Higher Education: School's Use of the Antitrust Exemption Has Not Significantly Affected College Affordability or Likelihood of Student Enrollment to Date
Published: 2006-09-21T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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.
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: 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  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  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  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  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 in- depth 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.
subsections: section_title: Background
paragraphs: 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.
subsections: section_title: Legal History of Antitrust Exemption for Higher Education Institutions
paragraphs: 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. 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  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. 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. 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. The exemption was extended most  recently in 2001, and is set to expire in 2008.
section_title: Determining a Student’s Financial Need
paragraphs: 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. 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  allowances. The student’s EFC is then compared to the cost of attendance  to determine if the student has financial need. (see fig. 1)
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.  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.
section_title: Twenty-Eight Schools Used the Antitrust Exemption to Develop a Common Methodology for Assessing a Family’s Financial Need
paragraphs: 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.
subsections: section_title: Highly Selective Private4- Year Colleges and Universities Formed a Group to Participate in Activities Allowable under the Exemption
paragraphs: 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. 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.)
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  members. 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.
section_title: Participating Schools Agreed to a Common Methodology for Assessing Financial Need, but Schools Varied in Their Implementation of the Methodology
paragraphs: 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  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 non- custodial 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.
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.
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  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.
section_title: 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
paragraphs: 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.
subsections: section_title: Cost of Attendance Increased at Schools Using the Exemption Corresponding to Increases at Other Private Schools
paragraphs: 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. For example, at other private  4-year schools there was a 7 percent increase in these costs, from $25, 204  to $27, 071. 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.
section_title: Percentage of Students Receiving Institutional Grant Aid and the Amount Schools Provided Them Increased
paragraphs: 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.
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  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.
section_title: 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
paragraphs: 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. 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.  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.
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.
section_title: Implementation of a Common Methodology Has Not Significantly Affected Affordability or Enrollment at Schools Using the Exemption
paragraphs: 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.  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 non- need-based grant aid, such as academic or athletic scholarships.  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.
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. 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).
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.
section_title: Concluding Observations
paragraphs: 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.
section_title: Agency Comments
paragraphs: 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 student- 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.
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.
section_title: Appendix I: Statistical Analysis of Expected Family Contributions at Schools Using the Exemption and Comparable Schools
paragraphs: 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  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.”
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.
Denoting the estimates of V1 and V2 for the two groups by  , the empirical distribution of  then compared with the empirical distribution of   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   ,  .
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   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.
We used the KSMIRNOV command in Stata to perform the tests.
Coefficient of  variation 1 (V1)
Coefficient of  variation 2 (V2)
Coefficient of  variation 1 (V1)
Coefficient of  variation 2 (V2)
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).
section_title: Appendix II: Econometric Analysis of Effects of the Higher Education Antitrust Exemption on College Affordability and Enrollment
paragraphs: 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.
subsections: section_title: Theories of the Effects of the Consensus Approach on Financial Aid
paragraphs: 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.
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  afford the higher prices. Our analyses allowed us to test these two  theories with the data available.
section_title: Sources of Data for the Model
paragraphs: 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.
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.
National Association of College and University Business Officers  (NACUBO): This source provided data on school endowment from 1992  through 2004.
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.
section_title: Selection of Control Schools
paragraphs: 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. 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.  These 36 schools comprised the treatment schools used initially to select  the comparable control schools. All 36 treatment schools were private; 13  were liberal arts schools and 23 were national universities. 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. 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. 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. 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  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). 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.
subsections: section_title: Determination of the Appropriate Time Periods for Assessing Effects and Classification of Schools that Only Attended the Meetings
paragraphs: 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. Therefore, the treatment schools  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.
section_title: Specifications of Econometric Models and Estimation Methodology
paragraphs: 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. We used a difference-in- difference 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. 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.
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.
subsections: section_title: Modeling the Effects of the Consensus Approach Methodology for Financial Need on Affordability and Enrollment
paragraphs: 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.
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. 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  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.
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.
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-need- based aid, which consisted mainly of merit aid.
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.
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. 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:  The exemption was captured by the implementation of the consensus  approach by a school. 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.
This variable was from the GAO survey of the CA and non-CA schools. for financial aid, or the preferences of the students. 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.
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):  AY1995: Equals one for the academic year 1995-1996, and zero otherwise  AY2003: Equals one for the academic year 2003-2004, and zero otherwise.
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.
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.
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.
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.
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). In  general, the values of the variables were similar between the two groups of  schools.
section_title: Comparison of Prices and Financial Aid in CA and Non-CA Schools
paragraphs: 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. 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.
section_title: Model Specifications and Estimation Methodology
paragraphs: Our econometric analysis is based on panel data, which pooled cross- sectional 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 random- effects estimation because the observations were not randomly chosen  and there were likely to be unobserved school-specific effects. The  reported estimates were based on the fixed-effects estimators, using  probability weights, and the standard errors were robust.
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). The regression equations  were specified generally as follows:   (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 school- level variables and between EMCA and the student-level variables.
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  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). 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  coefficient  approach implementation on price, while   measures the unconditional effect of the consensus  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 low- income (INCLO) students can be estimated by  δwhere 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 low- income (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,  ijtF is an indicator variable equal to one if the student is a financial  aid applicant and zero otherwise, and ijtA is an indicator equal to one if the  student is black, and zero otherwise. Then, using equation 1, the equation  for this example is:     (1.1)
Now consider a white student who is a financial aid applicant in school j  at time t.  The predicted price for a white student if j is a CA school is:  (1.2) and the predicted price if j is not a CA school is:  (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:  (1.4)
The coefficient  measures the effect of adopting the consensus approach  that is invariant across school and student type, the term  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:  (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  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 low- income students will have other characteristics and attend different  schools than non low-income students.  We will also discuss the  coefficientρˆ , 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.
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. See the  regression estimates for price and tuition in table 14, and those for the  financial aid variables in table 15.
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. 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. ) (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).
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. ) ) 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  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. ) (.). 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. The  regression estimates are in table 14.
The regression model for enrollment in table 14 is significant using the chi- square 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.
section_title: Estimation Results of the Effects of Attending Meetings and Implementing the Consensus Approach
paragraphs: 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.
subsections: section_title: Total Effects of Implementing the Consensus Approach (from table 16)
paragraphs: 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.
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.
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:The consensus approach implementation increased the amount of need- based total grant aid received by students in CA schools compared to non- CA schools by about $6,125, with a confidence interval of between $239  and $12,011. 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.
The consensus approach implementation did not significantly change the  amount of total aid received by students in CA schools compared to non- CA 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.
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.
section_title: Prior Levels and Differential Effects of the Consensus Approach on Affordability and Enrollment for Students with Particular Characteristics
paragraphs: 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  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.
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.
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.
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:  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 financial- aid students, compared to non-financial aid applicants, while the  likelihood of enrollment of Hispanic students increased, compared to  white students, in CA schools.
section_title: Limitations of the Study
subsections: section_title: Sample Selection bias
paragraphs: 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-in- difference 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. 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.
section_title: Measures of Price
paragraphs: 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.
section_title: Early Decision Admissions
paragraphs: 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. We did  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.
section_title: Excluded Schools of Comparable Selectivity
paragraphs: 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.
section_title: Limited Data Availability
paragraphs: 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.
section_title: Appendix III: Classification of 1999-2000 Academic Year and Schools Only Attending the 568 Group Meetings
paragraphs: 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.
subsections: section_title: Does Academic Year 1999-2000 belong to the Pre– or Post– Consensus Approach Implementation Period?
paragraphs: 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) y = β01 + β11 x1 + β21 x2 + u, u ~ N(0,σ2), for group = 1995-1996 (g1),  and  (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- 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.
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.
section_title: Do the Schools That Only Attended the 568 group Meetings belong to the Control or Treatment Group?
paragraphs: 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  2 are more appropriate for our subsequent analysis, we focus on sample 2,  the larger sample, for these tests.
subsections: section_title: To Which Group Did Brown Belong—Control or Treatment?
paragraphs: 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.
section_title: To Which Group Did Stanford Belong—Control or Treatment?
paragraphs: 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.
section_title: To Which Group Did USC Belong—Control or Treatment?
paragraphs: 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.
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.
section_title: Appendix IV: Comments from 568 Presidents’ Group
section_title: Appendix V: Consultants and Peer Reviewers
section_title: Appendix VI: GAO Contact and Staff Acknowledgments
subsections: section_title: Staff Acknowledgments
paragraphs: 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.
section_title: Bibliography
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