schools before the consensus 

approach implementation, where necessary. 

The total effect of the exemption on price as well as its specific effects on

particular groups will depend on which theory of the exemption is 

supported by the data. In particular, we expect price to be lower for 

disadvantaged students if the social benefit theory is valid; on the other 

hand, price will increase if the anti-competitive theory is valid. Similarly, 

the effects of the student-level variables would depend on the theories of 

the effects of the exemption. For the effects of the school-level variables, 

ENDOWSTU should be negative because with more resources there is less 

need to raise tuition and there will be more funds for grant aid. RANKAVG 

should be negative because as the quality of the school decreases tuition 

as well as grant aid should decrease. ENROLUG would be negative if 

higher growth in student enrollment perhaps means more revenues and

less need to raise tuition. On the other hand, if students’ education is on 

net subsidized by other sources of school income then ENROLUG would 

be positive as increased enrollment increases the costs to the school of 

providing education. And TENURED should be positive if more tenured 

faculty implies higher quality.35

We estimated equation 1 for price, as well as for tuition and the financial 

aid variables, using probability-weighted regression and robust standard 

errors, as well as the fixed-effects estimator for panel data.

36 See the 

 35The effects of these variables on tuition were expected to be similar to that of price. On 

the other hand, the effects of these school-level variables on the financial aid variables 

were expected to be opposite to that of price. 

36The statistical procedure we used is AREG in Stata. 

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regression estimates for price and tuition in table 14, and those for the 

financial aid variables in table 15.

37

The regression models for the price, tuition, and financial aid variables are 

all highly significant using the F-values of the models. See tables 14 and 15. 

Furthermore, the school-level variables generally have the expected 

effects. In particular, for the price equation, a student enrolled in a school

with an endowment per student (ENDOWSTU) of $250,000 paid about 

$5,000 lower price.38 Also, a student paid about $464 less for a school with 

a unit drop in its selectivity (RANKAVG). Although the effect is not 

significant, the positive sign for ENROLUG suggests that an increase in 

enrollment growth may result in a higher price paid, implying that 

education is net subsidized and increases in enrollment increases the cost 

of providing education; and vice versa. Finally, a student enrolled in a 

school with 10 percent higher tenured faculty (TENURED) paid about 

$3,310 higher. As discussed earlier, the effects of the student-level 

variables depend on which theory of the effects of the higher education 

exemption is relevant.39

Student enrollment equation: 

The regression equation for enrollment into a CA school (ENRCAijt) would 

depend on student characteristics. Generally, enrollment is the outcome of 

decision-making that included application, admission, and acceptance of 

the admission offer. The first and third decisions are made by the student,

and the second decision is made by the school. Therefore, in general, both 

student-level variables and school-level variables would be relevant. 

However, our approach, as indicated in equation 2, treated the CA schools 

essentially the same and likewise for the non-CA schools, with differences 

between the two groups other than the consensus approach 

implementation captured by the constant term in the regression. The 

 37The regression estimates for the financial aid variables excluded non financial-aid 

applicants, which reduced the number of observations but not the number of schools. 

Similar results were obtained for the price equation when the estimates were based on only 

financial-aid applicants. The regression estimates for tuition were obtained by excluding 

student-level variables because students at a school were charged the same tuition. 

38The $4,800 decrease is approximately equal to $250,000 x –(0.01935). 

39As discussed earlier, similar arguments can be obtained for the tuition and financial aid 

variables. 

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enrollment equation was thus specified as follows, excluding school-level 

variables as regressors: 

Prob( ENRCA 1 ) ( S AY 2003 AY 2003 S )

ijt ijt t t ijt

= = Φ α + ρ + δ + γ 

(2) 

Φ is the standard normal cumulative probability distribution function. 

Similar to equation 1, equation 2 includes student characteristics (with 

coefficients ρ), time fixed-effects captured by AY2003, and the interaction

of the time variable AY2003 with student characteristics (with coefficients 

γ).40

The time specific fixed-effect for AY2003 captures any shift, which is 

constant across students, toward or away from the CA schools, after the 

consensus approach implementation, while the interaction terms between 

the AY2003 and the student characteristics capture shifts toward or away 

from the CA schools by students with specific characteristics.41

The marginal effect of the consensus approach implementation is captured 

by the effects of AY2003 on enrollment in CA schools. Specifically, this 

equals, 

d [ Prob( ENRCA = 1 )] / d( AY 2003 ) = ( δ + S γ )φ(.), where φ is the 

standard normal probability density function. It should be noted that if 

AY2003 affects the probability of enrollment in CA schools, it would be a 

valuable suggestive evidence about the potential impact of the consensus 

approach implementation. However, it would not establish that the 

consensus approach implementation caused the shift. This is because it is

possible that such effects might be due to changes in other factors at CA 

schools versus non-CA schools (e.g., more rapid endowment growth in the 

latter than the former). The effect of the consensus approach

implementation is the change in the probability of enrollment in CA 

schools relative to non-CA schools as a result of the consensus approach

 40The model could not be estimated with school specific fixed-effects because they predict

successes or failures perfectly.

41In the estimated equations, the interaction terms between AY2003 and other variables

have the suffixes “*;” for example INCLO* is the interaction term between INCLO and 

AY2003. 

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implementation. The overall effect of the CA implementation as well as the 

effects of the consensus approach implementation on particular groups of 

students, such as low-income students and those who applied for financial 

aid, can be obtained similar to the discussion above for the price. 

The marginal effect of the student characteristics is captured by the 

effects of S on enrollment in CA schools. Specifically, this equals, 

d [ Prob( ENRCA = 1 )] / dS = ( ρ + AY 2003 γ )φ(.). The effect of the 

consensus approach implementation on how the probabilities of 

enrollment of low-income and minority students, and those who applied 

for financial aid, are affected can be obtained similar to the discussion for 

the price. 

Similar to the discussion for the price equation, the effects of the 

exemption and the student-level variables on enrollment into CA schools 

will depend on which theory of the exemption is valid. In particular, the 

social benefit theory will imply increased likelihood of enrollment into CA 

schools, especially of low-income students, because prices will be lower. 

While the opposite will occur with the anti-competitive theory because 

average price will be higher. 

We estimated equation 2 for student enrollment using the probit 

estimation, with probability weights and robust standard errors.42 The 

regression estimates are in table 14. 

The regression model for enrollment in table 14 is significant using the chisquare of the model. As indicated earlier, we expect the estimation results 

will enable us to determine if the likelihood of enrollment into schools 

implementing the consensus approach by various student groups is more 

consistent with the social benefit theory or the anti-competitive theory of

the effects of the higher education exemption. 

 42We could not use the panel data estimation technique for probit (XTPROBIT) because of

lack of convergence. Similar results were obtained when the estimates were based on only

students who applied for financial-aid. 

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Table 14: Regression Estimates of Effects of Consensus Approach Implementation on Price, Tuition, and Enrollment 

Variable Price Tuition Enrollment

EMCA 43,743.75a [0.002] -1,720.72 [0.512] n/a

AY2003 12,133.55a [0.000] 5,650.28a [0.000] 0.25751 [0.631]

Student-level 

FINAID -7,573.33a [0.000] n/a 0.01292 [0.854]

FINAID* 190.21 [0.911] n/a -0.30976 b [0.015]

ASIAN 1,084.13 [0.363] n/a -0.01069 [0.910]

ASIAN* -7,109.66c [0.055] n/a -0.08486 [0.613]

BLACK -9,444.68a [0.000] n/a 0.19398 [0.129]

BLACK* -566.20 [0.921] n/a -0.20422 [0.339]

HISPANIC -4,686.31a [0.007] n/a 0.06790 [0.574]

HISPANIC* -1,610.12 [0.588] n/a 0.35354b [0.021]

FOREIGN 3,398.09 [0.230] n/a -0.25968 [0.221]

FOREIGN* 10,588.17b [0.026] n/a 0.36734c [0.054]

OTHER -2,047.09 [0.407] n/a 0.02366 [0.887]

OTHER* -1,077.16 [0.727] n/a 0.30781c [0.059]

EFC 0.10871a [0.000] n/a 2.30e-06 [0.190]

EFC* -0.00745 [0.872] n/a -3.43e-06 [0.219]

INCLO -8,427.06a [0.000] n/a -0.15253 [0.196]

INCLO* -6,507.64 [0.274] n/a -0.15185 [0.489]

INCLOMD -8,696.19a [0.000] n/a -0.03045 [0.797]

INCLOMD* -1,789.68 [0.593] n/a 0.16593 [0.384]

INCMD -4,804.25a [0.000] n/a 0.06859 [0.506]

INCMD* 945.64 [0.771] n/a -0.16166 [0.303]

INCUPMD -1,434.39 [0.163] n/a -0.03378 [0.689]

INCUPMD* -1,715.73 [0.522] n/a -0.07025 [0.657]

SCORESAT -2.48 [0.430] n/a 0.00024 [0.227]

SCORESAT* -7.82 [0.383] n/a 0.00013 [0.764]

School-level 

ENDOWSTU -0.01935a [0.001] -0.00401a[0.008] n/a

ENDOWSTU* -0.01056b [0.044] 0.00038 [0.831] n/a

RANKAVG -464.33c [0.051] -151.06c [0.064] n/a

RANKAVG* -798.05a [0.000] 36.28 [0.792] n/a

ENROLUG 19,038.16 [0.785] 35,553.57 [0.133] n/a

ENROLUG* -45,843.9 [0.518] -44,159.24c [0.095] n/a

TENURED 33,100.97a [0.000] -610.06 [0.492] n/a

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Variable Price Tuition Enrollment

TENURED* -25,823.9b [0.014] 4,632.48 [0.315] n/a

Constant 29,651.89a [0.000] 28,746.01a [0.000] n/a

Test statistic of modeld22.97a [0.000] 375.71a [0.000] 37.68b [0.050]

R-squared 0.62 0.99 0.06

Sample size 518 28 518

Joint test for EMCA 2.85a [0.000] 1.15 [0.460] 18.70b [0.133]

Linear restriction test for EMCAe1.18 [0.240] -0.59 [0.586] n/a

Source: GAO analysis.

a

Statistically significant at the 1 percent level or lower. P-values are in brackets. 

b

Statistically significant at the 5 percent level or lower. P-values are in brackets. 

c

Statistically significant at the 10 percent level or lower. P-values are in brackets. 

d

F-statistic values for the price and tuition equations, and chi-square values for the enrollment 

equation. 

e

t-statistic values for the price and tuition equations, and z-statistic values for the enrollment equation. 

Notes: N/A means data are not available or applicable. 

* means interaction terms with EMCA for price and tuition equations, and interaction terms with 

AY2003 for the enrollment equation. 

Estimates of price and tuition are obtained using fixed-effects models. 

Estimates for enrollment are the marginal effects from a probit model. 

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Table 15: Regression Estimates of Effects of Consensus Approach Implementation on Financial Aid 

Variable Total grant aid Need-based grant aid Total aid

EMCA -28,705.63c [0.066] -15,512.93 [0.178] -50,805.28b [0.016]

AY2003 2,159.46 [0.348] -1,511.37 [0.522] 7,385.03c [0.062]

Student-level 

FINAID n/a n/a n/a

FINAID* n/a n/a n/a

ASIAN -722.86 [0.649] -740.76 [0.590] -2,638.73 [0.221]

ASIAN* 6,970.89 [0.142] 6,546.91c [0.066] 8,275.76 [0.221]

BLACK 7,914.65a [0.000] 4,796.63b [0.026] 8,425.58a [0.000]

BLACK* 2,546.02 [0.656] -2,267.21 [0.514] 965.32 [0.862]

HISPANIC 4,709.93b [0.023] 2,826.32 [0.140] 2,281.05 [0.389]

HISPANIC* 2,038.96 [0.570] 206.79 [0.947] 2,606.82 [0.622]

FOREIGN -5,754.55b [0.031] -4,634.40 [0.132] -11.961.86a [0.002]

FOREIGN* -10,965.98b [0.032] -10,414.55b [0.032] -11,659.21c [0.068]

OTHER 4,656.42 [0.169] 5,229.39 [0.129] 4,196.89 [0.349]

OTHER* -2,813.57 [0.481] -1,725.88 [0.667] -4,098.47 [0.496]

EFC -0.16058a [0.000] -0.149889a [0.000] -0.187923a [0.000]

EFC* 0.044382 [0.397] 0.03185 [0.472] 0.006624 [0.929]

INCLO 7,178.94a [0.000] 7,932.99a [0.000] 3,276.04 [0.177]

INCLO* 7,346.13 [0.239] 6,955.88c [0.096] 14,970.89 [0.020]

INCLOMD 8,227.84a [0.000] 8,747.21a [0.000] 7,153.01a [0.001]

INCLOMD* 2,830.62 [0.395] 3,585.08 [0.198] 3,678.72 [0.359]

INCMD 5,084.33a [0.000] 3,488.27b [0.015] 4,116.59b [0.040]

INCMD* 1,835.35 [0.617] 4,773.69 [0.134] 5,366.73 [0.339]

INCUPMD 2,215.09c [0.093] 1,699.48 [0.161] 1,643.62 [0.419]

INCUPMD* 600.56 [0.851] -473.12 [0.873] 2,503.61 [0.632]

SCORESAT 1.23 [0.729] -0.51436 [0.882] -3.05 [0.561]

SCORESAT* 10.81 [0.306] 1.97 [0.775] 18.18 [0.170]

School-level 

ENDOWSTU 0.00684 [0.317] 0.01056 [0.144] -0.00583 [0.512]

ENDOWSTU* 0.001786 [0.774] 0.00272 [0.644] 0.015595c [0.092]

RANKAVG 88.79 [0.773] 406.70 [0.114] 58.30 [0.888]

RANKAVG* 140.56 [0.571] 38.06 [0.859] 566.66 [0.178]

ENROLUG -36,447.82 [0.623] -238,689.4a [0.004] -12,713.12 [0.922]

ENROLUG* 30,780.66 [0.683] 246,969.5a [0.003] 32,081.77 [0.807]

TENURED -4,340.32 [0.545] 5,070.34 [0.486] 8,685.55 [0.530]

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Variable Total grant aid Need-based grant aid Total aid

TENURED* 15,405.02 [0.204] 16,399.34 [0.103] 11,726.97 [0.514]

Constant 8,727.25 [0.375] -1331.71 [0.878] 17,640.66 [0.209]

F value of model 14.81a [0.000] 16.36a [0.000] 8.79a [0.000]

R-squared 0.55 0.54 0.36

Sample size 409 409 409

F value of joint test 1.13 [0.328] 1.83b [0.026] 1.60c [0.067]

t value of linear restriction n/a 2.05b [0.041] 0.64 [0.525]

Source: GAO analysis.

a

Statistically significant at the 1 percent level or lower. P-values are in brackets. 

b

Statistically significant at the 5 percent level or lower. P-values are in brackets. 

c

Statistically significant at the 10 percent level or lower. P-values are in brackets. 

Notes: n/a means data are not available or applicable. 

*Means interaction terms with EMCA. 

The results of estimating equations 1 and 2 for the total effects of the CA

implementation on affordability and enrollment are summarized in table 

16, based on the regression results in tables 14 and 15. The results for price 

and enrollment in table 16 contain the key findings of the entire study, 

with the other variables (tuition and financial aid) providing information 

that supplements the findings for price. 44

Estimation Results of 

the Effects of 

Attending Meetings 

and Implementing the 

Consensus Approach43

 43Due to lack of sufficient data, we could not obtain separate estimates of the effects of 

attending meetings only or the effects of implementing the consensus approach only 

because it involved only two schools—Brown and Stanford. Also, we could not obtain 

separate estimates of the effects of implementing fully or partly the consensus approach 

because only three of the seven CA schools in sample 2 (Georgetown, Vanderbilt, and 

Wake Forest) had not fully implemented the CA. 

44All tests are performed using the 5 percent or lower level of significance. 

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Prices:45

For the average student, the consensus approach implementation did not 

significantly change the prices paid by students in CA schools compared to 

non-CA schools, including the effects on low-income and minority 

students and students who applied for financial aid.46

Total Effects of 

Implementing the 

Consensus Approach 

(from table 16) 

Tuition:47

The CA schools, compared to non-CA schools, did not significantly change 

the tuition they charged students as a result of the consensus approach 

implementation. 

Total grant aid:48

The consensus approach implementation did not significantly change the 

amount of total grant aid received by students in CA schools compared to 

non-CA schools. 

Need-based total grant aid:49

 45The results were similar when we limited the data to only students who applied for 

financial aid. 

46The effect of the consensus approach implementation on lower-middle income was 

positive and significant at the 10 percent level. We performed several tests for the total 

effects of the consensus approach on prices. First, the effect was significant at the 5

percent level when data for only students who applied for financial aid were used. Second, 

the total effect of the CA on prices was $3,488 and significant at the 5 percent level when 

ENROLUG and ENROLUG* were excluded from the model. Third, because prices are 

bounded at the lower end at zero and at the upper end at the cost of attendance, we also 

estimated the price equation using Tobit regressions. The total effect of the consensus 

approach on prices was negative and insignificant (at the 10 percent level). Unlike the

fixed-effects estimates, the Tobit estimates were unweighted and the standard errors were 

not robust. 

47The results are based on the seven CA and the seven non-CA schools in tables 11 and 12.

Similar results were obtained when we included the schools that had no SAT scores in AY 

2003-2004—three CA schools (Boston, MIT, and Pennsylvania) and two non-CA schools

(Tufts and Yeshiva). 

48The value of the effect of the CA on institutional grant aid was $1,331, but not significant. 

49The effect of the CA on need-based institutional aid was generally similar to need-based 

total grant aid. The effect was about $6,020 and significant at the 5 percent level, with a 

confidence interval of between $512 and $11,528. 

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The consensus approach implementation increased the amount of needbased total grant aid received by students in CA schools compared to nonCA schools by about $6,125, with a confidence interval of between $239 

and $12,011.50 The amounts of need-based grant aid received by students in 

CA schools compared to non-CA schools were higher for middle income 

students by about $20,221, with a confidence interval of between $6,718 

and $33,724. Asian students received higher need-based grant aid of about 

$14,628, with a confidence interval of between $5,051 and $24,206;

Hispanic students received higher need-based grant aid of about $9,532, 

with a confidence interval of between $1,006 and $18,059; and white 

students received higher need-based grant aid of about $6,017, with a 

confidence interval of between $178 and $11,856.

Total aid:51

The consensus approach implementation did not significantly change the 

amount of total aid received by students in CA schools compared to nonCA schools. However, low-income students in CA schools received higher 

total aid of about $12,121, with a confidence interval of between $1,837 

and $22,404.52

Enrollment:

The consensus approach implementation did not significantly change the 

overall likelihood of enrollment into CA schools compared to non-CA 

schools, for all types of students. 

 50The value of the effect of the CA on non-need-based grant aid was estimated to be about -

$6,873, though not significant; the F-test of the joint significance of EMCA and its

interactive terms had a p-value of 14 percent, and the test of the total effect of the CA had a

p-value of 2.1 percent. 

51The value of the effect of the CA implementation on self-help aid (loans, including PLUS, 

and work study) was $1,034, but not significant. 

52The value of the total effect of the CA on total aid was estimated to be about $7,140, 

though not significant; the F-test of the joint significance of EMCA and its interactive terms 

had a p-value of 20 percent, and the test of the total effect of the CA had a p-value of 1.4 

percent. 

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Table 16: Estimates of Effects of Consensus Approach Implementation on Affordability and Enrollment in CA Schools 

Relative to Non-CA Schools

Total effect of

consensus 

approach on... Price Tuition Total grant aid

Need-based

total grant aid Total aid

Probability of 

enrollment

All students $3,021 

[-$2,026, $8,068] 

-$433

[-$2,465, $1,599]

-$749

[-$6,967, $5,470]

$6,125b

[$239, $12,011]

-$2,886

[-$11,805, 

$6,034] 

38%

[8%, 67%]

Financial-aid 

applicants 

$2,177 

[-$3,319, $7,673] 

n/a n/a n/a n/a 22%

[-11%, 

54%]

Low income -$4,061 

[-$15,583, 

$7,461] 

n/a $3,688

[-$8,511,

 $15,887]

$1,956

[-$5,232,

 $9,144]

$12,121b

[$1,837, 

$22,404]

59%

[-52%, 

170%]

Lower-middle 

income 

$8,089c 

[-$263, $16,441] 

n/a -$3,671

[-$12,487,

 $5,145]

$6,556

[-$2,145,

 $15,257]

-$7,776

[-$19,776,

 $4,224]

95%

[6%, 

184%]

Middle income $2,320d 

[-$8,043, 

$12,682] 

n/a $1,618

[-$11,221,

 $14,457]

$20,221a

[$6,718, 

$33,724]

$1,178

[-$19,616,

 $21,971]

26%

[-41%, 

93%]

Upper-middle 

income 

-$1,048 

[-$7,641, $5,545] 

n/a -$973

[-$7,801,

 $5,855]

$2,769

[-$3,986,

 $9,524] 

-$3,054

[-$13,177,

 $7,068]

18%

[-47%, 

82%]

High income $3,699 

[-$824, $8,222]

n/a -$714

[-$6,905,

 $5,476]

$4,687c 

[-$449,

 $9,824]

-$3,856

[-$12,817,

 $5,104]

31%

[-6%, 

68%]

Asian students -$376 

[-$10,426, 

$9,674] 

n/a $5,726

[-$5,671,

 $17,123]

$14,628a

[$5,051,

 $24,206]

$3,694

[-$13,693,

 $21,082]

1%

[-78%, 

80%]

Black students $4,468 

[-$7,452, 

$16,387] 

n/a -$1,227

[-$13,238,

 $10,783]

$4,332

[-$4,992,

 $13,657]

-$6,542

[-$20,353,

 $7,269]

-26%

[-142%, 

91%]

Hispanic students $1,168d 

[-$6,744, $9,079] 

n/a $1,520

[-$8,300,

 $11,341]

$9,532b

[$1,006, 

$18,059]

$3,648

[-$8,981,

 $16,278]

108%

[-6%, 

222%]

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Total effect of

consensus 

approach on... Price Tuition Total grant aid

Need-based

total grant aid Total aid

Probability of 

enrollment

White students $2,588 

[-$2,403, $7,578] 

n/a -$491

[-$6,766,

 $5,784]

$6,017b

[$178, 

$11,856]

-$2,879

[-$11,922,

 $6,164]

19%

[-14%, 

52%]

Number of 

observations 

518 28 409 409 409 518

Schools 

CA Schools: Cornell University, Duke University, Georgetown University, University of Notre Dame, Vanderbilt University, Wake Forest 

University, Yale University 

Non-CA Schools: Brandeis University, Bryn Mawr College, New York University, Princeton University, Tulane University, University of 

Rochester, Washington University at St. Louis 

Source: GAO analysis.

a

Statistically significant at the 1 percent level or lower. 

b

Statistically significant at the 5 percent level or lower. 

c

Statistically significant at the 10 percent level or lower. 

d Effects were negative when data for only financial aid applicants were used. 

Notes: The values in brackets are the 95 percent confidence intervals for the estimates that are 

significant at the 5 percent level or lower. 

“n/a” means data are not available or applicable. 

All the monetary values are in 2005 dollars. 

Results are based on tables 14 and 15. 

The calculated values are based on ˆ ˆ, ˆ

δ + Iη + Sγ where the average values are for all students. 

The estimates are based on γ, k

η S k δ I ˆ ˆ ˆ + + where the average values are for the relevant k 

subgroup of students. 

Prior Levels and 

Differential Effects of the 

Consensus Approach on 

Affordability and 

Enrollment for Students 

with Particular 

Characteristics53

We discuss the estimates of affordability and the likelihood of enrollment 

in both the schools that adopted the consensus approach and those that 

did not, of students with particular characteristics, before the consensus 

approach was implemented. The estimates are reported in table 17, based 

on tables 14 and 15. These estimates could help explain the extent to 

which the consensus approach affected particular groups of students. For 

instance, if certain students were receiving higher financial aid awards 

prior to the consensus approach, they may be less likely to receive much 

higher awards as a result of its adoption. We also discuss the differential 

 53The results for financial aid applicants are relative to non financial aid applicants, those 

for the income groups are relative to the high-income students, and those for the racial 

groups are relative to the white students. 

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effects on students with particular characteristics that the consensus 

approach may have had on affordability and enrollment at those schools. 

The estimates are reported in table 18, based on tables 14 and 15. As 

already discussed, these estimates indicate how the consensus approach 

affected students with particular characteristics, assuming all the other 

characteristics of the students are held constant.

Prices: 

Some students paid lower prices prior to the CA implementation; in 

particular, financial aid applicants relative to non-financial aid applicants; 

low income, lower-middle income, middle-income students relative to 

high-income students; and black and Hispanic students relative to white 

students. But there were no significant differential effects of implementing 

the consensus approach on prices paid by these groups of students in CA 

schools. 

Total grant aid: 

Some students received higher total grant aid prior to the consensus 

approach implementation; in particular, low-income, lower-middle 

income, middle-income, black, and Hispanic students. 

Need-based total grant aid: 

Some students received higher need-based aid prior to the consensus 

approach implementation; in particular, low-income, lower-middle 

income, middle-income, and black students. But there were no significant

differential effects of implementing the consensus approach on prices paid 

by these groups of students. 

Total aid: 

Some students received higher total aid prior to the consensus approach 

implementation; in particular, middle-income, and black students. But 

lower-middle income students received lower total aid prior to the 

consensus approach implementation. Only low-income students in CA 

schools received higher aid, compared to high-income students, as a result 

of implementing the consensus approach. 

Enrollment:

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Students generally were not more or less likely to enroll in a CA school 

prior to the consensus approach implementation. However, implementing 

the consensus approach lowered the likelihood of enrollment of financialaid students, compared to non-financial aid applicants, while the 

likelihood of enrollment of Hispanic students increased, compared to 

white students, in CA schools. 

Table 17: Estimates of Affordability and Enrollment before the Consensus Approach Implementation for Particular Groups of

Students in Both CA and Non-CA Schools 

Students Price Total grant aid

Need-based

total grant aid Total aid

Probability of 

enrollment

Financial-aid applicantsd-$7,573aN/A N/A N/A 1%

Low incomee-$8,427a$7,179a$7,933a$3,276 -15%

Lower-middle incomee-$8,696a$8,228a$8,747a-$7,153a-3%

Middle incomee-$4,804a$5,084a$3,488b$4,117b7%

Upper-middle incomee-$1,434 $2,215c$1,699 $1,644 -3%

High income N/A N/A N/A N/A N/A

Asian studentsf$1,084 -$723 -$740 -$2,639 -1%

Black studentsf-$9,445a$7,915a$4,797b$8,426a19%

Hispanic studentsf-$4,686a$4,710b$2,826 $2,281 7%

White students N/A N/A N/A N/A N/A

Number of observations 518 409 409 409 518

Schools CA Schools: Cornell University, Duke University, Georgetown University, University of Notre 

Dame, Vanderbilt University, Wake Forest University, Yale University 

Non-CA Schools: Brandeis University, Bryn Mawr College, New York University, Princeton

University, Tulane University, University of Rochester, Washington University at St. Louis 

Source: GAO analysis.

a

Statistically significant at the 1 percent level or lower. 

b

Statistically significant at the 5 percent level or lower. 

c

Statistically significant at the 10 percent level or lower. 

d

The estimates are relative to non-financial aid applicants. 

e

The estimates are relative to high income students. 

f

The estimates are relative to white students. 

Notes: Results are from tables 14 and 15, based on the coefficient in equations 1 and 2. For 

instance, the value for price for financial-aid applicants is based on the estimated coefficient FINAID

in table 14. 

ρˆ

N/A means data are not available or applicable. 

All the monetary values are in 2005 dollars. 

Page 68 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

Table 18: Differential Effects of Consensus Approach Implementation on Affordability and Enrollment in CA Schools for 

Particular Groups of Students

Students Price Total grant aid

Need-based total 

grant aid Total aid 

Probability of 

enrollment 

Financial-aid 

applicantsd

$190 N/A N/A N/A -31%b

Low-incomee -$6,508 $7,346 $6,956c $14,971b -15%

Lower-middle 

incomee

-$1,790 $2,831 $3,585 $3,679 17%

Middle incomee$946 $1,835 $4,774 $5,367 -16%

Upper-middle 

incomee

-$1,716 $601 -$473 $2,504 -7%

High income n/a n/a n/a n/a n/a

Asian studentsf-$7,110c$6,971 $6,547c$8,276 -9%

Black studentsf-$566 $2,546 -$2,267 $965 -20%

Hispanic studentsf-$1,610 $2,039 $207 $2,607 35%b

White students n/a n/a n/a n/a n/a

Number of 

observations 518 409 409 409 518

Schools CA Schools: Cornell University, Duke University, Georgetown University, University of Notre Dame, Vanderbilt 

University, Wake Forest University, Yale University 

Non-CA Schools: Brandeis University, Bryn Mawr College, New York University, Princeton University, Tulane 

University, University of Rochester, Washington University at St. Louis 

Source: GAO analysis.

a

Statistically significant at the 1 percent level or lower. 

b

Statistically significant at the 5 percent level or lower. 

c

Statistically significant at the 10 percent level or lower. 

d

The estimates are relative to non-financial aid applicants. 

e

The estimates are relative to high income students. 

f

The estimates are relative to white students. 

Notes: Results are from tables 14 and 15, based on the coefficient in equations 1 and 2. For 

instance, the value for price for financial-aid applicants is based on the estimated coefficient FINAID* 

in table 14. 

γˆ

N/A means data are not available or applicable. 

All the monetary values are in 2005 dollars. 

Page 69 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

Limitations of the 

Study 

The findings of the study could be limited by the potential of selection bias 

if the CA schools had characteristics that we could not control for that 

made them more inclined to adopt the consensus approach and 

independently influenced the outcome variables. We believe that this is 

not a serious problem with the estimation since the difference-indifference approach includes CA schools before the implementation of the 

CA, implying the latter selection problem would require significant change

over a short time span in the character of these schools. Furthermore, a 

key factor that might motivate schools to join the 568 Group is the legacy 

of the Overlap group. The 568 Group has objectives that are similar to 

those stated by the Overlap group—to be able to offer financial aid to 

more needy students. Our test indicated that the chances of a former 

Overlap group member joining or not joining the 568 Group did not differ 

between the two groups of schools in our sample.54 Thus, the similarity 

between the two groups, in terms of a school joining the 568 Group, 

implied the potential for selection bias may be small. 

In our analysis, the total grant aid does not include self-help aid (loans and 

work study). However, if the true amount of total grant aid should include 

some proportion of self-help aid, then its exclusion would lead to an 

underestimation of total grant aid. Nonetheless, we believe this did not 

significantly affect our results since we found that the consensus approach 

implementation did not affect self-help aid. 

It may be that early admit students pay higher prices because early 

decision admission might be used by need-blind schools as a screening 

mechanism to indirectly identify a student’s willingness-to-pay. Under the 

early decision process a non-financial aid student is therefore more likely 

to be admitted than a financial-aid student of comparable quality.55 We did

Sample Selection bias 

Measures of Price 

Early Decision Admissions 

 54We tested for the equality of the proportions of CA schools and non-CA schools that were

members of the former of the Overlap group. We used the 11 CA schools and the 14 non-CA

schools in samples 1 through 4 in table 9. The CA schools had 5 Overlap members and the

non-CA schools had 3 Overlap members.

55See Kim (2005). 

Page 70 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

not expect the early decision process to affect our results because while 

the process might help identify a student with a higher willingness to pay, 

it is the student’s ability to pay that determines the need-based aid offered 

by the 568 Group. Furthermore, the total probability of enrollment of a 

financial-aid applicant was similar to that of a non-financial aid applicant 

both before and after the consensus approach implementation, even 

though the consensus approach implementation tended to decrease the 

likelihood of enrollment of financial-aid students. 

We could not include all the schools affiliated with the 568 Group in the 

analysis because of data limitations. (See the list of unmatched treatment 

schools in table 9.) However, there were several similarities (in terms of 

“best college” ranking, endowment, tuition and fees, and percentage of 

tenured faculty) as well as differences (in terms of freshmen enrollment) 

between the included and excluded CA colleges.

The data were available for only one academic year period after 

implementation of the consensus approach. This could mask potential 

effects of the consensus approach since these effects could be gradual,

rather than immediate, and therefore take time to for the effects to be 

captured. Also, the small sample size of the data could make the estimates 

less precise, especially for some of the subgroups of students we 

considered. However, we checked to ensure that the estimates were 

consistent with the data by estimating the predicted values corresponding 

to the observed mean values for price, the key variable of interest, and the

financial aid variables. The results, presented in table 19, show that the 

predictions of our model are consistent qualitatively with the observed 

data. 

Excluded Schools of 

Comparable Selectivity 

Limited Data Availability 

Page 71 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

Table 19: Comparison of Observed and Predicted Price and Financial Aid Variables in CA and Non-CA Schools: Pre— and 

Post—Consensus Approach Implementation Period

CA Schools Non-CA Schools

1995-1996 2003-2004 Difference 1995-1996 2003-2004 Difference

Price 

All students

Observed $28,039 $35,488 $7,449 $28,068 $30,838 $2,770

Predicted $30,791 $37,171 $6,380 $25,386 $27,882 $2,496

Financial-aid 

Observed $25,845 $32,897 $7,052 $24,960 $29,705 $4,745

Predicted $28,222 $34,352 $6,130 $22,347 $27,330 $4,983

Non financial-aid 

Observed $34,645 $44,504 $9,859 $37,714 $44,292 $6,578

Predicted $38,771 $46,625 $7,854 $35,127 $34,973 -$154

Low income

Observed $12,566 $10,095 -$2,471 $18,950 $21,886 $2,936

Predicted $14,272 $11,389 -$2,833 $13,613 $18,106 $4,493

Lower-middle-income

Observed $19,220 $30,437 $11,217 $17,623 $20,546 $2,923

Predicted $20,650 $32,075 $11,425 $15,156 $19,501 $4,345

Middle-income 

Observed $24,785 $34,201 $9,416 $22,560 $26,069 $3,509

Predicted $26,035 $36,438 $11,838 $21,092 $20,764 -$328

Upper-middle-income 

Observed $26,285 $32,310 $6,025 $29,429 $34,305 $4,876

Predicted $31,423 $35,121 $3,698 $26,566 $27,616 $1,050

High income 

Observed $32,616 $39,496 $6,880 $33,137 $34,138 $1,001

Predicted $35,538 $41,043 $5,505 $31,129 $32,582 $1,453

Financial aid—

All students

Total grant aid

Observed $9,142 $9,775 $633 $13,391 $13,960 $569

Predicted $10,285 $11,877 $1,592 $12,181 $13,194 $1,013

Need-based grant aid

Observed $7,771 $6,439 -$1,332 $11,863 $8,122 -$3,741

Predicted $7,443 $6,170 -$1,273 $12,277 $9,151 -$3,126

Page 72 GAO-06-963 Higher Education
Appendix II: Econometric Analysis of Effects

of the Higher Education Antitrust Exemption 

on College Affordability and Enrollment 

CA Schools Non-CA Schools

1995-1996 2003-2004 Difference 1995-1996 2003-2004 Difference

Total aid 

Observed $16,604 $16,046 -$558 $19,827 $22,255 $2,428

Predicted $17,998 $17,957 -$41 $18,425 $20,127 $1,702

Source: GAO analysis.

Page 73 GAO-06-963 Higher Education
Appendix III: Classifi

Academic Year 

the 

cation of 1999-2000 

and Schools Only Attending 

568 Group Meetings

Page 74 GAO-06-963 

Appendix III: Classification of 1999-2000 

Academic Year and Schools Only Attending 

the 568 Group Meetings 

We conducted tests to determine whether to use data collected in 

academic year 1999-2000 and whether schools that attended meetings of 

the 568 President’s group but did not implement the consensus approach

could be included in our analysis. First, the academic year 1999-2000 was 

very close to the establishment of the 568 President’s Group, which 

occurred in 1998. The 1999-2000 academic year might have been a 

transitional period, and it would therefore not be appropriate to use the 

data as part of the period before the 568 Group implemented the 

consensus approach. Second, there were five schools, among the schools 

with data available for our econometric analysis, that either only attended

the 568 Group meetings (Case Western Reserve University, Stanford 

University, and University of Southern California) or were members of the 

568 Group but had not implemented the CA as of 2003 (Brown University 

and Dartmouth College). We therefore investigated which group—control 

or treatment—each of the five schools belonged. 

We used the data for sample 4 to investigate if data collected in 1999-2000 

belonged in the pre-CA period (with data collected in 1995-1996). Although 

both samples 1 and 4 have data for 1995-1996 and 1999-2000, we chose 

sample 4 because it was the larger sample. See table 9 in appendix II for 

the list of the schools in each sample and the academic years for which 

data were available. 

The tests were performed using the Chow test, which is of the form:1

(1) y = β01 + β11 x1 + β21 x2 + u, u ~ N(0,σ2), for group = 1995-1996 (g1), 

and 

Does Academic Year 

1999-2000 belong to 

the Pre– or Post– 

Consensus Approach 

Implementation 

Period? 

(2) y = β02 + β12 x1 + β22 x2 + u, u ~ N(0,σ2) for group = 1999-2000 (g2).

Pooling the two groups of data we estimated, 

(3) y = β01 + β11 x1 + β21 x2 + (β02–β01)g2 + (β12–β11)g2x1 

+ (β22–β21)g2x2 + u, where g2 is an indicator variable. 

The test examines the hypothesis that the added coefficients are jointly 

zero: (β02–β01) = (β12–β11) = (β22–β21) = 0. 

An insignificant test statistic (a small test statistic and a large p-value) 

suggests that the above equality holds, and there is no difference between 

the estimates for 1999-2000 and the group with which it is compared (1995-

 1

See http://www.stata.com/support/faqs/stat/chow3.html for details. 

Higher Education
Appendix III: Classification of 1999-2000 

Academic Year and Schools Only Attending 

the 568 Group Meetings

1996). On the other hand, a significant statistic (a large test statistic and a 

small p-value) suggests that the above equality does not hold and the 1999-

2000 is different from the group with which it is compared (1995-1996). 

We combined 1999-2000 with 1995-1996 and tested if the coefficients for 

1999-2000 differed from that of 1995-1996, using sample 4. The tests were 

done for price, the key variable affecting student outcomes for schools. 

We performed a joint test that the added coefficients in equation 3 are 

jointly zero. The F-value is 1.71, and significant with a p-value of 0.0375. 

This implied that data collected in 1999-2000 did not belong to with the 

1995-1996 data in the pre-CA period.2

Similarly, we examined if 1999-2000 belonged to the post-CA period by 

combining 1999-2000 with 2003-2004, using sample 3. The F-value of the 

joint test is 8.36, and significant with a p-value of 0.0. This implied that 

1999-2000 data did not belong to with the 2003-2004 data in the post-CA 

period. 

These results suggest that it was more appropriate to exclude 1999-2000 

from the analysis, implying that samples 1 and 2, which have data for the 

pre-CA period (1995-1996) and the post-CA period (2003-2004) would be 

more appropriate. However, because sample 2 was larger than sample 1, 

our subsequent analysis used sample 2. 

We performed an analysis similar to that described above to determine 

whether schools that only attended meetings—Brown University, Case 

Western Reserve University, Dartmouth College, Stanford University, and 

University Southern California (USC)—belonged in the treatment or 

control group. We determined whether the behavior of each of these 

schools was more consistent with the control schools or the treatment 

schools after the consensus approach implementation, using data for 2003-

2004. Since we had determined from the above analysis that samples 1 and 

Do the Schools That 

Only Attended the 

568 group Meetings 

belong to the Control 

or Treatment Group? 

 2

As expected, the estimates from the pooling (equation 3) are the same as for the separate 

estimates (equations 1 and 2). Also the residual variances from equations 1 and 2 were 

similar, suggesting that the pooling was appropriate. This applies to all the other Chow

tests we performed. 

Page 75 GAO-06-963 Higher Education
Appendix III: Classification of 1999-2000 

Academic Year and Schools Only Attending 

the 568 Group Meetings

2 are more appropriate for our subsequent analysis, we focus on sample 2,

the larger sample, for these tests.3

Similar to the analysis in section above, we included Brown in the control 

group and tested if the coefficients for Brown differed from the control 

group. We performed a joint test and obtained an F-value of 25.68,

significant at 0.00. This implied that Brown did not belong to the control 

group. For the treatment group test, the F-value was 7.37, significant at 

0.00. This also implied that Brown did not belong to the treatment group. 

Thus, Brown did not belong to either the control or treatment group. 

The F-value for the control group test was 19.16, significant at 0.00, and 

the F-value for the treatment group test was 5.59, significant at 0.00. This 

implied that Stanford did not belong to either the control or treatment 

group. 

We tested for which group USC belonged by excluding the SAT scores 

variable (SCORESAT) from the model since the data were not available for 

2003-2004. The F-value for the control group test was 23.23, significant at 

0.00, and the F-value for the treatment group test was 12.54, significant at 

0.00. This implied that USC did not belong to either the control or 

treatment group.

To Which Group Did 

Brown Belong—Control or 

Treatment? 

To Which Group Did 

Stanford Belong—Control 

or Treatment? 

To Which Group Did USC 

Belong—Control or 

Treatment? 

Based on the above analysis, we determined that the best data for our 

analysis was sample 2, and we excluded all five schools that only attended 

the 568 Group meetings but did not implement the consensus approach. 

 3

The test was not performed for Case Western Reserve and Dartmouth because they are in

samples 4 and 3, respectively. Samples 3 and 4 cannot be used because there are no data 

for 1995-1996 and 2003-2004, respectively. 

Page 76 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’ Appendix IV Group : Comments from 568 Presidents’ 

Group 

Page 77 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 78 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 79 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 80 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 81 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 82 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 83 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 84 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 85 GAO-06-963 Higher Education
Appendix IV: Comments from 568 Presidents’

Group 

Page 86 GAO-06-963 Higher Education
Appendix V: Consultants and Peer Reviewers Appendix V: Consultants and Peer Reviewers 

Hashem Dezhbakhsh, Ph.D. 

Professor of Economics 

Emory University 

Dennis Epple, Ph.D. 

Thomas Lord Professor of Economics 

Graduate School of Industrial Administration 

Carnegie Mellon University 

Janet Netz, Ph.D. 

Founding Partner 

ApplEcon LLC 

Richard Romano, Ph.D. 

Gerald L. Gunter Professor of Economics 

Department of Economics 

Warrington College of Business 

University of Florida 

Lawrence White, Ph.D. 

Arthur E. Imperatore Professor of Economics 

Department of Economics 

Leonard N. Stern School of Business 

New York University 

Gordon C. Winston, Ph.D. 

Orrin Sage Professor of Political Economy, Emeritus 

Director of the Williams Project on the Economics of Higher Education 

Department of Economics 

Williams College 

Page 87 GAO-06-963 Higher Education
Appendix VI: 

A

GAO Contact and Staff 

cknowledgments 

Page 88 GAO-06-963 

Appendix VI: GAO Contact and Staff 

Acknowledgments 

GAO Contact Cornelia M. Ashby, Director, (202) 512-7215 

The following individuals made important contributions to the report: 

Sherri Doughty, Assistant Director; Andrea Sykes; John A. Karikari; 

Angela Miles; Daniele Schiffman; John Mingus; Dayna Shah; 

Richard Burkard; Susan Bernstein; Rachel Valliere; Robert Alarapon; 

Thomas Weko; and L. Jerome Gallagher. 

Staff 

Acknowledgments 

Higher Education
Bibliography Bibliography 

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Students’ College Choices?” National Bureau of Economic Research 

Working Paper, No. 9482. 2003. 

Bamberger, G., and D. Carlton.”Antitrust and Higher Education: MIT 

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Carlton, D., G. Bamberger, and R. Epstein. “Antitrust and Higher 

Education: Was There A Conspiracy to Restrict Financial Aid?” RAND 

Journal of Economics, vol. 26, no. 1 (Spring 1995): 131-147. 

Epple, D., R. Romano, S. Sarpca, and H. Sieg. “Profiling in Bargaining Over 

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Hill, C., and G. Winston. “Access: Net prices, Affordability, and Equity At a 

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Morrison, R. “Price Fixing Among Elite Colleges and Universities,” The 

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Netz, J. “Non-Profits and Price-Fixing: The Case of the Ivy League.” 

unpublished paper. November 1999. 

Netz, J. “The End of Collusion?: Competition After Justice and the Ivy 

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Salop, S., and L. White. “Antitrust Goes to College,” Journal of Economic 

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Bibliography 

Winston, G., and C. Hill. “Access to the Most Selective Private Colleges by 

High-Ability, Low-Income Students: Are They Out There?” unpublished 

paper. October 2005. 

(130463) Page 90 GAO-06-963 Higher Education
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