Documents Found!
As seen in
Less Work, Better Grades
Join
Course Hero
Access
best resources
Ace
your classes
Ace your courses with Course Hero!
|
|
|
Study Smarter, Score Higher
Here are the top 5 related documents
...July 18, 2004, Houston Chronicle
Focus on higher education upgrades in Texas; Get past debate over top 10% law, make plan better
By MARTA TIENDA WHILE the Texas top 10 percent law has succeeded in expanding higher education opportunities for studen...
...Texas' 10-Percent Plan: the Truth Behind the Numbers
By MARTA TIENDA and SUNNY NIU
Now that the U.S. Supreme Court has ruled that the Constitution permits colleges to have race-conscious admissions policies, many people in Texas are calling for the ...
...Detroit Free Press (MI) TEXAS' TOP-10-PERCENT POLICY HURTS MINORITIES' CHANCES FOR COLLEGE By MARTA TIENDA Date: March 26, 2003 Section: EDP; EDITORIAL Page: 10A One in a series of commentaries on the University of Michigan affirmative action cases, ...
Document Content (unformatted)
Course Hero has millions of student submitted documents similar to the one
below including study guides, homework solutions, papers, exam answer keys and textbook solutions.
Action Affirmative and the Texas Top 10% Percent Admission Law: Balancing Equity and Access to Higher Education Marta Tienda Princeton University tienda@princeton.edu Sigal Alon Tel Aviv University salon1@post.tau.ac.il. Sunny X. Niu Princeton University niu@princeton.edu This research was supported by grants from the Ford, Mellon, Hewlett and Spencer Foundations and NSF (GRANT # SES-0350990). We gratefully acknowledge institutional support from the Office of Population Research (NICHD Grant # R24 H0047879). Please direct all correspondence to Marta Tienda, Office of Population Research, Princeton University. February , 2008 Introduction Since the mid-1960s U.S. colleges and universities with selective admissions have used race and ethnic preferences affirmative action to diversify their student bodies, specifically targeting historically underrepresented groups. Although the 1978 Supreme Court decision1 outlawed use of quotas either to remedy past racial injustices or to approximate population composition, the Powell opinion invoked the First Amendment to endorse the value of institutional diversity both as an essential freedom for postsecondary institutions and as a means to achieve their educational missions. In the words of Justice Powell: The atmosphere of speculation, experiment and creation so essential to the quality of higher education is widely believed to be promoted by a diverse student body .it is not too much to say that the nation s future depends upon leaders trained through wide exposure to the ideas and mores of students as diverse as this Nation of many peoples. As the Nation of many peoples became even more diverse, organized opposition to affirmative action gained momentum, culminating in public referenda and lawsuits to end race preferences in college admissions. California voters passed Proposition 209 in 1996, outlawing consideration of race and ethnicity in college admissions, and two years later the Washington State electorate passed Initiative 200, which banned the use of race preferences. Law schools at both the University of Michigan and the University of Texas were sued, and the 1996 Hopwood decision2 imposed a judicial ban on use of race preferences in college admissions throughout the jurisdiction of the 5th Circuit Court. As other universities defended their use of racesensitive admissions practices, uncertainty about the future of affirmative action in 1 2 University of California Regents v. Bakke, 1978. Hopwood v. University of Texas (5th Cir. 1996) 2 college admissions triggered a search for race-neutral alternatives that could produce diverse student bodies. In one of the boldest of college admissions experiments, the 75th Texas legislature passed HB 588, which guarantees seniors who graduate in the top 10 percent of their class admission to any Texas public college or university. Admissions decisions for students who do not graduate in the top ten percent of their class are based on a broad range of objective and subjective criteria (see Long and Tienda, 2007; Barr, 2002). Signed into law on May 20, 1997, HB 588 popularly known as the top 10% law sought not only to recover the drop in black and Hispanic representation at its flagship institutions following the judicial ban on affirmative action, but also to increase the number of high schools that sent students to the four-year public universities (Montejano, 2001; Barr, 2002). Architects of the top 10% law expected that large numbers of black and Hispanic students would qualify for the admission guarantee because Texas high schools are highly segregated (Tienda and Niu, 2006a). Political support for HB 588 derived from its adherence to race-neutral admission criteria that were applied uniformly to all high schools, irrespective of size, wealth, or location (Tienda and Sullivan, 2008). Despite the apparent novelty of the Texas admission experiment, in fact, both public flagships the University of Texas at Austin (UT) and Texas A&M University (TAMU) have always weighed class rank heavily in their admissions decisions (Walker and Lavergne, 2001; Leicht and Sullivan, 2000). Even before the plan went into effect, college applicants who graduated in the top decile of their class were virtually ensured, albeit not guaranteed, admission to the public flagships. For example, at TAMU the admission rate of students who graduated in the top 10% of their high schools was 97 3 percent in 1996 and 100 percent since 1998 (Texas A&M, 2006). In effect, HB 588 largely transformed a de facto practice of admitting highly ranked students to a de jure guarantee of acceptance (Walker and Lavergne, 2001). Rather, the distinctive features of the Texas post-Hopwood college admission regime are the disregard of test scores for students who graduate in the top decile of their class and the use of school-specific class rank as a measure of merit in addition to the ban on race preferences. Other things equal, moreover, the top 10% law should benefit black and Hispanic applicants who graduate in the top decile of their high school class because their test scores, which on average are lower than those of whites and Asians, are disregarded for purposes of admission (Alon and Tienda, 2007). Black and Hispanic students also are less likely than white students to attend high schools that offer advanced placement courses and a broad range of extracurricular activities which are evaluated favorably by college admissions officers (Long, 2004; Long and Tienda, 2008). That the top 10% law was triggered by the judicial ban on affirmative action largely explains disproportionate research and policy attention on changes in campus diversity. Yet in crafting the legislation, the primary sponsor of HB 588, the late Irma Rangel, had a broader vision, namely to create a fair, race-neutral admissions structure providing students from all backgrounds and [all] parts of the state an opportunity to continue their educations (Giovanola, 2005). Specifically, HB 588 sought to recruit the very best students of each school in the state to the flagship universities (Montejano, 2001; emphasis in original). Not surprisingly, opponents complained that the percent plan disguises the use of race in admissions, and like affirmative action, excludes deserving applicants by giving 4 preference to allegedly less qualified candidates (Barr, 2002; Tienda and Niu, 200b). Rather than privileging minority students, who are presumed to be under-qualified for admission to selective institutions by critics of affirmative action, opponents of the top 10% law claim that using a single measure of merit favors students from underperforming schools. Essentially the Texas top 10% law altered the terms of the debate about privilege and access to the flagship universities by changing the exclusion criteria from individual attributes namely race and Hispanic origin to high school quality. Yet, with few exceptions (e.g., Montejano, 2001), most analyses of the impact of HB 588 have focused on changes in race and ethnic of freshmen classes, ignoring potential changes in the number and composition of feeder high schools (Barr, 2002; Staff, 1997; Long and Tienda, 2007). At issue is whether, to what extent, and in what ways the new admission regime restored diversity at the public flagships while also broadening access by increasing the pool of feeder high schools represented at the University of Texas at Austin and Texas A&M University. To address these questions, we first provide a thumbnail sketch of demographic trends that determine the pool of college-eligible students, and summarize research that evaluated the impact of HB 588 in diversifying the two flagship campuses. Subsequently we consider whether the top 10% law fostered changes in high school sending patterns, which Representative Rangel envisioned as a key mechanism to broaden college access in Texas. The concluding section discusses the broader implications of percent plans in promoting equity and broadening access to post-secondary institutions. 5 HB 588 and the Demography of Higher Education Even before the judicial ban on race preferences in college admissions and the top 10% law, the system of higher education in Texas experienced rising pressure from a higher than average demographic growth rate. The State of Texas recorded double-digit population growth rates since 1960, rose from fourth to second rank based on population size between 1970 and 1990 (Leicht and Sullivan, 2000), and continues to grow faster than the national average. Between 2000 and 2006 the population of Texas grew 12 percent, compared to about 6 percent for the nation as a whole (U.S. Census Bureau, 2007). Because births are the major component of growth, Texas also has a large schoolage population. During the 1990s, Texas rose from 9th to 5th in the proportion of population under 19 (Leicht and Sullivan, 2000). By 2005, 28 percent of Texas residents was under age 18, compared with the national population share of 25 (U.S. Census Bureau, 2007). Like other states where fertility of foreign-born women spurred population growth, Texas witnessed an increase in the size of high school graduate cohorts during the 1990s. Tienda and Sullivan (2008) report that between 1994 and 2004 the number of public high school graduates grew only 19 percent nationally, compared with 50 percent in Texas, 30 percent in California, 25 percent in Florida, and a meager 10 percent in New York State. These estimates are conservative because data for 2003 and 2004 were projected from earlier data that have already been surpassed. In Texas, for example, 6 actual annual increases in 2003 and 2004 exceeded the projections by 3 and 5 percent, respectively.3 National growth in the number of high school graduates is expected to slow over the next decade to a meager 2 percent. Even as other states witness modest increases or slight declines in their college-eligible population, in both Texas and Florida the number of public high school graduates is projected to grow at rates well above the national average. These anticipated changes in the number of high school graduates have direct implications for future college enrollment trends. Specifically, high levels of immigration coupled with above-replacement fertility not only keep the population young, but also continue to diversify the ethno-racial composition of the state (Tienda and Mitchell, 2006). Owing to their younger age structure, Hispanics comprise a higher share of the Texas school-age population compared with persons ages 25 and over (Murdock, et al., 2003). In Texas, immigration and differential fertility also altered the ethno-racial composition of the college-eligible population. As Table 1 shows, between 1994 and 2004, the number of Texas high school graduates increased 50 percent, albeit unevenly among demographic groups. Despite their elevated high school drop-out rates, the number of Hispanics who earned diplomas rose 78 percent during the decade. Consequently, the Hispanic share of high school graduates rose six percentage points between 1994 and 2004, the period covering the change in admission regime. During this decade the number of white high school graduates rose only 29 percent, hence their cohort share of diploma recipients fell from 56 to 48 percent, while the shares of black The WICHE projections on which Tienda and Sullivan (2007) base their calculations for 2003 and 2004 are 231,577 and 233,045 graduates, respectively. According to TEA data, actual 2003 and 2004 statistics are 238,109 and 244,165 graduates, respectively. 3 7 and Asian graduates inched up by one percent point each. These shifts in the composition of Texas high school graduates foretell the shape of things to come only about one in three high school graduates is projected to be white by 2014. Whether this translates to greater campus diversity depends both on college admission regimes and college readiness of future cohorts. Table 1 about Here The two defining features of Texas population change rapid increase and accelerating ethno-racial diversification will intensify pressure on the public higher education system as growing numbers compete for spots at the selective institutions. The Texas Higher Education Coordinating Board predicts that enrollment in public universities, community colleges, technical colleges, and private colleges will rise 15 percent between 2000 and 2010 (THECB, 2001). Enrollment at Texas public universities is projected to rise about 14 percent by 2010 compared with six percent for the private institutions. The failure of the 4-year post-secondary system to keep pace with population growth created a college squeeze that manifests as intensified competition for access to the most selective public institutions (Tienda and Sullivan, 2008). These conditions of rapidly growing demand for slots at post-secondary institutions coupled with slower growth of supply pose formidable public policy challenges for institutions seeking to equalize college access to under-represented groups. As important, the college squeeze creates an environment conducive to fundamental attribution errors, namely assuming that the top 10% law is responsible for falling admission rates among white students and those who attend the most competitive secondary schools (Jaschik, 2007; Haurwitz, 2007; Tienda and Sullivan, 2008). To wit, 8 affirmative action was blamed for squeezing out nonminority students with high standardized test scores, as a spate of lawsuits and public referenda attest, the top 10% law is criticized for squeezing out high-achieving students from the most competitive high schools by giving preference to highly ranked students from low performing schools. The next section reviews evidence in support of these claims. Trends and Differentials in College-Going Behavior at the Public Flagships Despite the intentions of Texas legislators to protect the hard-earned diversity at the public flagships, the change in college admission regimes from affirmative action to the percent plan can not guarantee increased diversity of selective colleges and universities because enrollment of rank-eligible minority graduates presumes both that they will apply for admission and have the financial means to enroll. Percent plans operating under statutory or judicial bans on race preferences may dampen the propensity of talented minority students to apply for admission if they do not realize that they qualify for the guarantee or if they perceive campus climate as unwelcoming (Niu, et al., 2008). Furthermore, the admission guarantee could also alter the college choice set of rank-eligible minority and nonminority students in different ways, making the net effect ambiguous (Niu, et al., 2006; Long and Tienda, 2008). Finally, the surge in the number of Texas high school graduates implies not only a growing demand for college access, but because the admission guarantee is school-specific, also an increase in the number of rank-eligible students. To evaluate the success of the top 10% law in restoring campus diversity in Texas, we assess application, admission and enrollment trends at the University of Texas 9 at Austin (UT) and Texas A&M University in College Station (TAMU), the two most selective among Texas public institutions.4 Both institutions considered race and ethnicity in their admissions decisions prior to the Hopwood decision, and both reported admission rates well below other public two- and four-year institutions (THECB, 1998). Combined, UT and TAMU enroll 23 percent of the student body attending four year public institutions in Texas (THECB, 2001). For perspective, the UT-Austin campus was one of the two the largest campuses in the U.S. in 2006, with a student body over of 48 thousand (The College Board, 2007).5 Enrollment at the Texas A&M College Station campus was approximately 45 thousand in that year. Undergraduates represent 77 and 82 percent, respectively, of the student body, and the freshman class alone constitutes approximately one quarter of all students.6 Admission Rates Most evaluations of the top 10% law conclude that the top 10% law is less efficient than affirmative action in achieving diversity of enrolled students, but none explicitly quantified the differential impact over time and across institutions.7 With few exceptions, empirical assessments of the percent plan s success are based on enrollment, even though the law provides admission guarantees to rank-eligible students. Long and Tienda s (2007) assessment of the top 10% law is a notable exception in that these authors evaluate changes in the racial and ethnic composition of admissions at the two public flagships and Texas Tech University following the judicial ban on affirmative Private institutions in Texas are bound by the Hopwood decision, but not by HB 588. With 48,562 students, Ohio State University registered just over 400 more students than UT-Austin. 6 For A&M, see http://www.tamu.edu/opir/reports/student.html; for UT see http://www.utexas.edu/student/admissions/research/index.html. 7 When the fate of the lawsuit against the University of Michigan was uncertain, administrators who sought campus diversity as a way of enhancing educational missions touted the success of the top 19% law. See Faulkner, 2000; 2002. 5 4 10 action and the shift to the top 10% regime. They show that the elimination of affirmative action and the implementation of the top-10% policy had sizable effects on the racial and ethnic composition of the Texas public flagships but the winners and losers differed across institutions and over time. Consistent with reports by the Texas Higher Education Coordinating Board (1998), Long and Tienda find that both UT-Austin and Texas A&M offered significant advantages to black and Hispanic applicants prior to the Hopwood decision. Both public flagships responded to changes in admission policies by shifting the weights they placed on applicant characteristics in ways that boosted the admissions probabilities of black and Hispanic applicants. These changes, however, did not fully compensate for the effects of the ban on affirmative action decision in lowering the odds of admission for blacks and Hispanics. Public universities were unable (or did not sufficiently attempt) to proxy race and ethnicity using other applicant attributes, although UT's Personal Achievement Index (PAI) sought to weight extracurricular and extraordinary circumstances in their admission decisions in ways that could have boosted minority applicants' admission probabilities. Finally, the authors find no evidence that Texas Tech University gave substantial preferences to minority applicants in the pre- or post-Hopwood period, and changes in TTU's post-Hopwood admissions policy lowered the probability of acceptance for minority applicants. Application and Enrollment Although the judicial and statutory bans on the use of race-sensitive criteria were aimed at institutional decisions about who to admit, there is mounting evidence that they also impacted application behavior and enrollment decisions of admitted students (Brown 11 and Hirschman, 2006; Long and Tienda, 2008). For example, Long (2004) finds that the elimination of affirmative action in Texas and California lowered minority students propensity to apply to the most selective institutions. Similarly, Brown and Hirschman (2006) show that the decrease in minority representation at University of Washington after Initiative 200 banned affirmative action largely stemmed from changes in application rather than admission rates. Nevertheless, the impact of the changed admission regimes on application behavior must be understood against the rapid growth of the college-eligible population during the period that admission regimes changed. In fact, both public flagships faced application pressures before and after the judicial ban on affirmative action, but particularly UT, where the number of applications surged from around 17,000 in 1996 to more than 27,000 in 2006 (The University of Texas at Austin, 2006). At TAMU, applications increased more modestly, rising from approximately 15,000 to more than 17,000 between 1996 and 2006 (Texas A&M University, 2006). Constrained by their physical carrying capacity, rejection rates rose at both institutions, however at UT a temporary increase in the size of the freshman class from 2000 through 2002 delayed the rise in rejection rates until 2003. As the share of applicants qualified for automatic admission rose, rejections increasingly involved applicants who were not in the top 10% of their senior class, but would be admissible based on other service and academic criteria. That many rejected applicants were graduates of highly competitive high schools that historically sent large numbers of students to UT and TAMU fueled criticism of the law (Jaschik, 2007). 12 To illustrate the initial impacts of the changed admission regime on college-going behavior in Texas, we use administrative data on applicants, admittees and enrollees for the period 1992-2002, which includes five years of the affirmative action regime (1992 1996) and five years of the top 10% regime (1998 2002).8 Table 2 summarizes the composition of in-state student applicant, admission and enrollee pools at the two public flagships before the Hopwood decision and under the top-10% admission regime. The uneven results are striking. At Texas A&M, the share of applications from black and Hispanic students fell slightly, while those from Asian origin students rose. White students comprised an increasing share of admittees and enrollees at Texas A&M. In contrast, at UT-Austin, the share of applications from black students increased, while those from Hispanic applicants were not significantly changed. White students experienced an increase in their raw numbers, but a drop in their share of applicants, admittees, and enrollees. The decline in white students' share of enrollees at UT-Austin was offset by the strong increase in Asian students' share. Thus, the competition for a fixed number of slots as UT-Austin along with the new admissions policy largely advantaged Asian students at the expense of white students. Table 2 about Here If enrollment diversity at the public flagships is the intended goal of the uniform admission law, the data reported Table 2 show declining shares of underrepresented minority students at both institutions (significantly so at Texas A&M) . Although the share of white students declined at UT-Austin, the 3.4 percentage point drop was largely due to an increase in enrollment of Asian origin students, not blacks or Hispanics. At 1997 was a transition year when the judicial ban on affirmative action was in force, but the top 10% law had not yet been implemented. 8 13 Texas A&M, enrollment shares of black and Hispanic students fell relative to their preHopwood levels. Thus, the Texas A&M campus is less diverse under the uniform admission regime compared to the pre-Hopwood period. Former president Gates claimed that the main problem was not so much that too few minority were students applying or gaining admission, but that too few were choosing to enroll (Schmidt, 2005). Ultimately, legislators will seek results on the desired outcome enrollment. High Schools as Sources of Unequal College Access Shifts in application, admission and enrollment trends at the Texas public flagships can not be attributed directly to changes in the admission regimes because the top 10% law was accompanied by additional changes that altered its overall impact. In addition to the changing demography of higher education, two noteworthy changes are the expansion of publically funded financial aid and the development of an aggressive outreach program designed to recruit and provide merit scholarships to rank-eligible students who graduate from high schools with low college-going traditions (Domina, 2007; Walker and Lavergne, 2001). Stated differently, the Hopwood decision forced university administrators to become more and more innovative in finding ways to encourage minority enrollment (Barr, 2002: 5). Authors of HB 588 were cognizant that a handful of feeder schools sent a disproportionate number of students to the public flagships; hence the legislation was designed to broaden the pool of high schools that sent students to UT and TAMU (Montejano, 2001; Barr, 2002). Recognizing that financial considerations were significant barriers to college attendance for many economically disadvantaged students 14 who qualified for the admission guarantee, administrators at UT and TAMU created two scholarship programs, the Longhorn Opportunity Scholarship and Century Scholarships, respectively (Domina, 2007; Walker and Lavergne, 2001). These programs were targeted to secondary schools that serve large numbers of poor students, which became the focus of aggressive outreach and recruitment of top-ranked graduates. Although the racial mix of high schools was not (and could not be) considered in selecting the Longhorn and Century high schools, because minority students are more likely than white or Asian students to attend poor schools (Tienda and Niu, 2006a), black and Hispanic students were expected to qualify for scholarships at these schools. As of the 2005-2006 school year, there were 58 and 70 high schools, respectively, participating in the Century and Longhorn programs (Domina, 2007).9 These changes in financial aid were further bolstered by the implementation of the TEXAS Grant program, which provides tuition assistance to students who demonstrate financial need and complete the state s recommended college preparatory curriculum (Domina, 2007).10 Accordingly, we evaluate whether, as aspired by its sponsors, HB 588 has succeeded in broadening college access to the public flagships for students from all parts of Texas and all socioeconomic backgrounds (Barr, 2002). Specifically, for UT and TAMU, respectively, Tables 3 and 4 consider changes in the composition of applicant and admit pools as well as admission rates according to type of high school attended. For these comparisons applicants to both institutions were classified according to a fivecategory school typology that sorted schools into three strata affluent, poor and Domina (2007:203) reports that although neither program guarantees that all students who qualify for the admission guarantee will receive a scholarship, in practice nearly every student from a participating school who matriculates at UT or TAMU receives an award. 10 Because these state funds are based strictly on financial need, they can be combined with other federal and private scholarship funds, including the Longhorn and Century scholarships. 9 15 average based on the share of economically disadvantaged students.11 Affluent schools are defined as the quartile of high schools with the lowest share of economically disadvantaged students; poor schools include the quartile of high schools with the highest share of economically disadvantaged students, and average schools represent the remainder. Because a subset of 28 high schools sent over one-quarter of the freshman class to the public flagships before the top 10% regime went into effect, these schools are separately identified, as are the subset of poor schools that were targeted for Longhorn and Century Scholarships (Tienda and Niu, 2006b). Table 3 about Here Notwithstanding claims that HB 588 privileges students from low performing high schools to the detriment of students from the most competitive high schools, for the state as a whole, feeder high students maintain their advantage in access to UT, at least through 2002. Graduates from feeder schools comprised a larger share of the applicant and admittee pools under the top 10% regime compared with affirmative action, and their admission likelihood also rose slightly. Although the shares of applicants and admittees from poor and Longhorn high schools dropped, their admission likelihood actually increased appreciably. This change is due almost entirely to the larger representation of top 10% graduates from Longhorn schools in the applicant pool even as the share of top decile students from poor high schools contracted. The diverging outcomes for topranked students from Longhorn and other poor schools not targeted for outreach and scholarship support underscores the need for financial aid for an admission guarantee to broaden access to underrepresented groups (Alon, 2007). In fact, students from Longhorn schools who graduated in the second decile of their class were even less likely 11 The quartile distribution is computed on an annual basis so that new schools can be accommodated. 16 to be admitted to UT after the top 10% law went into effect compared with their chances under the affirmative action regime. By comparison, the admission probability for students from feeder and affluent high schools remained unchanged across the two regimes. Table 4 about Here At TAMU, admission rates fell for applicants from feeder, affluent and average high schools and remained unchanged for students from poor and Century schools under the top 10% regime compared with affirmative action. Largely this resulted because of the larger number of applicants relative to the number of slots. Unlike UT, TAMU did not temporarily increase the size of its undergraduate class to accommodate the surge in applications.12 Applicants and admittees from feeder and affluent high schools remained unchanged as a share of the respective pools across the two admission regimes, but unlike Longhorn school applicants to UT, the share of Century school students in TAMU s applicant and admittee pools fell under the top 10% regime. The lower admission rates took their greatest toll among second decile applicants from feeder and affluent schools, whose enrollment probabilities fell by 11 and 7 percentage points, respectively. Admission rates of feeder and affluent school applicants ranked at or below the third decile also dropped considerably. At both TAMU and UT, applicants from average high schools were the key beneficiaries of the top 10% admission regime, which largely was driven by students who graduated in the top two deciles of their high school classes. This result indicates some broadening of access to the public flagships in that it requires a redistribution of slots 12 This decision proved unsustainable owing to the carrying capacity of the Austin campus, hence admission rates plummeted at UT after the size of the admitted class was scaled back (Tienda and Sullivan, 2008). 17 away from applicants who graduate from affluent and feeder schools. Even if this is a laudable social goal sought by the sponsors of the legislation and even if this is a direct consequence of the surge in demand stemming from rapid demographic growth, students from affluent and feeder schools who are denied admission will consider the admission regime unfair. Conclusion For several reasons, percent plans are inferior alternatives to affirmative action as a strategy to diversify college campuses (Long, 2004; Long and Tienda, 2007), or as we show, to broaden access to students who graduate from high schools with low college traditions. The most important is that admission mandates can only indirectly influence application behavior, which is a prerequisite for admission (Brown and Hirschman, 2006; Domina, 2007; Long and Tienda, 2008). Second, an admission guarantee can not ensure enrollment, which is particularly difficult for minority and low income students, especially those who graduate from high schools with low college going traditions (Tienda and Niu, 2006b). Finally, college choices appear to be highly constrained by the type of high school attended (Niu, et al, 2006; Niu and Tienda, 2008). At best, the top 10% regime tempered the decline in minority enrollment at the Texas public flagships, but the changing demography of the state reinforced this possibility. Neither affirmative action nor a percent plan, however, can resolve the tensions inherent in rationing scarce resources in this instance, admission slots against rising demand and competition for access to the most selective universities. 18 The highly uneven institutional consequences of HB 588 (Long and Tienda, 2007; 2008) also call into question the appropriateness of an admission regime that is so heavily predicated on a single metric class rank. The unequal institutional impacts of the automatic admission regime on students application and enrollment behavior partly reflect (1) differences in location of the two institutions a diverse capital city versus a small town over an hour from a major city (Tienda and Sullivan, 2008); (2) differences in the timing and comprehensiveness of outreach efforts to high schools with low collegegoing traditions (Domina, 2007); and (3) differences in institutional legacy whereby TAMU was deemed less appealing to minority students (Schmidt, 2005).13 We hasten to emphasize that the outcomes we describe are best described as short-term impacts. At this writing the top 10% admission regime has been in effect for a full decade and both intended and unintended consequences are more pronounced. For example, UT has had to contend with rising saturation with students who qualify for the admission guarantee, which has limited the ability of administrators to balance the undergraduate class. This problem has been less serious at TAMU, which has struggled, instead, to attract African American students who qualify for admission (either automatic or using multiple criteria). The divergent experiences of the two public flagships attest that a single-metric admission regime is not well suited to achieve common goals across post-secondary institutions that likely draw most of their applicants from different pools of sending schools. What is less debatable is that the indirect HB 588 has triggered powerful mechanisms that, combined with the changing demography of the state and the automatic 13 Until 1963, Texas A&M was a military-training college that was off-limits to black and female students, and compared to the Austin campus, is characterized by a more conservative culture than UT (Schmidt, 2005). 19 admission regime, have broadened access to the public flagships to high achieving students from the entire state of Texas. By strengthening ties between the top universities and high schools with low college going traditions, the Longhorn and Century Scholars program has begun to improve high school climate (Domina, 2007) and raise the number of economically disadvantaged students who attend the public flagships. Thus, even if HB 588 is rescinded in response to rising political opposition (Haurwitz, 2007), changes in high school sending patterns can persist, provided that financial incentives are maintained for needy students. References Alon, Sigal. (2007). The Influence of Financial Aid in Leveling Group Differences in Graduating from Elite Institutions. Economics of Education Review, 26(3):296-311. Alon, Sigal and Marta Tienda. (2007). Diversity, Opportunity and the Shifting Meritocracy in Higher Education. American Sociological Review, 72(3):487-511. Barr, Rita. 2002. Top 10 Percent Policy: Higher Education Diversity after Hopwood. Interim News, 77-9 (June 28). Austin: House Research Organization, Texas House of Representatives. Brown, Susan K. and Charles Hirschman. (2006). The End of Affirmative Action in Washington State and Its Impact on the Transition from High School to College. Sociology of Education, 79(2):106-130. The College Board. (2007). College Search. retrieved 9/16/07 http://www.collegeboard.com/student/index.html?student Domina, Thurston. (2007). "Higher Education Policy as Secondary School Reform: Texas Public High Schools After Hopwood" Educational Evaluation and Policy Analysis, 29(3):200-217 Faulkner, Larry R. (2000). Top 10 percent helps Students. San Antonio Express-News, October 25, p.5B. Faulkner, Larry R. (2002). Class Rank Predicts Student Success. USA Today, April 5, p.11A. 20 Giovanola, Anouck. (2005). Irma Rangel. Women s Legal History Project. http://womenslegalhistory.stanford.edu/papers05/Rangel_bio_Giovandola. Accessed 28 January 2008. Grutter v. Bollinger, Docket No. 02-241 (2003). Haurwitz, Ralph K.M. (2007). UT calls for limiting top 10 percent admissions. Austin American Statesman, Tuesday, February 27. Hopwood v. University of Texas, 78 F.3d 932 (5th Cir., 1996). Jaschik, Scott. (2007). 10 Percent Plan Survives in Texas. Inside Higher Education, 29 May. Leicht, K. T. & Sullivan, T. A. (2000). Minority student pipelines before and after the challenges to affirmative action. Unpublished manuscript, University of Texas at Austin. Retrieved September 11, 2007 from http://www.texastop10.princeton.edu/reports/misc/minority_pipelines.pdf Long, Mark C. (2004). "College Applications and the Effect of Affirmative Action." Journal of Econometrics, 121(1-2): 319-342. Long, Mark C. and Marta Tienda. (2007) Winners and Losers: Changes in Texas University Admissions post-Hopwood. Paper presented at the annual meeting of AERA, Chicago, April 2007. Long, Mark C. and Marta Tienda. (2008). Changes in Texas Universities Applicant Pools after the Hopwood Decision. Paper to be presented at the annual meeting of AERA, New York, April. Montejano, David. (2001). Access to the University of Texas at Austin and the Ten Percent Plan: A Three Year Assessment. http://www.utexas.edu/student/admissions/research/montejanopaper.html. Accessed 28 January 2008. Murdock, Steve H., Steve Whitre, Md. Nazrul Hoque, and Beverly Pecotte. (2003). The New Texas Challenge: Population Change and the Future of Texas. College Station: Texas A&M University Press. Niu, Sunny X., Marta Tienda and Kalena Cortes. (2006). College Selectivity and the Texas Top 10% Law: How Constrained are the Options? Economics of Education Review, 25:259-272. 21 Niu, Sunny X., Teresa Sullivan and Marta Tienda. (2008). Minority Talent Loss and the Top 10% Law. Social Science Quarterly. (in press) Niu, Sunny X. and Marta Tienda. (2008). Choosing Colleges: Identifying and Modeling Choice Sets. Social Science Research (in press). Regents of University of California v. Bakke, 438 U.S. 265 (1978). Schmidt, Peter. (2005) A New Route to Racial Diversity: Texas A&M Raises Minority Enrollments without Race-Conscious Admissions. The Chronicle of Higher Education, 51 (21): Page A22. Staff, Jenny. (1997). Texas after Hopwood: Revisiting Affirmative Action. Session Focus, No. 75-14. Austin, TX: Texas House of Representatives, House Research Organization, (April 22). Texas A&M University. (2006). Texas A&M University: Applications, Admits and Enrolled. Official 12th Class Day Data, Fall 2006. Report by the Office of Institutional Studies and Planning. Retrieved 9/16/07 from: http://www.tamu.edu/opir/reports/student.html; Texas Higher Education Coordinating Board [THECB]. (1998). Report on the effects of the Hopwood decision on minority applications, offers, and enrollments at public institutions of higher education in Texas. Retrieved December 18, 2002, from http://www.thecb.state.tx.us/cfbin/ArchFetch.cfm?DocID=16&Format=HTML Texas Higher Education Coordinating Board [THECB]. (2001). Enrollment Forecasts 2000-2015: Texas institutions of higher education. Study Paper #27. Retrieved December 18, 2002, from http://www.thecb.state.tx.us/reports/pdf/0380.pdf. Tienda, Marta and Teresa A. Sullivan. (2008). The Promise and Peril of The Texas Uniform Admission Law. In Martin Hall, Marvin Krislov and David L. Featherman (eds.), The Next Twenty Five Years? Affirmative Action and Higher Education in the United States and South Africa. Ann Arbor: University of Michigan Press. Tienda, Marta and Sunny Niu. (2006a). Capitalizing on Segregation, Pretending Neutrality: College Admissions and the Texas Top 10% Law. American Law and Economics Review, 8:312-346. Tienda, Marta and Sunny X. Niu. (2006b). Flagships, Feeders, and the Texas Top 10% Law: A Test of the Brain Drain Hypothesis. Journal of Higher Education. 76(4):712739. Tienda, Marta and Faith Mitchell, eds. (2006). Multiple Origins, Uncertain Destinies: Hispanics and the American Future. Washington D.C.: National Academies Press. 22 The University of Texas at Austin. (2006). Implementation and results of the Texas automatic admission law (HB588) at the University of Texas at Austin. Demographic analysis of entering freshman, fall 2006. Report by the Office of Admissions The University of Texas at Austin. Retrieved September 16, 2007 from: http://www.utexas.edu/student/admissions/research/index.html. University of California Regents v. Bakke, 438 U.S. 265 (1978) U.S. Bureau of the Census. (2007). State and County QuickFacts. Retrieved September 17, 2007 from: http://quickfacts.census.gov/qfd/states/48000.html Walker, Bruce and Gary Lavergne. (2001). Affirmative action and percent plans: What we learned in Texas. The College Board Review, 193:18-23. 23 Table 1. Composition of Public High School Graduates: Texas, 1994-2014 (%) Black Hispanic Asian White Total Grad 1994 12 29 3 56 163 2004 13 35 4 48 244 % 65 78 81 29 50 2014 12 44 6 37 265 Source: Texas Education Agency, Texas Public School Statistics, Pocket Edition, 1994-1995 & 2004-2006 Table 2. Composition of In-State Student Applicant, Admission and Enrollee Poolsa under Affirmative Action and Top 10% Regimes: Texas A&M and Austin (In Percent) Applicants Student Characteristicsb Texas A&M University Black Hispanic Asian White N Admittees Affirmative Action 1992-1996 5.1 15.1 5.3 73.4 42,564 Enrollees Affirmative Action 1992-1996 * * * * 4.1 12.8 3.5 78.8 25,802 Affirmative Action 1992-1996 4.7 13.6 5.4 75.0 54,585 Top 10% 1998-2002 3.9 * 11.8 * 6.4 * 75.7 65,404 Top 10% 1998-2002 3.5 11.7 5.8 77.1 48,489 Top 10% 1998-2002 2.8 * 9.7 * 3.6 82.5 * 30,442 University of Texas at Austin Black Hispanic Asian White N 4.7 17.3 14.1 63.1 54,652 5.1 * 16.9 16.4 * 60.1 * 62,266 4.1 17.7 14.5 63.1 41,189 4.4 16.1 * 17.8 * 60.4 * 48,867 4.2 15.6 15.1 64.5 26,112 3.9 15.2 18.8 * 61.1 * 31,908 Source: Texas Higher Education Opportunity Project (THEOP) administrative data. a: Students from Texas high school with senior class size greater than 9 students. b: The race/ethnic categories do not sum to 100% because the "other" category (Native American and unspecified) are not shown. *: p .001 for pre- post-Hopwood comparison. 24 Table 3. Composition of In-State Student Applicant and Admit Pools and Admission Likelihood a Before and After Hopwood: UT-Austin (in percent) Applicants Admittees Affirmative Action 1992-1996 *** *** *** *** *** 23.2 32.8 20.9 7.7 3.7 43,576 16.6 33.8 25.7 11.5 5.3 22,078 25.8 36.0 19.9 5.5 2.5 10,657 % Applicants Admitted Affirmative Action 1992-1996 *** *** *** *** 73.9 74.0 76.5 74.5 69.0 59,023 99.1 97.8 96.9 94.5 90.7 22,818 93.0 83.1 75.7 65.0 59.3 13,042 52.5 42.2 40.1 25.8 29.5 23,163 High School Type All Feeder Affluent Average Poor Longhorn N Top10% Feeder Affluent Average Poor Longhorn N Second 10% Feeder Affluent Average Poor Longhorn N b Afffirmative Action 1992-1996 23.2 32.7 20.1 7.6 4.0 59,023 16.2 33.4 25.7 11.7 5.7 22,818 22.7 35.4 21.5 6.9 3.4 13,042 Top 10% 1998-2002 25.5 29.1 23.6 6.1 3.4 69,521 17.6 29.9 30.7 9.6 6.2 27,148 26.0 32.8 27.4 5.7 2.3 13,896 Top 10% 1998-2002 25.5 28.8 24.5 6.2 3.5 53,221 17.8 30.0 30.7 9.5 6.1 26,659 29.2 33.3 26.0 4.3 1.4 11,480 Top 10% 1998-2002 76.7 75.9 79.6 78.1 79.9 69,521 99.1 98.5 98.1 97.3 96.1 27,148 *** *** *** *** *** *** *** *** *** * *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 92.8 84.0 78.1 * 62.5 50.2 ** 13,896 58.8 46.7 44.5 32.0 28.5 28,477 *** *** *** ** 3rd Decile and Lower Feeder 30.3 32.7 *** 34.0 36.3 *** Affluent 30.4 26.6 *** 27.5 23.4 *** Average 13.9 14.9 *** 11.9 12.5 Poor 4.0 2.9 *** 2.2 1.7 ** Longhorn 2.6 1.2 *** 1.6 0.7 *** N 23,163 28,477 10,841 15,082 a: Students from Texas high school with senior class size greater than 9 students. b: The high school type categories do not sum to 100% because the "missing/private" category are not shown. *: p<=.05, **: p<=.01, ***: p<=.001 for two regime comparison. Source: the administrative data component of the Texas Higher Education Opportunity Project (THEOP). 25 Table 4. Composition of In-State Student Applicant and Admit Pools and Admission Likelihood Before and After Hopwood: Texas A&M (in percent) Applicants Admittees Affirmative Action 1992-1996 *** *** *** *** *** 18.0 33.8 25.4 9.5 3.9 46,114 11.2 33.9 31.1 13.3 4.9 22,496 20.4 38.1 24.9 7.3 3.0 10,752 % Applicants Admitted Affirmative Action 1992-1996 72.4 76.4 82.5 85.3 84.2 59,114 99.3 98.0 97.5 97.4 96.5 22,994 92.2 82.3 79.0 78.1 74.8 12,964 High School Typeb All Feeder Affluent Average Poor Century N Top10% Feeder Affluent Average Poor Century N Second 10% Feeder Affluent Average Poor Century N Affirmative Action 1992-1996 19.4 34.5 24.0 8.7 3.6 59,114 11.1 33.8 31.2 13.3 4.9 22,994 18.4 38.3 26.2 7.7 3.3 12,964 Top 10% 1998-2002 20.5 33.3 25.7 6.0 2.5 70,040 Top 10% 1998-2002 18.3 32.3 28.3 7.0 2.9 51,591 Top 10% 1998-2002 65.6 *** 71.5 *** 81.1 ** 85.6 85.1 70,040 100.0 99.9 99.9 100.0 100.0 25,598 *** *** *** *** *** *** *** *** *** 11.5 33.1 34.9 *** 10.1 *** 3.8 *** 25,598 20.3 36.8 28.5 5.4 2.1 15,241 *** ** *** *** *** 11.5 33.1 34.9 *** 10.1 *** 3.8 *** 25,576 21.1 35.4 28.6 5.2 2.2 11,931 *** *** *** *** 81.2 *** 75.4 *** 78.5 75.9 80.6 15,241 47.7 43.1 47.9 50.2 58.3 29,201 *** *** *** *** ** 3rd Decile and Lower Feeder 28.2 28.5 27.8 28.2 54.8 Affluent 33.0 31.6 *** 30.1 28.2 *** 50.6 Average 15.7 16.2 15.9 16.1 56.1 Poor 4.7 2.8 *** 4.9 3.0 *** 58.1 Century 2.4 1.6 *** 2.9 2.0 *** 66.4 N 23,156 29,201 12,866 14,084 23,156 a: Students from Texas high school with senior class size greater than 9 students. b: The high school type categories do not sum to 100% because the "missing/private" category are not shown. *: p<=.05, **: p<=.01, ***: p<=.001 for two regime comparison. Source: the administrative data component of the Texas Higher Education Opportunity Project (THEOP). 26
Find millions of documents here - Study Guides, Homework Solutions, Papers, Exam Answer Keys and more.
Course Hero has millions of course related materials that will enable you to learn better,
faster and get an A in all your courses.
Below is a small sample set of documents:
Below is a small sample set of documents:
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
TEXASHIGHEREDUCATIONOPPORTUNITYPROJECT AdministrativeCollege ApplicationData DocumentationforPublicUseDataFiles December18,2008 Texas Higher Education Opportunity Project / Wallace Hall / Office of Population Research / Princeton University / ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
TEXASHIGHEREDUCATIONOPPORTUNITYPROJECT AdministrativeCollege TranscriptData DocumentationforPublicUseDataFiles November21,2008 Texas Higher Education Opportunity Project / Wallace Hall / Office of Population Research / Princeton University / P...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
February 24, 2005, The Daily Texan The Daily Texan - Top Stories Issue: 2/24/05 Top 10 percent may hurt minorities, report says By Melissa Mixon An unpublished study, reported by the Chronicle of Higher Education, said black and Hispanic students fr...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
February 23, 2005, American Association of Collegiate Registrars and Admissions Officers Two Studies Analyze Texas Admission Systems Written by: Stephen Kennedy-Johnston Published: 02/23/2005 Two studies, although not yet published, were presented ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Posted on Sun, Jul. 04, 2004 FORT WORTH STAR - TELEGRAM The idea of college must be cultivated By Richard Gonzales Special to the Star-Telegram In a Texas A&M banquet hall, bright Latino college students listened to a speaker rattle on about a form...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Texas AM U.: Texas Legislature meets to discuss future of top10 plan (C) 2003 The Ba...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
NEWS Expert has idea for top 10% / State could select college to which grad is admitted Associated Press 443 words 25 June 2004 Houston Chronicle 3 STAR 33 English Copyright 2004 Houston Chronicle AUSTIN AUSTIN - Students who are granted automatic ad...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
America\'s Newspapers Estimated printed pages: 3 Lubbock Avalanche-Journal (TX) June 25, 2004 Section: state Researcher tells state Senate committee top 10 percent law ought to be revised Texas system could choose campus, professor suggests Article Te...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
10% rule not hindering good students By Matt Flores San Antonio Express-News Web Posted : 01/20/2004 12:00 AM Humberto Aguilera still remembers the nervous twitch he felt as a Churchill High School senior as he anxiously awaited word from the admiss...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
June 24, 2004, 11:29PM State could select college to which grad is admitted Associated Press AUSTIN - Students who are granted automatic admission to Texas universities through the state\'s top 10 percent law should go to a school selected by the sta...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
nterview I w i t h . an Marta Tienda Marta Tienda, who served for eight years as a trustee of Carnegie Corporation of New York, is Maurice P. During Professor in Demographic Studies and Professor of Sociology and Public Affairs at Princeton Univers...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
http:/www.borderlandnews.com/stories/borderland/20040123-71954.shtml Borderland Friday, January 23, 2004 Top 10% plan has improved diversity at top Texas colleges Darren Meritz El Paso Times A program that gives students who graduate in the top 10...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Longhorns of the lower 90 By RICK CASEY Copyright 2004 Houston Chronicle PRINCETON Professor Marta Tienda likely doesn\'t expect that her study of Texas\' \"10 percent rule\" will end parental paranoia. When it comes to getting your children into the \"ri...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Study: Top 10 law not curbing college choices LAST UPDATE: 1/20/2004 7:24:05 PM WOAI.com Texas universities\' top 10 percent admission law does not keep smart students who fail to graduate at the top of their class from getting into the state\'s most ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Study: Top 10 law not curbing college choices 01/20/2004 Associated Press Texas universities\' top 10 percent admission law does not keep smart students who fail to graduate at the top of their class from getting into the state\'s most competitive univ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Jan. 20, 2004, 1:19AM Study: Top 10 law not curbing college choices By TODD ACKERMAN Copyright 2004 Houston Chronicle Texas universities\' top 10 percent policy is not squeezing out significant numbers of highachieving students from the state\'s most ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Study: 10 percent law isn\'t doing enough for diversity Sharon Jayson, AMERICAN-STATESMAN STAFF 460 words 24 October 2003 Austin American-Statesman B7 English Copyright (c) 2003 Bell & Howell Information and Learning Company. All rights reserved. Texa...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
March 12, 2003: A moment with. Marta Tienda Photo by Denise Applewhite The 1996 federal court ruling in Hopwood v. Texas struck down affirmative action in university admissions, leading Texas to create a program in which state students who graduate ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Sociology professor finds 10 percent plan flawed Brian Henn Princetonian Senior Writer The Texas \'10 percent plan\' for college admissions, heralded as a race-neutral alternative to affirmative action, does not succeed in boosting minority enrollment...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Study: Texas \'10 percent plan\' fails to sustain diversity By Eric Quiones Princeton NJ - The Texas \"10 percent plan,\" promoted as a tool to ensure diversity in higher education following a ban on affirmative action, has failed to sustain minority adm...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
Policy Brief Closing the Gap?: Admissions and Enrollments at the Texas Public Flagships Before and After Affirmative Action Marta Tienda, Princeton University; Kevin T. Leicht, The University of Iowa; Teresa Sullivan, University of Texas at Austin; M...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
http:/chronicle.com/daily/2003/01/2003012401n.htm January 24, 2003 Texas Admissions Plan Has Not Increased Diversity at Flagship Campuses, Study Finds By WILL POTTER The \"top 10 percent\" plan used for admission to public universities in Texas has ...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
...
Princeton >> TEXASTOP10 >> 10 (Fall, 2008)
A publication of the Population Reference Bureau Volume 30, Number 5 July 2002 News, numbers, and analysis Affirmative Action Alternative Put to the Test in Texas One by one, through the courts or through referenda, states like Texas, California, F...
Princeton >> MCIS2 >> 2 (Fall, 2008)
Recarving China\'s Past: Art, Archaeology, and Architecture of the \"Wu Family Shrines\" Saturday and Sunday, April 30May 1, 2005 Helm Auditorium, McCosh 50, Princeton University, Princeton, New Jersey Organized by the Princeton University Art Museum in...
Princeton >> MCIS2 >> 2 (Fall, 2008)
Recarving Chinas Past Art, Archaeology, and Architecture of the Wu Family Shrines March 5June 26, 2005 The act of recarving is based on a desire to preserve the past for present and future encounters. In any recarving, whether of history, literature...
Princeton >> MCIS2 >> 2 (Fall, 2008)
00b-front_pp6-21_final 1/14/05 6:01 PM Page 18 TIANJIN Shandong Region: HanDynasty Archaeological Sites HEBEI Y el Fushan lo w Ri ve r Linzi Zhangqiu Ji\'nan Qingzhou Weifang Anqui Qingdao Changqing Donge Tai Shan (Mount Tai) SHANDONG ...
Princeton >> ECO >> 467 (Fall, 2008)
FALL 2008 Prof. Markus K. Brunnermeier email: markus@princeton.edu http:/www.princeton.edu/~markus Office: 209 Dial Lodge Office Hours: Mo 4:25-5:30 pm ECO467: Institutional Finance Financial Crises, Risk Management and Liquidity Time and Locatio...
Princeton >> ECO >> 467 (Fall, 2008)
Installation Instructions for upTick Financial Simulation Software ECO467/567: Institutional Finance, AY Fall 2008 Preceptor: Ing-Haw Cheng The class will make use of a financial-simulation software tool developed at HBS called upTick. Through this p...
Princeton >> ECO >> 467 (Fall, 2008)
Institutional Finance Financial Crises, Risk Management and Liquidity Markus K. Brunnermeier Preceptor: Ing-Haw Cheng Princeton University Whats Institutional Finance? Traditional Finance frictionless Households 1 borrowing/lending insuring H...
Princeton >> ECO >> 467 (Fall, 2008)
Institutional Finance Financial Crises, Risk Management and Liquidity Markus K. Brunnermeier Preceptor: Ing-Haw Cheng Princeton University Lending/Insuring vs. Trading Lending/Borrowing + Insuring = trading assets/securities Bond Stock Der...
Princeton >> ECO >> 467 (Fall, 2008)
SEPTEMBER 5, 2006 GettingStartedwiththeupTickDemoInstallingtheSoftware OntheCoursePlatform,youwillneedtodownloadtheupTickClientandtheupTickDemo. YouwillusetheupTickClienttotradeduringtheclassroomsimulationsandtheupTickDemoto ...
Princeton >> ECO >> 467 (Fall, 2008)
Institutional Finance Financial Crises, Risk Management and Liquidity Markus K. Brunnermeier Preceptor: Ing-Haw Cheng Princeton University Overview Efficiency concepts EMH implies Martingale Property Evidence I: Return Predictability Mis...
Princeton >> ECO >> 467 (Fall, 2008)
Institutional Finance Financial Crises, Risk Management and Liquidity Markus K. Brunnermeier Preceptor: Ing-Haw Cheng Princeton University Market Making Limit Orders Limit order price contingent order Limit buy order: buy as soon as price dr...
Princeton >> ECO >> 467 (Fall, 2008)
AUGUST 21, 2004 Dynamic Markets: Price Formation I Introduction Thisweekweseektounderstandhowpricesareformed.Ingeneralterms,apriceistheoutcome ofanegotiationbetweenabuyerandaseller.Incertainmarkets,suchastheusedcarmarketorthe blocktradingmarket,this...
Princeton >> ECO >> 467 (Fall, 2008)
COMMUNICATIONS October 19, 1981 Initiation of Coverage EQUITY RESEARCH. AOE (NASD: AOE) RATING: BUY Current Price (10/18/1981 @ close) Current Ratio1 Quick Ratio Inventory Turnover Days to Sell Inventory Net Profit Margin (%) Total Debt/Total Ass...
Princeton >> ECO >> 467 (Fall, 2008)
Institutional Finance Merger Arbitrage February 28th, 2006 (based on slides by Coval and Stafford) Merger Arbitrage Returns are generated by isolating and bearing deal risk Risky application of the Law of One Price Conditional on deal success, ther...
Princeton >> ECO >> 467 (Fall, 2008)
INSTITUTIONAL FINANCE Lecture 08: Dynamic Arbitrage to Replicate Non-Linear Payoffs Originally prepared by Ufuk Ince, Ekaterina Emm, supplemental material courtesy of Wei Xiong, Princeton University 1 BINOMIAL OPTION PRICING Consider a European call...
Princeton >> ECO >> 467 (Fall, 2008)
Dont Fear the Repo Ying Jiang William Hessert Sang Hun Kang Dec 8 2008 FIN567 Overview Repurchase agreement: structure & history Tri-party repo General collateral repo Fall of Bear Stearns Primary dealer credit facility as a backstop for repos ...
Princeton >> ECO >> 467 (Fall, 2008)
Basel II Tamer Bakiciol Nicolas Cojocaru-Durand Dongxu Lu Roadmap Background of Banking Regulation and Basel Accord Basel II: features and problems The Future of Banking regulations Background of Banking Regulation and Basel Accord Banking Supe...
Princeton >> ECO >> 525 (Fall, 2008)
FALL 2006 Prof. Jos Scheinkman email: joses@princeton.edu http:/www.princeton.edu/~joses Office: 210 Dial Lodge Office Hours: TBA Prof. Markus K. Brunnermeier email: markus@princeton.edu http:/www.princeton.edu/~markus Office: 205 Dial Lodge Office ...
Princeton >> ECO >> 525 (Fall, 2008)
Eco 525: Financial Economics I Lecture 02: Risk Preferences and Savings/Portfolio Choice Prof. Markus K. Brunnermeier 21:58 Lecture 02 Risk Preferences Portfolio Choice Slide 2-1 Eco 525: Financial Economics I State-by-state Dominance - State...
Princeton >> ECO >> 525 (Fall, 2008)
Eco 525: Financial Economics I Lecture 04: State-price BETA Model Prof. Markus K. Brunnermeier 10:01 Lecture 04 State-price Beta Model Slide 04-1 Eco 525: Financial Economics I Overview Risk-adjustment in payoffs Risk-adjustment in returns...
Princeton >> ECO >> 525 (Fall, 2008)
Eco 525: Financial Economics I Lecture 05: Mean-Variance Analysis & Capital Asset Pricing Model (CAPM) Prof. Markus K. Brunnermeier 16:14 Lecture 05 Mean-Variance Analysis and CAPM Slide 05-1 Eco 525: Financial Economics I Overview Simple CAPM...
Princeton >> ECO >> 525 (Fall, 2008)
Eco525: Financial Economics I Lecture 06: Factor Pricing Prof. Markus K. Brunnermeier 09:55 Lecture 06 Factor Pricing Slide 06-1 Eco525: Financial Economics I Overview Theory of Factor Pricing (APT) Merits of Factor Pricing Exact Factor Pricin...
Princeton >> ECO >> 525 (Fall, 2008)
Eco525: Financial Economics I Lecture 07: Multi-period Model Prof. Markus K. Brunnermeier 20:27 Lecture 07 Multi-period Model Slide 07-1 Eco525: Financial Economics I Introduction accommodate multiple and even infinitely many periods. several...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Modeling Info Solution Concepts Markus K. Bru...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Rational Expectation Equilibria Classication of Models CARAGaussian Asset Demand Symmetric Information Info Eciency Noisy REE Information Acquisition Asset Pricing under Asymmetric Information Rational Expectati...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Share Auctions Classication of Models Unit Demand Auctions 2nd -Price RET Aliated Values Asset Pricing under Asymmetric Information Share Auctions Markus K. Brunnermeier Princeton University Share Auctions Cons...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Screening Models Classication of Models Static Uniform Price Discr. Price (Limit Order Book) Contrast Asset Pricing under Asymmetric Information Screening Models Markus K. Brunnermeier Princeton University Dyna...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Kyle (1985) Classication of Models Static Dynamic Dynamic Programming Dynamic Kyle Asset Pricing under Asymmetric Information Strategic Market Order Models Markus K. Brunnermeier Princeton University Extensions...
Princeton >> ECO >> 525 (Fall, 2008)
Asset Pricing under Asym. Information Epistomology Knowledge Partitions Knowledge Operator Group Knowledge Depth of Knowledge Public Events Asset Pricing under Asymmetric Information Knowledge & No Trade Theorems Markus K. Brunnermeier Princeton Uni...
Princeton >> ECO >> 525 (Fall, 2008)
Optimal Expectations Brunnermeier Savings Optimal Expectations Markus K. Brunnermeier and Jonathan Parker Princeton University Conclusion Octo...
Princeton >> ECO >> 525 (Fall, 2008)
1 Bubbles and Crashes Dilip Abreu Princeton University Markus K. Brunnermeier Princeton University Hedge Funds and the Technology Bubble Markus K. Brunnermeier Stefan Nagel Princeton University London Business School 2 Story of a typical tech...
Princeton >> ECO >> 525 (Fall, 2008)
Predatory Trading Brunnermeier & Pedersen Model Predation Exogenous Default Single Predator Multiple Predators Endogenous Default Systemic Risk Risk Management Valuation Predatory Trading Markus K. Brunnermeier Princeton, CEPR, NBER Lasse Heje Ped...
Princeton >> ECO >> 525 (Fall, 2008)
Market Liquidity and Funding Liquidity Markus K. Brunnermeier Princeton University Lasse Heje Pedersen New York University This version: June 2007 Abstract We provide a model that links an assets market liquidity i.e., the ease with which it is tra...
Princeton >> ECO >> 525 (Fall, 2008)
Problem Set 1 Fin 525: Financial Economics I Part 1: Asset Pricing in Discrete Time Prof. Markus K. Brunnermeier Due Date: TBA Problem 1 During the bagel hour on Thursday morning, Max (a fellow Ph.D. student) approaches you. He looks very tired and...
Princeton >> ECO >> 525 (Fall, 2008)
Problem Set 2 Fin 525: Financial Economics I Part 1: Asset Pricing in Discrete Time Prof. Markus K. Brunnermeier Due Date: Monday, October 2 Nota Bene: Please do not feel obliged to solve all of these problems.this problem set, like most, is long and...
Princeton >> ECO >> 575 (Fall, 2008)
FALL 2005 Prof. Harrison Hong email: hhong@princeton.edu http:/www.stanford.edu/~hghong Office: 210 Dial Lodge Office Hours: TBA Prof. Markus K. Brunnermeier email: markus@princeton.edu http:/www.princeton.edu/~markus Office: 205 Dial Lodge Office H...
Princeton >> FIN >> 501 (Fall, 2008)
Prof. Markus K. Brunnermeier Fin 501: Asset Pricing http:/courseinfo.princeton.edu Room: 103 BCF (Dial Lodge) Times: MW, 11:00 a.m. 12:20 p.m. Fall 2008 e-mail: markus@princeton.edu http:/www.princeton.edu/~markus Office: 205 Dial Lodge Office Hours...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 02: One Period Model Prof. Markus K. Brunnermeier 10:37 Lecture 02 One Period Model Slide 2-1 Fin 501: Asset Pricing Overview 1. Securities Structure Arrow-Debreu securities structure Redundant securities Mark...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 03: Risk Preferences and Expected Utility Theory Prof. Markus K. Brunnermeier 11:00 Lecture 03 Risk Aversion Slide 3-1 Fin 501: Asset Pricing State-by-state Dominance - State-by-state dominance - riskier incomp...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 04: One Period Model Aggregation, Efficiency Prof. Markus K. Brunnermeier 10:54 Lecture 02 One Period Model: Aggregation, Efficiency Slide 4-1 Fin 501: Asset Pricing Overview 1. Optimization and Representative Age...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 05: Sharpe Ratio, Bounds and the Equity Premium Puzzle Prof. Markus K. Brunnermeier Bounds and Equity Premium Puzzle Slide 4-1 Fin 501: Asset Pricing $1 invested in 1972 - show graph! Bounds and Equity Premium Puz...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 06: Mean-Variance Analysis & Capital Asset Pricing Model (CAPM) Prof. Markus K. Brunnermeier 10:13 Lecture 06 Mean-Variance Analysis and CAPM Slide 06-1 Fin 501: Asset Pricing Overview Simple CAPM with quadratic ...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501:Asset Pricing I Lecture 07: Multi-period Model Prof. Markus K. Brunnermeier Lecture 07 Multi-period Model Slide 07-1 Fin 501:Asset Pricing I Introduction accommodate multiple and even infinitely many periods. several issues: how to d...
Princeton >> FIN >> 501 (Fall, 2008)
Eco525: Financial Economics I Lecture 08: Factor Pricing Prof. Markus K. Brunnermeier Factor Pricing Slide 08-1 Eco525: Financial Economics I Theory of Factor Pricing (APT) Merits of Factor Pricing Exact Factor Pricing and Factor Pricing Erro...
Princeton >> FIN >> 501 (Fall, 2008)
Fin 501: Asset Pricing Lecture 10: Market Efficiency Prof. Markus K. Brunnermeier 11:45 Lecture 10 Market Efficiency Fin 501: Asset Pricing Overview Efficiency concepts EMH implies Martingale Property Evidence I: Return Predictability M...
Princeton >> FIN >> 501 (Fall, 2008)
Risk is the central element that influences financial behavior. Robert C. Merton (1999) PowerPoint Presentation Modified by Markus Brunnermeier for Fin 501 Originally Prepared by Ufuk Ince, University of Washington and Ekaterina Emm, Georgia State U...
What are you waiting for?