Return on College Quality IT\'s

RETURN on COLLEGE QUALITY it's not just a perverse status-consciousness that makes higher education the only industry in which competitors are rated on the caliber of their customers rather than on their product - or that drives U.S. News & World Report to rank colleges on how well they recruit and graduate already successful high schoolers.

It's that we have no other discriminating way to measure collegiate quality. (Hersh 2005)

Measurements" Related to Collegiate Quality Other than the ratings colleges receive from their graduates, Hersh (2005) notes in this paper's introductory quote, no other astute way exists to "measure collegiate quality." Hersh (2005) argues, albeit, that: "if we don't know what makes a school good or bad, then the anxiety-driven college-application process is a terrible waste, the U.S. News & World Report rankings are a sham, and all the money lavished on vast library holdings, expensive computer labs, wireless classrooms, and famous faculty members is going for naught." In light of the dearth of effective measuring tools for colleges, Hersh (2005) proposes that variables such the value of SAT scores, graduation rates, class sizes, faculty salaries, and alumni giving surely matter to college-obsessed parents. Despite the noted dearth of measuring tools for collegiate quality, this paper, nevertheless, examines "measurements" by five researchers regarding the return students receive on college quality. The studies include:

College Quality and Future Earnings: Where Should You Send Your Child to College? (1989)

Does it Pay to Attend an Elite Private College? Cross-Cohort Evidence on the Effects of College Type on Earnings. (1999)

How Robust is the Evidence on the Effects of College Quality? Evidence from Matching?(2003)

Determinants of undergraduate GPAs: SAT scores, high-school GPA and high-school rank. (2004)

Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and un-observables, (1999)

1. College Quality and Future Earnings: Where Should You Send Your Child to College?

a. In BRIEF:

In their study, College Quality and Future Earnings: Where Should You Send Your Child to College?, James, Alsalam, Conaty, and to (1989, 247) consider graduating students' future earnings, as a function of all the jointly supplied inputs. Research questions include:

Does it matter which college you attend'?

If it matters, which college characteristics or other aspects of the college experience lead to a higher value-added?

While institutional characteristics do not explain a large proportion of the variance in earnings, other aspects of the higher educational experience such as choice of major, number of maths credits taken, GPA, and postgraduate degree matter a great deal." (James, Alsalam, Conaty, and to (1989, 251)

Some institutional characteristics as selectivity and Private-East exert positive effects on future earnings. Expenditures per student, albeit, do not have such an impact.

Attending Harvard appears to be a positive investment, however, attending a local state university to major in Engineering. To take massive amounts of mouth also constitutes a good investment.

A number of other outputs of higher education, other than future earnings and productivity matter. (James, Alsalam, Conaty, and to 1989, 249) b. MOTIVATION:

As a voluminous literature exists "on the returns to quantity of higher education...," these researchers note that little work has been done on the causes and consequences of college quality. "This paucity of research is due in part to the difficulty in obtaining detailed information about student and institutional characteristics." (James, Alsalam, Conaty, and to 1989, 247) This researcher agrees with the way the authors view the context of the issue.

c. BACKGROUND: No specific note is made regarding previous research efforts completed. These authors purport they explore the causes and consequences of college quality. No policy is examined during this study.

d. EVIDENCE: Potential biases include authors' choices of variables. The authors admit:

study of this sort cannot completely avoid the possibility of selection bias in estimating coefficients. Students with unobservable characteristic~ (such as motivation) may work harder, enter remunerative majors and occupations, and also self-select themselves into certain colleges, thereby creating the appearance of large college effects when in fact they may be small. We have tried to minimize this problem by controlling for numerous student characteristics and also by controlling, in some equations. For college experience variables such as major and GPA that may proxy the unobserved student characteristics. These equations may help to mitigate selection bias at the college level but the problem recurs at the curriculum level: Are the effects of GPA, math and major real effects, or are they, too, due to unobserved student characteristics that are correlated with these choices? (James, Alsalam, Conaty, and to 1989, 249)

The most convincing evidence the authors present in support of their arguments includes: "Regardless of which variables are in the model, measured college effects [sic] are small, explaining 1-2% of the variance in earnings. Interestingly, these effects are largely unchanged when controls for family background and prior academic background are added in equation 3, although they decline when major and even more so, when occupation, are added in equations 4, 5, and 6. To the extent that college characteristics matter selectivity and Private-East..., (characteristics that are not readily replicable, seem most important." (James, Alsalam, Conaty, and to 1989, 249) No weak argument could be determined by this researcher as it appears the authors covered even their potential weak points. Ultimately, in regard to future earnings, findings of this study suggest, "The main conclusion to be drawn is that research and graduate programs do not help undergraduates, nor does the choice between college vs. university affect future earnings. (James, Alsalam, Conaty, and to 1989, 250)

2. Does it Pay to Attend an Elite Private College? Cross-Cohort Evidence on the Effects of College Type on Earnings a. In BRIEF:

Brewer, Eide, and Ehrenberg (1999, 104) report that even though a substantial and rising labor market premium links with college attendance, not much research relates how this premium varies across various types of institutions and across time. These authors "explicitly model high school students' choice of college type (characterized by selectivity and control) based on individual and family characteristics (including ability and parental economic status) and an estimate of the net costs of attendance." b. MOTIVATION:

Previous research reveals the labor market return to college overall has fluctuated. "Between the mid-1970s and the mid-1980s the proportionate difference in mean wages between male college graduates and male high school graduates grew by 15 to 30 percentage points (Bound and Johnson 1992; Katz and Murphy 1992; Levy and Murnane 1992, cited by Brewer, Eide, and Ehrenberg (1999, 105) it has not been confirmed, however, whether this higher return applies equally to all types attendees of four-year college or merely to those at certain types of institutions. This researcher did not find any areas to disagree with in this study.

c. BACKGROUND:

Previous research by Bound and Johnson 1992; Katz and Murphy 1992; Levy and Murnane 1992 (cited by Brewer, Eide, and Ehrenberg 1999, 104) notes that, overall, the labor market return to college regularly fluctuates. Contrary to some previous studies, in this study Brewer, Eide, and Ehrenberg (1999, 119) utilize longitudinal data to examine "how the labor market return changes across time for a given cohort, and how the return changed for those early 1980s. In addition, unlike previous attempts to determine the impact of college quality type on labor market outcomes, we allow for the fact that students systematically select the college quality type they attend on the basis of the net costs they face." d. EVIDENCE: Utilizing data from both the National Longitudinal Study of the High School Class of 1972 and High School and Beyond, Brewer, Eide, and Ehrenberg (1999, 104) determine the effects of college quality on wages and earnings. They also explore how this effect fluctuates across time. Brewer, Eide, and Ehrenberg (1999, 105) apply the framework Lee (1983) developed to analyze polychotomous choice models with selectivity, to college selection. The approach, a generalization of the familiar model of Willis and Rosen (1979), consists of a choice equation and an estimated outcome equation for each choice. "Even after controlling for selection effects, strong evidence emerges of a significant economic return to attending an elite private institution, and some evidence suggests this premium has increased over time." (Brewer, Eide, and Ehrenberg 1999, 104)

3. Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and un-observables, (1999) a. In BRIEF:

According to Dale, and Krueger (1999), characteristics related to a student's earnings capacity may influence some decisions made by elite colleges regarding his/her admission. As some characteristics are unseen by researchers who ultimately estimate wage equations, albeit, parsing out the effect of attending a selective college from the students' pre-college characteristics proves challenging.

Dale, and Krueger (1999, 1) find that students who attended more selective colleges do not earn more than other students accepted and rejected by comparable schools, but who ultimately attended less selective colleges.

b. MOTIVATION:

In the past, literature has addressed the question, "Does the 'quality' of the college that students attend influence their subsequent earnings?" When students are observed in the labor market, past findings note, students who attended colleges with higher average SAT scores or higher tuition tend to have higher earnings. "Attending a college with a 100 point higher average SAT is associated with 3to 7% higher earnings later in life." (e.g., Kane, 1998, cited by Dale, and Krueger 1999, 1) as higher education constitutes up 40% of total U.S. educational expenditures, understanding "the impact of selective colleges on students' labor market outcomes is central for understanding the role of human capital," Dale, and Krueger (1999, 1) stress.

Previous literature cited by Dale, and Krueger (1999, 1) includes work by "Hunt (1963), Solnion (1973), Wales (1973), Solmon and Wachtel (1975), and Wise (1975),...Brewer and Ehrenberg (1996), Behrman et al. (1996), Daniel (1997), Kane (1998), and others." c. BACKGROUND:

To remove the effect of unobserved characteristics that influence college admission, this study considers information on the set of colleges where students were accepted and rejected.

A difficulty interpreting past estimates a many estimates of the effect of college quality on students' subsequent earnings.

d. EVIDENCE:

In their study, Dale, and Krueger (1999, 1):

match students in the newly collected College and Beyond (C&B) Data Set who were admitted to and rejected from a similar set of institutions, and estimate fixed effects models. As another approach to adjust for selection bias, we [Dale, and Krueger, (1999)] control for the average SAT score of the schools to which students applied using both the C&B and National Longitudinal Survey of the High School Class of 1972."

Dale, and Krueger (1999, 2) employ two new approaches to amend non-random selection of students on the part of elite colleges. In one approach, they only compare college quality and earnings among students accepted and rejected by a comparable set of colleges; students comparable in provisions of observable variables. In their second approach, Dale, and Krueger (1999, 2) hold the average SAT score of the schools to which each student applied constant, "as well as the average SAT score of the school the student attended, the student's SAT score, and other variables. The second approach is nested in the first estimator."

The most convincing evidence the authors present in support of their arguments includes:."..the average tuition charged by the school is significantly related to the students' subsequent earnings." Dale, and Krueger (1999, 31) state, "we find a substantial internal rate of return from attending a more costly college. Lastly, the payoff to attending an elite college appears to be greater for students from more disadvantaged family backgrounds." Their weakest arguments evolve from the fact that after Dale, and Krueger 1999 (30) correct for students' unobserved characteristics, findings cast doubt on the perception that school selectivity, as measured by the average SAT score of freshmen who attend a college, constitutes a vital determinant of students' subsequent incomes.

4. How Robust is the Evidence on the Effects of College Quality? Evidence from Matching a. In BRIEF:

Black and Smith (2003, 1) contend: "The basic finding is that college quality matters for later labor market outcomes." investigated two potential weaknesses in the most commonly used econometric approach in the literature that estimates the labor market effects of college quality 23).

Based on an individuals' ability, substantial sorting exists into colleges of differing qualities for men and women in the National Longitudinal Survey of Youth (LSY). "Higher ability students disproportionately attend higher quality colleges. (some evidence of an asymmetry [found], with more high-ability students at low-quality colleges) Sorting on ability alone, however, does not break the support condition." (Black and Smith 2003, 24) Although some concerns arise, revolving around the conventional practice of utilizing "linear selection on observables models to investigate the labor market effects of college quality,...the matching estimates support the overall finding of the regression-based literature that college quality matters for labor market outcomes."

Estimates "based only on the 'thick support' region of propensity scores around 0.5 consistently turn out larger than those constructed using the full sample." b. MOTIVATION:

To inform their analysis of the support condition, Black and Smith (2003) utilize data from the 1979 cohort of the National Longitudinal Survey of Youth (NLSY) to examine how students of varying abilities, as measured by the first principal component of the ten tests that comprise the Armed Services Vocational Aptitude Battery. This researcher basically agrees with how the authors view the context of the issue. One consideration this researcher contends may have been minimized more should have been would be "the important role of family background for both labor market outcomes and college quality choices...." (Black and Smith 2003, 22) Although the researchers included this in their analysis, the approach seemed to somewhat minimize the familial factor.

c. BACKGROUND:

Previous research includes work by "Black, Daniel, and Smith (2003a,b), Brand (2002), Brewer, Eide, and Ehrenberg (1999), Dale and Krueger (2002), Light and Strayer (2000) and Turner (1998). The basic finding is that college quality matters for later labor market outcomes." (Black and Smith 2003, 1) This paper expands knowledge as it'd. EVIDENCE:

The authors initially interviewed respondents in 1979 and attempted to re-interview them annually (biannually since 1994) since then. "Of the five sub-samples that comprise the NLSY, we use only the representative cross-section and the minority over-samples." (Black and Smith 2003, 5) Covariates affect both college quality choice and labor market outcomes, Black and Smith 2003 (24) find. The authors present matching estimates regarding the impact on wages of attending a high-quality rather than a low-quality college. They also "present matching estimates using the Epanechnikov kernel with leave-one-out cross-validated bandwidths." (Black and Smith 2003, 18)

The authors' weakest argument evolves from their admission of they end up with large standard errors. "a problem with the data. Running linear regressions hides the problem by implicitly borrowing strength from comparison observations with lower probabilities of attending a high-quality college. (Black and Smith 2003, 23)

5.Determinants of undergraduate GPAs: SAT scores, high-school GPA and high-school rank. (2004) a. In BRIEF:

This study assesses the degree to which SAT scores, high-school GPA (HSGPA) and class rank predict success in college. The authors also explore whether race - sex differences exist regarding the potential of success in college.

The degree students with varying backgrounds are likely eligible for state-wide scholarships, as well as, student's ability to retain them after enrolment are also examined. (Cohn, Cohn, Balch and Bradley 2004, 577) Research questions include:

Who is likely to benefit most from statewide college scholarship programs in South Carolina?"

Is SAT a necessary requirement, and who is most likely to be affected by such a requirement?

What percentages of students by race and sex groups are likely to achieve a 3.0 GPA in college required to maintain their scholarships, given information about their high-school attributes?

What is the predicted college GPA for students, by race and sex, given the requirements for the various state scholarship programs? (Cohn, Cohn, Balch and Bradley 2004, 577) b. MOTIVATION: This paper tackles the question of the probability that students eligible for a scholarship will be able to maintain it in college. Students' scores on the SAT or ACT tests and/or high-school record (class rank and grade point average, GPA) routinely determine student's college admissions and eligibility for merit scholarships. This study assesses "the degree to which such factors predict success in college." (Cohn, Cohn, Balch and Bradley 2004, 578; 580) This researcher contends the view these authors' present regarding the context of the issue to be agreeable.