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
This study uses univariate probits to describe college attendance decisions and the decision among two-year
college students to transfer to a four-year college.
15
The results are expressed as Probit derivatives for ease of
exposition and interpretation.
16
Estimation results from the attendance models are reported in Table 4.
17
Table 5 presents the results of the transfer models.
4.1. Two- and four-year college attendance models We initially argued that women choose two-year over
four-year attendance as a result of practical consider- ations—such as family responsibilities—and that such
considerations might also explain why women transfer at lower rates than men. The results from the attendance
models partially bear out this expectation. The gender-
14
Leigh Gill 1997 report that individuals who leave school and then return still earn a wage premium on their initial
college credits. This indicates that the value of college credits does not depreciate fully over even several years.
15
Other researchers have used multinomial logit to examine the decision to attend different types of post-secondary insti-
tutions Manski Wise, 1983; Ordovensky, 1995; Hilmer, 1997. It seems likely that for the questions addressed in this
paper, the “independence of irrelevant alternatives” IIA con- dition required for that method would not be satisfied.
16
Probit derivatives are simply the change in the probability of an outcome caused by a change in one of the explanatory
variables. For y =
FxB, the Probit derivative is calculated y
9 =
fxbb, where b is the estimate of B. The probabilities are calculated at the x-variable means.
17
Several of the variables included on the right-hand side of the decision equations are the product of past decisions and may
therefore cause endogeneity bias. Methods for reducing such bias such as the Instrumental Variables method generally
require a variable that affects past decisions but does not other- wise directly affect the current decision. Identification based
solely on functional form is generally viewed as an unsatisfac- tory solution to this problem. Given the difficulties such correc-
tions pose, the model does not address endogeneity bias.
157 B.J.
Surette Economics
of Education
Review 20
2001 151–163
Table 4 Probit models of two-year and four-year college attendance probability derivatives listed, Huber–White t-scores in parentheses
a
Two-year attendance
b
Four-year attendance
b
1 Pooled 2 Women
3 Men 4 Pooled
5 Women 6 Men
Demographics
Female 1.19 4.03
0.64 1.89 Married
22.11 5.33 22.65 5.15
21.34 2.10 24.45 9.11
25.92 9.30 22.62 3.47
Young kids 22.50 5.87
22.97 5.46 22.03 2.63
-2.51 4.07 24.08 5.33
0.25 0.22 AFQT score
0.83 8.36 0.85 5.66
0.75 5.86 2.48 20.46
2.59 14.09 2.17 14.83
Black cross 1.51 2.33
1.37 1.51 1.71 1.89
4.30 4.72 4.87 3.95
3.77 2.94 Hispanic cross
1.89 2.16 1.41 1.13
2.43 2.05 4.15 3.46
6.06 3.37 2.16 1.52
Black over-sample 0.94 1.98
1.63 2.33 0.16 0.26
4.00 7.17 4.45 5.19
3.52 5.25 Hispanic over-sample
2.32 4.36 1.94 2.62
2.63 3.60 1.73 2.68
0.89 0.93 2.29 2.73
Parents education included
included included
included included
included Urban location
1.98 6.05 2.02 4.14
1.95 4.55 0.37 0.99
0.60 1.08 0.30 0.66
Age 13.7010.27
15.99 8.49 11.33 6.10
11.25 7.36 9.77 4.79
12.98 6.12 Age squared
215.6310.84 217.86 8.68
213.26 6.67 215.53 9.04
213.70 5.98 217.28 7.24
Age cubed 5.34 10.91
5.96 8.51 4.66 6.96
5.88 9.70 5.25 6.53
6.41 7.60 Time trend
0.00 0.32 0.05 0.33
20.02 0.12 20.17 1.34
0.27 1.40 20.11 0.67
Costs and benefits
College wage premium 3.60 2.64
3.46 1.79 3.78 2.05
24.33 2.87 21.08 0.48
27.35 3.83 HS graduate wage
4.23 2.41 1.71 0.64
6.07 2.68 24.27 2.24
24.96 1.59 23.46 1.64
Two-year tuition 23.02 6.39
23.45 5.22 22.68 4.09
2.06 3.54 2.26 2.65
1.60 2.19 Four-year tuition
20.93 2.45 21.00 1.87
20.76 1.47 21.27 2.85
21.68 2.56 0.78 1.42
Unemployment rate 0.22 5.12
0.19 3.04 0.25 4.43
0.16 3.20 0.21 2.87
0.13 2.04
Human capital
2-yr credits 4.38 21.49
4.22 14.46 4.44 18.96
1.44 3.94 0.52 1.07
2.12 4.25 4-yr credits
25.37 10.85 26.46 9.93
24.06 5.62 15.36 29.82
14.95 19.09 14.55 23.79
4-yr credits
2
0.59 6.15 0.81 7.30
0.28 1.74 22.43 18.14
22.37 11.32 22.30 14.92
AA degree n.a.
n.a. n.a.
0.43 0.48 20.18 0.15
1.80 1.43 Experience
20.94 7.22 20.97 5.21
20.98 5.60 22.14 9.06
21.78 5.29 22.46 8.44
Vocational training 20.39 0.73
20.88 1.23 0.03 0.04
21.33 1.67 21.72 1.61
21.07 1.13 Mean S.E. of dep. var.
0.086 0.280 0.092 0.288
0.080 0.270 0.184 0.387
0.179 0.382 0.185 0.388
Sample size 36,223
18,798 17,425
36,223 18,798
17,425 Pseudo-R
2
0.1719 0.1809
0.1682 0.4520
0.4434 0.4700
a
Standard errors are calculated using the Huber 1967 and White 1980 method to account for multiple observations of individuals.
b
Includes all high school graduates in each year they are observed in the data. Once individuals complete a bachelor’s degree, they are excluded from the estimation.
158 B.J. Surette Economics of Education Review 20 2001 151–163
Table 5 Probit transfer models Probability derivatives listed, t-scores in parentheses
Transfer from two-year college
a
1 Pooled sample 2 Women
3 Men
Demographic factors
Female 26.28 2.05
Married 217.39 3.51
216.51 2.90 222.15 2.16
Young children 215.17 2.59
216.07 2.60 27.36 0.41
AFQT score 9.48 8.57
9.36 6.09 10.11 6.09
Black random 4.00 0.60
21.30 0.15 11.87 1.17
Hispanic random 11.47 1.36
22.76 1.89 2.82 0.22
Black over-sample 9.84 2.15
4.30 0.70 14.97 2.16
Hispanic over-sample 4.07 0.78
23.84 0.54 12.15 1.60
Parents education included
included included
Urban location 4.27 1.10
10.65 1.97 23.78 0.67
Age 21.73 1.43
20.67 0.40 23.14 1.75
Time trend 2.50 1.54
0.11 0.50 4.20 1.75
Costs and benefits
College premium 210.94 0.97
26.90 0.43 217.35 1.07
High school wage 235.99 2.92
232.85 1.99 249.30 2.65
Tuition ratio 4-yr2-yr 21.00 2.22
21.31 2.07 20.89 1.35
Unemployment rate 20.22 0.49
20.29 0.46 0.19 0.27
Human capital
Two-year credits 27.37 4.91
210.91 5.35 23.15 1.38
AA degree 27.62 8.09
27.63 5.88 26.58 5.30
Experience 225.16 8.51
231.96 7.37 219.65 4.56
Ever train 27.58 2.50
26.70 1.64 210.63 2.30
Gap in schooling 8.94 1.81
10.34 1.56 6.60 0.88
Field of study
Vocational 23.79 5.23
18.19 2.98 29.38 4.25
Academic 36.58 4.25
44.33 3.87 27.28 2.05
Mean of dep. var. 0.415 0.493
0.385 0.487 0.452 0.498
Sample size 1390
764 626
Pseudo-R
2
0.2477 0.3022
0.2183
a
Includes only individuals who attended a two-year college. High school graduates who never attended either type of college and four-year college students who never attended a two-year college are excluded.
specific regressions show that having young children or being married affects women’s college attendance
decisions more than men’s. In the two-year and four-year attendance equations columns 2, 3, 5, and 6 of Table 4
the coefficients on “married” and “young kids” are larger in absolute value for women than for men.
18
Moreover, consistent with notion that two-year colleges are more
accommodating than four-year colleges, having young children and being married generally reduce four-year
attendance more than two-year attendance.
We also suggested that living in an urban area might proxy for proximity to college, and that gender differ-
ences in the effects of “proximity” thus defined might
18
Gender differences in the effects of marital status and hav- ing young children are statistically significant at the 5 level
in only the four-year attendance equation.
explain observed attendance patterns. The urban indi- cator is positive and significant in all three two-year
attendance equations, consistent with the hypothesis that proximity is an important determinant of two-year
attendance. However, this effect does not differ by gen- der, nor does it affect four-year attendance, which sug-
gests that proximity does not explain the gender differ- ence in attendance patterns.
To test whether differences in educational ability can explain the gender difference in transfer rates, the model
includes the AFQT score in the attendance models. All the columns in Table 4 show that the AFQT is an
important determinant of attendance and that it has a larger effect on four-year than two-year attendance.
However, there is no gender difference in the effects of AFQT on either two-year or four-year attendance. The
AFQT effects on attendance indicate that marginal stu- dents tend to attend two-year college, but there is no
evidence that the AFQT matters less for women.
159 B.J. Surette Economics of Education Review 20 2001 151–163
The attendance models include a set of economic fac- tors to shed further light on how students decide between
and use two- and four-year colleges. We focus first on the direct costs of attendance measured by two-year and
four-year tuition. The signs of the own-tuition effects are generally negative, as expected, and do not differ sig-
nificantly by gender. The signs of the cross-tuition effects are a priori ambiguous; their estimated values tell
us something useful about how students utilize the two types of college. The positive effect of two-year tuition
in the four-year attendance equations columns 4, 5, and 6 indicates that when two-year college is expensive, stu-
dents are more likely to attend four-year college and are less likely to attend two-year college. These cross-
effects are not symmetric, however. The negative effect of four-year tuition in the two-year attendance equations
columns 1, 2, and 3 suggests that when four-year col- lege is expensive, students are less likely to attend two-
year college. This latter effect is consistent with two- year colleges’ transfer role: when four-year college is
expensive and, therefore, more difficult to justify on economic grounds, the transfer option is less attractive
and attendance at two-year colleges is reduced.
19
The opportunity cost of college attendance is charac- terized by the wage earned by high school graduates in
each individual’s state of residence.
20
As expected, this cost reduces four-year college attendance. Contrary to
expectations, this opportunity cost generally raises two- year attendance. Turning next to the benefits of attend-
ance, the four-year college graduate wage premium raises the probability of two-year college attendance for
both women and men, but reduces the probability each particularly men attends four-year college. The latter
effect also contradicts expectations.
These contradictory results may stem from a number of causes; state level aggregate wages capture any num-
ber of state-specific effects making interpretation of such variables problematic. I retained them because human
capital theory argues strongly for their inclusion in col-
19
Intuitively, this asymmetry implies that the cross-price sub- stitution effect is small compared to the income effect in the
two-year attendance equation and vice versa in the four-year attendance equation. This asymmetry may stem from intertem-
poral considerations playing a larger role in the two-year decision equation—in effect reducing the cross-substitution
effect—than in the four-year decision equation.
20
Median wages by education level are obtained from the Current Population Survey CPS. They are calculated by state
and year for 18- to 40-year-olds reporting highest grade com- pleted equal to 12, and for 22- to 40-year-olds for individuals
reporting highest grade completed equal to 16 or more. There are not enough observations to calculate these variables by gen-
der for each state and year. It is straightforward, but uninforma- tive, to include the wage premium earned by individuals with
1 to 3 years of college.
lege attendance models. Note that none of the results are altered by excluding either the high school graduate
wage or the return to college or both. Despite controlling for the many factors that human
capital theory indicates should affect attendance, the female coefficient in the pooled two-year decision equ-
ation remains positive and statistically significant: women remain more likely than men to attend two-year
college. This suggests that the explanations advanced thus far for why women are less likely than men to attend
two-year colleges only tell part of the story. Family responsibilities, proximity, monetary costs, and ability
all play an important role, but they do not completely explain the significant gender difference in two-year
attendance rates.
As noted earlier, attendance decisions are almost cer- tainly driven in part by occupational preferences. We
cannot observe occupational preferences, but we can proxy for them using ex-post-occupational outcomes. In
models not reported, we include indicators for whether each individual ever worked in one of three female-
dominated occupations: the secretarial profession, the allied health profession, and primary or secondary school
teaching. Training for the first two professions is gener- ally provided by two-year colleges.
21
Training for the latter is generally provided by four-year colleges.
The inclusion of occupational indicators causes the coefficient on female in the pooled two-year college
regression to become very small and statistically insig- nificant. Women who subsequently work in either the
allied health or secretarial professions are much more likely to attend two-year colleges than those who sub-
sequently work in other fields. Working as a primary or secondary school teacher or in an allied health profession
significantly raises the probability of four-year attend- ance. Controlling for these variables “explains away” the
unexplained gender difference in two-year college.
The inclusion of such post-schooling outcomes is hard to justify on logical and econometric grounds—and for
that reason we present in Table 4 the models that exclude them. However, such models do tell us something
important about the attendance decision and are therefore worthy of note. They strongly suggest that women attend
two-year colleges at higher rates than men as a result of occupational preferences.
The question remains as to whether the entire set of explanatory variables explains the gender difference in
transfer rates as opposed to attendance rates. The four- year attendance model allows us to examine this ques-
tion, albeit indirectly. Controlling for other factors, one expects women and men with equivalent numbers of
two-year credits to be equally likely to attend four-year
21
Some of the allied health professions, most notably nurs- ing, may require a bachelor’s degree.
160 B.J. Surette Economics of Education Review 20 2001 151–163
college.
22
While this is not, strictly speaking, the same as transferring, it is very similar. The gender-specific
four-year attendance regressions columns 5 and 6 show that the effect of accumulated two-year credits on four-
year attendance is smaller for women than for men; one year of two-year credits raises the probability men attend
four-year college by 2 or 3 percentage points but has no effect on women’s probability of attendance. This differ-
ence is statistically significant at the 1 percent level. Put another way, women with one year of two-year credits
are significantly less likely than similar men to attend four-year college and have no higher a probability of
attending a four-year college than an otherwise similar high school graduate with no two-year credits.
Note that it is not the case that credits simply matter less in women’s schooling decisions. The effects of
accumulated two-year credits on further two-year attend- ance columns 2 and 3, and the effects of accumulated
four-year credits
on further
four-year attendance
columns 5 and 6, do not differ by gender. Women sim- ply appear less likely than similar men to use two-year
colleges as stepping stones to four-year colleges. This is so despite the inclusion of many variables that could
theoretically explain the gender difference. Moreover, the inclusion of the occupational indicators discussed
above but not included in the models in Table 4 does not alter this conclusion.
4.2. The transfer model The attendance models tell us much about what might
and might not explain the lower transfer rates observed among women. However, the effects of two-year credits
on four-year attendance described above does not meas- ure gender differences in transfer rates per se. Rather,
we infer lower rates of transfer for women from the fact that men and women with equivalent numbers of two-
year credits and other similar characteristics make sub- sequent educational decisions differently.
An alternative way to test whether women are less likely than men to transfer is to directly estimate a model
with an indicator for whether or not a student transferred at some point during the 12-year panel as the dependent
variable. High school graduates who never attend college and students who enrolled only in a four-year college are
not considered in the transfer model. Estimation results for three groups—one pooled and one each for women
and men—are reported in Table 5.
22
Four-year colleges vary in whether or not two-year credits can be applied by transfer students toward a bachelor’s degree.
Usually, such requirements involve grade received and course content. I have no way of identifying which credits would be
acceptable in transfer so all credits earned are included in these models.
The explanatory variables are similar to those used in the college attendance equations. However, because the
dependent variable is retrospective over up to 12 years, variables that can change with time are set to their values
at age 20.
23
Thus, marital status, the presence of children under the age of 5, tuition, the unemployment rate, urban
status, and others are fixed at their values when each individual was 20, even though the transfer could have
occurred after that age.
The pooled estimates in Table 5 column 1 show clearly that women are much less likely than men to
transfer. Even after controlling for a wide range of other factors that could explain this trend, being female
reduces the probability of transferring by about 6 per- centage points.
24
We postulated earlier that two-year col- leges’ more flexible class schedules better accommodate
domestic responsibilities, which may be borne dispro- portionately by women. Consistent with this hypothesis,
the gender-specific effects of having young children are much larger and negative for women column 2 than
for men column 3. Contrary to expectations, being mar- ried reduces men’s transfer rates more than women’s,
though this difference is not statistically significant. Finally, the effects of living in an urban area on the
transfer decision are substantially higher for women than for men, which is consistent with proximity mattering
more for women than for men.
25
None of these variables, however, fully explains the gender difference in trans-
fer rates. Because the transfer model is defined only for individ-
uals who attended college, it is possible to address the possibility that women attend two-year colleges at higher
rates than men because the fields of study offered there prepare them satisfactorily for their chosen occupations,
which may differ from the fields of study men choose. We include indicators for whether the field of study was
vocational or academic, as defined by a taxonomy pro- vided by the National assessment of vocational edu-
cation United States Department of Education, 1989. The omitted category is “no major field specified”.
Despite the inclusion of these indicators, the coefficient on “female” in the pooled regression is negative, indicat-
ing that women remain less likely than men to transfer. Women and men with similar numbers of two-year cred-
its, the same broad field of study, who are unmarried and have no small children, and are otherwise identical have
different rates of transfer to four-year college.
23
Setting these variables to their values at age 21 or 19 does not alter the qualitative results.
24
The coefficient on female is fairly robust, remaining close to 6 percent for a wide range of models. Changes in the model
rarely causes the t-statistic on this parameter to drop below 1.95.
25
The gender difference in the urban coefficient is statisti- cally significant at the 5 level.
161 B.J. Surette Economics of Education Review 20 2001 151–163
We also hypothesized that women’s lower transfer rates might be explained by differences in educational
ability; that more marginal women than men attend two- year college. The AFQT score is included in the transfer
model to address this possibility, but the effects of the AFQT score do not differ much across gender.
The AFQT is probably an imperfect proxy for edu- cational ability. Unobserved components of ability that
drive schooling decisions will be partially captured by accumulated college credits. The effects of two-year cre-
dits are much lower for women than for men. While hav- ing an associate’s degree raises the probability of trans-
ferring equally for men and women, completing an associate’s degree requires approximately 2 year’s worth
of credits. The combined effects of credits and associ- ate’s degree completion are very small in the female equ-
ation, while in the male equation the combined effects of credits and degree completion are quite large.
26
These results confirm the findings from the attendance models;
women who attend two-year college are less likely than similar men to transfer.
A third explanation advanced for why women transfer at lower rates than men is that they simply prefer two-
year colleges to four-year colleges due to occupational ambitions. To test this hypothesis, we estimated the
transfer model with indicators for whether an individual ever worked as a primary or secondary school teacher,
in the allied health profession, or as a secretary estimates not reported. Women who subsequently work
in the allied health profession or as teachers are more likely to transfer. However, the key results of this paper
are not affected by the inclusion of occupational dumm- ies; the coefficient on female remains negative and stat-
istically significant, and in fact, becomes larger and sig- nificant at the 1 level. We conclude from these
regressions that while occupation indicators have a lot of explanatory power, they do not alter the conclusion
that women are less likely than men to transfer.
4.3. Credits and labor market earnings Human capital theory predicts that credits affect col-
lege attendance by changing the tradeoff between current and future earnings. If the relative returns to two-year
and four-year credits differ for women and men, it might be economically rational for women to transfer at lower
rates. We tried to test this hypothesis by incorporating the rate of return to college in the attendance and transfer
models. Those results were somewhat unsatisfactory, due
26
For women, the effect of completed two-year credits, mul- tiplied by 2 years worth of credits, substantially offsets the
effect of having an associate’s degree 27.6 2210.9
+ 6.
For men the combined effect is about +
20.
in part to limitations in using state-level aggregate wages to proxy for current and future earnings.
To further evaluate this possible explanation, Table 6 reports rate of return estimates from four Mincer, 1974
human capital earnings functions based on the NLSY. Several previous authors have examined the returns to
two-year college credits and associate’s degree com- pletion Grubb, 1993; Kane Rouse, 1995a,b; Surette,
1997. The Grubb and Kane and Rouse analyses are based primarily on the National Longitudinal Survey of
the Class of 1972, and the college credit data is now over 20 years old. The NLSY data used here and in
Surette is more recent though still over 10 years old and is more relevant for explaining attendance behavior
during the 1980s. The estimates reported in Table 6 are broadly similar to those reported in previous studies.
The regressions presented in Table 6 explain the log of hourly earnings and the log of annual earnings, by
gender, as functions of two-year credits, four-year cred- its, degrees, and a large set of other explanatory variables
see footnote in Table 6 typically included in human capital models. The estimates show that women and men
do not differ appreciably in their returns to two-year cre- dits, and that for women, the returns to two-year and
four-year credits do not differ much. Men’s return to four-year credits are lower than their return to two-year
credits, and are much lower than women’s returns to four-year credits. This is the opposite of what one would
expect if differences in returns to college explain the transfer rate difference. These estimates suggest that on
economic grounds women should, if anything, be more likely than men to attend or transfer to four-year college,
not less.
5. Summary and conclusion
