155 B.J. Surette Economics of Education Review 20 2001 151–163
modest: it documents the fact that women differ from men in their college attendance and transfer decisions
and attempts to discover what factors can and cannot explain these differences.
3. A bit of economic theory
The decision to transfer is the result of a two-step pro- cess: the decision to attend two-year college initially, and
the subsequent decision to attend a four-year college. This paper focuses on what causes women and men to
differ at the second step. Properly addressing this ques- tion, however, also requires an examination of whether
and how women and men differ at the first step. Thus, the first part of this section models the decision to attend
both types of college among all high school graduates. The dependent variables are whether or not an individual
attended two-year college and whether or not an individ- ual attended four-year college in each year. The second
part of this section models the decision to transfer. “Transfer” is defined by whether or not an individual
who attended a two-year college ever subsequently attends a four-year college.
3.1. The attendance models The standard human capital model teaches that college
attendance decisions depend primarily on whether the discounted value of the premium college-educated work-
ers earn compared to high school graduates exceeds the costs of attendance. The model must therefore include
direct costs of college such as two-year and four-year tuition, opportunity costs such as foregone earnings, and
the wage premium college-educated workers earn. Attendance decisions will also be driven by demographic
and other characteristics that reflect preferences or econ- omic circumstances, such as marital status, having young
children, and accumulated human capital. The attendance model is estimated on the entire panel of high school
graduates who have not completed a bachelor’s degree.
11
The first and third columns in Table 1 summarize the set of variables used to model the decision to attend each
type of college. So what can explain women’s higher two-year attend-
ance rates and lower transfer rates? We postulate first that women are more likely to select two-year colleges
for practical reasons related to their child-rearing responsibilities, marital status, and economic circum-
11
Individuals can attend college in multiple years, and the panel nature of this model reflects that. Standard errors are cal-
culated using the Huber–White method to avoid overstating the model’s predictive power. Once an individual completes a bach-
elor’s degree she is excluded from the models.
stances, and that such factors explain both their higher two-year attendance rates and their lower transfer rates.
Two-year colleges generally offer more flexible class schedules and lower costs than four-year colleges. As
women tend to bear a disproportionate share of home- maker and child-rearing responsibilities, two-year col-
leges may appeal more to married women or mothers than to men. We therefore expect being married and hav-
ing young children to reduce college attendance more for women than for men, and for these two variables to have
larger negative effects on four-year than on two-year attendance. Gender differences in the monetary costs and
benefits of attendance could also explain differences in attendance patterns. If true, we expect women to be more
sensitive than men to tuition, opportunity costs, and the college wage premium.
The geographic proximity of college may be another practical determinant of attendance. Rouse 1995, for
example, finds a negative correlation between attendance and distance to the nearest two- and four-year college.
If the effects of proximity on transferring differ by gen- der—for example, if women are less likely to have
access to transportation—the female coefficients may capture these effects in the estimated models. Unfortu-
nately, the NLSY does not contain high-quality infor- mation on proximity to college. However, the data do
contain information about whether each individual lives in a predominantly urban county. Moreover, colleges
tend to be located in or near urban areas. Our urban indi- cator may therefore serve as a proxy for proximity to
college.
12
If proximity explains the observed gender dif- ferences in attendance patterns, one expects living in an
urban area to have a larger, positive effect on women’s than men’s attendance decisions.
Another explanation advanced for women’s lower transfer rates is that at the margin women who attend
college are of lower ability than men, with the most mar- ginal attending and remaining at two-year colleges.
This explanation is suggested by the fact that a larger percentage of female high school graduates attend col-
lege: if the distributions of educational ability of high school graduates are the same across gender, the mar-
ginal women may be of lower educational ability than their male counterparts. We control for student ability
and intelligence using the Armed Forces Qualification Test AFQT score, a measure like IQ. If more marginal
women attend two-year college than men, we expect the effects of the AFQT on attendance to be smaller for
women than for men.
12
The urban indicator probably also captures the effects of other factors related to living in an urban area—for example,
local attitudes toward education. Readers should keep in mind such alternative interpretations of the coefficient on the urban
indicator.
156 B.J. Surette Economics of Education Review 20 2001 151–163
A final plausible explanation for women’s higher two- year attendance rates and lower transfer rates is that
women simply prefer two-year colleges to four-year col- leges for reasons that are not directly observable. For
example, it is well known that women and men tend to end up in different occupations. If the female-dominated
occupations require training at two-year colleges, and the male-dominated occupations require training at four-year
colleges, one might expect women and men to make dif- ferent schooling decisions. We would like to control for
such preferences to test this explanation, but we cannot directly observe them. However, one may be able to infer
them based on post-schooling occupational outcomes. We estimate the attendance model both with and without
proxies for these preferences. Specifically, we model schooling decisions using indicator variables for whether
or not an individual ever works in several specific female-dominated occupations.
13
3.2. The transfer model The attendance model provides partial information
about whether and why women and men differ in their college attendance patterns and their rates of transfer to
four-year college. This section models the decision to transfer directly. We define “transfers” as individuals
who have attended two-year college at some point in their lives and have subsequently attended a four-year
college. Only the last observation of each individual is used in this model: for an individual observed in all 12
waves of the NLSY, she is a “transfer” if she attended a two-year college and subsequently attended a four-year
college at some point prior to 1990.
The transfer model is based on the same human capital theory that motivates the attendance model. The main
distinction is that the transfer model describes a decision about subsequent schooling, conditional on having pre-
viously chosen to attend two-year college. High school graduates who never attend college, and students who
attend only four-year college, are not included in this part of the analysis.
The non-transfers consist of three groups: those who continue to attend two-year college, those who complete
an associate’s degree, and those who leave school. Because all these individuals could subsequently enroll
in a four-year college at some point, it would be inappro- priate to exclude any of them from the analysis. The
model treats them as potential transfers, but includes a
13
As the inclusion of post-schooling outcomes in schooling decisions poses problems for interpreting causality, we present
models that exclude occupational indicators. The discussion in Section 4.1 outlines the main differences between the attend-
ance models with and without these variables.
variable to identify individuals who have a gap in their schooling history of more than one year.
14
The explanatory variables used to describe whether an individual transfers are very similar to those used in the
attendance model. Exceptions are that broad field of study is incorporated and the explanatory variables that
can vary with time are set to their values as of age 20. The inclusion of field of study addresses one of the limi-
tations of the attendance model. Expectations for vari- ables included in both the attendance and transfer mod-
els, where different from the expectations outlined above, are noted in the discussion of the results.
4. Multivariate results
