The relations between race, family characteristics, and where students apply to college

Social Science Research 39 (2010) 310–323

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The relations between race, family characteristics, and where students apply to college q Brian P. An * Department of Sociology, University of Wisconsin—Madison, 8128 William H. Sewell Social Sciences Building, 1180 Observatory Drive, Madison, WI 53706, USA

a r t i c l e

i n f o

Article history: Available online 23 August 2009 Keywords: Applying to college College transition Educational expectations Family background Race

a b s t r a c t This paper examines the impact of race, family background, and parental investments on a behavioral manifestation of educational expectations: where students apply to college. Submitting an application to colleges is an important step in the transition to college because the majority of four-year colleges require students to apply before enrolling. This paper documents the effects of social origins on the selectivity of where high school seniors applied, based on a national sample from the class of 2004. The results show general support for the contention that social background and parental investments influence where students apply to college. Minority students are more likely to apply to selective colleges than comparable white students. Parental education is positively associated with the selectivity of the college to which students apply. Family structure and sibship size, however, are not associated with applying to a selective college. Parental economic and interactional investments are also associated with where students apply to college. I further examine the interplay between family background and race and find that family background exerts a uniform influence on where students apply irrespective of race and ethnic origin. Ó 2009 Elsevier Inc. All rights reserved.

Introduction Over the past 60 years, the college participation rate among high school students has increased seven-fold, from 9 percent in 1939 to 66 percent in 2006. These increases are not specific to any particular social group driving the overall proportion upwards; all groups experienced increases in their rates of continuation (Clotfelter et al., 1991; National Center for Education Statistics, 2008; Roksa et al., 2007). As postsecondary expansion continues to increase the opportunities for Americans, qualitative distinctions across institutions become increasingly important as a mechanism that perpetuates stratified positions. In the United States, colleges vary in their prestige, resources, and the types of degree offered. College selectivity as a qualitative distinction across institutions A qualitative distinction that has received much media and research attention is college selectivity.1 Recent interest in institutional selectivity grew as a result of increased competition for postsecondary enrollment and waning support for q The research reported here was supported by the Institute of Education Sciences, US Department of Education, through Award # R305B090009 to the University of Wisconsin—Madison. The opinions expressed are those of the author and do not represent views of the US Department of Education. The author is grateful for Adam Gamoran, Sara Goldrick-Rab, Jeffrey Grigg, Eric Grodsky, Ruth López Turley, and three anonymous reviewers for helpful comments on earlier drafts. * Address: Center for Research on Educational Opportunity, University of Notre Dame, 1012 Flanner Hall, Notre Dame, IN 46556, USA. E-mail address: [email protected]. 1 Few studies distinguish between college quality and college selectivity, and use these concepts interchangeably (but see Black and Smith, 2006). Even the US News & World Report, purported as a measure of college quality, is largely influenced by average SAT or ACT scores of the enrolling students: a measure of selectivity (Webster, 2001). To avoid confusion, I refrain from using the term college quality and instead use the term college selectivity.

0049-089X/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2009.08.003

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affirmative action policies (Alon and Tienda, 2007; Melguizo, 2008). In 2003, selective institutions—defined as institutions that accept less than half of their applicants—accounted for 14 percent of all four-year institutions, but they received over 28 percent of all college applications (Hawkins and Clinedinst, 2006). Public awareness of college selectivity increased with the proliferation of college ranking guides and magazines, most notably, the US News & World Report (USNWR). Prospective students and their parents often rely on college guides and ranking magazines to provide objective and reliable information about colleges. The importance of institutional selectivity has led some colleges to participate in a high-stakes game of student recruitment. Colleges have changed their admissions policies and altered their admissions figures in an attempt to improve their rankings (Hunter, 1995). A senior administrator at Hobart and William Smith Colleges resigned after she failed to submit updated institutional information to USNWR, which forced USNWR to use older information that understated the institution’s performance on several key factors. As a result, Hobart and William Smith Colleges dropped from a second tier national liberal arts college to a third tier college (Brownstein, 2000). In addition to using college selectivity as a marketing strategy to recruit the most talented students, research has shown that students benefit from attending selective schools. Although the evidence is mixed regarding the earnings premium for attending a selective college, studies generally agree that attending a selective college positively affects other outcomes (for a summary of studies, see Brand and Halaby, 2006; Gerber and Cheung, 2008; Long, 2008). For example, research shows that employers use college selectivity as a signal of ability and potential productivity for newly hired employees (Ishida et al., 1997). Students who attend selective colleges are more likely to earn a college degree than students from non-selective colleges, above and beyond student sorting (Alon and Tienda, 2005; Brand and Halaby, 2006; Long, 2008; Melguizo, 2008). Undergraduates from selective institutions are also more likely to enroll in graduate school than undergraduates from non-selective institutions (Zhang, 2005). Attendance at a selective institution increases the likelihood of marrying a spouse from a similar institution and those from socially privileged backgrounds; thereby further contributing to social inequalities because of the labor market potential of dual earners with degrees from selective institutions (Arum et al., 2008). Social scientists further document differential returns to college selectivity by social origins and race. For example, Dale and Krueger (2002) find that the returns to college selectivity are greatest among low-income students. African American and Latino students benefit from attending selective colleges as much as, if not more than, comparable white students (Alon and Tienda, 2005; Melguizo, 2008; Small and Winship, 2007). Small and Winship (2007) estimate that black students with average characteristics graduate from college at a rate that is 13 percentage points lower than similar white students. By contrast, the black–white graduation gap is reduced to 3.6 percentage points at highly selective institutions. Yet researchers find that enrollment across institutional types differ by socioeconomic status (SES). Students from lowSES backgrounds are more likely to enroll in two-year and less selective colleges than those from high-SES backgrounds (Karen, 2002; Kim and Schneider, 2005; Roksa et al., 2007). Furthermore, recent evidence suggests the disparity in college attendance between those from different socioeconomic backgrounds is growing (Astin and Oseguera, 2004; Roksa et al., 2007). Given the increasing college enrollment patterns among all social groups, the rise in college cost, and the strong relation between family background and academic performance, members from the affluent class may attempt to differentiate themselves from those of other classes not only quantitatively across educational levels (i.e., vertical stratification), but also qualitatively within educational levels (i.e., horizontal stratification) (Gerber and Cheung, 2008; Haveman and Smeeding, 2006; Lucas, 2001; Rothstein, 2004). Although previous studies provide insights to students’ college destinations, these studies often mask the transition process through which individuals are allocated to different institutions (but see Brown and Hirschman, 2006; Espenshade et al., 2005). The transition from high school to college reflects a series of intermediary steps in which each step either exacerbates or attenuates socioeconomic and demographic differences. In the admissions process, for example, social group differences may occur along three stages: the application stage, the acceptance/rejection stage, and the matriculation stage. The possibility of student attrition exists at each stage, in which attrition is invoked either by the prospective candidates or the college admissions committee. Therefore, studying different stages of the transition process may provide a better understanding of how aggregate differences in college destinations occur. Educational expectations are an important factor that influences college transitions (Morgan, 2002). In this paper, I explore a behavioral manifestation of educational expectations: where students apply to college. Prior research shows that a significant amount of family background variation in college destination is attributed to differences in who applies to college (Bowen et al., 2005; Brown and Hirschman, 2006; Manski and Wise, 1983). Eligibility to four-year institutions can usually only be established if an individual submits an application, since 79 percent of four-year colleges and universities require some type of formal application for admission (Integrated Postsecondary Education Data System [IPEDS], 2006).2 If social group differences exist in the application to college, then it is likely that this stage contributes to the observed systematic differences in college destinations. In this study, I investigate differences in the selectivity of the college to which students apply using a nationally representative sample of high school students. I contend that this qualitative aspect of college decisions is important to the stratification process in the transition to college. Three main questions motivate this paper. First, do race and family background influence the application decision of students? Second, are parental investments associated with a student’s college decisions? Third, does the influence of family background and parental investments on where students apply differ by race? 2

All IPEDS results were calculated by the author using data from 2006.

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The association between race and college destinations The larger political and institutional context may lead to racial differences in where students apply to college. For example, affirmative action policies aim to influence the decisions and behaviors for some minority students (e.g., African American and Latino), but not for others (e.g., Asian Americans). In an effort to increase campus diversity and to improve occupational opportunity, postsecondary officials, especially those from selective institutions, sought to recruit qualified underrepresented minority students and to consider race in the admissions process (Bowen and Bok, 1998). Espenshade et al. (2004) show that selective colleges give an admission bonus for African American and Latino applicants. Scholars argue that racial considerations in the admissions process arose during the 1960s as a response to appease civil tensions between elites and racial groups that mobilized politically (Bowen and Bok, 1998; Karen, 1991). Although evidence shows a countermovement by dominant groups that led to a stagnation of minority attendance at selective colleges during the 1970s, race-based affirmative action continued throughout the 1980s and 1990s. Postsecondary institutions are under pressure to not only attract the most academically qualified students, but they are also under pressure to maintain the appearance of equality. Grodsky (2007) argues that institutions—especially selective institutions—participate in compensatory sponsorship where efforts to improve educational opportunities are given to social groups who historically suffered from constrained opportunities early in their educational careers. He finds that compensatory sponsorship generally occurs along racial lines, where institutions are less inclined to provide affirmative action towards economically disadvantaged students. These political and institutional efforts, therefore, shape the college-making decisions of minority students. Net of SES and academic achievement, African American and Latino students are more likely than white students to enroll in selective colleges (Ayalon et al., 2008; Bennett and Xie, 2003; Grodsky, 2007; Light and Strayer, 2002).3 As a response to the rollback of affirmative action policies in Washington (Initiative 200), Brown and Hirschman (2006) show that minority enrollments declined at the University of Washington (the state’s flagship public university) because of a decrease in the number of minority applicants instead of a change in admission rates. During this time, however, the number and average scores of minority students who took the SAT exam remained stable, indicating that some talented minority students may have realigned their set of college choices (Brown and Hirschman, 2006). For example, African American and Latino students in Texas were less likely than white students to send their SAT scores to selective Texas colleges following the Hopwood decision (which ended the use of race as a factor in college admission decisions), but they were more likely than white students to send their SAT scores to selective colleges outside of Texas (Hopwood v. Texas 5th Cir., 1996; Thomas, 2004). Moreover, studies show that the number of African American and Latino applications to selective colleges rebounded in states that ended their affirmative action policies in part because of increased college recruitment efforts to encourage talented minority students to apply to their schools (Brown and Hirschman, 2006; Card and Krueger, 2005). Scholars point to political mobilization of racial groups and institutional efforts to recruit minority students into their schools as a major driving force in raising the college enrollment of underrepresented minority students. These explanations provide less insight on the college decisions of Asian Americans, for whom a strategic adaptation explanation may be more fitting (Xie and Goyette, 2003). According to the strategic adaptation view, Asian Americans consciously pursue educational paths that allow them to advance up the social and economic ladder. In particular, Asian Americans tend to pursue paths that reduce the salience of subjective criteria (e.g., political resources, social capital) for upward mobility, and instead choose paths that allow them to improve their opportunities in the market economy: where fair competition is at least perceived as the norm. This explanation has been used to address the overrepresentation of Asian Americans in college participation and in the science and technical fields of college majors (Xie and Goyette, 2003). This approach can also be extended to account for the concentration of Asian Americans in selective colleges due to the prestige and quality (real or perceived) of the education these colleges provide. How family background affects the transition to college It is well established that the family plays a crucial role in the transmission of educational inequality. Compared to other industrial countries, the educational system in the United States is less standardized, less bureaucratized, and less centralized (Karen, 2002; Kerckhoff, 1995). Although these features in the educational system allows for social mobility, these same features also increase ‘‘client power” or the opportunity for affluent families to influence their child’s educational learning and experiences (Bidwell and Quiroz, 1991). Indeed, research shows that parents’ education and family income are positively associated with college enrollment (Karen, 2002; Kim and Schneider, 2005). There is further evidence that these associations are increasing over time. Roksa 3 Some research finds that blacks, but not Latinos, are less likely than whites to attend a selective college (Hearn, 1991; Karen, 2002), while other studies show no black–white or Latino–white differences on college selectivity (Davies and Guppy, 1997; Kim and Schneider, 2005). The discrepancy in findings is due, in part, to how college selectivity is operationalized. Studies that find a negative or no racial difference on college selectivity generally operationalize college selectivity as a continuous measure. This approach potentially conflates both the likelihood of attending selective colleges with the marginal distributions of students and colleges. By contrast, studies that show that black and Latino students are more likely than white students to enroll in selective colleges typically operationalize college selective as a discrete outcome and employ techniques that are insensitive to changes in the marginal distribution. I thank a reader who brought this explanation for the discrepant findings to my attention.

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et al. (2007) find that the association between parental education and participation at four-year colleges increased between the 1980s and 1990s. From 1971 to 2000, the probability of freshman who have highly educated parents entering selective colleges remained relatively unchanged at 20 percent. The probabilities among students with low- and middle-educated parents, however, declined where the decline was sharpest among those with less-educated parents (Astin and Oseguera, 2004). The high rate of divorce and single parenthood in the United States has led social scientists to examine the consequences of family structure on educational outcomes (Amato, 2001). Studies generally show that non-traditional (e.g., single-parent and step-parent) households are negatively associated with school achievement and educational attainment (Amato, 2001; Sandefur and Wells, 1999). Economic loss, however, is not the only consequence of marital dissolution. Studies show stepparents tend to exhibit lower levels of warmth and support for their non-biological child. Parental behaviors (e.g., monitoring school progress and parent supervision) account for some, but not all of the relation between family structure and educational outcomes (Astone and McLanahan, 1991; Thomson et al., 1994). Studies also show that the number of siblings (sibship size) a child has is related to his or her educational attainment. The resource dilution hypothesis contends that families are able to dispense a finite amount of resources towards each child in a household. As the number of siblings increase, the share of resources allocated to any given child decreases, which in turn lowers the academic achievement for each subsequent sibling (Downey, 1995; Steelman and Powell, 1991). There is less research devoted to understanding the relation between family structure and sibship size on the qualitative variation in college participation. However, prior studies provide insight to how these family characteristics might impact college decisions in at least two ways. First, family structure and sibship size may indirectly affect where students apply through its influence on academic achievement. Second, family disruptions and sibship size may influence where students apply by reducing the amount of economic and interactional resources available to parents to devote toward their child’s college. Research shows that as sibship size increases, parents increasingly believe that children are responsible for funding their own education (Steelman and Powell, 1991). The role of parental investments in college participation The amount of investments parents make towards their child’s future education influences their child’s college decisions. In this paper, I distinguish between two types of investments: economic investments and interactional investments (Charles et al., 2007). Economic investments represent the amount of financial resources parents devote for their child’s education. Interactional investments represent the commitment (e.g., discussions regarding college attendance) parents make in assisting their child’s educational decisions. Studies find that economic investments parents make are positively associated with their child’s educational attainment. Steelman and Powell (1989) show that the odds of continuing to the second year of college is 49 percent higher for students whose parents provided financial assistance during their first year of college than similar students whose parents did not provide financial assistance. Not surprisingly, the amount of income within a family strongly influences the amount parents are able to contribute to their child’s postsecondary education (Steelman and Powell, 1991). Although parental income is an important resource to finance a child’s college, parents generally use some combination of income, savings, and borrowing (Churaman, 1992). Saving for college and knowledge about grants and loans are positively related with attending a four-year college. This finding persists even after accounting for family income and parental education (Charles et al., 2007). In addition to economic investments, parents also provide interactional investments. The relationship between parents and children is important in transmitting parental resources and information to their children (Coleman, 1988). Given the myriad of considerations that factors into college choice, parents are able to provide assistance that helps students navigate through these choices. For example, studies show that the frequency of academic discussions with parents is positively associated with educational attainment. All things being equal, Kim and Schneider (2005) estimate that an increase in academic discussion increases the odds of attending a two-year and four-year college by 14 percent and 19 percent, respectively. Ascriptive factors and where students apply: hypotheses In this paper, I investigate the relations between race, family background, and parental investment on where students apply. On average, African American and Latino students are less likely than whites to enroll at selective institutions, whereas Asian American students are overrepresented at these institutions (Carnevale and Rose, 2004). Social scientists attribute these differences in schooling outcomes to racial differences in family background and academic achievement (Bowen et al., 2005; Fryer and Levitt, 2006; Goyette and Xie, 1999). These explanations, however, would predict that racial differences in enrollment to selective colleges would disappear after accounting for family background and academic achievement. But talented African American and Latino students are highly sought after by selective institutions, and recruitment efforts made by these institutions may alter the college decisions and behaviors of college-bound minority students (Bowen et al., 2005; Grodsky, 2007). Moreover, academic preparation alone does not account for the overrepresentation of Asian Americans at selective colleges (Ayalon et al., 2008). Although I do not explicitly consider the impact postsecondary institutions have on where students apply, I do recognize that college decisions individuals make partly reflect institutional factors. In particular, I hypothesize that underrepresented minorities (e.g., blacks and Latinos) apply to selective colleges at higher rates than comparable whites (hypothesis 1a or

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H1a). Moreover, I hypothesize that Asian Americans are more likely to apply to selective colleges (H1b) as a means to advance in the social and economic ladder (Xie and Goyette, 2003). I further expect to find that family background characteristics are associated with the selectivity of the colleges to which students apply (H2). In particular, family resources (e.g., parental education and income) exert a positive association with where students apply, while non-traditional (e.g., single-parent, other-adult configuration) households and sibship size are negatively related to where students apply. In addition, I hypothesize a positive association between parental investments (economic and interactional) and the college selectivity to where students apply (H3). Moreover, I examine the interplay between race, social background, and parental investments arguing that the institutional commitment towards race and the lack of commitment towards family background alters family decisions of where to apply. Therefore, I hypothesize that SES, high school achievement, and other investments exert a lower influence on applying to selective colleges for underrepresented minorities because of compensatory efforts that colleges provide to these students (H4). I further hypothesize that SES and economic investments have a lower association with the selectivity of where Asian Americans apply than for whites because of the importance of procuring objective criteria for social upgrading among Asian Americans. I expect to find, however, that the interactional investments and academic achievement in high school exerts a greater influence on where students apply for Asian Americans than for whites (H5).

Data and methods To examine the relations among race, family background, parental investments, and where students apply to college, I require data that contains a rich array of social background indicators and an indicator of where students apply to college. I used data from the Educational Longitudinal Study of 2002 (ELS:2002). ELS:2002 is a nationally representative sample of tenth grade students surveyed in 2002. I used the base-year (2002) and first follow-up (2004) surveys in ELS:2002. In general, students were high school sophomores in the base-year and they were seniors in the first follow-up. Prior research that uses nationally representative data typically examines students from the 1980s and 1990s. A benefit of ELS:2002 is that this data set contains a more recent cohort of students. Several changes occurred during the mid to late 1990s, among the most prominent was the retrenchment of affirmative action policies for college admissions (Gratz v. Bollinger, 2003; Grodsky and Kalogrides, 2008; Hopwood v. Texas 5th Cir., 1996; Washington Rev. Code Wash, 1998). ELS:2002 allows for investigation of student’s college decisions after (or at least during) important state and federal changes that occurred which had important consequences for college access.

Dependent variable The dependent variable is a binary outcome of whether or not a student applied to a selective college. I defined selective colleges as those that were classified as either highly competitive or most competitive based on the College Admissions Selector from the Barron’s Profiles of American Colleges (hereafter Barron’s Selector).4 The Barron’s Selector represents the degree of competition a typical prospective student faces when applying for admissions to a given college. In 2001, for example, highly competitive colleges and universities generally admitted students whose grades were no lower than a B, were in the top 20–35 percent of their high school class, and had a combined 1240–1308 SAT score or 27–28 ACT score. Moreover, highly competitive colleges typically accepted 33–50 percent of their applicants (Barron’s Educational Series, 2002). I chose the Barron’s Selector over other measures of college selectivity, such as the average or median SAT score of the incoming freshman and acceptance rates, for five reasons. First, defining selective colleges based on average SAT scores or acceptance rates requires the researcher to impose an arbitrary cut-off point classifying selective and non-selective colleges (Brand and Halaby, 2006). Instead, I rely on selectivity classifications constructed by authoritative college guides (e.g., Barron’s Profiles of American Colleges). Second, not all postsecondary schools require admission tests. In 2006, approximately 70 percent of four-year institutions that had an admission policy required SAT or ACT scores (IPEDS, 2006). Third, institutions that require SAT scores are generally more selective than institutions that do not require them (Turley et al., 2007). Fourth, acceptance rates are more sensitive to the number of applicant submissions and seat vacancies at a college: potentially leading to significant fluctuations across time. Between 2005 and 2006, about 24 percent of four-year institutions that had an admission policy experienced a 10 percentage point or half a standard deviation change in their acceptance rates; about 11 percent of institutions witnessed a 20 percentage point or one standard deviation change in their acceptance rates during this period (IPEDS, 2006). Fifth, SAT scores and acceptance rates capture a single component of college selectivity, whereas the Barron’s Selector is comprised of multiple components. Another popular measure of college rankings is the US News & World Report (USNWR). There is substantial overlap between Barron’s classification and USNWR rankings. Almost 90 percent of top-tier national colleges and universities found 4 Barron’s Selector classifies postsecondary schools based on their admissions competitiveness, ranging from ‘‘noncompetitive” to ‘‘most competitive.” The Barron’s classifications were determined by several admissions criteria such as: the median SAT or composite ACT examination scores, average high school class rank, grade point average of the incoming freshman, and college acceptance rate (for more information, see Barron’s Educational Series, 2002).

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in the USNWR were also classified as either highly or most competitive in the Barron’s Selector (Barron’s Educational Series, 2002; US News & World Report, 2002). Use of the USNWR, however, presents three issues. First, postsecondary institutions are categorized by mission, based on the basic Carnegie classification, and further designated as either a national institution or a regional institution. It is unclear which top-tier colleges (from national or regional rankings, or both) to include in the analysis. Second, the indicators that factor into the rankings are weighted differently based on their regional or national designation and ranked relative to their category peers, making it difficult to compare across institutional categories. By contrast, the Barron’s Selector classifies colleges based on common criteria, thereby allowing for comparability across institutions. Third, top-tier colleges in the USNWR are based on cut-off points by rank, such as the top 100 national universities and liberal colleges, whereas the Barron’s Selector classifies colleges based on meeting specific criteria. Independent variables The independent variables represent characteristics that students bring to high school and the experiences they have during high school. These include, race, gender, family background, parental investments, and student achievement (for a description of the variables used, see Table 1). Race was measured as white, black, Asian, and Latino where white was the omitted category. Due to the small number of Native Americans and students who responded as more than one race, they were dropped from the analysis. Family background is represented by four variables: parents’ education, family income, family structure, and number of siblings. Parental investments represent both economic and interactional investments. Economic investments consisted of three indicators. The first was measured as the amount of money parents set aside for their child’s college. Parents were surveyed in their child’s tenth grade year. The second was the importance of low college expenses (e.g., tuition, books, room and board). The third regarded the importance in the availability of financial aid. The importance of college costs and financial aid represent parents’ perceived ability or willingness to pay ‘‘fixed” costs (Perna and Titus, 2005). I used several measures to capture interactional investments. The first is the frequency with which children and their parents discussed plans to take the SAT/ACT. The second is the frequency with which children and their parents discussed applying to college. I also included two indicators of college expectation: the importance of the reputation of the college’s academic programs when choosing a postsecondary school and the importance of an easy admission standard when choosing a postsecondary school. A student’s college expectations reflect, in part, interests and personal investments parents make for their child (Sandefur et al., 2006). Status attainment research shows that significant others serve as social resources in which students use when deciding their educational plans (Hauser et al., 1983). College choice models further emphasize the importance of parental encouragement, which includes parental expectations, on the college choice process (Hossler et al., 1999).5 Each investment indicator was recoded into three discrete categories (e.g., not important, somewhat important, or very important; see Table 1). Parent–child discussions about applying to college and the importance in college academic reputation when choosing a school, however, were two exceptions. Less than eight percent of students either never discussed applying to college with their parents, or placed no importance to academic reputation when choosing a college. Therefore, I recoded parent–child discussions about applying to college and the importance in college academic reputation into two discrete categories (e.g., never or sometimes versus often). I included measures of academic achievement and grade point average (GPA) for academic courses. Academic achievement is a composite score of reading and math tests that were administered to students in the tenth grade. GPA represents a student’s average grades for academic courses taken in his or her freshman and sophomore years.6 Missing data I used multiple imputation techniques to handle missing information. Treatments to handle missing data, such as listwise deletion, lead to a considerable loss of respondents. Moreover, single imputation techniques produce over-precise standard error estimates. In contrast, multiple imputation creates M > 1 sets of imputed values by introducing random variation to the imputation procedure, thereby creating M valid but slightly different versions of the complete data (Collins et al., 2001). This 5 In supplementary analysis, I substituted indicators of the importance of academic programs and ease of admission solicited directly from the parent rather than from the student. I found that the substantive conclusions do not change using these direct parental indicators. Although the estimated coefficients of parental indicators were smaller than the student indicators reported in Table 2 (by approximately 40 percent), the coefficients remained statistically significant and in the same direction. 6 In earlier analyses, I included school-level indicators to examine the extent to which high schools serve as a means to facilitate the transition to college. I found some school effects, but they mostly reflected school characteristics—such as percent free/reduced lunch and percent minority—instead of school resources that are directly related to where students apply (e.g. college application and financial aid programs). That does not suggest that school resources had no impact on application decisions, but I found that these indicators (such as minority outreach programs) were related to whether a student applied to college or not instead of where he or she applied. In addition, I did not find significant cross-level interactions between school and family background on where students applied. Including school-level factors did not substantively alter any of the student- and family-level indicators. Because school main effects were not the focus of this paper and because I found no differential school effects by background, I excluded school effects from the models presented here. Models including school effects are available from the author on request.

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Table 1 Description of variables. Variables

Dependent variable College selectivity Applied to college Background Black Latino Asian Female Parent education (B.A or above) Log of family income Single-parent household Other adult configuration Number of siblings Investments Money saved category 1 Money saved category 2 College cost category 1 College cost category 2 Financial aid category 1 Financial aid category 2

Parent discussion about SAT/ACT category 1 Parent discussion about SAT/ACT category 2 Parent discussion about applying category 2 Academic reputation category 2

Description

Unweighted

Weighted

Mean

Mean

SD

A college that was categorized as highly competitive based on the Selector Whether a student applied to college or not (selection equation)

0.19 0.64

0.39 0.48

0.16 0.58

Student is Black Student is Latino, non-white Student is Asian (white is omitted category) Student is female At least one parent earned a bachelor’s or advanced degree

0.14 0.15 0.12 0.50 0.42

0.35 0.36 0.32 0.50 0.49

0.16 0.17 0.05 0.49 0.38

10.70 0.22 0.16

1.02 0.42 0.37

10.67 0.24 0.18

2.31

1.54

2.35

0.35

0.51

0.35

0.12

0.36

0.10

0.48

0.50

0.47

0.35

0.48

0.36

0.31

0.46

0.32

0.56

0.50

0.56

0.43

0.50

0.42

0.17

0.38

0.15

0.44

0.50

0.42

0.56

0.50

0.54

0.44

0.50

0.45

0.24

0.43

0.25

0.00 2.60

0.94 0.86

0.07 2.51

0.75 4.87 34.78

0.44 0.70 3.52

0.71 4.85 34.84

Log of family income from all sources in 2001 Single-parent household Other adult configuration (e.g., guardian, other relative) (Two-parent households are omitted category) Number of siblings. Range 0–6 Money set aside for teen’s college by 10th grade. Among those that saved money, category 1 represents approximately the bottom half of dollar amount saved Category 2 represents approximately the top half of dollar amount saved. Families that did not save money for college early in child’s high school is the omitted category How important is low expenses in choosing a school you would like to attend? Somewhat important How important is low expenses in choosing a school you would like to attend? Very important (not important is the omitted category) How important is the availability of financial aid, such as a school loan, scholarship or grant in choosing a school you would like to attend? Somewhat important How important is the availability of financial aid, such as a school loan, scholarship or grant in choosing a school you would like to attend? Very important (not important is the omitted category) The frequency students and parents discussed plans to take the SAT/ACT. Sometimes

Easy admission standards category 1 Easy admission standards category 2

The frequency students and parents discussed plans to take the SAT/ACT. Often (never is the omitted category) The frequency students and parents discussed about going to college. Often (never or sometimes are the omitted category) How important is a strong reputation of the school’s academic programs in choosing a school you would like to attend? Very important (not important or somewhat important are the omitted category) How important is easy admission standards in choosing a school you would like to attend? Somewhat important How important is easy admission standards in choosing a school you would like to attend? Very important (not important is the omitted category)

Achievement Academic achievement Grade point average

Standardized 10th grade test score composite (reading and math) GPA for academic courses in grades 9 and 10. Based on a four-point scale (A = 4.0)

Exclusionary variables Early college plans Unemployment rate Annual wages

Plans to continue education right after high school (10th grade) Unemployment rate; three average (2000–2002) Annual wages of state averaged across 2001–2003 (thousand)

Note: Sample size is 14,784, of which 9487 applied to college.

approach produces larger standard errors relative to single imputation because multiple imputation procedures introduce between-imputations variability to its calculation of standard errors. I created ten replications of the data. In order to yield regression estimates and standard errors, I followed Rubin’s procedure for combining regression results across M datasets (Schafer and Graham, 2002). Method of analysis Not all individuals applied to college. For example, approximately 64 percent of the ELS:2002 cohort applied to college during their senior year of high school. Examining only those who applied to college biases the influence of family background (and academic achievement) on where students apply because students who do not apply to college tend to differ

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systematically from students who apply to college. To address the issue of sample selection, I jointly estimate the likelihood of applying to college and where students apply. I implemented Heckman’s probit selection model to reduce bias due to sample selection (for formula of probit selection model, see Appendix A). The selection equation (e.g., applied to college) included the same indicators as the outcome equation (e.g., where students apply), and three additional indicators not found in the outcome equation. Although exclusion restrictions are not necessary to identify the model, without these restrictions, identification is achieved solely through distributional assumptions. I therefore included a measure that captures a student’s plan in his or her tenth grade to continue schooling after high school. I also included state-level unemployment rate and the state mean annual wage in the selection equation as two measures of opportunity cost (Alfonso, 2006). I also weighed the observations and used statistical methods to account for the sampling design and to adjust for oversampling, attrition, and non-response (Ingels et al., 2005). When reporting results, I express them in two ways. First, I report the estimated coefficients of the probit regression model (i.e., the probit score or index), which are in the Z metric or standard normal scores. Second, I provide average marginal effects, which represent the average of discrete or partial changes across all observations (Bartus, 2005).

Results Table 2 presents the regression results from the probit selection model predicting where students apply to college. The results reported contain the full array of indicators, which include race, gender, family background, parental investments, and academic achievement (for the regression results of the selection model, see Appendix B). Although there is no doubt that academic achievement is important in college decisions, the emphasis of this paper was the influence of ascriptive and family background factors on college decisions. I therefore concentrate on whether socio-demographic factors continue to influence where students apply to college, net of academic achievement.7 The findings show that minority students are more likely to apply to a selective college than white students. Supporting hypothesis 1a, African American and Latino students have a greater probability of applying to a selective college than similar white students. For example, African American students are 0.42 standard deviations more likely than whites to apply to a selective college. Put differently, among those who applied to college, the average probability of an African American student applying to a selective college is 5.9 percentage points (0.059  100, see Table 2) higher than the average probability of a white student. Asian American students are also more likely than whites to apply to a selective college (Supporting H1b). The average probability of an Asian American student applying to a selective college, conditional on applying, is 7.9 percentage points higher than the average probability of a white student. The results further show partial support for the hypothesis that family background factors are associated with where students apply. Consistent with hypothesis 2, parents’ education is positively associated with the selectivity of where students apply. A student with average characteristics who has at least one parent who earned a bachelor’s degree has a 3.8 percent greater probability of applying to a selective college than a similar student whose parents earned less than a bachelor’s degree. Although family income increases the probability of applying to any college (see Table B1), net of other factors, family income is not associated with applying to a selective college. The results for family structure and sibship size are also inconsistent with hypothesis 2. After the full array of indicators is accounted for, students from single-parent and other-adult families are as likely as students from two-parent households to apply to a selective school. Moreover, the number of siblings a student has provides little predictive power in determining where students apply to college. These results do not suggest that, net of other factors, family structure and sibship size have no influence on postsecondary schooling—the selection model indicates that family structure and sibship size are negatively related to whether a student applies to college (see Table B1)—but rather that they do not influence the selectivity of where a student applies. The findings show general support for the hypothesis that parental economic investments are associated to where students apply (H3). For example, money saved for college is associated with the college selectivity of where students apply. Only membership in a family that saved the largest amount of money, however, is correlated with applying to a selective college. Furthermore, all else being equal, an increase in the importance of college costs reduces the probability of applying to a selective college. For example, the average probability of a student who regards college costs as highly important is 5.9 percentage points lower than the average probability of a student who places no importance in college costs. Although the importance of college costs exerts a strong influence on applying to a selective college, there is little association between the importance of the availability of financial aid and the college selectivity of where students apply. Student–parent discussions regarding plans to take the SAT/ACT are positively related with college selectivity (confirming hypothesis 3). For example, students with average characteristics who frequently discuss plans to take the SAT/ACT with their parents have a 3.6 percent greater probability of applying to a selective college than similar students who do not 7 In supplementary analyses, I compared students from the ELS:2002 cohort to students from an earlier cohort (National Education Longitudinal Study of 1988 [NELS:88]) to examine whether there were significant changes in student decisions when applying to college. In general, there was little change in magnitude of the indicators across cohorts, despite continued changes to admission policies in the 1990s. The notable exception was that the estimated effect for college cost was almost twice as large in the ELS:2002 cohort than the NELS:88 cohort, and the difference across cohorts was marginally significant (p < 0.10). This finding suggests that college costs have become an increasingly important criterion for students when deciding to apply to a selective college. These results are available upon request.

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Table 2 Probit selection model and marginal effects in the college selectivity of where students apply. Variables

Coef.

Marginal effects

Background Black Latino Asian Female Parent education (B.A or above) Logged family income Single-parent household Other adult configuration Number of siblings

.420 .353 .538 .093 .285 .050 .017 .094 .018

(.087)*** (.076)*** (.071)*** (.046)* (.051)*** (.037) (.065) (.072) (.017)

.059 .048 .079 .012 .038 .006 .002 .012 .002

(.014)*** (.012)*** (.013)*** (.006)* (.008)*** (.005) (.009) (.009) (.002)

Investments Money saved (bottom half) Money saved (top half) College cost (somewhat important) College cost (very important) Financial aid (somewhat important) Financial aid (very important) Parent discussion about SAT/ACT (sometimes) Parent discussion about SAT/ACT (often) Parent discussion about applying (often) Academic reputation (very important) Easy admission standards (somewhat important) Easy admission standards (very important)

.019 .205 .286 .410 .048 .009 .125 .269 .051 .373 .372 .308

(.057) (.071)** (.056)*** (.070)*** (.070) (.078) (.061)* (.080)*** (.060) (.055)*** (.051)*** (.078)***

.002 .027 .043 .058 .006 .001 .016 .036 .007 .047 .051 .043

(.007) (.009)** (.008)*** (.008)*** (.009) (.010) (.008)  (.012)** (.008) (.008)*** (.006)*** (.009)***

.391 .305 2.561 .427

(.039)*** (.043)*** (.214)*** (.108)***

.051 .040

(.005)*** (.006)***

Achievement Academic achievement (math and reading composite) Grade point average Constant rho

Notes: Standard errors are in parentheses. Regressions are weighted, and standard errors are adjusted for sampling design. The sample size was 14,784, of which 9487 applied to college. Average marginal effects represent the average of discrete or partial changes across all observations.   p < .10. * p < .05. ** p < .01. *** p < .001 (two-tailed).

discuss plans to take a college exam with their parents. Student–parent discussions about applying to college, however, are not related with college selectivity. Consistent with hypothesis 3, students who regard the reputation of a college’s academic program as highly important are 0.37 standard deviations more likely to apply to a selective college than similar students who place little or some importance for the reputation of a college’s academic program in their college decisions. The 4.7 percentage point difference in the probability between a student with average characteristics who places great importance on a college reputation and a similar student who places little or some importance on a college reputation correspond to approximately 29 percent of the overall proportion of students who apply to a selective college (0.16, see Table 1). Also consistent is the finding that the average probability of a student who highly value easy admission standards is 4.3 percentage points lower than the average probability of a student who place little importance on easy admission standards. A similar result occurs for students who consider easy admission standards to be somewhat important. The difference in the estimated coefficient of easy admission standards between somewhat important and very important is not statistically significant (p < 0.49), suggesting that valuing easy admission standards, in general, rather than the magnitude of importance, in particular, impacts the selectivity of where students apply.8 Interaction effects between race and family characteristics In order to examine whether there is racial and ethnic variation in the influence of family background and parental involvement on where students apply (H4–H5), I separated the sample by race and re-estimated the association between family characteristics (e.g., family background and parental investments) and the selectivity of where students apply (see Table 3). There is little support for the hypothesis that family characteristics exerted less of an influence for underrepresented minorities than for whites. Although parental education and family background are not associated with applying 8 In supplementary analyses, I included state fixed effects to account for unobserved state-level variation that influences college decisions, such as state funding for public institutions, financial awards for students, and local labor markets. Including state fixed effects did not alter the results, suggesting that the results from Table 2 are robust from unobserved state-level variation that impacts college attendance. These results are available upon request.

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B.P. An / Social Science Research 39 (2010) 310–323 Table 3 Probit selection model in the college selectivity of where students apply by race.

Variables

Blacks Coef.

Latinos Coef.

Whites Coef.

.230* (.105) .301* (.130) .115  (.067) .063 (.169) .162 (.260) .078 (.048)

.079 (.061) .308*** (.065) .110* (.049) .038 (.085) .133 (.090) .011 (.023)

.111 (.156) .135 (.272) .366  (.197) .619** (.232) .244 (.280) .265 (.287) .081 (.206) .254 (.216) .199 (.165) .448** (.159) .486** (.177) .591** (.228)

.026 (.124) .191 (.226) .010 (.154) .206 (.160) .217 (.178) .154 (.189) .419* (.171) .669** (.226) .008 (.149) .747*** (.140) .371** (.118) .286 (.207)

.030 (.065) .199* (.081) .331*** (.065) .441*** (.088) .023 (.083) .044 (.085) .103 (.073) .248** (.093) .059 (.070) .384*** (.065) .403*** (.060) .304** (.104)

.327** (.109) .297  (.154) 2.390*** (.676) .168 (.287)

.507*** (.087) .586*** (.112) 2.854*** (.524) .740 (.482)

Female Parent education (B.A or above) Logged family income Single-parent household Other adult configuration Number of siblings

.132 (.145) .132 (.129) .011 (.085) .110 (.149) .295  (.171) .007 (.037)

.015 .153 .119 .074 .111 .047

Investments Money saved (bottom half) Money saved (top half) College cost (somewhat important) College cost (very important) Financial aid (somewhat important) Financial aid (very important) Parent discussion about SAT/ACT (sometimes) Parent discussion about SAT/ACT (often) Parent discussion about applying (often) Academic reputation (very important) Easy admission standards (somewhat important) Easy admission standards (very important)

.113 (.159) .078 (.250) .124 (.173) .183 (.210) .120 (.257) .197 (.282) .103 (.175) .194 (.202) .165 (.179) .022 (.165) .035 (.137) .090 (.174) .363*** (.089) .181 (.115) 1.382  (.803) .292 (.486)

Achievement Academic achievement (math and reading composite) Grade point average Constant rho

Asians Coef. (.144) (.170) (.098) (.176) (.207) (.049)

.402*** .314*** 2.872*** .458***

(.055) (.054) (.280) (.122)

Notes: Standard errors are in parentheses. Regressions are weighted, and standard errors are adjusted for sampling design. The sample size was 14,784, of which 9487 applied to college. Coefficients that were statistically different from whites (p < .05) are in bold.   p < .10. * p < .05. ** p < .01. *** p < .001 (two-tailed).

to a selective college for African American and Latino students (consistent with H4), the estimated coefficients are not statistically different from the estimated coefficients for white students. Table 3 provides some evidence that Asian American students differ from whites in their college application decisions. GPA and academic reputation have a greater association with college selectivity for Asian American students than for their white counterparts. These results provide some evidence that Asian American students rely more on objective criteria than whites for upward mobility (H5). Overall, however, the results show that family characteristics exert a uniform influence on where students apply to college across racial groups.

Conclusion As college participation continues to increase, distinctions across institutions become increasingly important as a means to allocate individuals to social positions. Social stratification is perpetuated by a greater tendency for advantaged students not only to attend college, but to attend schools that offer socially more desirable credentials. However, studies that examine the transition to college often mask the intermediary step between high school graduation and college enrollment. In this paper, I investigated whether race, family background, and parental investment influenced college decisions and whether the impact of family background differed across race and ethnic groups. Regarding race, the results were consistent with Grodsky’s (2007) argument that institutions, especially selective institutions, attempt to seek out students who as a group have been underrepresented at the postsecondary level. Although I was unable to decompose the contribution each agent has on the college decision, I contended that the decisions that students (and their families) make are partly a reflection of the actions that institutions make. Therefore, I reasoned that minority students would be more likely to apply to selective colleges than whites. Indeed, this was the case. All else being equal, blacks and Latinos were more likely to apply to selective colleges than comparable whites. I also found that Asian Americans applied to selective colleges at a greater rate than whites. This lends support for the strategic adaptation hypothesis. Xie and Goyette (2003) argue that Asian Americans tend to prefer occupations in which competence is based upon demonstrated skills. By concentrating on these skills, Asian Americans focus on the instrumental value rather than the intrinsic value of formal education. In their study, Xie and Goyette find that Asian Americans tend to attend college at higher rates than whites and major in fields that yield higher financial returns. I

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extended their hypothesis to include variation in the college selectivity as a means for Asian Americans to strengthen their credentials and demonstrate their skill because the selectivity of a college has been viewed as a proxy for college quality (Dichev, 2001). Overall, I found that family background marginally influenced where students apply. Even though family income, family structure, and sibship size influence whether a student applied to college, these factors were not related with the selectivity of where students apply. Although somewhat surprising, previous studies have found a similar pattern between family structure and college enrollment (Kim and Schneider, 2005). However, parental education continued to strongly influence college decisions. This finding may reflect the better understanding educated parents have in the college transition process than less-educated parents: as educated parents also have gone through the process. For example, educated parents may have a better grasp of their financial options, such as knowing that selective colleges and universities are better equipped than other colleges to supplement government financial assistance with institutional assistance to help compensate for the possible lack of financial means that some families can provide (Turley et al., 2007). Regarding parental investments, I examined qualitative distinctions of college participation (e.g., variation in college selectivity) whereas prior studies concentrated on quantitative distinctions (e.g., levels of attainment) (Charles et al., 2007; Freese and Powell, 1999). The results showed that the amount of economic and interactional investments parents made were related with where students applied. The amount of money saved for college, parental discussions about taking the SAT/ACT exam, and the reputation of a college’s academic program were positively associated with where students applied. Moreover, concerns regarding college costs and easy admission standards were negatively related to where students applied. Previous studies generally concentrated on the impact family background and race has on college participation, but few studies considered the interplay between race and family background. In this paper I addressed this issue by examining whether racial variation exists in the effects of family background. While there were some significant differences between race and family characteristics, overall, I found that social background and parental investments exerted a uniform influence on students’ college decisions. Continued efforts that unpack the ‘‘black box” in the transition from high school to college are needed in order to better understanding the source of social inequalities in college destinations. For example, this paper was unable to investigate how family background influences the initial ‘‘choice set” of colleges that the students are considering, and given their choices, how family background influences their later commitment to where to apply. Addressing this question would require a more detailed, qualitative description of a student’s college aspirations and expectations, which unfortunately is unavailable in the ELS:2002 data set. Moreover, this paper examined a single qualitative factor—college selectivity. However, other factors (e.g., location and school size) may be important in a student’s college decisions. In addition, although this paper attempts to address issues of self selection that influence college decisions, I was unable to adjudicate the causal direction between parental investments and where students apply to college. For example, money saved for college may affect where students apply to college. However, wanting to attend a selective college may make parents save money for college. I attempted to mitigate some of the causal ambiguity by restricting money parents saved for college to the tenth grade; however, saving money for college because of an expectation to attend a certain type of college may occur even prior to high school. Further research and better methodological designs are needed that allow for the estimation of causal effects of parental investments on college selectivity. As more studies concentrate on what happens during the transition from high school to college, social scientists will be better able to assess the fluidity in which students are able to make the transition across educational levels. Appendix A Formally, Heckman’s probit selection model (Van de Ven and Van Praag, 1981) approach assumes an underlying regression relationship exists:

Y 1i ¼ bX i þ ei

ð1Þ

in such a way that we observe only the binary outcome if Y P1i ¼ ðY 1i > 0Þ. For the ith individual, let Y 1i represent a latent variable, b represents a vector of coefficients that corresponds to a vector of independent variables Xi, and ei is the is the error term. However, where students applied to college are only observed for those that have applied to college:

Y 2i ¼ aZ i þ mi

ð2Þ

Let Y 2i represent a latent continuous variable that represents the propensity for a student to apply to a college, where Y2i = 1 if Y 2i > 0 and Y2i = 0 otherwise. Moreover, a are parameters that correspond to the explanatory variables Zi, and vi is the error term. I assume that ei and vi follow a bivariate normal distribution. The estimated q (rho)—the correlation between e and v—indicates whether selection on unobservables is an issue. If q is not statistically significant, then selection on unobservables is assumed to minimally influence equation 1 and therefore the joint estimation of Eqs. (1) and (2) are not required.

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Appendix B

Table B1 Selection model of students applying to college. Variables

Pooled

Blacks

Latinos

Asians

Whites

Background Black Latino Asian Female Parent education (B.A or above) Logged family income Single-parent household Other adult configuration Number of siblings

.289*** (.046) .087 (.049) .177**(.061) .186***(.030) .175***(.036) .051** (.020) .149***(.038) .162***(.038) .032** (.011)

.244***(.073) .154  (.083) .006 (.032) .206* (.094) .060(.101) .038(.025)

.175*(.079) .069 (.089) .040 (.033) .231**(.088) .085(.090) .074**(.028)

.273** (.093) .032 (.105) .020 (.053) .014 (.135) .043 (.129) .001 (.038)

.180*** (.039) .201***(.046) .100** (.033) .092  (.052) .200***(.051) .023 (.015)

Investments Money saved (bottom half) Money saved (top half) College cost (somewhat important) College cost (very important) Financial aid (somewhat important) Financial aid (very important) Parent discussion about SAT/ACT (sometimes) Parent discussion about SAT/ACT (often) Parent discussion about applying (often) Academic reputation (very important) Easy admission standards (somewhat important) Easy admission standards (very important)

.008 (.036) .160** (.059) .013 (.053) .028 (.056) .066 (.053) .175***(.053) .203***(.035) .252***(.058) .171***(.048) .327*** (.034) .265***(.038) .381*** (.050)

.057(.093) .050(.243) .052(.133) .265 (.160) .034(.166) .258(.164) .071 (.086) .207 (.127) .277** (.094) .371***(.083) .164 (.120) .303*(.122)

.034 (.102) .106 (.195) .113 (.127) .127 (.119) .076 (.178) .437** (.160) .179  (.093) .138 (.158) .228* (.100) .210**(.077) .280* (.115) .324* (.128)

.119 (.125) .217 (.160) .188 (.155) .220 (.172) .026 (.176) .200 (.188) .191 (.125) .331 (.200) .038 (.124) .318** (.116) .240  (.124) .304* (.144)

.002 (.050) .139  (.071) .014 (.071) .116 (.080) .033 (.068) .147* (.071) .243***(.044) .295***(.072) .131* (.054) .342***(.047) .273*** (.051) .433*** (.064)

.233*** (.023) .388***(.024)

.210***(.054) .377***(.056)

.193** (.064) .547***(.074)

.232*** (.030) .401*** (.033)

Achievement Academic achievement (math and reading composite) Grade point average Exclusionary variables Early college plans (10th grade) State-level unemployment rate State-level annual wages (thousand) Constant

.362***(.040) .059* (.025) .023***(.005) 1.913***(.268)

.311***(.091) .134* (.058) .033** (.011) 3.024***(.617)

.271*** (.051) .307*** (.053) .203* (.090) .088 (.059) .041** (.013) 2.038** (.655)

.344* (.137) .024 (.060) .013 (.017) 1.172 (.742)

.415*** (.050) .102** (.032) .020** (.007) 1.859***(.350)

Notes: Standard errors are in parentheses. Regressions are weighted, and standard errors are adjusted for sampling design. The sample size was 14,784, of which 9487 applied to college. Coefficients that were statistically different from whites (p < .05) are in bold. p < .10. * p < .05. ** p < .01. *** p < .001 (two-tailed).  

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