BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

Parental Transfers, Student Achievement, and the Labor Supply 

of College Students 

Charlene Marie Kalenkoski, Ohio University 

Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics 

Working Paper 416

March 2008

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Parental Transfers, Student Achievement, and 

the Labor Supply of College Students*

Charlene Marie Kalenkoski 

Ohio University 

Department of Economics 

Bentley Annex 351 

Athens, OH 45701 

Phone: (740) 593-2022 

Fax: (740) 593-0181 

kalenkos@ohio.edu 

Sabrina Wulff Pabilonia 

Bureau of Labor Statistics 

Division of Productivity Research and Program Development 

2 Massachusetts Avenue, NE Rm. 2180 

Washington, DC 20212 

Phone: (202) 691-5614 

Fax: 202-691-5664 

Pabilonia.Sabrina@bls.gov 

Abstract: Using nationally representative data from the NLSY97 and a simultaneous 

equations model, this paper analyzes the financial motivations for and the effects of 

employment on U.S. college students’ academic performance. The data confirm the 

predictions of the theoretical model that lower parental transfers and greater costs of 

attending college increase the number of hours students work while in school, although 

students are not very responsive to these financial motivations. They also provide some 

evidence that greater hours of work lead to lower grade point averages (GPAs). 

JEL Classification: D1, I2, J2 

Keywords: employment, transfers, GPA 

*Please address all correspondence to Sabrina Wulff Pabilonia. The views expressed in 

this paper are those of the authors and do not necessarily reflect the policies of the U.S. 

Bureau of Labor Statistics. The authors would like to thank Kweku Opoku-Agyemang, 

Anastasiya Osborne, Tatevik Sekhposyan, and Judy Yang for research assistance. The 

authors would also like to thank Alison Aughinbaugh, Michael Giandrea, Mark Long, 

Peter Meyer, David Ribar, Larry Rosenblum, Donna Rothstein, Leslie Stratton, Leo 

Sveikauskas, Bruce Weinberg, and Cindy Zoghi for comments and suggestions. The 

authors are especially indebted to Stan Panis and David Ribar for their aML 

programming assistance. 
I. Introduction 

According to a news release by the U.S. Bureau of Labor Statistics, 49 percent of all 

four-year college students aged 16-24 and 61 percent of all two-year college students aged 16-24 

were employed in October 2006 (U.S. Department of Labor 2007). Why do they work? One 

potential reason is that they lack adequate financial aid and parental transfers to cover their 

college-related expenses. Federal and state work study programs are designed to subsidize some 

of this employment in order to help more students afford college. 

Yet, there is an ongoing debate as to whether college student employment is beneficial or 

detrimental to students. On the one hand, college student employment may be beneficial in the 

long run if it provides students with valuable work experience. Stephenson (1981), Michael and 

Tuma (1984), Ruhm (1995, 1997), Light (1999, 2001), and Neumark and Joyce (2001) find 

positive effects of student employment on future labor market outcomes such as future wages, 

fringe benefits, occupational status, and likelihood of employment, holding schooling constant. 

However, when using a life cycle model that controls for unobserved individual heterogeneity 

and sample selection bias, Hotz et al. (2002) find that the positive effects of employment while in 

college on males’ future full-time wages are significantly diminished and may, in fact, be 

negative for some demographic groups. Hotz et al. (2002) further find that the return to full-time 

schooling in terms of wages is greater than any return to work while in school, suggesting that 

work that detracts from schooling reduces future wages. Indeed, college employment may have 

a detrimental effect on academic performance as time spent in market work reduces time 

available for attending classes, studying, or participating in other schooling-related activities. In 

addition, fatigue from spending long hours at work may negatively affect the quality of any 

schooling-related activity that does occur (Oettinger 1999). Loury and Garman (1995), as well 
2

as Jones and Jackson (1990), find that college grades have a substantial positive effect upon early 

career earnings. Therefore, it is important to analyze the effect of employment on student 

achievement as measured by student grades. 

Several studies have examined the relationship between market work and academic 

achievement in both high school and college. Of the high school studies, Ruhm (1995, 1997) 

and Tyler (2003) find that employment while in high school has a negative effect on both the 

number of years of schooling completed and 12th grade math achievement. Oettinger (1999) 

similarly shows a decline in the grades of minority high school students who work long hours. 

Dustmann and van Soest (2006) find that part-time employment while in school has a small 

negative effect on males’ exam performance in the U.K. Rothstein (2006), however, finds no 

significant effect of student employment on high school grades. Of the college studies, 

Ehrenberg & Sherman (1987) show that an increase in weekly hours worked decreases the 

probability that a student enrolls in college in a subsequent year and, for those who do enroll, 

reduces the probability that they graduate on time; however, they find only a small negative 

effect of working on the first-year grade point averages (GPAs) of male two-year college 

students and no effect of working on the first-year GPAs of male four-year college students. 

More recently, Stinebrickner and Stinebrickner (2003), Oettinger (2005), and Brennan et al. 

(2005) all provide evidence that working while in college has a harmful effect on students’ 

grades. 

This paper overcomes several limitations of existing research on the effects of college 

students’ employment on their academic achievement. First, earlier studies tend to rely on small 

samples. Of the U.S. studies, Ehrenberg and Sherman (1987) examine only male high school 

graduates that were enrolled in college full-time, while Stinebrickner and Stinebrickner (2003) 
3

and Oettinger (2005) each examine students from only one college. Similarly, the U.K. study by 

Brennan et al. (2005) only examines students attending a small number of universities in the 

U.K. This paper attempts to remedy this deficiency in the literature by using a recent sample of 

first-year U.S. college students from Rounds 1-8 of the National Longitudinal Survey of Youth 

1997 (NLSY97), a nationally representative survey, to provide results more applicable to the 

general college student population. 

Second, most existing studies of college student employment pay little attention to the 

reasons why such students work. One reason college students might work is that they might face 

borrowing constraints. Federally guaranteed student loan maximums are set well below the full 

cost of college, and credit cards available to college students often have high interest rates and 

low spending limits. Another reason is that financial aid awards (including guaranteed student 

loan awards) assume that parents will pay a certain amount towards their child’s education, their 

Expected Parental Contribution (EPC), but there is no mechanism to force reluctant parents to 

pay.1

 Two studies that do investigate the relationship between parental transfers and college 

student employment provide mixed evidence. Oettinger (2005) observes that college students 

work more if parents provide less financial support, a result similar to those for high school 

students found by Pabilonia (2001a) and Dustmann et al. (2004). Wolff (2006), however, finds 

that parental transfers have no effect on the employment of 16-22 year olds in France, although 

he makes no distinction between high school and college students. This paper attempts to 

address these gaps in the literature by focusing on financial motives for college employment, in 

 

1

 Kalenkoski (2005) shows that a substantial portion of parents transfer less than their EPC 

towards their child’s postsecondary education, suggesting that students must either choose a 

lower cost schooling alternative or fund the higher-priced schooling some other way, perhaps 

through student employment. 
4

particular the net price of schooling (tuition minus financial aid that does not need to be repaid) 

faced by a student and the transfers s/he receives from her/his parents.2

 

To illustrate these plausible motives, a simple variant of a time allocation model with 

parental transfers is presented. In this model, a student allocates his time between schooling and 

market work while his parents simultaneously make their own consumption and transfer 

decisions. Thus, parental transfers are treated as endogenous to schooling and work decisions as 

in Keane and Wolpin (2001) and Kalenkoski (2006), but in contrast to Oettinger (2005), who 

treats parental transfers as exogenous. The model motivates the testing of several key 

hypotheses. First, smaller parental transfers will result in longer hours worked while in college, 

all else, including the net price of schooling, held constant. Second, because we treat transfers as 

endogenous in the model, an increase in student work hours will reduce parental transfers. 

Third, an increase in the net price of schooling, holding parental transfers and everything else 

constant, will lead to an increase in hours worked. Finally, an increase in hours worked will 

lower student achievement. 

To test these hypotheses, we use data from the National Longitudinal Survey of Youth 

1997 (NLSY97) to estimate a simultaneous equations model consisting of a parental transfer 

Tobit, an hours worked Tobit, and a GPA regression equation via maximum likelihood. In this 

model, the parental transfer is an endogenous determinant of a student’s hours of work, the 

student’s hours of work is an endogenous determinant of the parental transfer, and hours worked 

is an endogenous determinant of the student’s GPA. Estimates are presented separately for fouryear and two-year college students because students attending these different types of colleges 

are likely to have different preferences regarding their postsecondary education and also because 

 

2 Students may work to support living expenses when setting up a new household in a dorm or 

apartment. This study will not consider these effects, nor the costs of room and board, due to 

lack of data.
5

they face different returns to work. Molitor and Leigh (2005), for example, show a larger wage 

return to working while attending a community college than while attending a four-year college, 

suggesting that work and schooling are more complementary for two-year than for four-year 

college students. In fact, many two-year colleges make an effort to coordinate class times with 

student work schedules by offering evening classes and courses that provide for the training 

needs of local employers (Kane and Rouse 1999). Therefore, the magnitude of the effect of 

employment during college on student achievement may vary by type of institution. 

Estimates from this model show that the NLSY97 data do support the hypotheses that a 

decrease in parental transfers increases the work hours of four-year college students. They also 

show that an increase in the net cost of schooling increases the number of hours worked by 

students at both four-year and two-year institutions. However, these effects are quite small. The 

estimates also support the hypothesis that an increase in hours worked negatively affects 

students’ grades, with larger negative effects for two-year students than four-year students. 

Therefore, it is plausible that while work study programs help students finance their college 

tuition and fees, they may be detrimental to students’ academic achievement and thus their longrun outcomes. The next section presents the theoretical motivation for the empirical analysis. 

Section III describes the data. Section IV presents the econometric model. Section V interprets 

the main results and those of several sensitivity analyses. Finally, Section VI concludes this 

paper.

II. Theoretical Motivation 

To motivate our empirical analysis, we present a simple, stylized theoretical model that 

illustrates the potential financial motives behind a college student’s labor supply. Let L be the 
6

fraction of time a student spends working, and let 1-L be the fraction of time the student spends 

in schooling-related activity, such as in-class time (credit hours) and study time. For simplicity, 

the model abstracts from leisure time. However, adding leisure to the theoretical model would 

not change any of the predictions that we use to motivate our empirical analysis.3

 Let academic 

achievement, A, be given by the function 

A = A(1-L, μ), (1) 

where ∂A/∂(1-L) > 0, that is, academic achievement is a positive function of the time a student 

spends in schooling-related activity4

, and μ is a vector of background characteristics including 

the child’s ability and existing knowledge and his family’s socio-economic characteristics, all of 

which may affect his production of academic achievement. There are two decision-makers in 

this model, a selfish child and an altruistic parent.5

 The child’s utility is given by 

Uc = Uc(Cc, A), (2) 

where Cc is the child’s consumption. This utility function is assumed to be strictly concave in Cc

and A. Note that the child’s utility is specified to depend directly on the child’s academic 

achievement. There are several reasons that the child may care about academic achievement. 

First, higher achievement is likely to increase the child’s future income. In this case A could be 

replaced with Y(A) in the utility function, where Y stands for future earnings and Y′(A) > 0. 

However, higher future earnings may not be the only reason the child may value academic 

achievement. Higher academic achievement in college may lead to more desirable future job 

 

3 Including leisure directly in the model would add another endogenous variable and hence 

another simultaneous equation to our empirical analysis. However, we are unable to estimate a 

system of four simultaneous equations where some of the variables are censored and we do not 

have data on leisure time.

4

 Using time-use data on students from one college, Stinebrickner and Stinebrickner (2004) 

found a large positive relationship between study-time and first-year GPA. 

5 There are other possible models where parental altruism is not assumed that could describe 

transfer behavior within families, such as an exchange model (Cox 1987). 
7

characteristics or a better future quality of life. The child may also enjoy some current 

consumption value of a college education. Rather than sort through all these possibilities, we 

leave utility in this general form. Assuming no borrowing against future earnings, the child’s 

budget constraint is given by 

wL + t = Ps(1-L) + Cc, (3) 

where w is the child’s wage, t is the transfer the child receives from the parent, and Ps is the net 

price of full-time schooling.6

The parent’s utility is given by 

Up = Up(Cp, Uc), (4) 

where Cp is the parent’s consumption.7 The parent’s budget constraint is given by 

Mp = Cp + t, (5) 

where Mp is the parent’s income, assumed to be exogenous. 

The parent and child make their decisions independently, given their knowledge about 

the other person’s decision rule. Thus, the child will choose the amount of time he or she spends 

in market work, L, in order to maximize his or her utility, given the parent’s transfer function. 

At the same time, the parent chooses t to maximize his or her utility, given the child’s labor 

 

6

 In reality, some students do take out student loans and incur credit card debt to finance their 

postsecondary education. However, as discussed in the introduction, students may not be able to 

borrow enough to completely cover the cost because they face federally guaranteed student loan 

maximums and high credit card interest rates. Thus, amounts not covered by parental transfers 

would need to be paid by the student through his or her own earnings. However, if one were to 

add loans as a choice variable in the model described here, it would not change the signs of the 

predictions that we test in the empirical analysis. Hence, we abstract from this complexity here. 

One should note, however, that incorporating loans into the theoretical model would require 

adding a loan equation to our simultaneous model and would generate the expected additional 

prediction that students work less when loans increase. However, we are limited in the number 

and types of regressions we can estimate simultaneously in aML. 

7 There are several ways the model could be extended to account for multiple children. A crude 

way would be to redefine Mp as the portion of the parent’s income that is available for this 

particular child and let it be a function of the number of siblings, e.g. Mp = Mp(N), dMp/dN < 0. 
8

supply function. The parent’s transfer function and the child’s labor supply function can then be 

solved to determine the Nash equilibrium, L* and t*. Note that the assumption of simultaneous 

moves by the parent and child makes transfers endogenous to the child’s decisions in this model. 

If the parent were to move first, parental transfers could be treated as an exogenous determinant 

of the child’s hours of work. In the empirical analysis we also estimate a specification where 

transfers are treated as exogenous. 

In order to obtain reaction functions, it is assumed that the academic achievement 

function is given by 

A = k(1-L) + μ, (6) 

where k is a constant greater than zero and the background factors, μ, enter additively. It is also 

assumed that the child’s utility function is Cobb-Douglas and is given by 

Uc(Cc, A) = Cc

α

A1-α, (7) 

where α is a constant between 0 and 1 and measures the relative importance of the child’s current 

consumption. Finally, it is assumed that the parent’s utility function is also Cobb-Douglas and is 

given by 

Up(Cp, Cc, A) = Cp

β

[Cc

α

A1-α]

1-β

, (8) 

where β is a constant between 0 and 1 and measures the relative importance of a parent’s current 

consumption.8

 

Rearranging (3) and substituting into (7) along with (6) gives 

 

8 For the most part using these specific functional forms does not change the predictions of the 

model that we use to motivate our empirical analysis. However, in a model with general 

functional forms and the assumptions of positive and diminishing marginal utility and marginal 

product, one is unable to determine the sign of the effect of the student’s hours of work on 

parental transfers. One is also unable to determine the sign of the effect of the student’s wage on 

his/her hours of work. However, this is also true in the model with specific functional forms, 

unless we make the assumption that the cost of full-time schooling is greater than the parental 

transfer amount. 
9

Uc(L) = [wL + t – Ps(1-L)]α

[k(1-L) + μ]1-α. (9) 

The child chooses L to maximize (9). Rearranging the first order necessary condition for a 

maximum gives the student’s labor supply (reaction) function: 

L = [α(w + Ps)(k + μ) + (1-α)k(Ps – t)]/[k(w + Ps)]. (10) 

For L to be positive, Ps must be greater than t. In other words, a student will work if the cost of 

full-time schooling is greater than the parental transfer. It can be shown that ∂L / ∂t < 0. That is, 

greater parental transfers mean less student labor supplied, all else equal. It can also be shown 

that ∂L / ∂Ps > 0. That is, given parental transfers, an increase in the price of schooling means 

more labor supplied, all else equal. Finally, it can be shown that the sign of ∂L / ∂w is negative.9 

Rearranging (5) and substituting along with the rearranged (3) and (6) into (8) gives 

Up(t) = (Mp – t)β

[(wL + t –Ps(1-L))α(k(1-L) + μ )1-α]

1-β

. (11) 

The parent chooses t to maximize (11) given L. Rearranging the first order necessary condition 

for a maximum gives the parent’s transfer (reaction) function: 

t = [α(1-β)Mp – L(βw + βPs) + βPs] / [α(1-β) + β]. (12) 

It can be shown that ∂t / ∂ Mp > 0, ∂t / ∂L < 0, ∂t / ∂Ps > 0, and ∂t / ∂w < 0. Thus, greater 

parental income leads to greater parental transfers, greater student labor supply leads to lower 

parental transfers, a higher price of schooling leads to greater parental transfers, and a higher 

student wage leads to lower parental transfers. 

Potential Endogeneity of the Net Price of Schooling 

The model we have just described treats the net price of schooling as exogenous. 

However, this variable may be endogenous to the child’s work and parental transfer decisions if 

 

9 This depends on the assumption that the net price of schooling is greater than the parental 

transfer.
10

a higher price of schooling reflects a choice of higher “quality” schooling (which is plausible 

given that a student who is willing to pay more must believe that he or she is getting more) and 

the student chooses how much he or she is willing to pay for quality jointly with his or her 

decision regarding how much to work in the market (perhaps because he or she will need to pay 

for a portion of the price with own earnings). Therefore, we modify the above model to treat the 

price of schooling as endogenous. We assume that a higher price of schooling reflects higher 

quality and that the student just chooses Ps directly along with L. We also assume that a higher 

price of schooling (reflecting higher quality) leads to higher achievement. Thus, the new 

achievement production function is given by 

A = kPs(1-L) + μ. (13) 

In addition to the work and transfer reaction functions, we also derive a net price of schooling 

reaction function for this model. It can be shown for the net price of schooling reaction function 

that, assuming Ps > 0, ∂ Ps / ∂ L > 0, ∂ Ps / ∂ t > 0, and ∂ Ps / ∂ w > 0. That is, an increase in 

student hours of work leads to a higher net price of schooling, an increase in the parental transfer 

increases the net price of schooling, and an increase in the student wage increases the net price of 

schooling. For the work reaction function ∂L / ∂t < 0 and ∂L / ∂w <0 as before. However, the 

sign on ∂L / ∂Ps is no longer positive but ambiguous.10 The relationships among the variables in 

the transfer reaction function retain the same signs as before. 

 

10 If it is instead assumed that the student’s ability positively affects the marginal product of 

schooling-related time, i.e., μ enters the achievement function multiplicatively rather than 

additively, it would give the same key results with one exception. It would allow us to positively 

sign ∂L / ∂Ps as in the model that treats price as exogenous.
11

III. Econometric Model 

We do not estimate a structural model. However, the exogenous price model we 

presented in Section II provides the motivation for testing several hypotheses. First, fewer 

parental transfers lead to an increase in hours worked while in college, all else – including the net 

price of schooling – held constant. Second, an increase in student hours worked leads to reduced 

parental transfers. Third, an increase in the net price of schooling, holding parental transfers and 

everything else constant, leads to an increase in hours worked. Finally, an assumption of the 

model, based on previous empirical evidence, is that an increase in hours worked reduces

student achievement, all else equal. To test these hypotheses, a system of simultaneous 

equations is estimated: 

 t* = γ1h + β1X1 + u1

h* = γ2t + β2 X2 + u2 (14) 

A = γ3h + β3 X3 + u3 , 

and 

t = t* if t* > 0 

t = 0 otherwise (15) 

h = h* if h* > 0 

h = 0 otherwise 

where t* is the latent variable measuring the parent’s desired transfer (it may be negative), t is 

the observed transfer made (it may be zero or positive), h* is the latent variable measuring the 

student’s desired hours of work (which may be negative), h is the observed hours worked (which 

may be zero or positive), A is the student’s GPA, X1, X2, and X3 are vectors of exogenous 

explanatory variables, γ1, γ2, and γ3 are coefficients on the endogenous right-hand-side variables, 
12

and β1, β2, and β3 are the coefficients on the exogenous explanatory variables. The error terms 

u1, u2, and u3 follow a trivariate normal distribution such that: 

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2

13 1 3 23 2 3 3

23 2 3

2

12 1 2 2

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2

1

3

2

1

,

0

0

0

~

ρ σ σ ρ σ σ σ

ρ σ σ σ ρ σ σ

σ ρ σ σ ρ σ σ

N

u

u

u

. (16) 

This model is similar to the simultaneous equations model with latent variables discussed in 

Maddala (1983) but includes an additional equation, the GPA equation. As in Maddala’s model, 

a logical consistency condition, 1 - γ1γ2>0, must hold for the model to be estimable. The model 

is estimated via maximum likelihood using the aML software package.11 

Identification of the endogenous variables in this model requires at least one variable to 

be included in X1 that is not in X2, one variable in X2 that is not in X1, and one variable in X2 that 

is not in X3. To identify parental transfers we use the respondent’s number of siblings as this 

variable should capture the degree to which there is competition for parental resources. A 

similar variable has been used for this purpose by Wolff (2006). We also include parents’ 

income and net worth (and their squares) in the transfer equation but exclude them from the other 

equations. These variables can be excluded from the hours worked equation because, according 

to the theoretical model, parental resources affect the hours worked by the student only through 

their transfer. Wolff (2006), Pabilonia (2001b), and Dustmann et al. (2004) make a similar 

parental income exclusion restriction. All of these variables used to identify parental transfers in 

 

11 Similar to previous studies, this analysis assumes that the decisions whether and where to 

enroll in college have already been made. While one might wish to estimate an enrollment 

probit or ordered probit along with the other three equations estimated here, we are limited in the 

number of equations we are able to jointly estimate. Thus our results may apply only to enrolled 

students.
13

the hours equation are also excluded from the GPA equation as they do not directly enter the 

production function in the theoretical model. 

With respect to identifying hours of work in both the transfer Tobit and GPA regression, 

we include several variables in the work equation that we exclude from the other equations. 

These include the county unemployment rate as a measure of local labor market conditions and 

an indicator for the existence of a state work study program in addition to the federal work study 

program. Wolff (2006) used an unemployment rate variable for similar purposes. We are 

unaware of any other study that uses a state work study program variable to identify hours. 

However, it makes sense to include it because such a program’s existence is exogenous to the 

student’s decisions and represents an additional outlet for student employment. A measure of the 

student wage is also included in the hours of work equation but excluded from the GPA equation 

to identify hours in the GPA equation. The student wage may be excluded from the GPA 

equation because it does not enter into the GPA production function in the theoretical model and 

we already control for student ability in the GPA equation. 

IV. Data 

The primary data used in this analysis come from the NLSY97 geocode file Rounds 1 

through 8. The NLSY97 youth respondents and one of their parents were first surveyed for 

Round 1 between January and October, 1997 or between March and May, 1998. This cohort of 

the NLSY is representative of the non-institutionalized U.S. population aged 12-16 on December 

31, 1996 and included 8,984 youth respondents in the initial round. Subsequently, youth 

respondents were interviewed annually. By Round 8, all respondents were between the ages of 

19 and 25 and thus were old enough to have completed a first term in college. 
14

For those respondents with available college enrollment information, in 1997 only a few 

of the youth respondents had completed a college term (either a semester, trimester, or quarter) 

after finishing high school. By Round 8, 3,520 youths could be identified as having completed at 

least one term in college. Only respondents’ first term college experience is examined in this 

paper in order to obtain the largest sample possible and to also insure that the college term 

dynamics are similar. Thus, a pooled cross-section of students’ first college experiences from 

the fall term of 1996 through the spring term of 2004 is examined. Note that the first term is also 

important for us to analyze because students are more likely to drop out of college in the first 

year (Stratton et al. 2005), and college drop-outs have significantly lower earnings than college 

graduates. However, we will still miss students who drop out before finishing their first term. 

These students are perhaps the poorest performers and thus may be the most negatively affected 

by work. Therefore, excluding them from the analysis potentially biases our results against 

finding a negative effect of work on GPA. This provides additional support for any statistically 

significant negative effects we do find. 

Of those respondents that can be identified as having completed at least one college term, 

we have valid information on our dependent variables for 2,356. When we delete observations 

for respondents with missing information on key independent variables, the sample is reduced to 

2,049 (See Appendix Table A1 for more details). In order to analyze college students separately 

by the type of institution they attended, whether a four-year or a two-year college, we drop an 

additional 18 respondents whose school type cannot be identified. We can identify 1,282 

students who attended colleges that offer a four-year bachelor’s degree or higher and 749 

students who attended colleges that offer at most a two-year degree, using data on colleges and 

universities provided by the U.S. Department of Education’s Integrated Postsecondary Education 
15

Data System (IPEDS).12 A comparison of the characteristics of these students with those from 

the full samples of four-year and two-year students indicates that our reduced sample is 

representative. For example, 43.8 percent of four-year students in our analysis sample are male 

compared to 45.7 percent of students in the full sample. Also, 6.1 percent of four-year students 

in our analysis sample are Hispanic compared to 6.8 percent in the full sample. Similarly, 11.9 

percent of four-year students in the analysis sample are black compared to 11.7 percent of the 

full sample. The results for two-year students are similarly representative. 

Both part-time and full-time students are included in the samples because, in the 

theoretical model, hours spent in schooling-related activity are chosen simultaneously with hours 

spent in market work. In addition, time spent in schooling-related activity is also more 

accurately captured as a continuous variable rather than a dichotomous one. Students have a 

wide range of credit hours for which they can register and can choose to study as much or as 

little as they like. According to the model, if one knows how many hours are spent in market 

work, one also knows how many hours are spent in schooling-related activity. 

Table 1 reports means and standard deviations for the variables used in our analyses. A 

comparison of respondent and family background characteristics for four-year and two-year 

college students highlights some unsurprising significant differences between the two samples. 

Four-year college students are more likely to be non-Hispanics and to have higher high school 

grades and ASVAB test scores than two-year college students. They are also more likely to have 

wealthier parents and parents who have a four-year college degree. 

The three key dependent variables used in our analyses — parents’ transfers to the 

student, the number of hours the student works per week, and the student’s GPA (our measure of 

 

12 IPEDS data are matched to the NLSY97 data using a college identification number (UNITID 

code) available in the geocode version of the NLSY97. 
16

achievement) — are measured as of the first college term. Parental transfers are the dollar value 

of schooling-related parental transfers measured in thousands of 1997 dollars.13 This variable 

comes from a series of questions in the NLSY97 about the sources of financial assistance 

received by the student during the student’s first term in college. Assistance includes financial 

aid received by a youth from parents (both biological parents, his biological mother and 

stepfather, and/or his father and stepmother) that the youth was not expected to repay. Seventytwo percent of four-year college students received a parental transfer in their first term (see Table 

1), $4,180 on average.14

The hours worked variable is the number of hours worked during a specific week during 

the first college term. A mid-term week was chosen because students’ work behavior may be 

different at the beginning and end of terms, when they are either newly searching for a job or are 

completing final examinations. The mid-term week selected depended upon the college term 

system reported and was the first week of February, May, October, or December. Table 2 shows 

the simple relationship between hours worked and parental transfers. On average, fewer hours 

worked is associated with greater average parental transfers. 

GPA is a continuous variable that is measured on a 0.0-4.0 scale. If the respondent selfreported his or her GPA on a different scale, his grade was converted to the 0.0-4.0 scale. 

Because the analysis uses first-term college students who are not yet familiar with the grading 

policies of specific professors and are most likely fulfilling core college requirements, students’ 

choice of courses should not have a great effect upon GPA. On average, four-year college 

students achieved a slightly higher GPA than two-year college students – 3.05 versus 2.90. 

 

13 The Consumer Price Index for All Urban Consumers (CPI-U) was used to convert all monetary 

values into 1997 dollars.

14 This is higher than the average transfer of $2,944 (converted by the authors to 1997 dollars) in 

Oettinger’s (2005) single public university sample; however, our sample includes not only public 

universities and colleges but also private ones that are generally more expensive. 
17

Table 3 shows the simple relationship between GPA and hours worked. Four-year college 

students who worked 20 hours or less had a slightly higher GPA on average than students who 

did not work. Those who worked more than 20 hours per week had the lowest GPA on average. 

However, two-year college students who worked any number of hours, including more than 20, 

earned higher GPAs than students who did not work, but those who worked 20 or fewer hours 

per week had the highest GPAs. 

The explanatory variables used in this analysis come from the NLSY97 and other data 

sources which have been matched to the NLSY97 using the state or county where the college 

was located and the college identification variable. A key explanatory variable is the net price of 

schooling (and its square). This variable is defined to be tuition and fees minus grants, tuition or 

fee waivers or reductions, fellowships, and scholarships for the first college term in which the 

student was enrolled, and it is measured in 1997 dollars. Information on tuition and fees for fulltime, full-year students at each institution comes from IPEDS. Per-term price is constructed by 

taking the standard tuition and fees for full-time full-year students and dividing by the relevant 

number of terms for each institution. It does not depend on a respondent’s actual credit hours 

and so is not endogenous to his or her schooling or work decisions. However, it is adjusted 

based on whether or not the student respondent was attending college in-state or out-of-state. 

The dollar value of grants, tuition or fee waivers or reductions, fellowships, and scholarships 

received by students used to construct this net price variable is created from the NLSY97 youths’ 

responses to the same series of questions as the parental transfer variable. The amount of this 

financial aid is subtracted from the per-term price to obtain the per-term net price of schooling 

variable used in the analysis. 
18

Other key explanatory variables are parents’ income and net worth (and their squares) as 

measured in 1996. Many respondents had missing values for these parental financial variables. 

Therefore, missing values are recorded as zeros and missing data indicator dummy variables for 

parents’ income and net worth are included in the regression analysis. Another variable intended 

to measure parental resources available to the respondent is the respondent’s number of siblings 

from Round 1 of the NLSY97. There is some concern that the number of siblings is potentially 

endogenous, as parents may trade off the quality and quantity of children (Becker & Tomes 

1976). However, given the length of time between birth and postsecondary attendance and the 

uncertain nature of financial aid awards over such long time horizons, this concern appears to be 

minimized.

Missing values are an even bigger problem for the respondent’s wage, as wage 

information is missing for most respondents in the NLSY97. Therefore, the minimum wage is 

used as a proxy for the student’s wage. The legal minimum wage is defined as the maximum of 

the state and federal minimum wages. The minimum wage seems to be an appropriate proxy for 

the student wage because most of the jobs students hold while attending college are temporary 

and require a low level of skills, i.e., jobs likely to pay the minimum wage or a wage correlated 

with the minimum wage (Wolff 2006, Dustmann et al. 1997). 

We include several other variables that are expected to affect work hours. One of these is 

the unemployment rate in the county where the student attended college, which was obtained 

from the Bureau of Labor Statistics’ Local Area Unemployment Statistics (LAUS) program. We 

also include an indicator for whether or not there was a state-supported work study program in 

the state where the respondent attended college over the period 1996-2004, the period covered by 

these data. A state work study program is a program similar to the federal work study program 
19

that subsidizes the employment of needy college students but is funded by the state rather than 

the federal government. This variable is constructed using historical information on state work 

study programs collected by the authors directly from the relevant state agencies. Seventeen 

states had a state-supported work study through most of the period under study.15 

Additional personal background variables such as age on December 31, 1996, whether or 

not the respondent is Hispanic, race (black and other nonwhite, with white as the omitted 

variable), mother’s highest level of education as of 1997, father’s highest level of education as of 

1997, the respondent’s high school grades, and the respondent’s ASVAB percentile score, are 

included in all equations to control for heterogeneous preferences and productivity in producing 

academic achievement. 16 

V. Results 

In Table 4, we present coefficient estimates and standard errors from our simultaneous 

model for key variables, separately for four-year and two-year college students.17 A likelihood 

ratio (LR) test confirms that the coefficients for these two groups are, indeed, significantly 

different. With respect to the financial motivation variables we find that increased parental 

transfers reduce the hours worked per week, although the effect is statistically significant only 

for four-year students. This may be because the higher cost of four-year institutions on average 

 

15 These states include California, Colorado, Florida, Idaho, Indiana, Iowa, Kansas, Kentucky, 

Michigan, Minnesota, Nevada, New Mexico, Ohio, Pennsylvania, Rhode Island, Utah, and 

Washington. California discontinued their program in August 2003 and Rhode Island and Iowa 

in July 2001. Iowa’s program was restarted in June 2005.

16 High school grades are self-reported and measured on a 8.0 scale with 1.0 being mostly below 

D’s and 8.0 being mostly A’s. ASVAB scores are a composite measure of math and verbal 

aptitude percentile score constructed by NLSY97 staff from the computer adaptive form of the 

Armed Services Vocation Aptitude Battery. This composite measure is similar to the AFQT 

score. 

17 Full coefficient estimates are available from the authors upon request.
20

may make four-year students rely more on parental transfers. The estimated negative coefficients 

representing the effect of hours worked on transfers are not statistically significant for either the 

four-year or two-year students, suggesting that parents’ transfer decisions do not depend on how 

much their children work in the market. We also find that the net price of schooling positively 

affects parental transfers and is statistically significant for both four-year and two-year students, 

suggesting that parents will provide more support for a child’s schooling the more it costs. Note 

that this positive effect is increasing in the price of schooling for four-year students but 

decreasing for two-year students. Again this may reflect the fact that four-year students are more 

reliant on their parents to fund their schooling than two-year students. The net price of schooling 

also has a positive effect on student work hours, although the effect is statistically significant 

only for two-year students. In fact, the effect on work hours is increasing in the net price of 

schooling for two-year students. Thus, it appears that students in four-year programs are more 

reliant on parents’ transfers and two-year students are more reliant on their own earnings to 

obtain funding. Together, these results suggest that financial considerations do play a role in 

how much students work, although differently for four-year and two-year students. 

With respect to the question of whether this work is harmful to students’ achievement, we 

find the expected significant negative effect of hours worked per week on first term GPA for 

both four-year and two-year college students, consistent with the results of single university 

studies (Stinebrickner and Stinebricker 2003, Oettinger 2005). Four-year college students who 

increase their hours of work by 15.20 hours (a one standard deviation increase) have on average 

a 0.18 lower GPA, a result similar to that found by Oettinger (2005), but one much smaller than 

that found by Stinebrickner and Stinebrickner (2003). Two-year college students who increase 

their hours of work by 19.78 hours have on average a 0.53 lower GPA. Single equation 
21

estimates (available from the authors) do not show this significant negative effect, emphasizing 

the importance of controlling for the endogeneity of hours worked. 

For both the four-year and two-year samples, the correlation between the unobserved 

determinants of hours worked and GPA, ρWG, is estimated to be positive and significant at the 

1% level, indicating that there is some unobserved variable, perhaps student motivation, that 

affects both the number of hours a student works and the student’s GPA in the same way. These 

statistically significant correlation coefficients provide support for our joint estimation of the 

hours worked and GPA equations. However, a likelihood ratio test that compares the 

simultaneous model and a restricted model where all of the correlation coefficients are 

constrained to be equal to zero results in marginal p-values of 0.18 for the four-year sample and 

0.25 for the two-year sample. Indeed, a likelihood ratio test comparing the fully simultaneous 

model to one where ρTG and ρTW and the coefficient on hours in the transfer equation are 

constrained to be equal to zero does not reject the simpler model with p-values of 0.39 for fouryear students and 0.29 for two-year students. These results taken together suggest that perhaps 

the transfer Tobit need not have been estimated jointly with the work and GPA equations (i.e. 

transfers could be treated as an exogenous determinant of hours of work), although work and 

GPA should be estimated jointly. However, the key results do not change between the models 

where transfers are treated as exogenous and where they are treated as endogenous. Therefore, 

we present the fully simultaneous results.18 

We now discuss our exclusion restrictions. As expected, both parental income and net 

worth have highly significant positive effects on parental transfers for four-year college students 

and parental income has a significant positive effect on parental transfers for two-year college 

students. These results are not surprising as wealthier parents can afford and thus may be willing 

 

18 The logical consistency condition for this model, 1 - γ1γ2>0, is satisfied.
22

to pay more for their children’s education. The significance of these effects is important given 

that these parental financial variables help identify parental transfers in the hours worked 

equation.19 We have also excluded them from the GPA equation as parents’ financial status does 

not enter the achievement production function in the theoretical model. However, as a 

robustness check, we also estimated a specification that includes income and net worth in the 

GPA equation because it is possible that students from wealthier families may have unobserved 

characteristics that affect academic outcomes, even after we control for parental education and 

student grades and test scores. These variables were insignificant in the GPA equation and our 

key results remained the same. 

The county unemployment rate helps to identify hours worked in both the transfer and 

GPA equations and is a negative and statistically significant determinant of hours worked in the 

two-year sample. Unfortunately, however, it is not statistically significant for the four-year 

sample. The net price of schooling also helps to identify hours worked in the GPA equation.20 

This variable is also significant and positive for the two-year sample only. Another variable that 

was included to identify hours in both the transfer and GPA equations, the existence of a state 

work study program in the state in which the student is in college, is not statistically significant 

for either sample.21 Finally, the minimum wage, a variable intended to proxy for the student 

 

19 The number of siblings in the household is also used to help identify parental transfers in the 

hours of work equation, although it is not statistically significant.

20 As a robustness check, we estimated a specification with the net price of schooling included in 

the GPA equation. This was done because the net price of schooling may reflect the quality of 

the institution attended and this quality may affect student GPAs. However, the net price of 

schooling was statistically insignificant in the GPA equation and the other key results remained 

unchanged. 

21 We also estimated a version of this simultaneous model where household siblings and the state 

work study program indicator are included in all equations rather than excluded from their 

respective transfer and work equations. This was done because one might challenge the former 

variable as previously discussed while the latter variable has not been used by other researchers. 
23

wage, should also have helped to identify hours of work in the GPA equation, but it is not a 

statistically significant determinant of hours of work for either four-year or two-year college 

students.22 A test for the joint significance of all four of these variables in the hours of work 

equation indicates joint significance for the two-year sample only. However, the county 

unemployment rate and net price of schooling variables are jointly significant in the hours of 

work equation with a p-value of 0.10 for the four-year sample. 

In Table 5 we present key marginal effects based on the coefficient estimates in Table 4. 

These are calculated for a one unit change in the explanatory variable for each observation and 

then averaged over all observations. However, it may be more illustrative to focus on standard 

deviation changes in the explanatory variables. Therefore, for the remainder of the discussion, 

the marginal effects for one unit changes presented in the table are multiplied by one standard 

deviation of the relevant explanatory variable. 

With respect to the magnitude of the negative effect of parental transfers on a four-year 

college student’s hours of work, a reduction of $5,000 in parental transfers results in the student 

working almost three hours more per week. This is a small effect, as the earnings from the three 

additional hours worked per week would be less than $5,000 at typical college student wages. 

With respect to the magnitude of the positive effect of the net price of schooling on 

parental transfers, an increase of $4,260 in the net price of schooling for four-year college 

students increases their parental transfers by $988.32, and a $1,380 increase in the net price of 

 

In the model using two-year college students, the effect of hours on GPA was insignificant and 

work study had a positive significant effect on GPA. 

22 Curiously, the estimated effect of the state minimum wage on parental transfers is positive and 

statistically significant, rather than negative, as expected. It is possible, however, that even 

though we intended for this variable to proxy for the student wage, it is capturing something else, 

perhaps economic conditions or general state support for youth.
24

schooling for two-year college students increases their parental transfers by $51.06.23 With 

respect to the positive effect of the net price of schooling on hours worked per week by two-year 

college students, a one standard deviation increase in the net price of schooling increases their 

hours worked per week by over two and a half hours. Similar to the effect of a reduction in 

parental transfers, the effect of an increase in the net price of schooling on these students’ hours 

of work are small; earnings from the increased hours would cover only a small portion of the 

increased cost. All of these results taken together imply that, while students’ work behavior is 

influenced by financial motivations, the effects are small. 

VI. Sensitivity Analyses 

We perform several sensitivity analyses to determine whether or not our results are robust 

to alternative specifications. One concern is that our model does not allow for the possibility that 

students can cover some of their schooling costs by taking out loans instead of using current 

earnings. Therefore, in Tables 6 and 7 we examine whether accounting for loans affects our key 

results. While ideally we would like to add a loan regression to our model, we are limited in the 

number and types of regressions we can estimate simultaneously in aML. Therefore, we account 

for a student’s loans per term by subtracting them from the current net price of schooling 

variable to obtain an alternative that reflects the cost of schooling that must be paid now rather 

than later. For our key estimates, whether or not we subtract loans from the net price of 

schooling does not affect our results.24 

 

23 The marginal effect for the net price of schooling accounts for both the linear and the squared 

term. 

24 Students were asked to provide information on loans from relatives and friends as well as 

government-subsidized and other types of loans. We include all types of loans. 
25

Another sensitivity analysis we perform is to estimate our model for full-time students 

only.25 We do this because full-time and part-time students may behave differently in response 

to various personal and household characteristics and constraints. However, we caution the 

reader that, because of the time constraint, hours of schooling (and thus full-time student status) 

is an endogenous variable in our model. As for our main results, we provide estimates separately 

for four-year, full-time students and two-year, full-time students. For four-year, full-time 

students, the effect of transfers on hours of work is negative and significant as it is for all fouryear students, suggesting that a reduction in parental transfers causes these students to work 

more in the market. The effect of hours on transfers is also negative and significant as predicted 

by the model. Note, however, that this is the only specification where we find empirical support 

for this prediction of the theoretical model. The four-year, full-time student specification also 

gives support for the joint estimation of the transfers and work tobits, as ρTW is significant and 

positive. As for all four-year students, the net price of schooling positively affects parental 

transfers and the effect of hours on GPA is estimated to be negative and statistically significant. 

For two-year, full-time students, as for all two-year students, the estimated coefficients for hours 

worked in the transfer equation and transfers in the hours worked equation are both statistically 

insignificant, suggesting no relationship between parental transfers and work hours for these 

students. As for all two-year students, the effect of hours worked on GPA is negative and 

significant. 

Finally, in Tables 8 and 9 we consider a model in which the net price of schooling is 

treated as endogenous. In this model, we estimate an equation for the net price of schooling 

jointly with the hours worked and GPA equations. In this model, the net price of schooling now 

 

25 There were two few students (279) who attended school part-time to provide separate 

estimates.
26

enters the GPA equation. We do not estimate a transfer equation (and thus treat transfers as 

exogenous) because, as previously discussed, our likelihood ratio tests suggest it may be 

unnecessary to estimate transfers jointly with hours of work and GPA. In addition, estimating 

four equations jointly in aML is not possible. However, it may be the case that transfers should 

be jointly estimated with the price of schooling and we are ignoring this quite plausible 

possibility. Hence, we need to treat these results with caution. In order to identify the effect of 

the net price of schooling in the hours of work and GPA equations, we include the average instate tuition for public four-year institutions and the average state grant per 18-24 year old (in the 

state where the respondent’s high school was located) in the net price of schooling equation but 

not in the other equations. These variables were obtained from the Digest of Education Statistics 

and converted to 1997 dollars (U.S. Department of Education 2007). Both variables are highly 

significant in the net price of schooling equation, suggesting their value for identification. 

Similar to our preferred specification, transfers negatively affect a student’s hours of work. 

However, unlike the preferred specification, the net price of schooling does not affect the hours 

worked by two-year students and students’ hours of work have no statistically significant effect 

on their GPA in this endogenous price specification. Note, however, that the negative and 

statistically significant estimate of ρPW for two-year students indicates that there is some 

unobserved factor that is positively associated with the net price of schooling and negatively 

associated with a student’s hours of work. Perhaps this unobserved factor is the quality of the 

institution attended. Higher quality schools cost more and, because they are more academically 

demanding, their students may work less in the market. It is possible that treating the net price of 

schooling as exogenous biased the effect of working while in school on GPA downward, at least 

for two-year students. 
27

VII. Conclusion 

Student work is often proposed as a means of financing a student’s postsecondary 

education, and sometimes it is subsidized via state and federal work study programs. Thus it is 

important that we analyze the motivations for and the effects of such employment. In this paper, 

we motivate and estimate a model using data from the NLSY97, a nationally representative 

survey, that treats parental transfers, student work hours, and student GPA as simultaneously 

determined and that accounts for censoring in parental transfers and work hours. We test several 

hypotheses regarding the financial motives for and academic effects of college student 

employment and find empirical support for the hypothesis that a decrease in parental transfers 

increases the work hours of four-year college students. We also find that an increase in the net 

price of schooling increases the number of hours worked by both four-year and two-year college 

students. Finally, we find that an increase in hours worked negatively affects students’ grades, 

with larger negative effects for two-year students than four-year students. This result is 

important, as it is the first using data from a large, nationally representative survey to find a 

detrimental effect on student grades of working while in college. However, this result is not 

robust to all alternative specifications. In addition, our results are based only on the first-term 

experiences of college students and we focus on only one measure of academic performance. 

More research that explores student experiences beyond the first term as well as other measures 

of academic performance is needed. 
28

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31

Table 1. Sample Means and Standard Deviations 

 Four-Year Students

(N = 1,282) 

Two-Year Students 

(N = 749) 

Variables Mean S.D. Mean S.D. 

Worked .46 .72 

Hours of work 10.08 15.20 21.86 19.78 

Hours of work (conditional on working) 21.98 14.68 30.39 15.51 

College GPA 3.05 0.72 2.90 0.91 

Parental transfer received .72 .50 

Parental transfer (in 1,000s) 3.01 4.99 .50 1.08 

Parental transfer (conditional on any) (in 

1,000s) 

4.18 5.28 0.99 1.27 

Age on December 31, 1996 14.43 1.33 14.45 1.40 

Male .44 .45 

Hispanic .06 .15 

Black .12 .12 

Other race (nonwhite) .01 .02 

High school grades (0-8 scale) 6.76 1.25 5.83 1.44 

ASVAB 63.13 32.70 43.77 32.61 

ASVAB scores missing .12 .17 

Net price of schooling (in 1,000s) .56 4.26 .41 1.38 

Net price of schooling minus loans1

 (in 

1,000s) 

-.85 5.77 .13 1.76 

Mother’s education missing .22 .28 

Mother high school degree .33 .39 

Mother 4 year degree .33 .15 

Father’s education missing .15 .18 

Father high school degree .26 .28 

Father 4 year degree .34 .16 

Parents’ income missing .08 .10 

Parents’ income (in 10,000s) 5.98 5.97 4.35 4.56 

Parents’ net worth missing .26 .27 

Parents’ net worth (in 10,000s) 22.39 59.50 13.24 46.86 

Number of siblings 1.40 1.20 1.48 1.27 

State work study program .43 .44 

County unemployment rate 4.14 1.52 4.90 2.09 

State minimum wage 4.88 0.37 4.96 0.49 

Note: Survey weights used. 

1

 This may be negative because we do not include room and board in the price of schooling and 

loans may cover some of these expenses. 
32

Table 2. Parental Transfers, by Type of College and Hours Worked

Four-Year College Students Two-Year College Students 

 Hours worked Hours worked 

 Not 

Working 1-20 More than 20 

 Not 

Working 

 

1-20 More than 20

Average 

Parental 

Transfer 

(in 1,000s) 

3.59 

(5.58) 

2.64 

(3.81) 

1.89 

(4.37) 

 .52 

(1.10) 

.69 

(1.56) 

.40 

(0.77) 

Number of 

Observations 

699 323 260 227 157 365 

Note: Standard deviations are in parentheses. Survey weights used. 

Table 3. Grade Point Average, by Type of College and Hours Worked

Four-Year College Students Two-Year College Students 

 Hours worked Hours worked 

 Not 

Working 1-20 More than 20 

 Not 

Working 

 

1-20 More than 20

Average 

College GPA 

3.04 

(0.74) 

3.13 

(0.64) 

2.95 

(0.76) 

 2.82 

(1.04) 

2.93 

(0.78) 

2.94 

(0.90) 

Number of 

Observations 

699 323 260 227 157 365 

Note: Standard deviations are in parentheses. Survey weights used.
33

Table 4. Maximum Likelihood Estimates of the Simultaneous Tobit Model for the Relationship between 

First-term Parental Transfers, Hours Worked, and GPA for College Students 

 Four-Year Students Two-Year Students 

Dependent Variable: Parental Transfers (in 1,000s) 

Hours worked per week -0.005 -0.018 

(0.033) (0.017) 

Net price of schooling (in 1,000s) 0.364*** 0.119* 

(0.037) (0.068) 

Net price of schooling squared 0.008*** -0.035** 

(0.001) (0.016) 

Parents’ income (in 10,000s) 0.449*** 0.152*** 

(0.119) (0.056) 

Parents’ income squared -0.011*** -0.006** 

(0.004) (0.003) 

Parents’ net worth (in 10,000s) 0.055*** 0.015 

(0.017) (0.010) 

Parents’ net worth squared -0.000*** -0.000 

(0.000) (0.000) 

Number of siblings -0.197 -0.059 

(0.149) (0.059) 

State minimum wage 1.013** 0.098 

(0.478) (0.164) 

Dependent Variable: Hours Worked Per Week 

Transfers -1.186*** -0.058 

(0.521) (2.495) 

Net price of schooling (in 1,000s) 0.290 2.237*** 

(0.311) (0.712) 

Net price of schooling squared -0.018 0.313* 

(0.025) (0.184) 

State work study program available 0.620 -1.850 

( 1.724) (1.784) 

County unemployment rate -0.670 -1.103** 

 (0.565) (0.481) 

State minimum wage 1.786 0.117 

(2.477) (1.875) 

Dependent Variable : GPA (4 point scale) 

Hours worked per week -0.012*** -0.026*** 

(0.004) (0.005) 

Coefficients of correlation ρ 

ρTW 0.072 0.123 

(0.141) (0.248) 

ρTG -0.047 0.089 

(0.045) (0.137) 

ρWG 0.302*** 0.606*** 

(0.079) (0.060) 

Log-likelihood -7,333.36 -4,362.85 

Number of Observations 1,282 749 

Notes: Significance levels: * = p<.10;**=p<.05;***=p<.01. Standard errors are in parentheses. Each equation also 

includes age, gender, race/ethnicity, high school grades, ASVAB percentile scores, parents’ education levels, and an 

intercept, and missing variable indicators. The transfer and hours worked equation also include the state minimum 

wage rate. 
34

Table 5. Marginal Effects for Key Variables in Simultaneous Equations Model

Four-Year College Students Two-Year College Students 

Independent Variables 

Dependent Variable: 

Parental Transfers (in 

1,000s) 

Dependent 

Variable: 

Hours Worked 

Per Week 

 Dependent Variable: 

Parental Transfers 

(in 1,000s) 

Dependent 

Variable: 

Hours Worked 

Per Week 

Hours Worked 

Per Week 

-0.003 

(0.00003) 

 -0.008 

(0.0001) 

 

Parental Transfers 

(in 1,000s) 

 -0.567 

(0.004) 

 -0.044 

(0.002) 

 

Net Price of Schooling 

(in 1,000s) 

0.232 

(0.003) 

0.132 

(0.002) 

 0.037 

(0.001) 

1.870 

(0.024) 

Number of 

Observations 1,282 749 

Notes: Marginal effects are calculated for each observation using the unconditional expected value and then 

averaging across observations. Bootstrapped standard errors are in parentheses. 
35

Table 6. Maximum Likelihood Estimates of the Simultaneous Equations Model for the Relationship between 

First-term Parental Transfers, Hours Worked, and GPA for College Students 

 I. Accounting for Loans II. Full-time Students 

Four-Year 

Students 

Two-Year 

Students 

Four-Year 

Students 

Two-Year 

Students 

Dependent Variable: Parental Transfers (in 1,000s) 

Hours worked per week 0.008 -0.022 -0.060* 0.000 

(0.033) (0.017) (0.035) (0.022) 

Net price of schooling (in 1,000s) 0.290*** 0.091* 0.386*** 0.127 

(0.033) (0.056) (0.036) (0.088) 

Net price of schooling squared 0.006*** 0.027*** 0.009*** -0.036* 

(0.001) (0.006) (0.001) (0.021) 

Parents’ income (in 10,000s) 0.488*** 0.154*** 0.419*** 0.106 

(0.122) (0.054) (0.115) (0.073) 

Parents’ income squared -0.012*** -0.006** -0.010** -0.003 

(0.004) (0.003) (0.004) (0.004) 

Parents’ net worth (in 10,000s) 0.049*** 0.015 0.052*** 0.024* 

(0.017) (0.010) (0.016) (0.013) 

Parents’ net worth squared -0.000*** -0.000 -0.000*** -0.000 

(0.000) (0.000) (0.000) (0.000) 

Number of siblings -0.225 -0.057 -0.226* -0.049 

(0.152) (0.059) (0.141) (0.078) 

State minimum wage 1.016** 0.153 0.991** 0.199 

(0.481) (0.158) (0.464) (0.215) 

Dependent Variable: Hours Worked Per Week 

Parental Transfers (in 1,000s) -0.994* -0.023 -1.547*** 1.783 

(0.513) (2.559) (0.541) (2.758) 

Net price of schooling (in 1,000s) 0.001 2.053*** 0.480 2.531** 

(0.295) (0.675) (0.313) (0.930) 

Net price of schooling squared -0.013 0.151 -0.016 0.477* 

(0.017) (0.168) (0.025) (0.299) 

State work study program available 0.591 -1.824 0.028 -1.045 

(1.730) (1.753) (1.574) (3.645) 

County unemployment rate -0.709 -1.140** -0.499 -0.768 

(0.570) (0.480) (0.522) (0.538) 

State minimum wage 1.602 -0.434 2.531 -2.828 

(2.479) (1.853) (2.362) (2.353) 

Dependent Variable : GPA (4 point scale) 

Hours worked per week -0.012*** -0.027*** -0.017*** -0.022*** 

(0.004) (0.005) (0.003) (0.006) 

Coefficients of correlation ρ 

ρTW 0.002 0.157 0.301** -0.177 

(0.138) (0.239) (0.148) (0.270) 

ρTG -0.056 0.100 -0.001 -0.031 

(0.045) (0.134) (0.053) (0.135) 

ρWG 0.307*** 0.622*** 0.416*** 0.523*** 

(0.079) (0.056) (0.059) (0.092) 

Log-likelihood -7,342.62 -4,360.28 -10,349.06 -1,604.40 

Number of Observations 1,282 749 1,234 542 

Notes: Significance levels: * = p<.10;**=p<.05;***=p<.01. Standard errors are in parentheses. Each equation also 

includes age, gender, race/ethnicity, high school grades, ASVAB percentile scores, parents’ education levels, and an 

intercept, and missing variable indicators. The transfer and hours worked equation also include the state minimum 

wage rate. In Specification I, we subtract loans from the net price of schooling variable. 
36

Table 7. Marginal Effects for Key Variables in Simultaneous Equations Models

I. Accounting for Loans II. Full-time 

Four-Year Students Two-Year Students Four-year Students Two-Year Students 

Dependent Variable : Parental Transfers (in 1,000s) 

Hours Worked 

Per week 

0.005 

(0.00003) 

-0.009 

(0.0001) 

-0.037 

(0.0004) 

0.0001 

(0.0000) 

Net Price of 

Schooling (in 

1,000s) 

0.191 

(0.002) 

0.050 

(0.001) 

0.251 

(0.003) 

0.044 

(0.001) 

Dependent Variable: Hours Worked Per Week 

Parental 

Transfers (in 

1,000s) 

-0.475 

(0.003) 

-0.017 

(0.0001) 

-0.741 

(0.005) 

1.311 

(0.008) 

Net Price of 

Schooling (in 

1,000s) 

-0.003 

(0.001) 

1.653 

(0.014) 

0.224 

(0.002) 

2.021 

(0.047) 

Number of 

Observations 

1,282 749 1,234 542 

Notes: Marginal effects are calculated for each observation using the unconditional expected value and then 

averaging across observations. Bootstrapped standard errors are in parentheses. 
37

Table 8. Maximum Likelihood Estimates of the Simultaneous Equations Model for the Relationship between 

First-term Net Price of Schooling, Hours Worked, and GPA for College Students 

 Four-Year Students Two-Year Students 

Dependent Variable: Net price of Schooling (in 1,000s)

Hours worked per week -0.015 0.028*** 

(0.016) (0.008) 

Parental Transfers (in 1,000s) 0.163*** 0.045 

 (0.011) (0.050) 

Avg. in-state tuition for public 4-year institutions (in 

1,000s) 0.342*** 0.226*** 

(0.072) (0.037) 

Average state grant per 18-24 year old 0.002** 0.002*** 

(0.001) (0.0004) 

State minimum wage -0.119 -0.367*** 

(0.207) (0.086) 

Dependent Variable: Hours Worked Per Week

Parental Transfers (in 1,000s) -1.650*** -3.509*** 

(0.567) (1.269) 

Net price of schooling (in 1,000s) 1.298 -2.110 

(2.523) (4.107) 

State work study program available 1.840 -5.931** 

(2.342) (2.298) 

County unemployment rate -0.685 -1.185 

(0.814) (0.752) 

State minimum wage 3.249 -1.617 

(3.484) (3.090) 

Dependent Variable : GPA (4 point scale)

Hours worked per week 0.0001 0.002 

(0.002) (0.003) 

Net price of schooling (in 1,000s) -0.005 -0.048 

(0.006) (0.037) 

Coefficients of correlation ρ 

ρPW 0.045 -0.525*** 

(0.219) (0.147) 

ρPG 0.076 0.013 

(0.048) (0.069) 

ρWG 0.001 0.063 

(0.064) (0.085) 

Log-likelihood -4,936.30 -3,351.73 

Number of Observations 1,282 749 

Notes: Significance levels: * = p<.10;**=p<.05;***=p<.01. Standard errors are in parentheses. Each equation also 

includes age, gender, race/ethnicity, high school grades, ASVAB percentile scores, parents’ education levels, and an 

intercept, and missing variable indicators. 
38

Table 9. Marginal Effects for Key Variables in Simultaneous Equations Model for the 

Relationship between First-term Net Price of Schooling, Hours Worked, and GPA for 

College Students

Four-Year Students Two-Year Students 

Independent Variables 

Dependent Variable: 

Net Price of 

Schooling (in 1,000s) 

Dependent 

Variable: 

Hours Worked 

Per Week 

 Dependent Variable: 

Net Price of 

Schooling (in 

1,000s) 

Dependent 

Variable: 

Hours Worked 

Per Week 

Hours Worked 

Per Week 

-0.013 

(0.00004) 

 0.021 

(0.0002) 

 

Parental Transfers 

(in 1,000s) 

0.137 

(0.0005) 

-0.744 

(0.006) 

 0.035 

(0.0003) 

-1.680 

(0.007) 

 

Net Price of Schooling 

(in 1,000s) 

 0.585 

(0.005) 

 -2.793 

(0.011) 

Number of 

Observations 1,282 749 

Notes: Marginal effects are calculated for each observation using the unconditional expected value and then 

averaging across observations. Bootstrapped standard errors are in parentheses. 
39

Appendix 

Table A1. Sample Size 

NLSY97 Round1 8,894 

Completed academic year college term (i.e. not including 

summer classes) 

3,520 

College GPA 2,614 

Parental transfer data 2,396 

Hours worked data 2,356 

Valid net price of schooling data 2,086 

Number of siblings 2,069 

High school grades 2,050 

Matchable county code for colleges 2,049 

Sample in four-year college 1,282 

Sample in two-year college 749 

Sample of four-year, full-time students 1,234 

Sample of two-year, full-time students 542 

Note: Sample constructed using stepwise deletion.