Undergraduate ﬁnancial aid and subsequent alumni … ﬁnancial aid and subsequent alumni giving...

The Quarterly Review of Economics and Finance45 (2005) 123–143

Undergraduate financial aid and subsequentalumni giving behavior

Kelly A. Marrc, Charles H. Mullinb, John J. Siegfrieda,∗a Department of Economics, Vanderbilt University, Nashville, TN 37235, USAb Bates White LLC, 2001 K Street NW, Suite 700, Washington, DC 20006, USAc The Coca Cola Company, 1 Coca Cola Plaza, Atlanta, Georgia 30313, USA

Received 23 April 2003; accepted 28 August 2003Available online 28 September 2004

Abstract

We investigate alumni giving behavior during the eight years after graduation with data on 2822Vanderbilt University graduates. We estimate both the likelihood of making a contribution and theexpected gift size, conditional on contributing. The type of financial aid received as an undergraduateappears to have a greater influence on subsequent alumni generosity than the amount received. Addinga scholarship to a loan-only package or eliminating a small loan from a mixed loan-grant packagemay increase the likelihood of a subsequent contribution.© 2004 Board of Trustees of the University of Illinois. All rights reserved.

JEL classification:I22; L31

1. Introduction

Alumni provided over $6.8 billion of voluntary support to colleges and universities in2000–2001 (Chronicle of Higher Education, 2002, p. 33). Although these donations are lessthan 4% of higher education revenues, they represent critical revenues for certain institutions(Baade & Sundberg, 1993; Leslie & Ramey, 1988; Mulugetta, Nash, & Murphy, 1999). Inparticular, America’s research universities averaged almost $76 million each in voluntary

∗ Corresponding author. Tel.: +1 615 322 2871; fax: +1 615 343 8495.E-mail addresses:[email protected] (C.H. Mullin), [email protected]

(J.J. Siegfried).

1062-9769/$ – see front matter © 2004 Board of Trustees of the University of Illinois. All rights reserved.doi:10.1016/j.qref.2003.08.005

124 K.A. Marr et al. / The Quarterly Review of Economics and Finance 45 (2005) 123–143

support in 2000–2001, of which alumni support averages 28%. And over the past 30 years,the share of revenues of the nation’s thirty major private research universities accountedfor by alumni giving has been rising (Ehrenberg & Smith, 2001, p. 2). Moreover, alumnisupport is frequently less encumbered with restrictions than are alternative revenues, andthus can be allocated to the highest valued incremental use.

In a seemingly unrelated event, the structure of financial aid recently underwent a dra-matic change. Colleges and universities have begun to rely on financial aid policies as atool to maximize institutional revenues and to manage enrollment (McPherson & Schapiro,1998). Merit aid has grown in importance relative to need-based aid, and institutions usefinancial aid packages that consist of grants, loans, employment opportunities, and self-helpto influence enrollment behavior as well as to provide access to post-secondary education.Additionally, the share of higher education costs shouldered by state governments has fallenin recent years (McPherson & Schapiro, 1998, Tables 3.1 and 3.2). In response, federal andinstitutional aid has risen, but virtually the entire rise has been in the form of loans ratherthan grants (Dynarski, 2002). For federal Perkins loans and institutionally funded loansindividuals write loan repayment checks directly to their alma mater or its representative.

In this paper, we attempt to link undergraduate financial aid decisions to alumni giving.In particular, we explore whether undergraduate college loan obligations affect alumnicontributions. Are young alumni less likely to donate to their alma mater if they havealready “just sent them a check”? Alternatively, do young graduates view financial aid,including loans, as an enabling opportunity, without which they might not have been able toearn a degree at all? In short, do post-graduation loan obligations affect either the likelihoodof giving by young alumni to their alma mater or the expected amount of their gift if theydo contribute?

To explore these questions we estimate two equations. First, to predict which alumniare more likely to contribute, we employ a probit model in which the dependent variabledistinguishes alumni who donated to their alma mater at least once during the eight yearsimmediately following their graduation from those who did not. The explanatory variablesinclude measures of both the existence and amount of various types of undergraduate finan-cial aid. Second, we estimate the expected gift size on the same undergraduate financial aidvariables for contributing alumni. However, the vast majority of gifts in our data are small(97% of alumni have donated under $1000 over the eight years). Thus, whether or not a giftwas made contains most of the information in the data. Due to this fact, we did not expect toand do not learn much from this second equation, conditional on the results of the first. Forthis reason, we emphasize the likelihood of giving throughout the paper. Finally, althoughat times we attempt to place a causal interpretation on some of the coefficients from theseequations, their first contribution is primarily descriptive. Absent a formal model of theallocation of financial aid and selection of college activities, a causal interpretation is risky.

Clotfelter (2003)also addressed the effects of financial aid on subsequent alumni do-nations. He observed no relationship between a binary variable representing the receipt ofsome need-based financial aid and alumni contributions for a cohort of 1951 freshmen ata sample of selective colleges and universities. However, he found a significant negativeeffect for the 1976 cohort at those same institutions. An absence of detail about the amountand composition of the need-based financial aid limits Clotfelter’s analysis. The data usedin this study, although with shortcomings of their own, address these latter concerns.

K.A. Marr et al. / The Quarterly Review of Economics and Finance 45 (2005) 123–143 125

Our data consist of students who received their bachelor’s degrees from Vanderbilt Uni-versity between May 1988 and May 1990. For those alumni who received financial aid, thedata distinguish need-based loans, need-based scholarships, merit scholarships, and athleticscholarships. We have eight full years of contribution history for each individual and treatthe three consecutive classes of students as a single cohort. During their first eight years afterreceiving their degree 1473, or 52.2% of the graduates, donated to Vanderbilt at least once.Since Vanderbilt has very few students of non-traditional age, these donations effectivelyrepresent the cumulative giving history of graduates who are approximately 30 years old.Vanderbilt’s alumni giving rate is low relative to its peers. For example,US News& WorldReportpublishes an average one-year annual giving rate from the 2001 and 2002 fiscalyears of 26% for Vanderbilt, lowest among the twenty top ranked doctoral universities inthe country.

These data have two major limitations. First, they are from a single university, limit-ing the extent to which one may safely generalize the results. Second, they lack a directmeasure of alumni’s incomes. Alumni income is a major factor in determining contribu-tions (Bruggink & Siddiqui, 1995; Clotfelter, 2003; Monks, 2003), so its omission couldbias our results concerning the impact of financial aid. In particular, if the receipt of fi-nancial aid (after controlling for all other covariates) correlates with a graduate’s futureincome, then omitted variable bias is likely. However, a student’s future income doesnot directly enter financial aid formulas. Instead, a student’sparents’ past income andfamily wealth primarily determine the amount of financial aid received during college.Additionally, expectations about scholastic performance govern the division of that aidbetween loans and scholarships. Both family wealth and scholastic performance corre-late positively with future income (Altonji & Dunn, 1991; Behrman & Taubman, 1990;Jones & Jackson, 1990). Therefore, our results are only as robust as our controls for thesevariables.

Fortunately, we have the variables that determine the allocation of financial aid betweenloans and scholarship and two measures of parental income—parental income as reportedby the students when they were freshmen and the per capita income for the zip code inwhich the students’ parents reside. After conditioning on these variables, the incomes ofgraduates are unlikely to correlate with financial aid receipt.1 So, while we might explainmore of the variation in giving with a better measure of students’ post-graduate income, theomission of students’ income is unlikely to bias our estimate of the relationship betweenfinancial aid and giving.

We hypothesize that graduates’ willingness to donate to their alma mater depends ontheir satisfaction with their undergraduate experience, an important component of which istheir financial-aid history.2 If this hypothesis is true, our detailed information about both the

1 Family wealth may affect the college or university children attend and, in turn, alumni’s incomes. However,our analysis controls for university attended.

2 Stutler and Calvario (1996)identify “satisfaction with financial aid services” as one of the nine categories ofgraduates’ undergraduate experience that distinguish alumni donors from non-donors. Alternative models of themotivation for giving include pure altruism, avoidance of social stigma, tax incentives, recognition for generosity,a response to past or deterrence to future solicitation, and quid pro quo for services rendered indirectly such asaccess to elite social circles or business contacts.


amount and composition of financial aid awarded to these students, as well as other specificinformation such as grade point averages and social affiliations, is immensely valuable.

Finally, college and university alumni seldom make large donations during their firsteight years after graduation. The largest contributor in our sample gave $35,698; the aver-age contributor gave $293. However, alumni fund-raisers believe that contribution patternsdevelop early in life and that past giving relates to future giving (Lindahl & Winship, 1992;Okunade & Justice, 1991; Worth, 1993, p. 67). In particular, the relatively few individualswho make large donations to their alma mater after they reach the pinnacle of their careersare most likely to have established a pattern of giving earlier in life. Therefore, to the ex-tent that loan repayments discourage individuals from making regular contributions in theyears immediately following graduation, need-based loans may reduce the pool from whichlarger donations might develop subsequently. Thus, the net present value of any effect ofloan obligations on either the propensity to contribute or the amount of contributions madeby individuals during the eight years immediately following graduation may be much largerthan is evident from our empirical estimates.

Although a $36,000 gift is modest compared to the largest contributions received byVanderbilt over an eight-year period, it is substantial in the context of giving by youngalumni. Of the 1472 contributors, there are some substantial outliers: six gave more than$5000 and an additional 41 donated over $1000. Because the pattern of giving rather thanthe size of gifts seems to be important for young alumni, we focus attention primarily onwhether a contribution is made. When estimating determinants of the amount given, wereport two sets of results: one that includes all contributors, and another that excludes the47 individuals who contributed in excess of $1000.

2. Model, estimation, and identification

First, we present the model and general estimation approach. Second, we address iden-tification concerns stemming from the lack of alumni income in the data set.

2.1. Model and estimation

We model the decision to donate and the amount donated as a two-step process. First,the individual decides whether or not to contribute based on the index function:

d∗ = Xβ + µ. (1)

Whend* exceeds zero, we observe a donation (d = 1). Otherwise, there is no donation (d= 0). We assume that the error term is normally distributed and estimate the implied probitmodel.

Second, we approximate the magnitude of the donation as a linear function,

y∗ = Zγ + ε (2)

whereZ may contain, but is not restricted to, the variables inX. We impose no cross-equation restrictions betweenβ andγ, even for coefficients on the same explanatory variable


(i.e. we do not compute the Tobit specification). Moreover, the data strongly reject suchcross-equation restrictions. For example, receiving financial aid may differentially affectthe likelihood of giving and the expected magnitude of the gift. Finally, we do not observey* for all respondents. Instead, we observe the gift size only for the sub-population makingpositive donations, but observe a gift size of zero for all others. So,

y = y∗, if d = 1

y = 0, if d = 0.

In general, the selection imposed by the first stage correlates with the error term in the secondstage. This correlation results in omitted variable bias for traditional regression analysis. Inparticular, ifε has a normal distribution, then

E{y∗|d = 1} = Zγ + E{ε|d = 1} = Zγ + ρσελ(Xβ) (3)

whereρ is the correlation betweenε andµ, σ� is the standard deviation ofε, andλ(Xβ) isthe inverse Mills ratio.

To correct for selection, we compute the inverse Mills ratio based on the estimatesin the first stage and include the estimated inverse Mills ratio as an additional regressorin the second stage. Finally, we correct standard errors both for the heteroscedasticityinduced by selection and the fact that the inverse Mills ratio is an estimated regressor.Heckman (1979)demonstrates that this procedure yields consistent estimates. However, inour application, the second-stage regression contains all of the explanatory variables thatare in the selection equation. In such a case, functional form assumptions alone identify thesecond-stage regression. In an attempt to demonstrate the robustness of the estimator to thechoice of functional form, we compute estimates under multiple distributional assumptions.We also compute estimates without any form of selection correction.

2.2. Identification

For illustrative purposes, consider the linear probability model which conditions solelyon alumni income and financial aid,

d = α1 + α2 × Income+ α3 × Aid + µ. (4)

Assume thatµ is uncorrelated with both Income and Aid. Additionally, assume that need-based aid is a linear function of parental income (PI),

Aid = β1 + β2 × PI + ε. (5)

Although Aid is not a direct function of a student’s future income, we still expect it tocorrelate negatively with future income. In particular, being from a lower-income familyboth increases the amount of aid offered (β2 is negative) and decreases expected futureearnings. Typically, a negative correlation between Income and Aid results in a negativebias for the OLS estimator ofα3 when the regression omits Income.3

3 The bias in this simplified model equalsα2 × cov(Aid, Income)/Var(Aid), which is negative sinceα2, theeffect of income, is positive and the covariance between Aid and Income is negative.


When the determinants of Aid that correlate with Income are known, conditioning onthe correlates can eliminate the omitted-variable bias. Substitute (5) into (4) to have

d = α1 + α3β1 + α3β2 × PI + α3 × ε + (α2 × Income+ µ)

≡ γ1 + γ2 × PI + γ3 × ε + η. (6)

The new error term,η, reflects that Income will be omitted from the regression.ε isorthogonal to PI by construction. Therefore, any bias in the estimation ofγ2 (due to thepositive correlation between PI and Income) will not bias the estimation ofγ3. By assump-tion, ε is orthogonal toµ. Therefore, the OLS estimator of (6) identifiesγ3 (which equalsα3), the impact of financial aid on future contributions, ifε is uncorrelated with Income.Thus, the key identifying assumption is that the residual variance in Aidafter controllingfor parental incomeis uncorrelated with alumni’s post-graduate income.4

This approach generalizes to more covariates in either (4) or (5). The key assumptionremains that after conditioning on the covariates known to affect Aid, the residual variancein Aid is uncorrelated with any omitted variables. In our application, parental income andwealth determine the total amount of need-based aid. Then, the total need-based aid award isdivided between need-based scholarships and need-based loans on a sliding scale governedby student’s predicted academic performance, a measure of ability. Students receive a greaterfraction of their aid in scholarships as their predicted performance rises. A reviewer ofapplications combines high school grades, SAT scores, and subjective measures (such asthe quality of the essay, apparent motivation, etc.) into a reader’s grade, ranging from C−to A+.5 Thus, parental income, parental wealth, and predicted performance (i.e. ability)determine need-based aid packages. We control for all of these factors in our analysis.

3. Data

The data consist of 2822 full-time students who entered Vanderbilt University as fresh-men between August 1984 and August 1986 and graduated between May 1988 and May1990.6 Transfer students (either into or out of Vanderbilt) are excluded from the sample.Contributions from each graduate in our sample pertain to the first eight years after grad-uation. This eight-year window allows sufficient time for individuals to complete mostpost-graduate professional programs, even allowing for a few years of pre-professionalprogram work experience.

Vanderbilt’s Alumni and Development office provided the date, amount, form (cash,stock, or in-kind), and destination unit of every gift made by individuals in our sample.

4 Although the presence of alumni income does not affect identification, including it in the analysis wouldimprove the efficiency of the estimator.

5 Starting in 1986, predicted grade point average (PGPA) replaced the reader’s grade. The same factors de-termined the PGPA, but the score was left as a continuous variable, in lieu of the discrete reader’s grades. For1986, we map the PGPA into the corresponding reader’s grades. (Other specifications were tried, but they did notqualitatively alter the estimates of interest.)

6 Data are unavailable for earlier cohorts.


They also indicated when corporate employers matched gifts. We identify as gifts to theundergraduate college contributions designated for undergraduate academic units, athletics,reunion events, libraries, and undesignated contributions. This definition excludes gifts des-ignated for schools without undergraduate programs or for the university hospital. A binaryvariable indicating if a gift was made to the undergraduate college is the dependent variablein the likelihood of giving probit model. The total amount contributed is the dependentvariable in the second equation.

We divide the explanatory variables into four categories: financial aid, socio-demographic, college experience, and charitable incentives.Table 1reports descriptivestatistics and results of a two-tailed test for a difference in the means between donors andnon-donors for each of these variables. Our expectations regarding the relationship of eachvariable to both the probability of contributing and the expected size of the contributionare described below. These expectations are the same for each dependent variable. For ex-ample, if we expect a variable to increase the likelihood of giving, then we expect it toincrease the gift size as well. Therefore, in the following discussion we often refer to thetwo outcomes collectively as “charitable behavior.”Table 2reports the predicted signs ofthese relationships, along with the empirical estimates.

A few general points will expedite the presentation. First, most of the variables arebinary; only exceptions are noted explicitly. Second, all contributions are expressed in 1998dollars.

3.1. Financial aid variables

Financial aid eases the budget constraint of the student and her family. Additionally, afinancial aid award may bestow psychological benefits on the recipient. To the degree that re-cipients appreciate either of these attributes, we expect financial aid to induce more-generouscharitable behavior. Thus, we expect non-need-based aid and need-based scholarships toincrease giving. Although need-based loan obligations are also enabling, they may makealumni feel as though they are already supporting the university through their repayments,moderating the likelihood of giving. Therefore, we have no expectation about the sign ofthe net effect of need-based loans.

With the exception of athletic scholarships, the amount of each type of financial aid ismeasured in $2000 intervals, $1–2000, $2001–4000, etc. We prefer this non-parametricapproach because it imposes few constraints. As evidenced by the point estimates, thecorrect functional form is non-linear and we are uncomfortable making conjectures(e.g., it is quadratic) about it. Reducing the cell size substantially below $2000 incre-ments would decrease the sample size in many cells to the point of hindering statisticalinference.

3.1.1. Need-based loansNeed-based loans include both institutionally funded loans, federal Perkins loans that the

university administers, and federal Stafford loans administered through commercial lendingorganizations. Repayment usually commences six to nine months after graduation andextends over a 10-year period. Thus, repayments for the typical graduate are due throughoutour sample giving period.


Table 1Descriptive statistics

Variable Mean S.D. Mean

Min Max Donors Non-donors

Donate 0 1 0.52 0.50 1 0*

Total donations 0 35,698 153 1,139 293 0*

Financial aidNeed-based loan

$1–2,000 0 1 0.06 0.23 0.05 0.07*

$2,001–4,000 0 1 0.10 0.30 0.08 0.12*

Above $4,000 0 1 0.04 0.18 0.04 0.04

Need-based scholarship$1–2,000 0 1 0.03 0.17 0.03 0.03$2,001–4,000 0 1 0.06 0.23 0.06 0.06$4,001–6,000 0 1 0.06 0.24 0.05 0.07*

Above $6,000 0 1 0.03 0.16 0.02 0.03*

Merit-based scholarship$1–$2,000 0 1 0.01 0.10 0.01 0.01$2,001–$4,000 0 1 0.00 0.07 0.00 0.00$4,001–$6,000 0 1 0.00 0.06 0.00 0.00$6,001–$8,000 0 1 0.01 0.11 0.01 0.01Above $8,000 0 1 0.01 0.11 0.01 0.01

Athletic Scholarship 0 1 0.04 0.19 0.03 0.04

Socio-demographicWhite 0 1 0.95 0.23 0.96 0.93*

Female 0 1 0.50 0.50 0.51 0.48

Family incomeBottom decile 0 1 0.12 0.33 0.12 0.12Ninth decile 0 1 0.10 0.29 0.09 0.10Eighth decile 0 1 0.10 0.30 0.09 0.12*

Seventh decile 0 1 0.09 0.29 0.08 0.10*

Sixth decile 0 1 0.10 0.30 0.10 0.10Fifth decile 0 1 0.09 0.28 0.09 0.09Fourth decile 0 1 0.11 0.31 0.10 0.12**

Third decile 0 1 0.09 0.29 0.11 0.07*

Second decile 0 1 0.10 0.30 0.12 0.09*

Top decile 0 1 0.10 0.30 0.11 0.09**

College experienceExpected performance

A+ 0 1 0.02 0.15 0.03 0.02A 0 1 0.07 0.26 0.08 0.07A− 0 1 0.12 0.32 0.11 0.12B+ 0 1 0.26 0.44 0.27 0.26B 0 1 0.38 0.49 0.38 0.39B− 0 1 0.09 0.29 0.10 0.09C+, C, C− 0 1 0.04 0.19 0.03 0.04No grade 0 1 0.01 0.09 0.01 0.01*

Fraternity (men) 0 1 0.27 0.44 0.28 0.25**


Table 1 (Continued)

Variable Mean S.D. Mean

Min Max Donors Non-donors

Sorority (women) 0 1 0.31 0.46 0.35 0.26*

Athlete 0 1 0.09 0.29 0.10 0.09

Cumulative GPA 1.27 4 2.95 0.44 2.98 2.93*

MajorEconomics 0 1 0.15 0.36 0.17 0.14*

Education 0 1 0.06 0.24 0.05 0.06Human/Org. Development 0 1 0.06 0.25 0.06 0.07Humanities 0 1 0.18 0.39 0.18 0.19Mathematics/Engineering 0 1 0.28 0.45 0.29 0.26**

Performing arts 0 1 0.01 0.08 0.00 0.01**

Psychology 0 1 0.09 0.29 0.09 0.09Science 0 1 0.08 0.27 0.06 0.10**

Social science 0 1 0.17 0.38 0.18 0.16∗ Significant difference between means at 5% level.

∗∗ Significant difference between means at 10% level.

3.1.2. Need-based scholarshipsNeed-based scholarships are institutionally funded need-based grants that do not re-

quire repayment. We exclude Pell Grants because students recognize them as entitlementsthat are independent of the college or university they attend. For this reason, Pell Grantsshould not affect the student’s attitude toward Vanderbilt.7 Furthermore, we omit CollegeWork Study Program (CWSP) awards. Although most student aid packages at Vanderbiltinclude a CWSP award, the data do not permit us to determine if a student chose to take itup.8 Fig. 1displays a scatter plot of need-based scholarships and need-based loans for allalumni receiving aid. As seen in the figure, conditional on receiving aid, these two variablesessentially are uncorrelated.9

3.1.3. Merit scholarshipsMerit scholarships are non-need-based grants to students awarded for academic or ex-

tracurricular achievements in high school. We expect more contributions from former meritscholarship holders for two reasons. First, as previously mentioned, the honor of receivinga merit scholarship enhanced their undergraduate experience. Second, despite controllingfor expected scholastic aptitude, former merit scholarship holders are more likely to be highachievers and consequently high earners.10

7 When added to the model, Pell Grants have a statistically insignificant effect. However, their inclusionstrengthens our findings. The coefficients on need-based scholarships in excess of $4000 increase 10–30%; fur-thermore, the coefficient for need-based scholarships between $4001 and $6000 becomes statistically significant.None of the remaining estimates are affected.

8 Students claim about 75% of CWSP dollars offered. However, the CWSP offer is approximately the samefraction of most student financial aid packages, so its omission is unlikely to bias the estimated coefficients.

9 Excluding students with merit and/or athletic scholarships does not alter this relationship.10 We are unable to control for each of the extracurricular achievements that contribute to the receipt of these

awards.


Table 2Parameter estimates of a probit model of the likelihood of donating

Variable Expected sign Probit estimates

Coefficients Marginal effect P-value

Intercept −0.68Financial aid

Need-based loan$1–2000 ? −0.40 −0.151 0.012*

$2001–4000 ? −0.41 −0.157 0.003*

Above $4000 ? −0.20 −0.076 0.221

Need-based scholarship$1–2000 + 0.32 0.121 0.036*

$2001–4000 + 0.35 0.134 0.013*

$4001–6000 + 0.19 0.074 0.108Above $6000 + 0.12 0.048 0.255

Merit-based scholarship$1–2000 + 0.13 0.050 0.292$2001–4000 + −0.14 −0.055 0.357$4,001–6000 + 0.37 0.140 0.198$6001–8000 + 0.10 0.038 0.348Above $8000 + 0.16 0.060 0.256

Athletic scholarship + −0.20 −0.078 0.228

Socio-demographicWhite ? 0.20 0.078 0.075**

Female ? −0.03 −0.011 0.713

Family incomeBottom decile − −0.03 −0.012 0.384Ninth decile − −0.06 −0.022 0.301Eighth decile − −0.22 −0.085 0.021*

Seventh decile − −0.19 −0.074 0.042*

Sixth decile − −0.10 −0.038 0.182Fifth decile − −0.09 −0.035 0.209Fourth decile − −0.24 −0.091 0.013*

Third decile − 0.14 0.055 0.096**

Second decile − 0.10 0.039 0.171Top decile (Omitted) (Omitted)


A+ (Omitted) (Omitted)A − −0.01 −0.005 0.473A− − −0.14 −0.054 0.249B+ − −0.06 −0.022 0.392B − −0.11 −0.041 0.305B− − −0.02 −0.007 0.469C+, C, C− − −0.21 −0.080 0.199No grade − −0.55 −0.209 0.053**

Fraternity (men) + 0.20 0.075 0.003*

Sorority (women) + 0.34 0.130 0.000*


Table 2 (Continued)

Variable Expected sign Probit estimates

Coefficients Marginal effect P-value

Athlete + 0.22 0.084 0.020*

Cumulative GPA + 0.17 0.066 0.003*

MajorEconomics ? 0.15 0.057 0.046*

Education ? −0.09 −0.036 0.398Human/Org. Development ? −0.07 −0.026 0.541Humanities (Omitted) (Omitted)Mathematics/Engineering ? 0.16 0.062 0.015*

Performing Arts ? −0.52 −0.200 0.112Psychology ? 0.03 0.012 0.722Science ? −0.30 −0.116 0.003*

Social Science ? 0.12 0.045 0.103∗ Significant at 5% level.

∗∗ Significant at 10% level.

3.1.4. Athletic scholarshipsAthletic scholarships are need-blind grants that are allocated through a recruitment pro-

cess that increases self-esteem. For this latter reason and others similar to those identifiedfor merit scholarships, we expect positive coefficients for this variable.

0

2,500

5,000

7,500

10,000

12,500

0 2,500 5,000 7,500 10,000

Need-Based Scholarships

Nee

d-B

ased

Loa

ns

Fig. 1. Scatter plot of need-based loans vs. need-based scholarships.


3.2. Socio-demographic variables

3.2.1. WhiteNinety-five percent of Vanderbilt graduates in the sample are white. Therefore, we make

no attempt to differentiate among various minority groups due to an insufficient number ofdisaggregated minorities in the sample.

3.2.2. FemaleEckel and Grossman (1998)found that women contribute more than men. However,

on average, men earn more than women, providing access to more resources to supportdonations. Consistent with greater ability to pay,Okunade (1996)found that male graduatesof the University of Memphis contributed more than female graduates.Clotfelter (2003)finds no significant difference in contributions between men and women who were enrolledin 14 select private colleges and universities in 1976. Recent cross-sectional evidence from415 colleges and universities (Cunningham & Cochi-Ficano, 2002) confirms the irrelevanceof gender for giving. Our inability to control for a graduate’s earnings makes the directionof the effect of this variable an empirical question.

3.2.3. Parental incomeThere are two measures of parental income: (1) per capita income for the zip code in

which the student’s family resided at the time they applied to Vanderbilt and (2) parentalincome as reported by freshmen to the Cooperative Institutional Research Program (CIRP).The first measure is available for all students, but potentially is less accurate than thestudent’s self report of parental income. The second measure is available for about onethird of the sample.11 The two measures have a correlation of 0.3. In order to minimizeconstraints on the functional form, we specify parental income as a set of nine binaryvariables, each representing a decile of the within-sample distribution of parental income.Because parental income levels of Vanderbilt students without need-based financial aid arehigh, the fifth decile might correspond to the first or second decile in the United States.

3.3. College experience variables

Both social interactions and academic success determine the quality of a student’s collegeexperience. The more favorable a student’s experience, the more likely she is to reward theuniversity with charitable gifts. The variables identified below proxy for these two attributesof a student’s undergraduate experience.

3.3.1. Expected performanceAs part of the admissions process, each applicant is assigned a “reader’s grade” ranging

from C− to A+. The grade reflects an applicant’s standardized test scores, high schoolgrades, high school quality, extra curricular activities, essay quality, and other credentials

11 Approximately 90% of the students complete the CIRP freshman survey; 80% of those report parental income,and 50% provide their social security number.


that likely would influence the success of the applicant while in college at Vanderbilt and inthe labor market after graduation. Applicants with higher reader’s grades are also likely toreceive relatively more grant than loan aid in their financial aid package. Because alumnicontributions are affected by graduates’ incomes, failure to control for ability would leadto spurious correlation between undergraduate loan and grant aid amounts and subsequentalumni contributions.

3.3.2. Greek affiliationHarrison, Mitchell, and Peterson (1995)found higher alumni giving at institutions with

a higher percentage of students who pledged fraternities and sororities. The usual interpre-tation of such results is that students who participate in the extensive social and communityactivities arranged by Greek organizations feel a stronger sense of attachment to the univer-sity. Furthermore, membership in a fraternity or sorority requires substantial dues, whichtends to bias Greek membership toward students from relatively wealthier families, possi-bly increasing subsequent contributions. On the other hand,Okunade, Wunnava, and Walsh(1994)found that alumni who were members of social Greek organizations donated signif-icantly less to the University of Memphis for academic purposes. They hypothesized thatthis finding reflected the competition between Greek organizations and academic units forthe support of their common alumni. We identify membership in fraternities and sororitiesby separate variables. Because Greek organizations are single-sex, we include three binaryvariables to identify the effects of sex and Greek membership: male, non-Greek is the omit-ted category; female reflects female non-Greek; the combination of female and sororityreflects females in Greek organizations; fraternity reflects males in Greek organizations.

3.3.3. AthletesThis variable includes both scholarship and non-scholarship varsity athletes. As with

Greek affiliation, participation on a varsity athletic team generates a stronger sense ofattachment to the university through group membership. In addition, former athletes receivesolicitations from a special club of former players in addition to the usual appeal for alumnicontributions. Thus, we expect former athletes to contribute more.

3.3.4. Cumulative grade point average (GPA)Higher GPAs may lead to increased satisfaction and/or to higher earnings (Jones &

Jackson, 1990), both of which should increase charitable giving. To the extent that highercollege GPA measures satisfaction with the college experience, this variable measures anattitudinal result of the college experience rather than a student’s potential for college andlabor market achievement. Because expected performance (the reader’s grade) explainsonly 20% of the variation in cumulative GPA, we include both in the estimated model.

3.3.5. Undergraduate majorA series of binary variables distinguish nine groups of majors.12 Students majoring in

two areas are included in each; thus, the sum of the means of these variables exceeds one.

12 Human and organizational development is a quasi-business major that de-emphasizes finance and accounting.


We expect to observe differences across majors because disciplines attract different types ofstudents and provide distinct earnings potentials (Hecker, 1995). For example, a stereotypeof an education major is a relatively more patient and generous person, who, therefore, mightbe more likely to donate, although in modest amounts because of low expected earnings.The academic area variables also capture systematic differences in student satisfactionacross departments or divisions of the university. For example, the positive coefficientassociating economics majors with a greater likelihood of giving might be interpreted ina self-serving way by these authors as reflecting the superior undergraduate experiencereceived by economics students at Vanderbilt! Alternatively, it might reflect higher earningsof economics graduates (Okunade & Justice, 1991). In the end, we make no prediction ofthe expected sign of the coefficient of any major.

3.4. Charitable incentives

3.4.1. Corporate matching programsA corporate matching program reduces the effective price of a contribution, which should

increase charitable behavior. Unfortunately, we have information about employers’ match-ing policies only for graduates who made contributions. Consequently, we include thecorporate matching variable only in the gift size equation (this is the only difference inexplanatory variables between the two equations). Furthermore, we know only the totalamount contributed by a graduate’s employer, not the rate at which individual donationswere matched. Therefore, we define this variable as the dollar value of the employer’sdonations divided by the dollar value of all the graduate’s donations.

We expect a substantial positive elasticity with respect to the match rate, but that doesnot necessarily imply a positive coefficient on this variable. The dependent variable is thedollars spent on contributions to Vanderbilt, not the quantity of dollars Vanderbilt receives.Economic theory only predicts that when the price of a good falls (the cost of sending adollar to Vanderbilt), the quantity purchased will rise. It does not require individuals toincrease the amount they spend on that good. For example, suppose a graduate who wouldgive $300 with no corporate match receives a 100% corporate match. Economic theorypredicts that the graduate will give in excess of $150, ensuring that Vanderbilt receives inexcess of $300. Furthermore, if this graduate’s elasticity were 0.5, then we would observe agift of $225 resulting in a net receipt of $450 for Vanderbilt. Under this latter scenario, thecorporate match results in a smaller contribution by the alumnus. In order to have a positivecoefficient on this variable, it is necessary and sufficient for the elasticity to exceed one.

4. Empirical results

Tables 2 and 3contain the estimates of each explanatory variable’s effect on thelikelihood of making at least one contribution during the eight years after graduationand on the expected size of such contributions, respectively.13 We discuss these two

13 Attempts to distinguish between frequent and non-frequent donors (where frequency ranged from three tosix donations) were relatively uninformative. In short, (1) the coefficients on the financial aid variables increase


Table 3Parameter estimates of the expected gift size equation

Variable Expected sign Selection corrected No selection correction

All gifts All gifts Gifts < $1000

Intercept −869 701 201

Financial aidNeed-based loan

$1–2000 ? 1009 −54 −93*

$2001–4000 ? 1128 29 −74*

Above $4000 ? 540 −4 −69*

Need-based scholarship$1–2000 + −1087 −214 82*

$2001–4000 + −1090 −152 53*

$4001–6000 + −677 −151 57*

Above $6000 + −520 −142 12

Merit-based scholarship$1–2000 + 1619** 2000 3$2001–4000 + 369 −30 −39$4001–6000 + −1389 −294 12$6001–8000 + −221 64 81Above $8000 + −457 −32 −42

Athletic scholarship + 608 27 11

Socio-demographic

White ? −713 −184 4

Female ? −7 −75 −29**

Family incomeBottom decile − −93 −182 4Ninth decile − 21 −142 5Eighth decile − 354 −251 11Seventh decile − 351 −172 24Sixth decile − 127 −145 −4Fifth decile − 6 −247 −4Fourth decile − 443 −207 24Third decile − −171 254 24Second decile − −456 −154 10Top decile (Omitted)


A+ (Omitted) (Omitted)A − 218 164 −34A− − 376 −38 −24B+ − 100 −81 −43B − 267 −52 −28B− − 223 154 −24C+, C, C− − 409 −192** −59No grade − 1927 504 −65

Fraternity (men) + −494 39 −17

Sorority (women) + −761 170** −7


Table 3 (Continued)

Variable Expected sign Selection corrected No selection correction

All gifts All gifts Gifts < $1000

Athlete + −600 27 5

Cumulative GPA + −522 −51 −1

MajorEconomics ? −337 81 14Education ? 179 −77 −11Human/Org. Development ? 85 −106 40**

Humanities (Omitted)Mathematics/Engineering ? −485 −33 −6Performing Arts ? 1088 −170 −87*

Psychology ? −208 −124 −16Science ? 622 −168** −23Social Science ? −410 −81 2

Charitable incentivesPercent giving match + 23* 23* 5*

Selection correctionInverse Mills ratio + 4221

∗ Significant at 5% level.∗∗ Significant at 10% level.

sets of estimates in order, but first we address the issue of controlling for post-graduateincome.

Children’s income correlates positively with their parents’ income. Parental income helpsdetermine which students receive need-based financial aid. Combining these two facts, theexpected income of alumni correlates negatively with the receipt of need-based financialaid. Therefore, the omission of alumni income from the empirical analysis could inducea negative bias on the coefficients for need-based loans and scholarships. However, it isthe failure to control for differences inexpectedpost-graduate income rather than a failureto control for income itself that causes the bias. The relevant statistic to control for thescholarship-loan mix in financial aid packages is the expected future income of studentsconditional on their parents’ income and anticipated scholastic performance. In other words,if we adequately control for alumni’s parents’ income and anticipated performance (i.e.ability), then we have controlled for all aspects of future alumni income (including expectedinheritances) that are endogenous with respect to the receipt of financial aid.

The two measures of parental income available are zip code based per capita incomeand student self-reported parental income. Student self-reported parental income probablyis a better measure, but is available for only 953 out of 2822 graduates. To test the va-lidity of the zip code based parental income controls, we restricted the sample to those

in magnitude for frequent donors, but the changes are not statistically significant; and (2) except for cumulativeGPA and participation in varsity athletics, the college experience variables were unable to distinguish betweenthese two groups of donors.


who reported parental income. Then, we ran the analysis in duplicate for this restrictedsample, once using the zip code data and again utilizing the self-reported parental-incomedata. Fortunately, the choice of control does not qualitatively alter the point estimates. Inswitching from the zip code data to the self-reported data, all coefficient estimates changeby less than half of one standard error. The average change is one fifth of a standard error.Given these results, we proceed using the entire sample and the per capita income con-trol based on the zip code in which the student’s parents resided at the time of collegeapplication.

4.1. Likelihood of giving

Most of the variables have an economically substantial and frequently statistically sig-nificant impact on the probability of making a donation. We discuss the variables in thesame clusters in which they were introduced: financial aid, socio-demographic, and collegeexperience.

4.1.1. Financial aidThe type of financial aid received appears to be much more important than the quantity

of aid received. All quantities of need-based loans lower the probability of giving duringthe first eight years after graduation between 8 and 16%. This is to be expected becausethe typical student loan is paid off over 10 years, encompassing all eight years of ourcontributions data. This finding contrasts sharply with Cunningham and Cochi-Ficano’s(2002) results, which found no relationship between need-based loans and alumni givingacross institutions. There is weak evidence that the largest loans, those in excess of $4000,have the smallest negative effect. This result may imply that students view larger loans asenabling opportunities, leading to increased giving (or at least leading to less of a negativeimpact on giving).

In contrast to need-based loans, need-based scholarships raise the probability of givingbetween 5 and 13%. However, the estimated increase in the likelihood of giving decreaseswith the size of the scholarship awarded. We lack a good explanation for this pattern, al-though it may be linked to institutional caps on need-based loans or the manner in whichmerit scholarships interact with need-based aid. Finally, merit based scholarships increasethe likelihood of donating, but the pattern is erratic with respect to the size of the scholarshipand statistically insignificant for each size category. These findings corroborate the impor-tance of need-based scholarships vis-a-vis merit scholarships for future alumni generosityidentified byCunningham and Cochi-Ficano (2002)using cross-sectional averages across415 institutions in 1996–1998.

4.1.2. Socio-demographicsUnlike Eckel and Grossman (1998), we find no differences in generosity between men

and women. On the other hand, there is strong evidence that parental income matters. Thevariables characterizing parental income are jointly significant at the 1% level. Althoughindividual coefficients are noisy, giving seems to rise with parental income. In particular,graduates from families in the top three within-sample deciles all contribute more than thosefrom families in the bottom seven deciles of the income distribution.


4.1.3. College experienceThe college experience variables have the most consistent substantial effects on the

likelihood of giving. Although individual coefficients are insignificant, donations seem toincrease with expected performance. Recall that expected performance is a combination ofprevious scholastic performance (such as SAT scores and high school GPA) and subjectiveattributes (such as motivation). Since we control for actual scholastic performance (finalGPA), this finding most likely reflects a relationship between those subjective attributes andalumni giving.

Consistent withHarrison et al. (1995), we find that members of non-academic groups−fraternities, sororities, and athletic teams− respond more favorably to appeals for donationsafter graduation. The estimated effects are 7, 13 and 8%, respectively, and all are statisticallysignificant.

Students who enjoyed academic success donate slightly more often as alumni. A onestandard deviation increase in GPA (about 0.44 on a 4.0 scale) raises the likelihood of givingby 3%.

Three majors− economics, mathematics/engineering, and science− have large, sta-tistically significant effects on the likelihood of contributing, but the differences across allmajors are substantial. Performing arts and science majors exhibit a probability of giving thatis 20 and 12% lower, respectively, than the humanities benchmark. Economics, mathemat-ics/engineering, and social science exhibit a probability of giving that is, respectively, 6, 6,and 5% higher than the benchmark. The giving behavior of education, human/organizationaldevelopment, and psychology majors is indistinguishable from humanities majors. We an-ticipated that science majors would earn higher incomes than humanities majors, and, con-trary to what the evidence reveals, that this earnings differential would tend to make sciencegraduates more likely to donate. Perhaps the science graduates, many of whom have goneon to medical school, are not yet in a financial position at age 30 that reflects the net presentvalue of their expected lifetime income stream.

4.2. Expected gift size

The first column ofTable 3presents estimates from the standard selection correctionmodel outlined in Section2, where identification is based purely on the normality assump-tion for the error term.14 The second column repeats the analysis without correcting forselection. Finally, the third column repeats the analysis of the second after omitting the 47individuals who contributed in excess of $1000 (3.2% of givers and 45% of money).

As we anticipated, our effort to predict the amount contributed contains little useful infor-mation. The coefficient on corporate matches is the only statistically significant parameterestimate in the initial selection corrected regression. Second, the treatment of large giftsmatters. Least squares estimation disproportionately weights large gifts. When the sampleis restricted to those giving less than $1000 (i.e. assigning zero weight to the 47 largest gifts(out of 1473)), the point estimates for most of the financial aid variables change sign.15

14 Multiple distributional assumptions for the error terms were tried, and yielded qualitatively similar results.These alternative specifications are available from the authors upon request.

15 The same restriction has no impact on the likelihood of giving estimates.


Although not shown in the table, omitting the 47 largest donations decreases the standarderrors for the financial aid variables by an order of magnitude. This occurs because theoutlying observations no longer drive the result. However, the coefficient estimates remainstatistically insignificant.

On the positive side, the gift size regressions provide evidence that gift size is inverselyrelated to price. Contributions that were subject to a corporate employer match were largerthan other gifts. Including all gifts, those whose gifts were matched gave 8% more ($23.16on a base of $293). Restricting the sample to gifts under $1000, those whose gifts werematched gave 6% more ($5.07 on a base of $85).16 Recall that the dependent variable inthese regressions is the portion of the gift made by the alumnus, not the total size of thegift Vanderbilt received. Thus, the fact that the alumni increase their portion of the giftwhen their employer matches gifts implies that the (negative) elasticity of donations withrespect to price exceeds one. Without knowing the matching rate for all gifts, an exactestimate of the elasticity is not possible, but the point estimates imply an elasticity closeto 1. For example, if the averaging matching rate were 100% (50%), the elasticity wouldbe−1.08 (−1.16). This estimated elasticity of giving with respect to price is quite in linewith Clotfelter’s conclusion upon reviewing the considerable literature on this issue that theelasticity ranges between−0.9 and−1.4 (Clotfelter, 1985, p. 274).

5. Conclusion

Colleges and universities use financial aid to help achieve a variety of goals—strengthenthe academic quality of the student body, insure a diverse student population, provide op-portunities for intergenerational mobility in income and wealth, and strengthen the financialcondition of the institution. Although much has been written about the implications of var-ious financial aid strategies for the short-run financial status of colleges and universities,there is also the possibility that financial aid decisions made today carry implications for fu-ture voluntary contributions by alumni, and therefore the financial condition of institutionsin the long run.

The empirical results of this exploratory analysis suggest that discrete changes in financialaid packages affect the willingness of alumni to contribute. It appears that loans (whetherlarge or small) decrease giving, while grants (whether large or small) increase giving.Therefore, a loss in future contributions increases the cost of loans. Conversely, an increasein future contributions lowers the cost of grants.

For example, the marginal effect of small need-based scholarships on graduates’ contri-butions over the first eight post-graduate years is 0.12. For the mean aggregate contributionof $293, adding a small grant, say $1000, to an otherwise loan-only financial aid packagecould be expected to return about $35 in contributions over the first eight years. A completeanalysis would require an estimate of expected incremental gifts over the remainder of agraduate’s lifetime, adjust the gifts to reflect the expected rate of inflation in contributions,and discount the net contributions to their equivalent net present value. On this basis, the

16 As expected, omitting the larger gifts reduces the response since donors with a greater responsiveness tomatching gifts are more likely to exceed $1000 in giving and be dropped from the sample.


return from expected future contributions is modest, but not trivial. If the rate of inflationof contributions equals the discount rate, and if donors contribute at the same rate for 48more years, the return would be $245, in which case the $1000 scholarship actually “costs”the university only $755. Gifts of young alumni (those who had graduated in the most re-cent 10 years) constituted only 11.4% of giving to the Vanderbilt annual fund in 1999. Ifgifts were distributed equally over the first ten post-graduate years, the first eight classes ofgraduates would account for only 9.1%. Figured on this basis, the return would be $385,and the nominally valued $1000 scholarship would cost the university only $615. But thesecalculations are shortsighted. As a well known guide to college fund raising (Worth, 1993,p. 67) states:

“In addition to its immediate impact on the current budget, the annual fund is aprincipal means of involving new donors, identifying those who have a particularinterest in the institution, and developing their habit of giving. Over time, the annualgiving program can be the incubator for major donors, whose cumulative impact onthe institution can be substantial.”

Finally, it is clear that students’ undergraduate experiences affect their willingness tocontribute as alumni. Decisions regarding Greek organizations, athletics, and grading poli-cies all bear, to one degree or another, on the likelihood students will continue to supporttheir college or university after they graduate.

Acknowledgement

We thank Greg Perfetto of the Provost’s Office of Special Projects and Melanie Fordof the Office of Alumni and Development at Vanderbilt University for enormous help inassembling the data used in this project, and participants in the NBER Higher EducationGroup, as well as David Reiley, John Kraft, James Monks and two anonymous referees foruseful comments on an earlier draft.

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