Directory UMM :Data Elmu:jurnal:E:Economics of Education Review:Vol20.Issue3.2001:

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Economics of Education Review 20 (2001) 245–262

www.elsevier.com/locate/econedurev

Do parental income and educational attainment affect the

initial choices of New Hampshire’s college-bound students?

Robert K. Toutkoushian

*

Office of Policy Analysis, Myers Financial Center, 27 Concord Road, University System of New Hampshire, Durham, NH 03824, USA

Received 20 February 1999; accepted 25 June 1999

Abstract

While other studies have explored the application and enrollment decisions of students, none have explicitly con-sidered how selected factors affect an individual’s choice to initially consider a particular institution. This study uses data on where New Hampshire seniors have their SAT scores sent to examine what factors influence a student’s decision to consider attending different types of institutions in the region, and whether student choice is affected by parental education and income. The results show that first-generation students and students with college-educated parents con-sider attending similar postsecondary institutions. Likewise, having a low family income does not appear to restrict college-interested students from considering more exclusive and/or expensive institutions. Students are also found to be most interested in institutions where their ability more closely matches the average ability profile of enrolled students.

JEL classification: I2

Keywords: Educational economics; Human capital

1. Introduction

Now more than ever, colleges and universities are faced with the challenge of designing enrollment man-agement strategies that can satisfy multiple goals. Insti-tutions of all types increasingly compete for high-caliber students to help raise their student profiles, and work to attract a sufficient number of students to fund their operating budgets. At the same time, federal and state governments have pressured the higher education com-munity to ensure that a postsecondary education is within the reach of students who have traditionally been underrepresented in higher education. This would include students from lower-income families and

stu-* Tel.:+1-603-862-0966; fax:+1-603-868-2756.

E-mail address: [email protected] (R.K.

Toutkoushian).

dents from families where neither parent has attended college (“first generation students”). Title IV of the Higher Education Act of 1965 established the Pell Grant program to target students from lower-income families, and the five TRIO programs to assist first-generation stu-dents from lower-income families. The hope of pol-icymakers is that these students would have the same options as their counterparts in choosing if and where to attend college.

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high school educations, students with college-educated parents are more likely to graduate from high school and continue on to college. At the same time, his data show that the college continuation rate for first-generation stu-dents has been rising since 1990. While interesting, stat-istics such as these raise more questions than they answer. Are students with lower parental income and education predominantly found at less-expensive and/or less reputable institutions, and if so, is it because they are less qualified for admission or simply less interested in these types of schools? It is important to determine if given their academic qualifications, disadvantaged stu-dents have the same opportunities as other stustu-dents to choose such postsecondary options, and the same interest in pursuing a postsecondary education.

During the past 30 years, researchers have investigated how students make decisions about where to attend col-lege, and how these decisions are affected by individual, institutional, and economic characteristics. These are known collectively as studies of the student demand for higher education. These studies make it possible to deter-mine if enrollments of disadvantaged students are rising and whether these students have equal chances of being selected from applicant pools holding constant relevant characteristics such as academic ability/achievement.

Studies of individuals currently enrolled in postsec-ondary institutions can only address certain aspects of whether students have appropriate access to higher edu-cation. Descriptive studies of students at an institution by definition only take into account those students who have applied for admission, were offered admission by the institution, and were willing and able to attend. Little is often known about those students who do not apply to and/or enroll in the institution. This is especially important with regard to answering questions about access because traditionally disadvantaged students may be less likely than others to even consider pursuing a four-year degree, much less attend a highly selective (and/or expensive) private institution. Time-series stud-ies that examine shares of high school graduates apply-ing to and/or enrollapply-ing in postsecondary institutions assume that all high school graduates are interested in and capable of attending college. Likewise, cross-sec-tional studies of individual students who enroll from the applicant pool do not take into account that some stu-dents may be underrepresented in the applicant pool. Ehrenberg and Sherman (1984) refer to this as a “selec-tion bias” problem, and express concern as to how it may influence what is known about student demand.

A student’s decision-making process is comprised of four separate decisions: (1) whether to pursue a postsec-ondary education; (2) whether to consider a specific insti-tution; (3) whether to apply to an institution that is under consideration; and (4) whether to enroll in one of the institutions to which the student is admitted. Paulsen (1990) and Weiler (1994) propose similar categorizations

of the student decision process. The set of institutions to which a student is considering enrolling is commonly referred to as the student’s consideration set. Paulsen (1990) argues that the “search phase” for students described in the second and third stages is perhaps the most important component of student demand.

One difficulty in looking at the student search phase is knowing which institutions students are considering for their postsecondary education. An approach that has not been widely explored is to use information on the institutions where students have their college entrance exam scores sent to represent a student’s consideration set. This is a useful proxy measure for several reasons. Most students who plan to attend college after high school take either the Scholastic Aptitude Test (SAT) or the American College Testing Program (ACT) test. Furthermore, the majority of colleges and universities in the United States require all of their applicants to submit test scores to the institution prior to being considered for admission. Institutions usually require applicants to take a specific test, with the SAT being the most popular choice for colleges and universities in the eastern and western regions of the country and the ACT being more prevalent in the midwestern United States.

This study uses data on all SAT takers in one parti-cular state — New Hampshire — to examine the college search phase of students and determine if family income, first-generation status, and other factors such as aca-demic ability/achievement and educational plans play a role in the types of institutions students initially consider for their postsecondary education. Demand models are estimated for nine different institutions that are among those most frequently designated by New Hampshire high school students. The institutions vary with regard to size, selectivity, cost, affiliation (public versus private), and emphasis on research. It is important to note at the onset that this study does not address whether per-sonal and economic factors influence a student’s decision to pursue a college degree, or their final destination for postsecondary education, but rather their initial choice of colleges/universities to consider.

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R.K. Toutkoushian / Economics of Education Review 20 (2001) 245–262

2. Literature review

Student demand for higher education refers to the will-ingness and ability of potential college students to pursue a postsecondary education. The term “demand” could refer to an individual’s interest in pursuing higher edu-cation in general or choosing a single institution or cate-gory of institution (e.g., public). Economic models of student choice assert that individuals will pursue a col-lege education when the expected net return from colcol-lege (benefits minus costs) exceed the expected net return from all other ways in which they could spend their time (Becker, 1990; Clotfelter, 1993). Human capital theory suggests that time spent by individuals in college is viewed as an investment in their future (Becker, 1962). When the benefits from attending college rise, holding costs constant, not surprisingly the theory predicts that the demand for college will rise. Likewise, an increase in the costs of attendance would lead to a predicted decrease in the number of individuals interested in higher education.

Studies of student demand date back to the work by Ostheimer (1953). As noted by Becker (1990), beginning in the 1960s statistical models, such as the one used by Campbell and Siegel (1967), tried to explain changes in higher education demand over time. There are many reviews of student demand studies that have been pub-lished including Jackson and Weathersby (1975), Radner and Miller (1975), Cohn and Morgan (1978), Hossler, Braxton and Coppersmith (1989), Becker (1990) and Paulsen (1990). Interested readers are directed to these sources for a more extensive description of early studies on this topic.

The variables chosen to explain student demand vary depending in part on whether time-series or cross-sec-tional data are being used. Time-series studies examine the relationships between factors of interest and the demand of groups of students for postsecondary edu-cation. In this instance, the dependent variable is typi-cally either the proportion or number of eligible students enrolling in college each year (Campbell & Siegel, 1967; Sies, 1973; Lehr & Newton, 1978; Doyle & Cicarelli, 1980; Shim, 1990; Parker & Summers, 1993; Hsing & Chang, 1996; Wetzel, O’Toole, & Peterson, 1998). In contrast, cross-section studies focus on how selected fac-tors influence the postsecondary decisions of individual students (Christiansen, Melder, & Weisbrod, 1975; Ehrenberg & Sherman, 1984; Fuller, Manski, & Wise, 1982; Venti & Wise, 1983; Carter & Savoca, 1984; Kodde & Ritzen, 1988; Savoca, 1990; Moore, Studen-mund, & Slobko, 1991; Rouse, 1994; Weiler, 1994; DesJardins, Dundar, & Hendel, 1999).

Studies using time-series or cross-sectional data vary with regard to how student demand is defined. The majority of studies to date have used enrollments as the measure of student demand. Venti and Wise (1982) note

that while much research focuses on college attendance, other important aspects of college choice that come before the enrollment decision are often ignored. Spies (1973), Becker (1990) and Savoca (1990) argue that this presents some analytical problems for those interested in explaining how key factors influence student demand. Attendance figures for most institutions depend not only on student demand but also the supply of spaces made available by the institution. Therefore, it is difficult to identify if changes in enrollments represent changes in student demand or institutional supply.1 _{Studies of}

enrollment behavior often ignore how changes in factors such as tuition rates affect the size and composition of the applicant pool. Savoca (1990) further notes that the enrollment sensitivity to tuition may be underestimated in empirical studies if tuition and enrollments affect each other.

As a result, some researchers turned their attention to the decision of individuals to apply for admission. Spies (1973) used data on a sample of students who scored 1100 or higher on the SAT to examine how factors influence whether they apply to select groups of mostly private institutions. Venti and Wise (1982) and Savoca (1990) analyzed cross-sectional data on students from the National Longitudinal Study of the High School Class of 1972 to estimate student demand models based on whether individuals applied to college. Student appli-cation behavior has also been examined by Chapman (1979) and Paulsen (1990), and others. These studies show that many of the same aspects of student demand posited about enrollment behavior are often found when looking at the application behavior of students.

A few studies have used data on students who have taken a college entrance exam to study application behavior. In addition to Spies (1973), Tierney (1983) used cluster analysis to group Pennsylvania students who had taken either the SAT or ACT according to the type of institution to which they had their test scores sent. Weiler (1994) matched data on students having their SAT scores sent to a particular institution with those who applied for admission and estimated models to explain what factors influenced a student’s decision to apply to the school, given that they were considering the insti-tution. More recently, DesJardins et al. (1999) matched cross-sectional data on all students within a five-state area who took the ACT to applicants at the University

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of Minnesota to examine how particular characteristics explained whether a test taker in the region applied to the university. These studies showed the potential value in using data on students taking college entrance exams to represent the college-bound population and examining the different aspects of the college decision-making pro-cess for individuals. None of these studies, however, analyzed why students initially considered attending particular institutions.

Regardless of the analytical approach (time-series ver-sus cross-section) and the measure of student demand (applications versus enrollments), the main objective of all of these studies is to determine how individual, insti-tutional, and/or economic factors influence student demand for higher education. Cross-sectional studies typically include measures of individual attributes that may influence a person’s propensity to attend college. These factors include gender, race/ethnicity, parental educational attainment and income, and academic ability and/or achievement. Student ability is of particular inter-est to colleges and universities because of the rising com-petition for high-ability students and the effects of hav-ing more high-quality students on performance measures such as retention and graduation rates. Venti and Wise (1982, 1983), Fuller et al. (1982) and Tierney (1983) argue that individuals sort themselves into institutions where their academic ability more closely resembles that of the present student body. Cook and Frank (1993) found that as student quality rises, so does the reputation of the institution, which in turn makes it easier to attract high-ability students. Litten and Hall (1989), in a survey of high school students, found that students often judge college quality on the basis of the achievements and pro-file of the current student body. Spies (1973), Venti and Wise (1983) and Weiler (1994) argue that the self-selec-tion is based on how students perceive their chances of completing a degree, and the expected gains if they are able to complete a degree. If the gains from college are only received by successfully completing a degree, then many students will be discouraged from applying to and/or attending institutions where their ability falls well below that of the average student. Student ability can also affect institutional choice if students believe that colleges and universities provide employers with an inexpensive screening mechanism for workers (Arrow, 1973; Spence, 1974; Cook & Frank, 1993).

Despite the wide variations in empirical approaches to estimating student demand functions, a number of fairly consistent findings have emerged from the published studies to date. First and foremost is that student demand is relatively insensitive to changes in the cost of attend-ance (Becker, 1990; McPherson & Shapiro, 1998; Wet-zel et al., 1998). Second, student ability and achievement measures such as standardized test scores and high school grades are found to affect the overall demand for

higher education as well as the type of institution demanded by students.

A priori, income should have a positive effect on stud-ent demand in that as family income rises studstud-ents are better able to afford a college education.2 _{The income}

effect could also affect the types of institutions con-sidered by students. One major concern of policymakers is that students from lower-income families may be more restricted than other students in their choice of postsec-ondary education provider as a result of their lower ability to pay for college. Empirical studies have found that income has a positive effect on student demand for higher education (Spies, 1973; Venti & Wise, 1983; Becker, 1990; Shim, 1990; Rouse, 1994). Venti and Wise (1982), Savoca (1990) and Weiler (1994) found that family income has a positive effect on the prob-ability of a student applying to college. Other studies have suggested that family income may affect the types of institutions chosen by students. McPherson and Shapiro (1998) argue that there is a growing gap in the four-year college participation rates of students from low- and high-income families, with students from lower-income families being increasingly served by two-year colleges. Doyle and Cicarelli (1980) found that fam-ily income has a negative effect on student demand for public four-year institutions, and DesJardins et al. (1999) showed that students from low- and medium-income families are more likely than other students to apply to a particular four-year public university.

While fewer studies have examined the effect of par-ental education on student demand, those that have, gen-erally have shown that the demand for higher education rises with parental education. Rouse (1994) found that first-generation students were more likely to begin their postsecondary education in two-year versus four-year institutions. Venti and Wise (1982) and Savoca (1990) showed that students with highly-educated parents had a greater probability than other students of applying to col-lege. In contrast, Spies (1973) found that parental edu-cation had no effect on a student’s interest in selective institutions. Kodde and Ritzen (1988) focused on the impact of parental education on student demand for higher education in The Netherlands. They found that parental education by itself had little effect on enrollments, but had a much larger effect on enrollments when considered in conjunction with family income.

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They attributed this result to the connection between par-ental educational attainment and family income.

3. Data description

The data used here were obtained from the College Board, and contain records on all seniors attending high school in New Hampshire in March 1996 who had taken the SAT at least once (n=9323). This represents almost 80% of the high school seniors in the state for this year. The sample includes a small number of students who reside in other states but attend private high schools in New Hampshire. Only the most recent test score infor-mation on each student is included in the dataset, regard-less of how many times a student may have taken the exam. Table 1 shows how New Hampshire SAT takers compare to national SAT takers. Note that New Hampshire SAT takers are fairly similar to national test takers in terms of their academic achievement/ability and parental educational attainment. At the same time, New Hampshire students have slightly higher family incomes than national test takers. The largest distinction, how-ever, can be seen in terms of race/ethnicity. Given the small number of minority students in New Hampshire, these data will not be useful for examining issues per-taining to access to higher education by race/ethnicity, and are not representative of the nation.

The College Board administers a survey to students known as the Student Descriptive Questionnaire (SDQ).

Table 1

Comparison of New Hampshire and national SAT takers, 1996a

Category New Hampshire National

Academic ability/achievement:

Mean grade point average 3.06 3.20

Mean SAT-math score 514 508

Mean SAT-verbal score 520 505

Plans for advanced placement 38% 52%

Family income:

Median family income US$48,514 US$47,302

Families below US$30,000 22% 27%

Highest degree for parents:

HS diploma or less 36% 39%

Associate degree 10% 8%

Bachelor degree 31% 28%

Graduate degree 23% 25%

Race/ethnicity:

White 93% 69%

Black or African American 1% 11%

Hispanic or Latino 1% 8%

Asian 3% 9%

All other 2% 3%

a _{Sources: 1996 College-bound seniors: a profile of SAT program test takers, New Hampshire and national reports. Median family} income is found by linear interpolation between income class endpoints.

The SDQ asks students to provide information on items such as parental educational attainment, family income, student academic achievement, courses taken during high school, and postsecondary plans including highest degree and intended major. After eliminating obser-vations with missing data on these independent variables, there were 5787 students remaining in the sample.3

Since students are generally required to submit their SAT scores for admission consideration to the insti-tutions examined in this study, whether a student has his or her test sent to each institution is a good early indi-cator of his or her interest in attending. Several qualifi-cations deserve mention before proceeding. The number of students submitting test scores to each institution is not a perfect measure of student demand since some stu-dents who are willing and able to attend a given school

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Fig. 1. Educational attainment of parents of SAT-takers in 1996 and of all New Hampshire adults 25 years and older, 1990.

may not submit their test scores because they feel that they would have little chance of being accepted. This is especially true for the more selective institutions in this study. Likewise, some students may send their test scores to an institution but have no intention of enrolling if offered admission.4_{Finally, since students who take the}

SAT have already made the decision to consider pursu-ing a four-year postsecondary education, these data can-not address how various factors influence a student’s decision to consider postsecondary education. As shown in Fig. 1, this may be particularly relevant for New Hampshire, in that the parents of SAT-takers have higher levels of formal education than the adult population in the state. The group of SAT takers in a state will also omit those high school seniors who are interested in post-secondary institutions that do not rely on the SAT, such as two-year colleges and four-year institutions that util-ize the ACT or do not require applicants to take either standardized test.

The College Board permits students to have their test scores sent to up to four institutions at no cost, and more submissions can be made for an added fee. On average, students submitted their SAT scores to 4.52

4 _{It is not possible to determine if students submitting test} scores to a college eventually applied for admission and enrolled since the data do not contain either the student’s name or social security number.

institutions/scholarship agencies.5_{Twenty-five percent of}

the SAT takers in the sample submitted their test scores to exactly four institutions/agencies, and 44% of the SAT takers submitted their scores to more than four institutions/agencies. To illustrate one facet of the test-sending behavior of students, Table 2 shows the results from a model where the number of SAT scores submitted by students are regressed against variables representing their educational aspirations, parental income and edu-cational attainment, ability/achievement, intended major, and gender.6_{It can be seen that New Hampshire seniors}

5 _{The College Board lists approximately 5000 different} enti-ties to which a student may have his or her SAT scores sent. This list includes four-year colleges and universities, two-year colleges, other postsecondary institutions, and various scholar-ship agencies. It would be extremely difficult to omit all non-four-year colleges and universities from the calculation shown here. To examine whether these results are influenced by stu-dents sending their test scores to scholarship agencies as com-pared to colleges/universities, the analysis was repeated after excluding all designated agencies with college codes below 1000. This group includes most of the scholarship agencies in the data. After omitting these entities, it was found that on aver-age students submitted their SAT scores to 4.40 remaining agencies, which is very close to the 4.52 average for all entities. In addition, the general results of the regression model explaining the number of test score designations did not change when only entities with college codes of 1000 or greater were analyzed.

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Table 2

Factors influencing the number of institutions/scholarship agencies receiving SAT scores from New Hampshire seniors, 1996a

a _{Dependent variable is the number of institutions and} schol-arship agencies receiving SAT scores from an individual stud-ent. The regression model also controls for intended major (23 variables). Standard errors are shown in parentheses. **

p,0.01, *p,0.05.

with graduate postsecondary aspirations submit their SAT scores to more institutions than other students. Likewise, students of higher ability/achievement levels submit SAT scores to more institutions than their peers. While the results show that family income has a positive effect on the number of SAT scores submitted by stu-dents, the income effect may be of little practical sig-nificance given that the magnitude of the coefficient sug-gests that a US$10,000 increase in family income translates into less than 0.1 additional test score sub-missions. Finally, no evidence could be found that stu-dents from first-generation families submit fewer or more test scores than students of college-educated parents.

For the purpose of this study, a New Hampshire stud-ent is said to be considering a college if he or she has submitted test scores to the institution. Nine institutions have been singled out for analysis in this study. The institutions were chosen on the basis of their popularity among New Hampshire students and range in terms of size, scope, selectivity, and tuition charges.7_{Table 3}

pro-7 _{These institutions were among the top 10 designated} choices of New Hampshire SAT takers in 1996. For the logistic regression models that follow, it was necessary to select

insti-vides some general information on the nine institutions. The first three schools — Keene State College, Plymouth State College, and the University of New Hampshire — are the only four-year, public residential campuses in the state of New Hampshire. They are the lowest-cost pro-vider, in terms of tuition and fees, among these alterna-tives for New Hampshire students planning to go to col-lege. The University of Vermont and the University of Massachusetts are public land-grant institutions in the region with profiles that are somewhat similar to the Uni-versity of New Hampshire. Note, however, that New Hampshire students would pay higher tuition charges at these institutions than they would pay at in-state public institutions because public colleges and universities in the region impose much higher tuition rates on non-resi-dent stunon-resi-dents than they do on resinon-resi-dent stunon-resi-dents. The next three institutions — Northeastern University, Boston University, and Boston College — are private insti-tutions located in Massachusetts. These instiinsti-tutions receive considerable interest from New Hampshire’s col-lege-bound population. Boston College and Boston Uni-versity are viewed as being more selective institutions, as reflected in their lower acceptance rates and higher average SAT scores of new students. Finally, Dartmouth College is a private, Ivy League institution located in New Hampshire that draws significant interest from high-ability students across the nation. It is very selec-tive, as can be seen by their low acceptance rate and high average SAT scores of new students.

To begin the analysis, New Hampshire seniors who have taken the SAT are grouped according to whether they submitted test scores to each institution. In Table 4, the profiles of students sending test scores to each institution are compared to each other and to the state as a whole.

Table 4 highlights the dramatic differences in the pro-files of New Hampshire students who consider each of these institutions. The more selective schools in the group receive a greater share of their test scores from students who plan to pursue a doctorate degree, have high SAT scores and grade point averages, and intend to apply for advanced placement in college. Conversely, the public state colleges in New Hampshire receive the most interest from students who plan to stop their post-secondary education at the bachelor’s degree, have lower SAT scores and grade point averages, and do not plan to apply for advanced placement. The data also show that the New Hampshire state colleges receive interest from a higher proportion of lower-income and first-gen-eration students than the other seven institutions, and that the more selective schools have the highest income

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Table 3

Selected characteristics of nine institutions

Institution Status State Carnegie FY95 enrollmentsb _{Tuition and fees, Acceptance}

classificationa _{FY95 (US$)}c _{rate, Fall}

1997 (%)d Undergraduate Graduate

Keene State College Public NH Comprehensive I 3443 26 3464 77

Plymouth State College Public NH Comprehensive I 3498 48 3381 79

University of New Hampshire Public NH Doctoral II 10,788 917 4559 76

University of Vermont Public VT Research II 7075 932 15,958 85

University of Massachusetts Public MA Research I 16,751 2431 11,813 73

Northeastern University Private MA Research II 10,747 2829 13,566 70

Boston University Private MA Research I 15,304 7355 18,690 55

Boston College Private MA Doctoral I 9437 2114 17,103 46

Dartmouth College Private NH Doctoral II 3926 1248 19,650 22

a _{Institution classifications are based upon the Carnegie classification scheme.}

b _{Figures represent headcounts of full-time students in the Fall of 1994 as reported to IPEDS.}

c _{Resident tuition rates are shown for Plymouth State College, Keene State College, and the University of New Hampshire, and} non-resident tuition rates are shown for the University of Vermont and the University of Massachusetts. All other institutions have the same tuition rates for resident and non-resident students.

d _{Data are for new freshmen as of the Fall 1997 (source: US news and world report, 1998).}

files and fewest first-generation students out of the pool of all SAT takers.

It should be remembered that students can have their test scores submitted to more than one of these insti-tutions. Table 5 shows that there is a considerable degree of overlap between these nine institutions in terms of New Hampshire seniors’ interest in each. Each column represents those students who had their SAT scores sent to a particular institution, and the rows show the percent-ages of these students who also had their test scores sent to the other eight institutions. Not surprisingly, the over-lap is greatest between the three public four-year insti-tutions in New Hampshire.

These descriptive statistics alone do not answer ques-tions as to whether all college-interested students in New Hampshire have appropriate access to different types of postsecondary institutions because they do not control for the correlation between family income and parental education, nor the correlation between either of these measures and other relevant factors affecting student choice such as academic ability/achievement. If students form consideration sets based in part on matching their ability to that of the institution, and first-generation and lower-income students on average have lower levels of academic achievement, then some of the differences in their consideration rates of more selective schools may be attributed to academic ability rather than their parental income or educational attainment. To examine this issue,

the following model of student demand is specified for each of the nine institutions:

ln

S

pi 1−pi

D

5Xib1ui (1)

where piis the probability that the ith New Hampshire

SAT-taking senior had his or her test scores sent to a specific institution, Xiis the set of characteristics for the

ith individual,bis a vector of the parameters to be esti-mated showing how each variable affects the log odds ratio, and uiis an error term. Logistic regression is used

to estimate the parameters in Eq. (1). Similar empirical approaches have been used by Christiansen et al. (1975), Venti and Wise (1982), Ehrenberg and Sherman (1984), Weiler (1986), Leppel (1993), Rouse (1994) and DesJar-dins et al. (1999).

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Table 4

Comparison of New Hampshire SAT-taking seniors sending test scores to nine institutions, 1996a

SAT-taking seniors % NH test takers Intended terminal degree Mean SAT scores

considering sending test scores

to each institution

Bachelors (%) Masters (%) Doctorate (%) Math Verbal Total

Keene State College 15 36 29 6 475 480 955

Plymouth State College 14 39 26 6 472 475 947

University of New Hampshire 44 27 33 16 529 526 1055

University of Vermont 9 20 38 22 543 543 1086

University of Massachusetts 6 25 34 17 538 538 1076

Northeastern University 8 23 42 14 540 527 1067

Boston University 9 14 38 28 575 566 1141

Boston College 7 14 38 30 585 572 1157

Dartmouth College 7 9 27 44 624 609 1233

All NH test takers 26 28 16 518 522 1041

SAT-taking seniors Family income % taking any AP Mean HS grade First generation

considering test point average (%)

Mean (US$) Below $30,000 (%) US$30,000–70,000 Above US$70,000

(%) (%)

Keene State College 48,256 24 58 18 20% 2.84 26

Plymouth State College 47,583 26 56 18 20% 2.81 28

University of New Hampshire 53,055 19 56 25 36% 3.13 18

University of Vermont 56,777 15 55 30 38% 3.25 14

University of Massachusetts 54,120 20 52 28 34% 3.09 17

Northeastern University 54,309 18 57 25 40% 3.20 21

Boston University 55,456 20 49 31 53% 3.35 15

Boston College 62,660 12 46 42 57% 3.43 13

Dartmouth College 60,662 16 47 37 72% 3.58 10

All NH test takers 52,311 21 54 25 36% 3.08 20

a _{Statistics are based on only those SAT takers without missing values on the independent variables (n}=_{5787). Since the highest income category in the dataset is US$100,000}

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Table 5

Overlap between institutions in the number of SAT scores received by New Hampshire seniorsa % also sending test scores to Of students sending SAT scores to each institution

KSC PSC UNH N.East UMass UVM BC BU Dart

KSC – 52 23 15 16 12 7 8 10

PSC 49 – 22 12 15 10 6 7 7

UNH 67 68 – 66 69 65 61 62 53

N.East 8 6 11 – 18 13 16 23 9

UMass 6 6 9 13 – 10 8 11 5

UVM 7 6 13 16 15 – 16 12 13

BC 3 3 9 14 9 13 – 28 19

BU 5 5 12 27 17 12 37 – 15

Dart 5 3 9 8% 6 11 21 12 –

a _{Abbreviations are as follows: KSC}=_{Keene State College, PSC}=_{Plymouth State College, UNH}=_{University of New Hampshire,} N.East=Northeastern University, UMass=University of Massachusetts, UVM=University of Vermont, BC=Boston College, BU= Bos-ton University, Dart=Dartmouth College.

Table 6

Factors influencing a New Hampshire student’s decision to submit SAT scores to four-year public institutions in New Hampshire Keene State College Plymouth State College University of New Hampshire

Variable (1) (2) (1) (2) (1) (2)

Associate degree 20.051* 20.043* 20.035+ 20.034+ 20.114** 20.103*

(2.27) (2.28) (1.74) (1.92) (2.88) (2.57)

Masters degree 20.020+ 20.003 20.031** 20.014 0.034* 0.040*

(1.99) (0.31) (3.18) (1.60) (2.10) (2.42)

Doctorate degree 20.112** 20.055** 20.101** 20.049** 20.071** 20.041+

(6.51) (3.60) (6.00) (3.16) (3.37) (1.86)

Male 20.016+ 20.012 0.012 0.014 0.029+ 0.034*

(1.65) (1.40) (1.34) (1.63) (1.94) (2.20)

First generation student 0.012 0.003 0.019+ 0.010 20.041* 20.040*

(1.17) (0.37) (1.90) (1.10) (2.25) (2.19)

Family income (US$1000s) 0.002* 0.001+ 3.1e204 22.5e205 0.005** 0.005**

(2.19) (1.81) (0.46) (0.04) (4.33) (4.08)

Family income squared 21.7e205** 21.2e205* 26.8e206 22.7e206 24.6e205** 24.2e205**

(2.72) (2.18) (1.13) (0.50) (4.68) (4.27)

Advanced placement – 20.032** – 20.031** – 20.046**

(3.36) (3.30) (2.83)

Grade point average – 20.048** – 20.046** – 0.003

(6.03) (5.93) (0.22)

SAT score (100s) – 0.154** – 0.083** – 0.221**

(7.01) (4.15) (6.50)

SAT score squared – 20.009** – 20.005** – 20.011**

(7.81) (5.03) (6.77)

Attend public HS 0.042** 0.034** 0.024* 0.018+ 0.134** 0.125**

(3.58) (3.30) (2.17) (1.85) (7.73) (7.17)

Years study arts 20.003 0.001 20.006+ 20.002 20.014* 20.011*

(0.91) (0.40) (1.84) (0.54) (2.54) (2.09)

Years study English 0.019 0.011 0.015 0.007 0.003 0.008

(1.41) (1.00) (1.22) (0.66) (0.13) (0.37)

Years study languages 20.018** 24.6e204 20.015** 0.002 0.050** 0.052**

(3.86) (0.11) (3.51) (0.52) (6.35) (6.12)

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255

Table 6 (continued)

Keene State College Plymouth State College University of New Hampshire

Variable (1) (2) (1) (2) (1) (2)

Years study matha _0.008 _0.014+ _0.007 _0.014+ _0.052** _0.053**

(0.86) (1.79) (0.82) (1.78) (3.18) (3.15)

Years study science 20.014* 20.005 20.018** 20.008 0.054** 0.054**

(2.34) (0.87) (3.06) (1.51) (4.96) (4.88)

Years study social science 20.002 20.004 20.002 20.004 20.017+ 20.022*

(0.28) (0.83) (0.39) (0.80) (1.83) (2.29)

Major in agriculture 20.054 20.048+ 20.028 20.027 0.112* 0.109*

(1.60) (1.66) (0.93) (1.00) (2.46) (2.37)

Major in biological sciences 20.024 20.004 20.076** 20.052* 0.171** 0.177**

(0.97) (0.21) (2.68) (2.06) (5.05) (5.18)

Major in business 0.011 0.010 0.043** 0.041** 0.053* 0.046+

(0.66) (0.71) (2.76) (2.94) (1.96) (1.70)

Major in communications 20.010 20.007 20.031 20.026 0.057 0.054

(0.37) (0.33) (1.16) (1.06) (1.43) (1.34)

Major in computer science 0.039 0.069** 0.022 0.055* 0.048 0.066

(1.45) (2.93) (0.84) (2.35) (1.05) (1.42)

Major in education 0.130** 0.110** 0.113** 0.099** 0.062* 0.058+

(7.86) (7.55) (7.08) (6.85) (2.06) (1.92)

Major in engineering 20.066** 20.027 20.077** 20.040+ 0.122** 0.137**

(2.59) (1.22) (3.05) (1.76) (3.69) (4.10)

Major in general studies 0.022 0.061 0.037 0.069 0.037 0.077

(0.36) (1.10) (0.62) (1.27) (0.36) (0.75)

Major in health professions 20.021 20.022 6.5e204 20.002 0.055* 0.047+

(1.17) (1.42) (0.04) (0.11) (2.03) (1.74)

Major in public affairs 20.005 20.017 0.032 0.017 20.065 20.072

(0.20) (0.73) (1.34) (0.81) (1.45) (1.61)

Major in social sciences 0.040* 0.046** 0.011 0.020 0.050+ 0.052+

(2.30) (3.00) (0.59) (1.26) (1.77) (1.84)

Chi-square (df) 360 (36)** 582 (40)** 337 (36)** 516 (40)** 454 (36)** 521 (40)**

a _{Calculated t-statistics are shown in parentheses. The omitted category for intended major is “undecided”, and the omitted category} for highest planned degree is “bachelors degree”. Each model also contains controls for 11 other majors which were not statistically significant (not shown). **p,0.01, *p,0.05,+p,0.10 (two-tailed tests).

apply for advanced placement in any subject, and com-bined SAT score.8_{The variable definitions are provided}

in Appendix A. To aid in interpretation, all of the coef-ficients from the logit model have been transformed to show the impact of each variable on the probability of

8 _{The sensitivity of individual choice to tuition and fees} can-not be directly estimated in this model due to the cross-sectional nature of the data. Although the net price of attendance will vary due to financial aid, this information is not available on the College Board data. Even if it were available, it may not be relevant for explaining college consideration behavior because many students would not have known this information at the time of deciding whether to initially consider each institution. An indirect measure of cost may be inferred through family income, since students with higher family incomes can expect on average to receive less financial aid and thus face higher net costs of attendance. Changes in family income would be expected to have similar impacts on the financial aid offers of other institutions in the students’ potential choice set, and thus

a student having his or her SAT scores submitted to each institution.9

There are clearly large differences in the ways that New Hampshire seniors initially sort themselves across the nine institutions depending on their educational plans and abilities. The New Hampshire state colleges are most attractive to those residents planning to stop their post-secondary education at the bachelors degree. In contrast, those with intentions to pursue a doctoral degree are sig-nificantly more likely to submit their test scores to the selective private institutions shown in Table 8. For example, in model (1) a student who plans to pursue a doctorate degree is 5% more likely than a similar student

the price effect implied by income changes on the demand for one particular institution is probably minimal.

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Table 7

Factors influencing a New Hampshire student’s decision to submit SAT scores to selected out-of-state institutions Northeastern University University of Massachusetts University of Vermont

Variable (1) (2) (1) (2) (1) (2)

Associate degree 20.029+ 20.024 20.063* 20.056* 20.062* 20.051+

(1.82) (1.58) (2.26) (2.12) (2.12) (1.88)

Masters degree 0.014** 0.013** 0.005 0.005 0.021** 0.020**

(3.61) (3.32) (0.88) (0.99) (3.89) (3.84)

Doctorate degree 20.017** 20.017** 24.0e204 0.004 0.003 0.006

(3.04) (2.99) (0.06) (0.52) (0.48) (0.95)

Male 0.006 0.007+ 0.011* 0.007 20.005 20.003

(1.52) (1.87) (2.17) (1.56) (0.90) (0.55)

First generation student 0.010* 0.011** 20.002 24.4e204 20.011 20.008

(2.30) (2.59) (0.30) (0.07) (1.50) (1.19)

Family income (US$1000s) 0.001** 9.4e204** 28.9e205 28.2e205 5.5e204 4.5e204

(3.21) (3.09) (0.24) (0.23) (1.31) (1.15)

Family income squared 28.5e206** 27.7e206** 5.4e207 5.8e207 24.0e206 22.9e206

(3.17) (2.99) (0.17) (0.19) (1.14) (0.90)

Advanced placement – 21.1e204 – 20.010* – 20.013*

(0.03) (2.01) (2.47)

Grade point average – 0.009* – 20.010* – 0.009+

(2.38) (2.16) (1.77)

SAT score (100s) – 0.044** – 0.051** – 0.084**

(4.17) (3.73) (5.44)

SAT score squared – 20.002** – 20.002** – 20.004**

(4.28) (3.49) (5.50)

Attend public HS 20.006 20.007+ 20.001 20.002 0.004 2.5e205

(1.41) (1.71) (0.21) (0.30) (0.62) (0.01)

Years study arts 20.001 26.5e204 9.7e204 0.001 20.002 20.001

(0.56) (0.49) (0.55) (0.74) (0.90) (0.65)

Years study English 20.015** 20.013* 20.006 20.005 20.018* 20.015*

(2.62) (2.29) (0.81) (0.71) (2.27) (2.00)

Years study languages 0.012 23.5e204 0.007* 0.006* 0.019** 0.017**

(0.55) (0.16) (2.53) (2.15) (6.07) (5.25)

(continued on next page)

who plans to stop at the bachelors degree to initially sider attending Dartmouth College, and even after con-trolling for academic ability and achievement, they are still 2% more likely to consider Dartmouth. The Univer-sity of New Hampshire, UniverUniver-sity of Vermont, and Northeastern University receive the most interest from students who plan to acquire a masters degree. With regard to gender, male students are between 2 and 3% more likely than female students to be interested in the University of New Hampshire or Dartmouth College, and are slightly less interested than female students in Boston College.

What is striking is the way in which New Hampshire seniors match their academic ability/achievement levels with those of the institution. The results for SAT score show that for all institutions except Dartmouth College, there is a quadratic relationship between a student’s SAT score and the likelihood of their submitting test scores to the institution. Table 9 shows the SAT values at which the log odds ratios are maximized for each institution,

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257

Northeastern University University of Massachusetts University of Vermont

Variable (1) (2) (1) (2) (1) (2)

Years study matha _0.016** _0.014** _0.015* _0.014* _0.008 _0.006

(3.05) (2.78) (2.35) (2.41) (1.19) (1.00)

Years study science 0.009** 0.008* 29.0e204 20.001 0.009* 0.007+

(2.85) (2.44) (0.25) (0.39) (2.09) (1.77)

Years study social science 0.001 7.4e204 0.008* 0.007* 0.002 0.001

(0.56) (0.30) (2.33) (2.05) (0.46) (0.22)

Major in agriculture 20.016 20.016 0.030* 0.028* 0.009 0.008

(0.90) (0.91) (2.36) (2.33) (0.58) (0.56)

Major in biological sciences 24.6e204 20.002 20.003 20.002 0.005 0.004

(0.05) (0.20) (0.23) (0.15) (0.46) (0.43)

Major in business 0.005 0.003 0.012 0.011 20.014 20.015+

(0.67) (0.42) (1.36) (1.36) (1.41) (1.67)

Major in communications 0.022* 0.021* 0.024* 0.022* 20.020 20.019

(2.26) (2.16) (2.25) (2.18) (1.38) (1.44)

Major in computer science 0.013 0.012 20.014 20.013 20.031 20.026

(1.05) (1.00) (0.80) (0.79) (1.58) (1.45)

Major in education 20.025* 20.025* 28.6e204 28.8e204 20.025* 20.024*

(2.25) (2.27) (0.08) (0.09) (2.26) (2.32)

Major in engineering 0.035** 0.033** 20.002 21.8e204 20.019 20.017

(4.56) (4.36) (0.15) (0.02) (1.62) (1.53)

Major in general studies 0.018 0.021 20.477 20.451 0.008 0.012

(0.71) (0.84) (0.03) (0.03) (0.24) (0.39)

Major in health professions 0.040** 0.037** 20.013 20.011 0.017* 0.014+

(5.71) (5.49) (1.34) (1.26) (2.03) (1.82)

Major in public affairs 0.028** 0.027** 20.010 20.010 20.036+ 20.034+

(2.74) (2.76) (0.64) (0.62) (1.91) (1.90)

Major in social sciences 0.027** 0.025** 0.009 0.009 20.020* 20.019*

(3.66) (3.55) (1.07) (1.08) (2.06) (2.11)

Chi-square (df) 269 (36)** 297 (40)** 93 (36)** 120 (40)** 237 (36)** 286 (40)**

a _{Calculated t-statistics are shown in parentheses. The omitted category for intended major is “undecided”, and the omitted category} for highest planned degree is “bachelors degree”. Each model also contains controls for 11 other majors which were not statistically significant (not shown). **p,0.01, *p,0.05,+p,0.10 (two-tailed tests).

likely to be interested in either Boston College or Dart-mouth College.

Turning to parental education, there is little evidence that after controlling for other relevant factors first-gen-eration students in New Hampshire initially consider dif-ferent types of postsecondary institutions than students with college-educated parents. The only exception was the University of New Hampshire, where first-generation students were 4% less likely than students with at least one college-educated parent to consider the institution. While in the first model first-generation students were about 2% less likely than their counterparts to consider Dartmouth College, the difference becomes insignificant after including controls for student ability/achievement. Interestingly, first-generation students are slightly more likely than students with at least one college-educated parent to consider Northeastern University, even after controlling for student ability/achievement measures.

With regard to family income, the results are mixed

but overall suggest that New Hampshire seniors from lower-income families are not more restricted than other students in the types of colleges and universities that they initially consider. After controlling for the various fac-tors in models (1) and (2), income did not have a quad-ratic effect on a student’s likelihood of considering five of the nine institutions, which includes three of the four highest-priced institutions examined here. Of these five institutions, only Boston College received less interest from students in lower-income families than from other students.10_{For three of the remaining four institutions,}

the probability of a student submitting test scores at first

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Table 8

Factors influencing a New Hampshire student’s decision to submit SAT scores to selective private institutions Boston College Boston University Dartmouth College

Variable (1) (2) (1) (2) (1) (2)

Associate degree 20.011 0.001 20.027 20.016 4.1e204 0.014

(0.47) (0.08) (1.33) (0.87) (0.02) (1.13)

Masters degree 0.022** 0.015** 0.023** 0.017** 0.013* 0.003

(4.34) (3.47) (5.15) (4.15) (2.39) (0.72)

Doctorate degree 0.026** 0.014** 0.028** 0.016** 0.051** 0.018**

(4.45) (2.76) (5.10) (3.22) (9.01) (4.45)

Male 20.008+ 20.007+ 20.001 20.003 0.018** 0.011**

(1.81) (1.67) (0.21) (0.71) (3.97) (3.21)

First generation student 0.003 0.008 20.002 0.004 20.016* 20.004

(0.41) (1.38) (0.29) (0.70) (2.25) (0.84)

Family income (US$1000s) 3.3e204 2.7e204 24.0e204 23.9e204 26.4e204+ 24.3e204+

(0.86) (0.81) (1.26) (1.34) (1.82) (1.73)

Family income squared 4.5e207 4.0e207 3.3e206 3.0e206 6.6e206* 3.9e206+

(0.15) (0.15) (1.26) (1.25) (2.28) (1.90)

Advanced placement – 0.007+ – 0.005 – 0.016**

(1.75) (1.24) (4.50)

Grade point average – 0.016** – 0.009* – 0.029**

(3.59) (2.25) (7.51)

SAT score (100s) – 0.062** – 0.051** – 0.017+

(4.69) (4.52) (1.70)

SAT score squared – 20.003** – 20.002** – 23.3e204

(4.50) (4.06) (0.82)

Attend public HS 0.003 0.001 20.002 0.002 20.032 3.6e204

(0.64) (0.33) (0.34) (0.38) (0.66) (0.10)

Years study arts 20.005** 20.005** 4.8e204 23.3e204 0.001 20.001

(3.01) (3.32) (0.33) (0.25) (0.60) (0.90)

Years study English 20.011 20.006 20.007 20.004 20.004 20.002

(1.36) (0.88) (1.08) (0.56) (0.58) (0.30)

Years study languages 0.023** 0.013** 0.013** 0.005* 0.020** 0.002

(7.12) (4.55) (5.07) (2.22) (6.62) (1.12)

(continued on next page)

rises with family income and then declines. This suggests that students from lower- and upper-income families were less likely than students from middle-income famil-ies from submitting test scores to these schools. Differen-tiating the model with respect to family income shows that the likelihoods of students submitting test scores to Keene State College, the University of New Hampshire, and Northeastern University are maximized at family income levels of US$46,600, 56,000 and 61,400, respect-ively. Finally, the results for Dartmouth College show that students from lower- and upper-income families were more likely than students from middle-income fam-ilies to initially consider the college. Overall, the results show that having a lower family income does not cause a student to avoid initially considering the higher priced and/or more selective institutions within this group.11

11 _{There is the possibility that some of the income effect on} college consideration could be captured by other variables in the model. For example, parents who attended college are likely

Not surprisingly, the results also show that a New Hampshire student’s interest in particular institutions varies along with his/her academic preparation and inter-ests. For example, seniors who plan on majoring in edu-cation are more likely than other seniors to consider attending one of the in-state public institutions. Schools such as the University of New Hampshire and Northeast-ern University receive more interest from students plan-ning to major in engineering and those with more course-work in the sciences, while the University of Vermont, Boston University and Northeastern University are more

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259

Boston College Boston University Dartmouth College

Variable (1) (2) (1) (2) (1) (2)

Years study matha _0.010 _0.004 _0.011* _0.007 _8.5e

204 20.007

(1.49) (0.67) (2.02) (1.32) (0.14) (1.53)

Years study science 0.020** 0.013** 0.004 22.1e204 0.011** 0.002

(4.24) (3.28) (1.06) (0.07) (2.69) (0.69)

Years study social science 20.002 20.002 0.001 0.002 20.005 20.002

(0.66) (0.58) (0.52) (0.60) (1.59) (0.83)

Major in agriculture 20.049* 20.040* 20.531 20.480 20.024 20.008

(2.12) (2.03) (0.05) (0.05) (1.24) (0.63)

Major in biological sciences 20.016+ 20.017* 0.007 0.003 0.012 0.004

(1.71) (2.11) (0.74) (0.37) (1.36) (0.64)

Major in business 7.4e204 0.002 0.001 0.002 7.2e204 0.006

(0.09) (0.23) (0.16) (0.30) (0.08) (0.99)

Major in communications 0.003 0.002 0.039** 0.035** 20.010 20.007

(0.24) (0.19) (4.03) (3.93) (0.67) (0.69)

Major in computer science 20.002 20.006 20.008 20.014 0.029* 0.010

(0.16) (0.52) (0.52) (0.98) (2.44) (1.14)

Major in education 20.026* 20.019* 20.002 0.001 20.007 0.003

(2.43) (2.00) (0.25) (0.09) (0.59) (0.41)

Major in engineering 20.020+ 20.024** 0.021* 0.013 0.002 20.009

(1.95) (2.58) (2.30) (1.58) (0.18) (1.30)

Major in general studies 0.012 0.009 0.006 0.002 0.062** 0.035*

(0.49) (0.42) (0.21) (0.07) (3.02) (2.38)

Major in health professions 20.022** 20.018* 0.017* 0.018* 0.004 0.006

(2.70) (2.54) (2.27) (2.51) (0.52) (1.04)

Major in public affairs 20.041* 20.028 20.024 20.017 20.008 0.009

(2.01) (1.63) (1.38) (1.07) (0.46) (0.75)

Major in social sciences 0.008 0.005 0.014+ 0.012 3.6e204 20.001

(1.04) (0.77) (1.81) (1.58) (0.04) (0.20)

Chi-square (df) 351 (36)** 423 (40)** 265 (36)** 334 (40)** 390 (36)** 676 (40)**

a _{Standard errors are shown in parentheses. The omitted category for intended major is “undecided”, and the omitted category for} highest planned degree is “bachelors degree”. Each model also contains controls for 11 other majors which were not statistically significant (not shown). **p,0.01, *p,0.05,+p,0.10 (two-tailed tests).

attractive to students interested in the health sciences. Finally, it is somewhat surprising to learn that seniors attending public high schools in New Hampshire were just as likely as their private high school counterparts to consider attending most of the private colleges and universities within this group. At the same time, public high school students were more likely than private school students to consider attending one of the three in-state public institutions (see DesJardins et al., 1999 for a similar finding).

4. Summary and discussion

This study shows how data on students taking college entrance exams in a state can be used to gain a better understanding of the initial college consideration choices of students, and whether parental educational attainment and income affect these choices. This approach uses data

on most of the college-bound students in a state, and can thus better contrast those who are interested in an institution with those who are not interested. This is especially important for policymakers who want to deter-mine the degree to which institutions are meeting the interests and needs of a state’s college-bound population, and for institutional analysts in gaining a better under-standing of the types of students who are most (and least) interested in attending. The statewide data also permit separate demand models to be estimated for a range of institutions.

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Table 9

Comparison of log odds maximizing and actual SAT scores for each institutiona

Institution SAT score that maximizes the log odds SAT scores of incoming freshmenb ratio

25th percentile 75th percentile

Dartmouth College 1600c ₁₃₃₀ ₁₅₂₀

Boston University 1278 1160 1360

Boston College 1214 1190 1390

University of Massachusetts 1166 1000 1220

University of Vermont 1090 1020 1220

Northeastern University 1058 970 1190

University of New Hampshire 1030 1020 1220

Keene State College 889 870 1070

Plymouth State College 814 860 1070

a _{Log odds maximizing scores are found by differentiating the equations shown in Tables 6–8 (model (2)) with respect to SAT} score.

b _{Score is set equal to the maximum possible combined SAT score since there is evidence of a positive, linear relationship.} c _{Source: US news and world report, 1999. Includes resident and non-resident freshmen.}

College, and Boston University. Although lower-income students were slightly less likely than other students to consider several of the institutions examined here, income had no bearing on whether a New Hampshire student was interested in attending the more expensive institutions in the group such as Dartmouth College, Boston University, the University of Vermont and the University of Massachusetts. Since college-interested students from lower-income families are not scared away by higher tuition and fee charges, it suggests that they have formed expectations that if they are qualified for admission they have a good chance of receiving suf-ficient financial aid awards to enable them to attend.

The fact that family income and educational attain-ment do not affect the initial choices of New Hampshire seniors who are interested in attending college should be encouraging to policymakers and other education stake-holders, but should not be interpreted in and of itself as showing that parental income and education “do not matter”. It is still possible, as suggested by Rouse (1994), McPherson and Shapiro (1998) and Mortenson (1998a, 1999), that disadvantaged students in the state are less likely than their counterparts to even consider attending a four-year college or university. The magnitude of this problem is probably small in New Hampshire in light of the high proportion of high school seniors (80%) who annually take the SAT. Even if the initial choices made by students interested in college are not affected by their family income and education status, disadvantaged stu-dents may face more barriers than other stustu-dents in terms of gaining admission to selective and/or expensive insti-tutions, and being able to enroll in these institutions if they are offered admission. Nonetheless, these results, when combined with the lower college participation rates

of disadvantaged students, suggest that institutional pol-icymakers should focus more attention on enabling these students to achieve their postsecondary aspirations, rather than convincing them of the need to pursue a post-secondary education.

Finally, there is reason to believe that the experiences in New Hampshire are probably not representative of the nation as a whole. New England features a very strong private college sector which is not seen in other parts of the country, and the close proximity of states to each other in the region fosters considerable movement of stu-dents across state borders. In addition to having few min-ority students, New Hampshire has the lowest child pov-erty rate in the nation and is among the leading states in terms of the percentage of students from low-income families who go on to college (Mortenson, 1998b). Nonetheless, the data show that the college-interested population in the state is diverse with regard to student ability, parental income and educational attainment, and other measures thought to influence demand for higher education. The same methodology can and should be applied to other states and institutions to determine if similar findings emerge in other parts of the country.

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261

415). The less-selective institutions in this study receive less interest than other institutions from high-ability stu-dents. Together with their high overall admission rates, the less selective institutions are already offering admis-sion to most of the high-ability students who do apply, and thus have no direct policy lever through which to readily enroll more high-ability students. Furthermore, the competition for these students has increased as public and private colleges alike strive to raise their attending student profiles. Spies (1990) and Cook and Frank (1993) have found that there is a rising concentration of high-ability students at a small number of elite institutions. While gains in admitted student profiles could be achi-eved through raising admission standards and enrolling fewer students at the lower end of the academic ability/achievement range, this would most certainly have a negative impact on access and create financial pressures if enrollments decline as a result.

Acknowledgements

I would like to thank the following individuals for their helpful comments on earlier drafts of this paper: William Becker, Stephen DesJardins, Paula Hollis, Edward MacKay, Mark Rubinstein, Robert Stonebraker, participants in the weekly seminar series of the Econom-ics Department at the University of New Hampshire, participants in the annual meetings of the Northeast Association for Institutional Research (November 1998) and the Association for Institutional Research (May 1999), faculty and administrators at Keene State College, Plymouth State College, and the University of New Hampshire, and two anonymous referees. I am also grateful to the College Board for making these data available for analysis.

Appendix A. Data definitions

Variable Definition/method of

construction

Associate Equals one if the highest

planned degree is an associate degree, zero otherwise.

Bachelors Equals one if the highest

planned degree is a bachelors degree, zero otherwise.

Masters Equals one if the highest

planned degree is a masters degree, zero otherwise.

Doctorate Equals one if the highest

planned degree is a doctoral or related degree, zero otherwise. First generation Equals one if both parents’

education is listed as either

grade school, some high school, high school diploma/equivalent, or business/trade school

Family income Midpoint of family income class as reported on the SDQ. Incomes in excess of US$100,000 were recoded to equal US$100,000.

Advanced placement Equals one if a student plans to apply for advanced placement or exemption in any subject, zero otherwise.

High school GPA Self-reported high school grade point average.

SAT-math Score on the math portion of the

SAT

SAT-verbal Score on the verbal portion of the SAT

Intended major Intended majors were grouped into 22 majors based on the three-digit codes used by the College Board (omitted category is “undecided”).

Years study selected Total years in high school spent

subjects studying each subject.

Public high school Equals one if the student currently attends a public high school in New Hampshire, zero otherwise.

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