Measuring the Impact of the U.S. News & World Report College Rankings on College. Admissions

Transcription

1 1 Measuring the Impact of the U.S. News & World Report College Rankings on College Admissions Alec Joshua Hubel Senior Economics Thesis Advisor: Biswajit Banerjee Haverford College April 28, 2011

2 2 Abstract The college rankings issued by U.S. News & World Report have become of considerable concern to the field of higher education. Previous research has found significant impacts between an institution s rank and its admissions statistics. This paper attempts to quantify the extent of those impacts. Using fixed effects econometric analysis, this paper has found a substantial link between an institution s rank and acceptance rate, yield rate, volume of applications received, and the standardized test scores of an incoming freshmen class. These results suggest that these more impressive rankings may be correlated with more favorable admissions statistics.

3 3 Table of Contents I. Introduction... 4 II. Literature Review... 8 III. IV. Data.11 Methodology V. Results VI. VII. VIII. IX. Caveats and Further Research...24 Conclusion Bibliography. 28 Appendix

4 4 I. Introduction Since its inception in the early 1980 s, the U.S. News & World Report college rankings have become an immensely popular source of information on institutions of higher education. As of 1998, the Best Colleges issue sells approximately 3.5 million copies and generates over $5 million in revenue (Machung 1998). The Best Colleges section of the U.S. News & World Report website receives millions of page views every month (Bowman & Bastedo 2009). With such a high amount of publicity, it is inevitable that these rankings have the ability to significantly impact the realm of higher education. An incentive base problem can easily emerge because of the sway that these rankings have. Because college administrators are aware of the impact of college rankings, there is much incentive to make changes to an institution that would boost an institution s ranking. However, this may not necessarily result in a better undergraduate experience. For example, the U.S. News & World Report rankings as well as rankings sponsored by other publications take the selectivity of an institution, as measured by acceptance rate, in to account when measuring the quality of an institution. If a college begins to advertise themselves towards prospective students that would have little chance of admission, they could yield a greater amount of deniable applications, dropping the acceptance rate. While this may have a positive impact on an institution s ranking, it does nothing to provide a higher quality education. This problem is seen with the increasing popularity of early-decision programs (Machung 1998). In early-decision programs, students are allowed to apply to a school before the standard application process begins. However, if a student is accepted, they are bound to attend the institution and must withdraw applications from all other

5 5 institutions. These programs may have advantages for students who are absolutely sure of which institution they wish to attend, but in the case of students who are unsure or have to consider the different financial aid packages they are offered, applying via early decision limits options. If a large proportion of a school s student body gains admittance via an early-decision process, the percentage of accepted students that decide to ultimately attend the school, also known as the yield rate, will be high. This yield rate is a significant factor in the U.S. News & World Report rankings. As such, schools have an incentive to expand their early-decision programs even though it has no significant impact on the quality of education a school can offer. Rankings are calculated based on a wide variety of variables ranging from standardized test scores to faculty statistics to general reputation. Each variable is given a weight, and then the weights are summed. The school with the highest sum is awarded the number one rank. Unfortunately, there is a significant problem with this method because the weights are relatively arbitrary. There is standardized rubric that is widely used to assess the quality of an education. Furthermore, the methodology that U.S. News & World Report uses to calculate its rankings is not static. Every year, the magazine tweaks its methodology in order to make their rankings more accurate (Machung 1998). However, this causes rankings to be especially erratic. Colleges, especially at the upper echelons, tend to be tightly clustered in the rankings. A slight change in methodology can lead to a school dropping or rising by several ranks, while there may be no significant change in the quality of the institution. For example, in 1993 Georgetown University ranked 17 th among national universities. The following year, Georgetown dropped to 25 th. It is highly unlikely that

6 6 there was a substantial change at Georgetown over that period. What actually caused the drop was U.S. News & World Report changing the way they defined faculty salaries. In 1993, Georgetown ranked 20 th in the faculty resources category. The next year, their ranking plummeted to 74 th place in the same category (Machung 1998). Again, it was not a change at Georgetown that resulted in the drop, but a change in the ranking methodology. The fact that a high proportion of each institution s rank is based on reputation is also cause for concern. In 2010, a weight of 25% was given to the reputation of an institution 1. This is the single greatest weight of all the factors considered in the U.S. News & World Report rankings. As reputation is an opinion and not an easily quantifiable measure, it is debatable whether or not it should not be given so much weight. In order to gather reputation data, U.S. News & World Report sends surveys to college administrators across the country. The surveys ask the administrators to rate the undergraduate reputation of an institution on a scale from 1 (marginal) to 5 (distinguished). These surveys are then used to calculate an institution s reputation data. While the fact that the reputational data is based off of the opinions of administrators in institutions of higher education gives the notion a little more validity, this also creates certain problems. Administrators may have an incentive to downplay their opinion of rival institutions while promoting their opinion of institutions that are in their favor. Additionally, these rankings are unable to include certain facets of a college that contribute to the overall quality, yet are not quantifiable. The presence of community service opportunities or a senior capstone project certainly adds to the undergraduate 1 colleges 2011 about the rankings methodology

7 7 experience yet they are not included in the ranking. If a high percentage of the student body has demonstrated leadership qualities or has exceptional writing skills, this is also excluded from the ranking methodology yet it certainly creates a higher quality institution. With all these problems, many officials within higher education have criticized U.S. News & World Report as well as other publications that rank colleges. Some have even gone so far as to refuse to cooperate with the publication and submit no data. Reed College did just that in They were subsequently dropped to the bottom of that year s ranking (Machung 1998). The existence of all these potential issues with college rankings, coupled with their widespread clout among the public, is reason enough to pursue further research on the impact they may have on the higher education landscape.

8 8 II. Literature Review There have been several sources of previous research on the effects of U.S. News & World Report college rankings on the undergraduate admissions process 2. Empirical analysis of the topic began with a working paper by James Monks and Ronald G. Ehrenberg entitled The Impact of U.S. News & World Report College Rankings on Admissions Outcomes and Pricing Policies at selective Private Institutions. This study, as well as ones written by Marc Meredith (2004) and Nicholas A. Bowman and Michael N. Bastedo (2009) found positive empirical evidence, proving that U.S. News & World Report college rankings do have an impact upon the admissions statistics of institutions of higher education. All three studies found that an improvement in ranking will result in a higher level of standardized test scores for the incoming freshmen class as well as lower (more selective) acceptance rates. This would imply that a better ranking allows a school to be more competitive and attract a more intelligent student body. Papers by Monks and Ehrenberg (1999) and Bowman and Bastedo (2009) also found that rankings had a significant impact on the yield rate of an institution. This would show that a better ranking makes an institution more desirable, because applicants are more likely to attend if they have been accepted. Certain studies (Bowman and Bastedo 2009, Meredith 2004) have also found that a better ranking results in a greater amount of incoming freshman coming from the top 10% of their high school class. The paper by Monks and Ehrenberg also seeks to determine if rankings have an impact on the pricing policies of higher education institutions. While they found no significant relationship between ranking and the sticker price of tuition they were able to 2 See Table 1

9 9 find that rankings had an impact on aid-adjusted tuition (tuition minus grant aid per student) and required self-help (the amount of tuition that students on financial aid are expected to fund themselves). Less favorable rankings resulted in a reduction of aidadjusted tuition as well as lower self-help levels. This would imply that, in an effort to attract more qualified students, colleges would make themselves more affordable to those students that would require financial aid. While these three studies have extremely convincing results, their samples are relatively small and selective. The papers by Monks and Ehrenberg (1999) and Bowman and Basetdo (2009) focus almost exclusively schools in the top echelons of higher education. Monks and Ehrenberg draw their data from the schools within the Consortium on Financing Higher Education (COFHE). Of the 30 schools in this organization, all but one are ranked within the U.S. News & World Report top-25 liberal arts colleges or national universities. Furthermore, none of the schools are public institutions. The study written by Bowman and Bastedo (2009) only draws data from schools that have ranked within U.S. News & World Report s top-50 liberal arts colleges or national universities. In the study written by Meredith (2004), data is only drawn from schools categorized as national universities. The selective nature of these samples prevents the results from being generalized to all of higher education. In 2005, Amanda Griffith and Kevin Rask published a study entitled The Influence of the US News and World Report collegiate rankings on the matriculation decision of high-ability students: This study took a different approach to quantifying the impact of the U.S. News & World Report rankings. Instead of aggregating the impacts on a wide selection of schools, Griffith and Rask took data solely

10 10 from students who had applied and been accepted to Colgate University. They found that if a student had been accepted to multiple schools, the respective ranks of the schools they had been accepted to had a significant impact on which institution they chose to attend. Given the option between two schools, one with a better ranking and one with a worse ranking, the student would be more likely to attend the schools with the better ranking. Furthermore, this impact is more significant among full-pay students as opposed to those on financial aid. Intuitively, this makes perfect sense. If a student is not financially constricted, they will be able to rely more on rankings when making college decisions. Conversely, if a student is bound by financial limitations, they may only be able to attend the school that offers the best financial package, regardless of the ranking.

11 11 III. Data Variable Descriptions Table 2 Variable Name Description Mean Std. Dev. prank lagged rank variable satv75 75th percentile of SAT verbal scores satm75 75th percentile of SAT math scores act75 75th percentile of ACT scores apply Number of applications received admit Number of applicants admitted acceptrate Percent of applicants accepted enroll Number of students enrolled in incoming freshmen class yrate Yield Rate Percent of accepted students that enroll poutstate Price for out of state students totalstudents Total size of student body avgaid Average amount of grant aid given by school totalexpenses Total yearly expenses for the school 7.07E E+08 faculty Total number of faculty studfaculty student/faculty ratio NE Northeast region dummy AL Atlantic region dummy SO South region dummy MW Midwest region dummy WE West region dummy Private Private institution dummy Public Public institution dummy liberal Liberal Arts College dummy university National University dummy The data set for this analysis was constructed with the purpose of portraying a wide range of higher education institutions. The top 80 schools from the Liberal Arts Colleges category as well as the top 80 schools from the National Universities categories in the Best Colleges 2011 issue of U.S. News & World Report (published August 2010) were selected to be included in this sample 3. These 160 institutions represent a wide variety of size, quality, location, and tuition level. There are 124 private institutions and 36 public institutions (including 3 military academies) included in the sample. Data on these institutions was collected over the time frame. 3 U.S. News & World Report distinguishes between liberal arts colleges and national universities based on classifications set forth by the Carnegie Foundation for the Advancement of Teaching.

12 12 The data on these 160 schools was collected from the Integrated Postsecondary Education Data System (IPEDS). IPEDS is a data collection program maintained by the National Center for Education Statistics, a subsection of the US Department of Education s Institute of Education Sciences. IPEDS collects, manages, and makes publicly available standardized data from every institution of higher education that receives federal student financial assistance. Over 6,700 institutions submit standardized data to IPEDS every year. Data that is available through IPEDS includes but is not limited to: institutional characteristics (name, location, public/private status), financial data (tuition prices, financial aid), enrollment (size of student body, admissions statistics), and institutional resources (size of faculty, salaries). Ranking data was collected from the U.S. News & World Report: Best Colleges issues that were published from 2000 to These issues are published in late August or early September, just before high school students start submitting their college applications. For this study, college data for the current year is paired with ranking data for the previous year as to quantify the impact of the most recent U.S. News & World Report ranking on an incoming class of college freshmen. For example, if a high school senior goes through the college application process during the academic year, the most recent ranking data that they would observe would be published in late August or early September of This student would begin their college education in the academic year. As such, data from the academic year is paired with rankings from the 2004 edition of U.S. News & World Report: Best Colleges issue. In order to impose controls on the econometric model, variables were selected that represent facets of a higher education institution that would impact admissions

13 13 statistics. In order to represent the size of the school, data for the total size of the student body and total yearly expenditures of a school are included in the data set. Selecting variables that represent the quality of an institution is a very difficult task, as numbers cannot sum up the value of an undergraduate education. However, there are certain metrics that can give a general idea of the relative worth of a specific educational experience. Acceptance rate is included to signify how competitive entrance in to an institution is. Yield rate (the percentage of accepted students that decided to attend) is included to measure the desirability of a school. A high yield rate will indicate that, even with other postsecondary education options, students have a strong desire to attend a particular institution. A student/faculty ratio was created for every institution in order to indicate the quality of classroom education a school has to offer. It is assumed that a lower student faculty ratio, or a high amount of faculty relative to students, will create a more productive learning environment. The 75 th percentiles SAT and ACT scores for the incoming freshmen class is included in order to gauge the intellectual capacity of the students at a college. While it is often debated whether or not standardized test scores are accurate representations of an individual s intellect, it is an easily available and standardized metric, lending itself to econometric analysis. A more accurate indicator may be a metric that describes the success of students after graduation, but this information is neither easily available nor standardized. Furthermore, certain financial variables have been included. The price of out-ofstate tuition and the average amount of financial grant aid that a college provides have been included in order to signify the affordability of an institution. The application fee for each observation has also been included to signify the practicality of applying to an

14 14 institution. These variables are meant to show the desirability of an institution without reflecting its quality. Variables Means by Year Table 3 Year prank satv satm act apply admit acceptrate enroll yrate poutstate totalstudents avgaid totalexpenses faculty studfaculty Certain dummy variables have also been created to determine if admissions statistics vary within subsets of the data. Dummy variables for geographic regions have been included to account for varying admissions statistics across different sections of the country. Based on state, colleges in the data set were categorized in the Northeast, Atlantic, Midwest, South, or West regions. Dummy variables were also created for every year within the sample to account for trends in college admissions that are witnessed over time. Lastly, dummy variables were created to differentiate between public and private institutions and schools that are categorized by U.S. News & World Report as either liberal arts colleges or national universities. For this study, five different admissions statistics will be used as dependent variables. These variables are the number of applications received, the percent of applications that are accepted, the yield rate and the 75 th percentile of SAT math and verbal scores of the incoming freshmen class. The yield rate and number of applications are being analyzed as to determine the attractiveness of a school to potential students as

15 15 compared to its ranking. The acceptance rate is being analyzed to determine the selectiveness of a school as compared to its ranking. The 75 th percentile of SAT math and verbal scores is being analyzed to determine the quality of student that a school attracts as compared to ranking.

16 16 IV. Methodology As previous studies have done (Monks and Ehrenberg 1999, Meredith 2004, Bowman and Bastedo 2009) this study will employ fixed effects regression analysis to quantify the impact of U.S. News & World Report college rankings on the five dependent variables previously listed. These regressions were first run for all observations within the sample. Next, the regressions were repeated for national universities, liberal arts colleges, public institutions, and private institutions. Lastly, the dataset was subdivided in to 3 rank based groups: observations with a rank of 50 or better, observations with a rank of 25 or better, and observations with a rank of 10 or better. Regressions were then ran over these three subsets. A variety of metrics discussed in the previous section were used as independent variables in the regressions. It should be noted that due to the numerical order of rankings, the standard interpretation of coefficients must be specified. As a lower ranking number is considered a better position (i.e. one would prefer to ranked 4 th as opposed to 5 th ) the coefficient associated with the rank variable implies what would happen if a college witnessed a decline in rank by one. If the coefficient on rank is positive, the dependent variable will increase with an increase (worsening) in rank and decrease with a decrease (improvement) in rank. If the coefficient on rank is negative, the dependent variable will decrease with an increase (worsening) in rank and decrease with decrease (improvement) in rank. For example, if the rank coefficient in the regression for the 75 th percentile of SAT-math scores is -5, then it would be interpreted as a 1 unit increase in rank (for example, moving from 4 th to 5 th ) coinciding with a decline of the 75 th percentile of SATmath scores by 5 points, ceteris paribus. Conversely, an improvement in rank by one

17 17 (moving from 5 th to 4 th ) would be linked with an increase in the 75 th percentile of SATmath scores by 5 points, ceteris paribus. The west region will be used as the excluded regional dummy variable will be the excluded year for the year dummy variables. In the regressions that covered all institutions, public/private and liberal arts college/national university dummy variables are used. The dummy variables for public institutions and national universities are excluded. I expect the results to suggest that a better rank is correlate with an institution becoming more selective (via a lower acceptance rate), having a higher degree of appeal (via a higher yield rate and a larger volume of applications), and attracting more intelligent students (as signified by higher SAT scores).

18 18 V. Results Yield Rate see Table 4 As anticipated, a better rank is associated with a higher yield rate. This implies that given a more impressive ranking, students are more likely to attend an institution, assuming they have already been accepted. Across all institutions, a decline in rank by one is associated with a decline in yield rate by 0.14 percentage points. Additionally, a school s acceptance rate held a large amount of predicting power when determining the yield rate of an institution. A one percentage point increase in acceptance rate is associated with a 0.19 percentage point decrease in yield rate. Apparently, more selective schools are more attractive to prospective students as schools are more likely to have a high yield rate if they have a low acceptance rate. In the regression that covered all institutions, neither the dummy variable for private institutions nor the dummy variable for liberal arts colleges had any statistical significance. This would suggest that there is no substantial difference, in terms of yield rate, between private and public institutions nor between national universities and liberal arts colleges. When the regressions were divided between private and public institutions, certain trends emerged. In the regression that covered private institutions, the coefficient for rank was, similarly to the regression covering all institutions, negative and statistically significant. However, in the regression that covered public institutions, the coefficient on the rank variable was no longer statistically significant. This is expected, considering there are a myriad of other factors, outside of quality and prestige, which would motivate students to attend public institutions. These include, but are not limited to, affordability and the proximity to a student s home.

19 19 A similar trend is seen when the sample is divided between liberal arts colleges and national universities. The coefficient on rank for the regression covering national universities is, similarly to the original regression, negative and statistically significant. However, in the regression that covered solely liberal arts colleges, the same coefficient was no longer statistically significant. This also may be due to the fact that there are often many factors outside of an institution s reputation that would motivate a student to attend a liberal arts college. Factors such as the availability of professors to students or a liberal arts education are not factored in to the U.S. News & World Report Rankings, but are often considered when applying to liberal arts colleges. Lastly, the data set was broken in to the schools ranked within the top 50, top 25, and top 10 liberal arts colleges or national universities. In all three regressions, the coefficient on rank was negative and statistically significant. Furthermore, the size of the coefficient increased as the regressions focused on more elite schools. The coefficients were , , and for the regressions covering the top 50, top 25, and top 10 schools, respectively. This would imply that for higher ranked schools, rank plays a larger role in predicting the yield rate of an institution. Acceptance Rate see Table 5 As previous studies have found, a better rank is linked with a lower acceptance rate (Bowman & Bastedo 2009, Meredith 2004, Monks & Ehrenberg 1999). With a more impressive rank, schools are able to be more selective with the students the applicants they approve. Across all institutions, the coefficient on rank is 0.002, implying that a decline in rank by one will be associated with an increase in acceptance rate by 0.2 percentage points. Not surprisingly, SAT scores are also highly correlated with

20 20 acceptance rank. This trend continues when the regressions are run again over data sets comprising solely liberal arts colleges and national universities. The coefficients for the SAT variables are negative and statistically significant, implying that a higher 75 th percentile SAT math or verbal score is associated with a lower acceptance rate. Naturally, schools that attract higher quality students (as measured by standardized test scores) are able to be more selective with their applicant pool. As with yield rate, in the regression that covered all institutions, neither the dummy variable for private institutions nor the dummy variable for liberal arts colleges had any statistical significance. This implies that there is no substantial difference, in terms of acceptance rate, between private and public institutions nor is there a difference between national universities and liberal arts colleges. When the regression is ran over a data set comprised only of private institutions, similar effects are seen. The coefficient on rank is positive and statistically significant, signifying a connection between better ranks and lower acceptance rates. However, when the regression is run over a data set that only includes public institutions, the coefficient is no longer statistically significant. This implies that for public institutions, there is no significant connection between ranking and acceptance rate. This may be due to the fact that there are other factors at play when the admissions offices of public schools decide who to accept. Often times, public schools are mandated to accept a certain amount of students from within the same state while accepting a smaller proportion from out-ofstate. For the data sets that only included schools ranked within the top 50 and top 25, the same trend as the original regression is seen. Better ranks are correlated with lower

21 21 acceptance rates. However, with schools ranked 10 or better, there is no longer any statistically significant connection between rank and acceptance rate. This may be explained by the fact that the few elite schools in this subset have not only been consistently part of the top 10, but have also been consistently selective. Schools like Harvard or Yale will always have low acceptance rates, regardless of slight ranking fluctuations, due to their legendary prestige. Number of Applications see Table 6 As seen in Bowman & Bastedo s 2009 study, a decline in rank is connected with a decline in the number of applications that a school receives. As expected, a drop in ranking, and consequently a drop in prestige, will be correlated with fewer high school seniors wishing to attend a particular institution. Across all institutions, the coefficient on rank is -40.9, implying that a decline in rank by one will be linked to a school receiving 40 fewer applications. Besides ranking, the most telling independent variables are the ones that measure the size of a school: the total amount of students and a school s total expenditures. Naturally, a larger school will receive more applications than a smaller one. For the regression that covered all institutions, the dummy variable for liberal arts schools is negative, statistically significant, and very large. This tells us that on average, a liberal arts college will receive approximately 2,800 less applications than a national university. This is easily explained by the fact that liberal arts colleges tend to be much smaller than national universities. When the data is divided between liberal arts colleges and national universities, the same connection holds. In both these regressions, the coefficient on rank is negative and statistically significant, implying that an improvement in rank is correlated with a

22 22 higher volume of applications received. Interestingly, the coefficient is much larger for national universities than for liberal arts colleges (-65.2 and -11.4, respectively). This may be due to the fact that there are usually many other factors at play when students decide to apply to a liberal arts college. As mentioned in the discussion of yield rates, one-on-one opportunities with professors and a liberal arts education, along with other intangibles, are features that are considered when applying to a liberal arts college, but do not factor in to rank. When the regression is ran again over data sets that only include schools ranked within the top 50, top 25, and top 10 of liberal arts colleges or national universities, some interesting trends appear. While among the top 50 schools, the coefficient on rank is similar to that in the original regression (negative and statistically significant), the coefficient becomes statistically insignificant for the regressions on the top 25 and top 10 schools. This is likely explained by the fact that elite schools that are ranked 25 or higher will always hold a certain level of appeal. A slight fluctuation in rank will not make elite schools any less appealing to high school seniors. SAT Scores see Tables 7 & 8 The results concerning standardized test scores falls in line with expectations as well as the results generated by previous studies (Meredith 2004, Monks & Ehrenberg 1999). Across all universities, an improvement in rank is correlated with an increase of 0.8 and 0.6 points in the 75 th percentile of SAT math and verbal scores, respectively. This would agree with standard intuition that a greater level of prestige, as measured by rankings, is correlated with a higher quality of incoming freshmen, as measured by standardized test scores. In the regression that covered all institutions, the dummy

23 23 variable for private institutions is positive, statistically significant, and rather large (20.5 and 31.9 for math and verbal, respectively). This signifies that the incoming freshmen of private institutions tend to have higher SAT scores than those of public institutions. The significance of this correlation stands when the regression is ran among solely public institutions, private institutions, liberal arts colleges, and national universities. In all of these regressions, the coefficient on rank is negative and statistically significant, signifying an improvement in rank being related to better SAT scores. It is notable that for both the SAT math and SAT verbal regressions, the coefficient on rank among national universities is larger when compared to liberal arts colleges. This may suggest that during the admissions process, national universities tend to favor standardized test scores over more intangible factors such as admission essays and a track record of extracurricular activities.

24 24 VI. Caveats and Further Research Though these results do show that the U.S. News & World Report rankings have a significant impact on college admissions, there are some issues with the analysis to consider. As the methodology of the rankings factor in many variables, including the ones used as dependent variables in the previous analysis, there may be endogeneity between the rank variable and the dependent variables. A possible solution to this would be to include an instrumental variable. Meredith (2004) suggests the possibility of recalculating the rankings while excluding the independent variable that is being analyzed, and using that ranking as an instrumental variable. Furthermore, there are series of missing data points within the dataset used for this analysis. This is largely due to the fact that for the earlier years in the data set, only the top-25 schools were ranked. Because of this, there is no ranking data for some observations. If this same study could be repeated in several years, when more full rankings would be available, the results may be more accurate. Another possible shortcoming in the data may emerge from the fact that college rankings are not necessarily accurate portrayals of quality. Firstly, there is the issue of representing the actual quality of an academic institution. An educational experience is nearly impossible to quantify. Factors such as student-faculty relationships, opportunities for innovative and hands-on learning experiences, and the value of being surrounded by highly intelligent individuals are not things that can be represented by numbers. Furthermore, there can be much debate over whether one facet of an undergraduate experience is more valuable than another. The quality of an education can not be boiled

25 25 down to numbers. By doing so, U.S. News & World Report is creating a perception of quality that is not entirely accurate. Additionally, the model that this study uses imposes a linear relationship among observations. This means that according to the results a movement from 10 th place to 11 th place would generate the same impact as a movement from 49 th place to 50 th place. It is doubtful that this is the most accurate assessment of the impact that college rankings has on admissions statistics. 4 It would also be interesting to use a similar data set and methodology to examine financial data of college institutions as opposed to admissions indicators. For example, it would be worth examining if public universities received greater amount of government funding if they climbed in ranks. Unfortunately, that type of question is beyond the scope of this paper. 4 In order to compensate for this, specifications were tried where rank was substituted with the log of ran. The results, in terms of sign and statistical significance were the same as the original regressions.

26 26 VII. Conclusion The results of this study are consistent with those found in previous research. The rankings created by U.S. News & World Report have a significant impact on admissions indicators. An improvement in ranking can cause a school to become more attractive to potential students, boosting the yield rate and number of applications received. A stronger ranking will also allow a school to be more selective, possibly yielding a higher qualified student body. Additionally, a stronger ranking will cause the incoming freshmen class to have higher standardized test scores. It would be beneficial to the entire higher education landscape if these rankings were not valued to the degree that they are. College administrators must constantly keep their eye on their institutions rank, as it has a very real impact on the school s admissions prospects. If administrators were able to divert attention away from improving statistics that correlate to ranking, but not actual educational quality, there would be greater resources available to provide better undergraduate experiences. There are a few possible solutions that may lessen the problems that current rankings present. It would be helpful if rankings were created that captured more of the intangibles that can drastically alter the educational experience but are not factored in to current popular rankings. For example, schools would receive higher rankings for offering independent research opportunities or close faculty mentorship. This may be a more accurate way of assessing the quality of institutions. It would also be beneficial if the higher education industry could come together and agree on a rubric for ranking schools. While this would not eliminate the impact of

27 27 rankings, it would help create a standardized system of assessing the quality of an institution. There would no longer be any debate over whether or not rankings were accurate measures of quality, or whether rankings intrinsically favor some institutions over others. Because it can be proved that these rankings do have an impact, ranking systems should strive to be as accurate a measure of quality as possible. If there is minimal disconnect between the actual quality of an institution and the perceived quality of an institution based on its ranking, then changes in ranking will only come from actually changes in quality, not changes in ancillary metrics that solely impact rankings.

28 28 VIII. Bibliography "About the Rankings US News and World Report." US News & World Report News & Rankings Best Colleges, Best Hospitals, and More. Web. 02 Apr < the rankings>. Bowman, Nicholas A., and Michael N. Bastedo. "Getting on the Front Page: Organizational Reputation, Status Signals, and the Impact of U.S. News and World Report on Student Decisions." Research in Higher Education 50.5 (2009): Print. Griffith, A., and K. Rask. "The Influence of the US News and World Report Collegiate Rankings on the Matriculation Decision of High ability Students: " Economics of Education Review 26.2 (2007): Print. Machung, Anne. "Playing the Ranking Game." Change 30.4 (1998): Web. Meredith, Marc. "Why Do Universities Compete in the Ratings Game? An Empirical Analysis of the Effects of the U.S. News and World Report College Rankings." Research in Higher Education 45.5 (2004): Print. National Bureau of Economic Research. The Impact of U.S. News & World Report College Rankings on Admissions Outcomes and Pricing Policies at Selective Private Institutions. By James Monks and Ronald G. Ehrenberg ed. Cambridge, MA: National Bureau of Economic Research, Print. NBER Working Paper Ser. Thompson, Nicholas. A Review of the Methodology for the U.S.News & World Report's Rankings of Undergraduate Colleges and Universities. Rep. National Opinion Research Center, Print.

29 29 Title Authors Year of Study Getting on the Front Page: Organizational Reputation, Status Signals, and the Impact of U.S. News and World Report on Student Decisions The Influence of the US News and World Report Collegiate Rankings on the Matriculation Decision of High ability Students: Why Do Universities Compete in the Ratings Game? An Empirical Analysis of the Effects of the U.S. News and World Report College Rankings Bowman & Bastedo Griffith & Rack IX. Appendix Other Studies Results Table 1 5 Data: Data: Years Schools Meredith Schools Ranked 50 or higher Colgate University and schools that share applicants with Colgate University 233 National Universities Selected Results Acceptance Rate ( ), Students from the top 10% of their high school class (+), Number of applications (+), Yield Rate (/), SAT scores (/) Likelyhood to attend (+) Acceptance Rate ( ) Students from the top 10% of their high school class (+) SAT scores (+) Notes Moving in/out of top 50 for all schools in data set Data from students who applied and were accepted, but did not necessarily attend Colgate University 5 Selected Results: The sign in parentheses indicates the movement of the independent variable, given an improvement in rank. A slash (/) indicates no significant relation

30 30 The Impact of U.S. News & World Report College Rankings on Admissions Outcomes and Pricing Policies at Selective Private Institutions Monks & Ehrenberg Consortium on Financing Higher Education (COFHE), 30 highly ranked schools Acceptance Rate( ) Yield Rate (+) SAT scores (+)

31 YIELD RATE Table 4 Variable All Schools Private Public Liberal Arts College National University Top 50 Top 25 Top 10 N prank * * * * ** * totalstudents * *** ** E 06 * * * * faculty 2.18E E E E E E E 06 totalexpenses 8.11E 12 ** E E 10 *** E E E 12 satv E 06 satm ** act ** acceptrate * * * * * * poutstate * * E 06 * * * *** E 06 avgaid * * E 06 ** * * ** E 06 Private liberal Region Dummies None SO AL, SO None NE NE, AL, SO NE NE Year Dummies 2004~ ~2008 None 2003~ None None None *=Significant at the 1% level **=Significant at the 5% level ***=Significant at the 10% level Region Dummies: Variables with statistical significance (10% significance level) are listed Year Dummies: Years with statistical significance (10% significance level) are listed 31

32 32 ACCEPTANCE RATE Table 5 Variable All Schools Private Public Liberal Arts College National University Top 50 Top 25 Top 10 N prank * * * * * * totalstudents 1.08E E E E E E E E 06 faculty 6.65E E E E E E E 06 totalexpenses ** E E E E E E E 11 satv75 * * * * * ** satm75 ** * ** act yrate * * * ** * * poutstate * * E 06 * ** * ** E 07 avgaid 1.64E 06 ** E 06 *** E 07 * E E 07 Private liberal Region Dummi MW MW MW None MW MW, SO MW MW Year Dummies None None None None None None None *=Significant at the 1% level **=Significant at the 5% level ***=Significant at the 10% level Region Dummies: Variables with statistical significance (10% significance level) are listed Year Dummies: Years with statistical significance (10% significance level) are listed

33 NUMBER OF APPLICATIONS Table 6 Variable All Schools Private Public Liberal Arts College National University Top 50 Top 25 Top 10 N prank * *** * ** totalstudents * * * * * * * * faculty * totalexpenses * * E E E 07 * E 07 * satv satm75 *** act ** ** yrate * * * * * poutstate * ** * ** ** * *** appfee * * ** * ** avgaid * ** * ** Private Liberal * Region Dummies AL, SO, MW NE, AL, SO AL, SO, MW AL AL, SO, MW AL, SO, MW None None Year Dummies 2008 None None None 2007~2008 None 2004~2008 None *=Significant at the 1% level **=Significant at the 5% level ***=Significant at the 10% level Region Dummies: Variables with statistical significance (10% significance level) are listed Year Dummies: Years with statistical significance (10% significance level) are listed 33

34 SAT Math Table 7 Variable All Schools Private Public Liberal Arts College National University Top 50 Top 25 Top 10 N prank * * * * * * * totalstudents *** *** ** ** *** totalexpenses 3.21E 10 * E 09 ** E E E E 09 studfaculty ** * acceptrate * * * * * * yrate ** poutstate ** ** ** *** avgaid ** ** ** Private * liberal * Region Dummie AL, SO AL, SO None AL, SO None None NE, AL None Year Dummies 2002~ and ~ ~ ~ ~ ~2004 None *=Significant at the 1% level **=Significant at the 5% level ***=Significant at the 10% level Region Dummies: Variables with statistical significance (10% significance level) are listed Year Dummies: Years with statistical significance (10% significance level) are listed 34

35 SAT Verbal Table 8 Variable All Schools Private Public Liberal Arts College National University Top 50 Top 25 Top 10 N prank * * * * * * ** totalstudents ** * totalexpenses 9.67E 11 ** E 09 ** E E E E 10 studfaculty * *** ** * ** acceptrate * * * * * * * yrate * poutstate avgaid * *** *** ** Private * liberal Region Dummies AL AL, SO SO AL, SO NE, SO, MW None SO None Year Dummies 2004~2005 None 2004~ ~2005 None None 2004~2006 None *=Significant at the 1% level **=Significant at the 5% level ***=Significant at the 10% level Region Dummies: Variables with statistical significance (10% significance level) are listed Year Dummies: Years with statistical significance (10% significance level) are listed 35