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

Letter

Background

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.

Legal History of Antitrust Exemption for Higher Education Institutions

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.

Determining a Student’s Financial Need

For many students, financial aid is necessary in order to enroll in and complete a postsecondary education. In school year 2004-2005, about $113 billion in grant, loan, and work-study aid was awarded to students from a variety of federal, state, and institutional sources. 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.

Twenty-Eight Schools Used the Antitrust Exemption to Develop a Common Methodology for Assessing a Family’s Financial Need

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.

Highly Selective Private4- Year Colleges and Universities Formed a Group to Participate in Activities Allowable under the Exemption

Twenty-eight schools, all of which have need-blind admission policies as required under the law, formed the 568 Presidents’ Group in 1998 with the intent to engage in activities allowed by the antitrust exemption. 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.

Participating Schools Agreed to a Common Methodology for Assessing Financial Need, but Schools Varied in Their Implementation of the Methodology

Of the four activities allowed under the antitrust exemption, the 28 schools engaged in only one—development of the consensus approach for need analysis. With respect to the other three activities allowed under the exemption, the schools either chose to not engage in the activities or piloted them on a limited basis. For example, three schools in the group attempted to share student-level financial aid data through a third party. However, they reported that because the effort was too burdensome and yielded little useful information, they chose not to continue. The group also expressed little need or interest in creating another common aid application form as such a form already existed. Schools also decided to leave open the option to award aid on a non-need basis.

According to the officials representing the 28 schools, the main purpose of the group was to discuss ways to make the financial aid system more understandable to students and their families and commit to developing a common methodology for assessing a family’s ability to pay for college, which they called the consensus approach. Developing an agreed upon 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.

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

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.

Cost of Attendance Increased at Schools Using the Exemption Corresponding to Increases at Other Private Schools

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.

Percentage of Students Receiving Institutional Grant Aid and the Amount Schools Provided Them Increased

Over the same time period, the percentage of students who received any form of institutional grant aid at schools using the exemption increased by 3 percentage points, from 37 to 40 percent, as illustrated by figure 3.

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.

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

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.

Implementation of a Common Methodology Has Not Significantly Affected Affordability or Enrollment at Schools Using the Exemption

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.

Concluding Observations

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.

Agency Comments

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.

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

We compared variation in expected family contributions (EFCs) between students who were admitted to both schools using the exemption and comparable schools that did not. We collected data on student EFCs from 27 of the 28 schools using the exemption and 55 schools that had similar selectivity and rankings as schools using the exemption. The data included the student’s EFC calculated by the schools as of April 1, 2006, based on their need analysis methodology. We determined that these data would most likely reflect the school’s first EFC determination for a student and thus would be best for comparison purposes. We then matched students across both sets of schools to identify students accepted to more than one school (which we call cross-admits).

Our sample consisted of data for the following three types of cross-admit students: 1. Students accepted to several schools using the exemption and several schools that were not (type 1 students); 2. Students accepted to only schools using the exemption (type 2 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).

Appendix II: Econometric Analysis of Effects of the Higher Education Antitrust Exemption on College Affordability and Enrollment

To estimate the effects of schools’ implementation of the consensus approach to need analysis on affordability (measured by price) and enrollment of freshmen students, we developed econometric models. This appendix provides information on theories of the exemption effects on student financial aid, the data sources for our analyses and selection of control schools, specifications of econometric models and estimation methodology, our econometric results, and limitations of our analysis.

Theories of the Effects of the Consensus Approach on Financial Aid

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.

Sources of Data for the Model

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.

Selection of Control Schools

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.

Determination of the Appropriate Time Periods for Assessing Effects and Classification of Schools that Only Attended the Meetings

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.

Specifications of Econometric Models and Estimation Methodology

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.

Modeling the Effects of the Consensus Approach Methodology for Financial Need on Affordability and Enrollment

The basic tenets of financial need analysis are that parents and students should contribute to the student’s education according to their ability to pay. The CA schools used the consensus approach for its need analysis methodology and to determine the expected family contribution (EFC) for each student based on that methodology. Conversely, the non-CA schools primarily used a need analysis methodology called the institutional methodology (IM). The difference between the cost of attendance (COA) and the EFC determines whether a student has financial need. If so, the school then develops a financial aid package of grants, loans, and work study from various sources. The actual amount that students and families pay depends on how much of the aid received is grant aid. Therefore, the implementation of the consensus approach was expected to affect the price paid and the financial aid received by students, and by implication, their enrollment into schools.

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.

Comparison of Prices and Financial Aid in CA and Non-CA Schools

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.

Model Specifications and Estimation Methodology

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.

Estimation Results of the Effects of Attending Meetings and Implementing the Consensus Approach

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.

Total Effects of Implementing the Consensus Approach (from table 16)

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.

Prior Levels and Differential Effects of the Consensus Approach on Affordability and Enrollment for Students with Particular Characteristics

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.

Limitations of the Study

Sample Selection bias

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.

Measures of Price

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.

Early Decision Admissions

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.

Excluded Schools of Comparable Selectivity

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.

Limited Data Availability

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.

Appendix III: Classification of 1999-2000 Academic Year and Schools Only Attending the 568 Group Meetings

We conducted tests to determine whether to use data collected in academic year 1999-2000 and whether schools that attended meetings of the 568 President’s group but did not implement the consensus approach could be included in our analysis. First, the academic year 1999-2000 was very close to the establishment of the 568 President’s Group, which occurred in 1998. The 1999-2000 academic year might have been a transitional period, and it would therefore not be appropriate to use the data as part of the period before the 568 Group implemented the consensus approach. Second, there were five schools, among the schools with data available for our econometric analysis, that either only attended the 568 Group meetings (Case Western Reserve University, Stanford University, and University of Southern California) or were members of the 568 Group but had not implemented the CA as of 2003 (Brown University and Dartmouth College). We therefore investigated which group—control or treatment—each of the five schools belonged.

Does Academic Year 1999-2000 belong to the Pre– or Post– Consensus Approach Implementation Period?

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.

Do the Schools That Only Attended the 568 group Meetings belong to the Control or Treatment Group?

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.

To Which Group Did Brown Belong—Control or Treatment?

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.

To Which Group Did Stanford Belong—Control or Treatment?

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.

To Which Group Did USC Belong—Control or Treatment?

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.

Appendix IV: Comments from 568 Presidents’ Group

Appendix V: Consultants and Peer Reviewers

Appendix VI: GAO Contact and Staff Acknowledgments

Staff Acknowledgments

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.

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