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DOI: 10.1177/0003122412472680

2013 78: 70 originally published online 3 January 2013American Sociological Review Laura T. Hamilton

More Is More or More Is Less? Parental Financial Investments during College

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American Sociological Review78(1) 70 95 American SociologicalAssociation 2012DOI: 10.1177/0003122412472680http://asr.sagepub.com

Higher education in the United States haslong been sponsored by parents funds. Overtime, however, the burden on parents hasgrown heavier and more substantial. Duringthe past three decades, the federal govern-ment has increasingly transferred a greater

proportion of aid from grants to loans that areoften carried in part by parents (Baum andSteele 2007; Paulsen and St. John 2002). At

the same time, the proportion of revenuecoming from state and local funding hasgradually declined (Fain 2009; McPhersonand Schapiro 1998). 1 At most schools, tuitionhas risen to make up the difference. In the

past decade alone, average tuition and fees at private four-year colleges and universitiesincreased at an annual rate of 3 percent ininflation-adjusted dollars; public schools sawan even steeper annual increase of 5.6 percent(Baum and Ma 2010).

472680ASR XXX10.1177/00031224124726

80American Sociological ReviewHamilton2012

aUniversity of California-Merced

Corresponding Author:Laura T. Hamilton, School of Social Sciences,Humanities and Arts, 5200 North Lake Road,Merced, CA 95343E-mail: [email protected]

More Is More or More Is Less?Parental Financial Investmentsduring College

Laura T. Hamilton a

AbstractEvidence shows that parental financial investments increase college attendance, but weknow little about how these investments shape postsecondary achievement. Two theoreticalframeworks suggest diametric conclusions. Some studies operate from a more-is-more perspective in which children use calculated parental allocations to make academic progress.In contrast, a more-is-less perspective, rooted in a different model of rational behavior,suggests that parental investments create a disincentive for student achievement. I adjudicate

between these frameworks, using data from nationally representative postsecondary datasetsto determine what effect financial parental investments have on student GPA and degreecompletion. The findings suggest seemingly contradictory processes. Parental aid decreasesstudent GPA, but it increases the odds of graduatingnet of explanatory variables andaccounting for alternative funding. Rather than strategically using resources in accordancewith parental goals, or maximizing on their ability to avoid academic work, students aresatisficing: they meet the criteria for adequacy on multiple fronts, rather than optimizing theirchances for a particular outcome. As a result, students with parental funding often performwell enough to stay in school but dial down their academic efforts. I conclude by highlightingthe importance of life stage and institutional context for parental investment.

Keywordscollege completion, grade point average, higher education, parental investment, satiscing,young adulthood

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Hamilton 71

These trends reflect a gradual shift in theresponsibility for U.S. higher education fund-ingfrom state and federal subsidies to indi-vidual families (Lucas 1996; Price 2004). As

public schools begin to operate more like private schools and rely heavily on tuitiondollars, and federal funds for higher educa-tion fail to absorb the cost to students, the roleof parents becomes even more central. Par-ents often become the primary financiers ofhigher education.

To send their children to college, parentsoften make difficult financial decisions.Financing childrens education comes at a

considerable cost for many, as they dip intofamily savings or pull from retirementaccounts. Yet, little work examines whether

parental dollars translate into quantifiable benefits for students. Simply put, can parents purchase a better college outcome for theirchildren? Do parental dollars boost student

performance? Or is there a point of diminish-ing returns, even a negative influence? Thesequestions are important not only for individ-

ual parents as they make decisions about howmuch to fund their children, but also for poli-cymakers in understanding implications of afunding structure that rests squarely on par-ents shoulders.

The central goal of this article is to deter-mine what effect financial parental investmentshave on two key postsecondary education out-comes: grade point average (GPA) and degreecompletion. I rely on data from three nationallyrepresentative datasets of postsecondary stu-dents collected by the National Center for Edu-cation Statistics (NCES) to address thefollowing empirical questions:

1) What is the direction and magnitude of pa-rental aids influence on student GPA and

bachelors degree completion, net of studentsociodemographics, parental socioeconomicstatus (SES), family structure, academicability, student characteristics, and institu-tion characteristics?

2) What are the effects of parental aid when ac-counting for other sources of financial aid,in both relative and absolute terms?

Provision of Parental Aid

Deil-Amen and Turley (2007) note that rela-tively little research within the sociology of

higher education focuses on college financing.One exception is examination of factors shap-ing how much parental aid parents provide.Higher socioeconomic status parents are morelikely to assume their children will attend col-lege, have the resources to pay for it, andmake the necessary financial plans (Flint1992; Olson and Rosenfeld 1984). Familystructure also plays a role. Married parentscontribute larger amounts of money to their

childrens college education (Turley andDesmond 2011). When there are greater num- bers of siblings, especially if they are closelyspaced, each child may receive less (Downey1995; Steelman and Powell 1989). Parentsmay invest more in children with higher aca-demic aspirations and in children who demon-strate higher levels of achievement, althoughevidence for the latter is mixed (Flint 1997;Powell and Steelman 1995).

The cost of education also matters. Pricierschools often require greater parental contri- butions, and students self-select based onability to pay (Paulsen and St. John 2002).Institutions typically provide scholarships

based on prior achievement, and students canalso receive funds from programs such as

National Merit Scholarships. This type of aidtends to disproportionately benefit studentsfrom affluent families who arrive withstronger records (Carnevale and Strohl 2010).

Need-based aid is usually calculated using theexpected family contribution (EFC) and doesnot take into account the actual amount of

parental assistance students will receive. 2 Thenet cost to families includes the EFC plus anyadditional costs not met by merit, need, orother forms of aid (e.g., military or employer

benefits). Students and their parents mustdecide how to cover these costsusing dif-ferent types of loans, savings, or earnings.

Even if we match students on all the abovecharacteristics, we might expect considerableheterogeneity in how much aid parents offertheir children. Indeed, attendance at a four-year

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72 American Sociological Review 78(1)

college epitomizes a relatively new lifestage, young adulthooda period of the lifecourse devoted to academic, career, and self-development (Rosenfeld 2007; Settersten and

Ray 2010). In many ways, young adulthoodmirrors the development of adolescence aroundthe turn of the twentieth century, in that it is notyet fully institutionalized or universal (Fursten-

berg, Rumbaut, and Settersten 2005; Osgoodet al. 2005; Zelizer 1985). Compared to parent-ing younger children, there is currently lessconsensus about what amount of financial sup-

port constitutes good parenting for college stu-dentseven within social class categories.

Past research on intergenerational effectsof parental funding for college underscoresthis point. Accounting for income and educa-tion, parents who received financial supportfor college from their own parents are signifi-cantly more likely to provide higher levels ofaid for their offspring (Flint 1997). Here wesee evidence that a cultural understandingabout young adulthoodtransmitted acrossgenerationsshapes parental investment in

college. College funding thus cannot beunderstood as entirely a matter of financialcalculus. Not all parents who can afford to doso will cover the costs of college, and somewith limited resources will find ways to pro-vide more than expected.

Effects of Parental Aid

Sociological knowledge of the effects of parental support for older youth is limited,although recent work notes links to collegeattendance. For example, Charles, Roscigno,and Torres (2007) demonstrate that parentalinvestmentsincluding financial support forcollegecan explain the BlackWhite gap incollege attendance. Kim and Schneider (2005)show that parental social capital helps parentschannel outside information and financialresources to increase the likelihood that stu-dents successfully transition into higher edu-cation. These findings are consistent with agrowing awareness that extending parentalinvestment later into the life course providesyouth with distinct advantages (Osgood et al.

2005; Settersten and Ray 2010). However, theextent to which parental investments, specifi-cally parental aid, shape specific academicoutcomes for college students remains largely

unexplored.As Bowen and Shapiro (1998) note, thisissue plagues work on college funding moregenerally. Most scholarship has focused onthe link between aid and access and has notadequately addressed the effects of aid on col-lege achievement and completion. Somerecent research has begun to move beyondaccess. For example, scholars have shownthat in some cases merit-based aid can boost

achievement (Henry and Rubenstein 2002;Stater 2009), and other researchers have begun to trace the receipt of grants and schol-arships to degree completion (DesJardins,Ahlburg, and McCall 2002; Dynarski 2003).Similarly, Paulsen and St. John (2002) indi-cate that unmet need is a barrier to persis-tence. Parental aid, however, has been largelyexcluded from these efforts (but see Steelmanand Powell 1989).

Inattention to effects of parental aid duringcollege is surprising given the importance of postsecondary achievement for students lifechances. For example, GPA can fundamen-tally shape students movement into advanceddegree programs and boost earning power(Jones and Jackson 1990; Loury and Garman1995; Mullen, Goyette, and Soares 2003).Degree completion is associated with greateraccess to higher paying and more privilegedoccupations, a higher probability of marriage,

better health, greater civic participation, andintellectual development (for a review, seeStevens, Armstrong, and Arum 2008). Under-standing what role parental investments playin influencing these key educational out-comes is thus central to the goals of stratifica-tion research.

Two frameworks offer divergent views onhow parental investments shape student GPAand degree completion. Studies from statusattainment, human capital, and cultural capitaltraditions operate from a more-is-more per-spective. This lens suggests that children use

parents calculated allocations in service of



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Hamilton 73

higher education. According to this approach,increasing parental investments improve, or atthe very least do not harm, student perfor-mance. In contrast, I examine a more-is-less

perspective that is rooted in a different rationalchoice model of human behavior and is oftenimplied in recent concerns over young adultsfinancial dependence on their parents. Thetheory of moral hazard suggests that parentalinvestments may create a disincentive for stu-dent achievement.

Traditional Approaches to ParentalInvestment: More Is More

The first framework, more-is-more, drawsfrom sociological traditions that identify

parental investment in education as a keymechanism driving the reproduction of advan-tage. For example, status attainment models which dominate stratification research insociologypostulate that an individualssocioeconomic destination is a function ofability, background, aspirations, and parental

investments in education. Research has exam-ined many forms of parental investments,including social psychological aspects (e.g.,educational aspirations), financial capital, cul-tural capital, and social capital (Blau andDuncan 1967; Coleman 1988; Downey 1995;Sewell and Hauser 1976). Central to this par-ticular investigation, Steelman and Powell(1989) identify financial investments in post-secondary education as a previously little rec-ognized factor in status attainment processes.

These models often imply that parentalinvestments link class origins and destina-tions via a series of calculated exchanges,characteristic of a rational choice perspective.For example, in the human capital model(Becker 1964) parents intentionally directfinancial resources to their childrens educa-tion, expecting children to optimize thesefunds by accruing skills and credentials nec-essary for future socioeconomic success.Scholarship from a cultural perspectiveper-haps best illustrated by Bourdieu (1984; seealso Lareau 2011)suggests that parents cul-tivate valuable social tastes, interests, skills,

and dispositions through exposure to certaineducational and extracurricular contexts. This

process can occur through calculated intent ora less direct absorption of ones social and

cultural environs.This research shares a basic tenet: parentalinvestments in education have a positiveeffect on childrens academic, occupational,and economic fates. There is little room instatus attainment, human capital, or culturalapproaches for ineffective or even problem-atic parental educational investments. Thesecore sociological traditions implicitly (andsometimes explicitly) suggest that more is

better than less, and that parents cannot investtoo much in their children.

Young Adulthood and Moral Hazard:More Is Less

A more-is-more perspective is deeplyingrained in the sociology of education. Inrecent years, however, concern about parentalinvestment has grown as the responsibilities

ascribed to parents continue to expand andextend further into the life course (Acocella2008). Intentionally crafting opportunities forchildrens intellectual, social, and emotionalgrowth has become an accepted norm ofmiddle-class parentingand one that maygenerate its own set of problems (Hays 1996;Lareau 2011; Warner 2006). For youth reach-ing the end of adolescence, this parentingmay be primarily financial in nature.

Scholars have begun to note the possiblehigh cost of creating a young-adult life stagewhere parents free youth from the realities offinancial responsibility (Danziger and Rouse2007). Some evidence indicates that allowingyouth to postpone adult statuses, like full-time employment, may have unintendedeffects on their independence. For example,

Newman and Aptekar (2007) document anincreasing delay in leaving the natal home inWestern Europea pattern also seen in theUnited States. Similarly, Lareau (2011)describes early educational benefits of anintensive logic of childrearing, but her workalso hints at issues of entitlement among



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young adults who are used to having theirneeds met by parents. These emerging obser-vations raise questions about how far into thelife course parental investments continue to

be beneficial.Such concerns tap into an alternative theo-retical framework for understanding parentalfinancial aid in college, where more may actu-ally be less. Moral hazard theory, like priorsociological research on parental investment, isderived from a rational choice perspective onhuman interactions (Heimer 1988). The assump-tions, however, are much different. Moral hazardarises in situations of information asymmetry:

individuals insulated from risk behave in waysnot compatible with their investors goals. Thismay occur because they are not fully accounta-

ble for potential consequences and may pursuedifferent interests. 3 When applied to this case,moral hazard theory suggests that parental aidcan provide an educational disincentive for chil-dren. Children may direct more effort to schoolwhen they personally feel the economic costs of

poor performance.

In earlier stages of education, childrenspend the majority of time under the watchfulgaze of teachers or parents. During college,

particularly when students live on-campus andaway from home, parents are less able tomonitor their childrens academic behaviors toensure sanctioned use of educational resources.Parental aid may also have unique characteris-tics distinguishing it from most other forms ofaid. For example, grants and scholarships areoften merit-based and come with strict perfor-mance standards that may keep studentsfocused. Similarly, work-study and veteran

benefits require students to have personalresponsibility in obtaining funds, which maytranslate into greater investment in academic

performance. Loans are the only other sourceof aid that is generally not tied to performanceand may divert financial responsibility fromstudents during collegeespecially if stu-dents are not responsible for payments.

Some studies suggest this more-is-lessframework may be promising. For example,Davila and Mora (2004) invoke moral hazardto explain why children of self-employed

entrepreneurial parents underperform in rela-tion to their peers. They posit that these stu-dents are less motivated because of anticipatedeconomic security through the family busi-

ness. Economists Bodvarsson and Walker(2004) found that among a small sample ofstudents at two Midwestern schools, parentalaid for tuition and books significantly weak-ened self-reported academic achievement.This study suggests the need for more rigor-ous testing. Finally, a large (although con-flicted) literature on effects of studentemployment on performance indicates theimportance of personal financial responsibil-

ity. Several studies have found no effects(Curtis and Nummer 1991) or positive effectsof low to moderate levels of student employ-ment on GPA (Kalenkoski and Pabilonia2008; Pike, Kuh, and Massa-McKinley 2008).

This scholarship suggests that young adult-hood, sponsored by parental funds, may pro-vide a context in which more is less. Here,

parental funds set the stage for moral hazard byoffsetting costs associated with low academic

performance. From this perspective, parentalaid will not boost GPA or the likelihood ofgraduating and may even prove detrimental.

Reverse Causality and SelectivityProcesses

These two perspectives point to parental aidas a causal mechanism in college students

performance. However, the casual arrow may be reversed. For example, more may look likemore if, as noted earlier, parents invest morein better performing students (Powell andSteelman 1995). In contrast, research fromearlier levels of schooling suggests parentsmay invest more in students who are disad-vantaged in some way, as a compensatorymechanism (Hamilton, Cheng, and Powell2007; Teachman, Paasch, and Carver 1997).

Selectivity processes may also create a situ-ation where less motivated or less talented stu-dents are more likely to receive parentalinvestments. Because parental aid increasesaccess to college, students with parental assis-tance will likely display a wider range of ability



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Hamilton 75

and motivation. In contrast, students who makeit to college with little to no parental help maynot only be exceptionally talented but alsouniquely motivatedfor which there is no

good empirical proxy (Gambetta 1987; Torche2011). Unobserved heterogeneity among thecollege student population may thus drive asimilar empirical finding as processes of moralhazard, but for different reasons. More maylook like less if students receiving greateramounts of support are qualitatively differentfrom those who do not receive similar help.When relevant, I address these possibilitieswith selectivity analyses using fixed-effects

models.

DATA, MEASURES, ANDMETHODSPrimary analyses rely on two nationally rep-resentative postsecondary datasets collected

by the National Center for Education Statistics(NCES): the Baccalaureate and Beyond Study1993 (B&B93) and the Beginning

Postsecondary Students Study 1990 to 1994(BPS90/94). These datasets are linked to the National Postsecondary Student Aid Study(NPSAS), which includes unparalleled dataon elements of college students funding situ-ations, institution and student characteristics,and academic records. Unlike more recent

NPSAS-linked data, these datasets offer con-tinuous, rather than dichotomous or categori-cal, measures of parental aid.

Where GPA is the outcome of interest, Ifocus on the B&B93. This dataset includes anationally representative sample of around12,000 undergraduate students in their finalyear at their U.S. bachelors granting institu-tion during 1992 to 1993. The B&B93 is

preferred for its large and representative sample of students in four-year schools, but asample of students in their final year is notappropriate for analyses in which bachelorsdegree completion is the key outcome. Forthese analyses, I turn to the BPS90/94, whichfollows a nationally representative sample offirst-year students attending postsecondaryinstitutions of any type in the United States

during 1989 to 1990. This dataset includesaround 4,000 students who started at four-year schools and were not lost before the finalfollow-up.

For both datasets, I used multiple imputa-tion techniques appropriate for data missingcompletely at random (MCAR) or missing atrandom (MAR). I generated m = 10 completedatasets with multiple imputation by chainedequations, which uses the observed data tosimulate plausible missing values (Royston2005). I analyzed each of the m datasets indi-vidually and combined results to produceestimates that account for missing data uncer-

tainty (Little and Rubin 2002). As indicated by von Hippel (2007), I used the dependentvariable in imputation but excluded casesmissing on the dependent variable from anal-ysis (although including them does not alterthe findings presented here). 4 As a result, thesample size for the B&B93 analyses is 10,870students; for the BPS90/94 it is 3,810 stu-dents. 5 Table 1 reports the percent of imputedvalues in the analytic sample. The BPS90/94

shows fewer imputed values overall, giventhat cases not present in the final wave wereexcluded prior to imputation. 6

For the GPA analyseswhich produce asurprising findingI also included analysesfrom the Beginning Postsecondary StudentsStudy 1996 to 2001 (BPS96/01) to confirmthe B&B93 findings and address selectivity

processes. As with the BPS90/94, I restrictedthe sample to students who started their firstyear in four-year schools. For this dataset, Ionly included students who were age 30 yearsor younger because older respondents werenot asked about parental assistance.

The BPS96/01 provides limited informa-tion on parental aid and student GPA at three

points throughout college: 1996, 1998, and2001 (2001 data include final GPAs forrespondents who graduated). This allows me toestimate fixed-effects models, which makecomparisons within students rather thanbetween them. These models estimate param-eters for variables that experience any amountof temporal variance, even if only within asubset of cases, and control for effects of



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Table 1 . Descriptive Statistics for B&B93 and BPS90/94 Analytic Samples

B&B93 (N = 10,870) BPS90/94 ( N = 3,810)

Variables % Imputed Mean % Imputed a Mean

Key Independent Variables Parental aid 20.92 3.44 3.57 4.77 Parental aid/total funding 27.50 .27 16.46 .37Dependent Variables Student GPA (x100) n/a b 306.43

BA by 1994 n/a b .59Sociodemographics Female 2.39 .57 .00 .52 Age 2.29 24.99 .00 18.798 Race Black 10.38 .06 .03 .08 White (reference) 10.38 .86 .03 .86 Asian 10.38 .04 .03 .05 Other race 10.38 .03 .03 .01 Hispanic 10.28 .06 .21 .05Parental SES Income 1.92 45.77 .00 48.75 Independent status .20 .40 .00 .08 Education HS degree or less (reference) 7.23 .31 1.97 .27 Some college 7.23 .19 1.97 .23 College degree 7.23 .29 1.97 .25 Advanced degree 7.23 .25 1.97 .25Family Structure Parents married 13.31 .76 .00 .74 Number of members in college 27.17 1.28 3.65 1.40Academic Ability SAT/ACT score 19.83 987.41 36.72 980.69 1989 to 1990 GPA 17.14 262.02Student Characteristics Major Business and management .45 .15 .00 .20 Computer science .45 .03

Education .45 .15 .00 .08 Engineering (reference) .45 .07 .00 .09 Health .45 .07 .00 .06 Humanities .45 .11 .00 .14 Life sciences .45 .08 .00 .06 Math .45 .02 .00 .01 Physical sciences .45 .02 .00 .02 Social/behavioral sciences .45 .17 .00 .16 Vocational/technical .45 .03 .00 .02 Other major .45 .11 .00 .07 Undeclared .00 .10 Enrolled full-year .20 .65 .00 .87 Full-time intensity 2.25 .66 2.97 .85 Out-of-state student 12.10 .29 1.29 .30

(continued)



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Hamilton 79

legally independent students, I use the stu-dents own income and include a variableindicating independent status in the analyses.Because there is a correlation between inde-

pendent status and the log of parental aid ( r = .470, p < .001 for B&B93; r = .260, p < .001for BPS90/94), including it provides a con-servative estimate of effects of parental aid.

Family structure. Turley and Desmond(2011) demonstrate that parents who are mar-ried to each other provide the greatest financialcontributions. Research also indicates that stu-dents from divorced families do not perform

as well in school as do those from nondivorcedfamilies (Amato 2001). I include a dummyvariable indicating marriedas opposed tosingle, remarried, or divorcedparents.

Strain placed on family resources by otherfamily members also shapes provision of

parental aid (Downey 1995; Powell and Steel-man 1995). Therefore, I account for the num-

ber of family members in college. Thismeasure, like income, is reported on the basis

of dependency.

Academic ability. Although imperfect,SAT scores are perhaps the most commonlyused predictor of academic ability and I thusinclude them here (Camara and Echternacht2000). In cases where SAT scores were notavailable, I used the SAT-converted ACTscore. Because research has identified collegeGPA as a factor shaping persistence, degreecompletion analyses also include first-yearGPA as an independent variable (Cabrera,

Nora, and Castaneda 1993). The B&B93 andBPS90/94 do not include high school GPA,

but the supplemental fixed-effects analysesusing the BPS96/01 account for this time-invariant factor.

Student characteristics. I include aseries of dummies for major, with engineering(which has the lowest average GPA) as thereference. 13 I also account for enrollmentstatus (full-year or part-year), intensity (full-time or part-time), and residency (in-state orout-of-state). Finally, I include a categorical

measure of employment status during the aca-demic year. I define full-time employment as35 hours or more per week and part-time asless than 35 hours. No employment is the ref-

erence category.

Institution characteristics. Becausestudent GPAs are higher on average at privateinstitutions, I include a measure of institutioncontrol (public versus private) (Rojstaczer2002). When accounting for student quality,increasing selectivity depresses GPA butincreases the likelihood of graduation (Alex-ander and Eckland 1977; Alon and Tienda

2005). I therefore include institutions scoreson the 1992 Barrons Admissions Competi-tiveness Index. Barrons Index ranges fromnoncompetitive (1) to most competitive (6)and is based on four criteria: SAT/ACT scoresof students accepted in the previous year, GPArequired for admission, class rank required foradmission, and percentage of applicantsaccepted the previous year. I also include ameasure of tuition cost, because it directly

shapes how much money parents provide.14

Alternative funding. In the final set ofanalyses for each dependent variable, Iinclude measures of aid from alternativesourcesgrants and scholarships, loans,work-study, and other aid (e.g., veteran bene-fits). 15 I pulled information on alternative aidsources from federal and institutional files, as

part of the NPSAS. Because educationalfunds are often derived from student employ-ment, I also include a measure of moneyearned starting June 30 of the year in questionand ending July 1 of the following year.

Analytic Approach

The article is organized in two main sections.First, I examine the influence of parentalfinancial aid on student GPA. Then, I move toits effects on degree completion. For eachoutcome variable I proceed as follows. I esti-mate a bivariate model, in which parental aidis used to predict the outcome of interest. Ithen include student sociodemographics and



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parental SESbecause these variables arecentral to most education and stratificationresearchand move to a full model addingfamily structure, academic ability, student

characteristics, and institution characteristics. Next, I account for alternative funding,including grants and scholarships, loans,work-study, other aid (e.g., veteran benefits),and funds earned through student employ-ment. I treat parental aid both as a relativemeasurein which it is divided by the totalaid packageand as an absolute measure(alongside the absolute amounts derived fromalternative sources). Finally, for the GPA

analyses, I present supplemental, fixed-effectsestimates as a way of cross-checking patternsand addressing the possibility that uniquecharacteristics of college students withoutsupport are driving the findings.

RESULTSStudent GPA

Table 2 presents coefficients for the regres-sion of GPA on parental aid (logged) andother explanatory variables. Model 1 indi-cates that at the bivariate level, an increase in

parental aid is associated with a decrease instudent GPA ( b = 2.182, p < .001). 16 Thiseffect is apparent even though parental aidoperates as a proxy for a host of differentsocial processes linked to parental back-ground.

Model 2 helps to disentangle effects ofsocial class from parental aid with the addi-tion of controls for sociodemographics and

parental SES. The model again indicates thatas parental aid increases, student GPAdecreases ( b = 2.233, p < .001). At the sametime, parental income and education work atcross-purposes with parental investments. Asincome increases, so does student GPA, andhaving a parent with an advanced degree (asopposed to a high school degree or less) isassociated with a significant increase in GPA.Model 2 also shows that women and olderstudents have significantly higher GPAs.White students (the reference category) have

the highest GPAs, followed by Asians, stu-dents of other races, and Blacks. Studentswho identify as Hispanic have significantlylower GPAs than other respondents.

Model 3 adds variables capturing familystructure, academic ability, student character-istics, and institution characteristics. Notably,the significant negative effect of parental aidon student GPA persists and, if anything, ismagnified ( b = 4.570, p < .001). 17 Here, the

positive effect of parental education is nolonger apparent. In fact, having a parent withsome college or a college degree significantlydecreases student GPA. As a long tradition of

educational research suggests, the positiveeffect of parental education on student perfor-mance operates primarily indirectly through anumber of mechanisms such as advantages onstandardized tests like the SAT and greaterlikelihood of admission to more selectiveinstitutions. Net of these benefits, greaterselectivity in who attends college amongfirst-generation students, as opposed to thosewith college-educated parents, becomes visi-

ble (see Torche 2011).Students with married parents have, onaverage, higher GPAs. As SAT score increases,so does student GPA. All majors have signifi-cantly higher GPAs than the reference cate-gory of engineering. 18 Students enrolledfull-year and full-time have higher GPAs.Out-of-state students, who are typically fromwealthier families or recruited for merit, alsoachieve greater GPAs. Although working

part-time during the academic year has noadverse effects on GPA, full-time employ-ment does. Students at private institutionshave significantly higher GPAs; however, asselectivity increases, student GPA decreases. 19

These findings indicate that parental aidsnegative effect is not due to family, student,or institution characteristics. It is possible,however, that parental aids effects may not

be the same for students from different socialclass backgrounds. I thus estimate a modelwith an interaction term that allows the effectof parental aid to vary by income. The inter-active variable is significant, indicating someheterogeneity. 20



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Hamilton 81

Table 2 . Regression Coefficients for Student GPA on Parental Aid (Log) and ExplanatoryVariables, B&B93 ( N = 10,870)

Model 1 Model 2 Model 3

Parental Aid (log) 2.182***

2.233***

4.570***

Sociodemographics Female 16.788 *** 17.506 ***

Age 1.165 *** 1.084 ***

Race Black 32.273 *** 18.268 ***

Asian 7.040 ** 3.517 Other race 10.402 ** 6.231 *

Hispanic 11.503 *** 5.415 *

Parental SES Income 3.802 *** 3.125 ***

Independent status .239 2.227 Education Some college 1.276 2.748 *

College degree .141 4.319 **

Advanced degree 5.799 *** 1.970Family Structure Parents married 3.448 **

Number of members in college .819Academic Ability SAT/ACT score .096 ***

Student Characteristics Major Business and management 6.690 **

Computer science 8.458 **

Education 20.420 ***

Health 15.432 ***

Humanities 14.815 ***

Life sciences 9.163 ***

Math 10.820 **

Physical sciences 7.864 *

Social/behavioral sciences 8.156 ***

Vocational/technical 8.276 **

Other major 5.631 **

Enrolled full-year 10.554 ***

Full-time intensity 6.345 ***

Out-of-state student 2.222 *

Employment during school Full-time 7.209 ***

Part-time 1.593Institution Characteristics Private institution 12.103 ***

Selectivity 3.856 ***

Institution cost (log) 1.605

Note: Omitted categories are White, high school degree or less, engineering, and no employment duringthe school year.* p < .05; ** p < .01; *** p < .001 (two-tailed test).



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Figure 1 illustrates this finding, depictingthe relationship between parental aid and

GPA at selected values of parental income,holding all other explanatory variables at themean. Income values correspond to 10, 25,50, 75, and 90 percent of the parental incomedistribution. As the graph demonstrates, themost notable differences are in GPAs of stu-dents with no aid, with the advantage going tostudents in the top half of the income distribu-tion. For example, students whose parentsmake $90,000 annually and receive no aidhave an average GPA of around 3.15, whereasstudents whose parents make $5,000 start

below 3.05. However, as aid increases, thecurves begin to converge. By $16,000 in aid,all students are pulled below the 3.00 mark a critical threshold for many graduate pro-grams and employers. Across values of

parental income, the highest level of parentalaid is associated with an average GPA ofaround 2.95. 21

Patterns across the selected incomes, whileslightly different, are highly consistent. Thecurve for the most privileged students is thesteepest, but everyone experiences a signifi-cant reduction in GPAparticularly in the

first $8,000 of aid. Regardless of class back-ground, the toll parental aid takes on GPA is

modest. Yet, any reduction in student GPAdue to parental aidwhich is typically offeredwith the best of intentionsis both surprisingand important. Even small disparities in GPAare magnified in an increasingly competitivelabor market and disadvantage graduateswhen their records are considered next tothose without such deductions.

Alternative funding. Next, I look at howalternative aid shapes the relationship between

parental aid and GPA, net of explanatory vari-ables. Table 3 presents regression coefficientsfor GPA on a relative measure of parental aid(Model 1). Here parental aid is divided by thetotal amount of funding, including grants andscholarships, loans, work-study, other formsof aid (e.g., veteran benefits), and fundsearned through student employment.

Because parental aid largely determineshow much alternative aidparticularly non-merit-based aidstudents receive, the abso-lute and relative measures are highlycorrelated ( r = .828, p < .001) and have asimilar effect. Model 1 indicates that as the

2.85

2.9

2.95

3

3.05

3.1

3.15

3.2

0 4 8 12 16 20 24 28 32 36 40

S t u d e n

t G P A

Parental Aid in Thousands of Dollars

Income = 5K Income = 15K Income = 37K

Income = 60K Income = 90K

Figure 1. Estimated Effect of Parental Aid on Student GPA, B&B93 ( N = 10,870)Note: Model includes controls for student sociodemographics, parental SES, family structure, academicability, student characteristics, and institution characteristics.



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Hamilton 83

Table 3 . Regression Coefficients for Student GPA on Relative and Absolute Parental Aid,Alternative Aid, and Explanatory Variables, B&B93 ( N = 10,870)

Model 1: Relative Model 2: Absolute

Parental Aid/Total Funding 10.943 *** Parental Aid (log) 3.637 ***

Alternative Funding Grants and scholarships (log) 7.421 ***

Loans (log) 3.949 ***

Work-study (log) .069 Other forms of aid (log) 3.580 Student employment (log) .517Sociodemographics Female 17.536 *** 17.299 ***

Age 1.103 *** 1.074 ***

Race Black 18.179 *** 19.155 ***

Asian 3.574 3.961 Other race 6.256 * 6.737 *

Hispanic 5.467 * 5.946 **

Parental SES Income (log) 3.021 *** 4.128 ***

Independent status 2.661 3.105 *

Education Some college 2.906 * 2.650 *

College degree 4.557 ** 4.327 **

Advanced degree 2.335 1.943Family Structure Parents married 3.080 ** 3.362 **

Number of members in college .794 .492Academic Ability SAT/ACT score .096 *** .093 ***

Student Characteristics Major Business and management 6.542 ** 6.966 **

Computer science 8.434 ** 8.269 **

Education 20.477 *** 20.907 ***

Health 15.450 *** 15.932 ***

Humanities 14.503 *** 15.024 ***

Life sciences 9.122 *** 9.437 ***

Math 10.581 ** 10.484 **

Physical sciences 7.931*

8.457*

Social/behavioral sciences 7.953 *** 8.581 ***

Vocational/technical 7.881 ** 8.361 **

Other major 5.516 * 5.998 **

Enrolled full-year 10.742 *** 9.661 ***

Full-time intensity 6.299 *** 5.785 ***

Out-of-state student 2.065 2.771 **

Employment during school Full-time 7.848 *** 7.977 ***

Part-time 2.488 * 2.063Institution Characteristics Private institution 11.827 *** 10.284 ***

Selectivity 4.003***

3.791***

Institution cost (log) 2.299 * 2.578 *

Note: Omitted categories are White, high school degree or less, engineering, and no employment duringthe school year.* p < .05; ** p < .01; *** p < .001 (two-tailed test).



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proportion of funding derived from parentsincreases, student GPA decreases ( b =10.943, p < .001). Effects of other independ-ent variables also remain much the same. In

this model, institutional cost reaches signifi-cance. As with selectivity, it has a negativeimpact on GPA.

Model 2 presents absolute measures of allfunding forms in thousands of dollars(logged). The negative effect of parental aid

persists ( b = 3.637, p < .001). Here independent status positively affects GPA andreflects the fact that independent studentsreceive less parental aid overall. Loans are the

only other aid source to have a similar effecton student GPA ( b = 3.949, p < .001), andthe coefficients are not significantly different.On the other hand, as money for grants andscholarships increases, so does student GPA(b = 7.421, p < .001). Funds from work-study,other sources of aid, and student employmentdo not significantly affect student GPA.

These findings indicate that the negativeeffects of parental aid and loans do not extend

to all forms of aid. In fact, funds from mostaid sources are not associated with losses toGPAand they may even benefit students.There is thus variation in the effect of a dollardepending on funding source. I return to thisissue in the conclusion.

Supplemental analyses. Cross-sec-tional data are limited in the extent to whichthey can distinguish effects of family funding

processes from spurious effects of studentcharacteristics. For example, it may be thatthe association between parental aid and stu-dent performance is due to greater academictalent and motivation among less privilegedstudents who make it to college, in compari-son to the less select pool of more privilegedstudents who often receive more parental aid.It is also possible that by the final year of col-lege, many students with little parentalfunding and low GPAs have simply droppedout, creating the appearance that lower levelsof parental aid lead to higher student GPAs.

To address these possibilities and confirm patterns established in the B&B93 analyses, 22

I turn to the BPS96/01 panel data, which fol-low a group of students who started college in1996, and estimate fixed-effects models inwhich GPA is regressed on parental aid. Here,

I examine the effect of parental aid within theeducational careers of individual students, effectively controlling for the potential impactof differences between students. I include allstudents, even those who do not persist in col-lege to senior year, so as to avoid potentiallyexcluding low achieving students who receivelittle parental aid.

I use a dichotomous indicator of aid whether parents paid any tuition and fees

and student-reported GPA, because thesemeasures are available across all threewaves. 23, 24 The full model includes year andcollege majora potentially time-varyingfactor that influences student GPA. I alsoincorporate a variable that marks respondentsas transfer students if they left their originalinstitution. This is a rough indicator of chang-ing institutional context, because the paneldata do not offer detailed measures of institu-

tion characteristics for all three time points.As Table 4 indicates, when accounting fortime-invariant unobserved heterogeneity thereis still a significant, negative effect of parentalaidin this case, providing any help withtuitionon GPA. The effect is apparent at the

bivariate level ( b = 21.603, p < .001) andwhen controlling for time-varying factors ( b =4.990, p < .001). As expected, student GPAimproves with time. Transferring is also asso-ciated with a higher GPA and may be due tomoving to a less selective school or entering aninstitution that provides a better fit, motivatingacademic focus. In addition, most majors havea positive effect on GPA, as compared to engi-neering. These results provide strong evidencethat selectivity processes are not driving thenegative relationship between parental aid andGPA, and that this relationship is not an artifactof using the B&B93 dataset. 25

Degree Completion

It is possible that parental aid works in theopposite direction or simply has no effect



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Hamilton 85

with regard to degree completion. As myfocus is now on persistence, I turn toBPS90/94, the only large-scale, nationallyrepresentative dataset that follows a sampleof first-year students and includes a continu-ous measure of parental aid. Table 5 providescoefficients from a logistic regression modelof BA completion. I start with a bivariatemodel (Model 1) that indicates a significant,

positive relationship between parental aid andBA completion within five years ( b = .615,

p < .001). In this case, class advantages likelymagnify the benefits of parental aid. Thechallenge is to see if parental aids positiveeffects remain when accounting for parentalclass background.

As Model 2 indicates, even net of socio-demographics and parental SES, parental aidsignificantly increases the likelihood ofobtaining a bachelors degree ( b = .432, p

american sociological review 2013 hamilton 70 95

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