285 J. Monks Economics of Education Review 19 2000 279–289
Table 4 Returns to college characteristics. Dependent variable: log hourly wages
Specification 1 Specification 2
Specification 3 Intercept
1.533 1.295
1.242 0.035
0.064 0.066
Experience 0.077
0.071 0.071
0.006 0.006
0.006 Experience squared10
2 0.019
2 0.017
2 0.017
0.004 0.004
0.004 Tenure
0.037 0.035
0.035 0.005
0.005 0.005
Tenure squared10 2
0.025 2
0.023 2
0.023 0.005
0.005 0.005
Male 0.155
0.128 0.131
0.026 0.022
0.022 White
0.035 2
0.049 2
0.069 0.029
0.029 0.030
Armed Forces Qualification Test 0.111
0.107 0.022
0.022 Public institution
2 0.045
2 0.033
0.027 0.027
Masters, doctoral or research 0.137
0.126 0.031
0.032 Specialized institution
0.189 0.169
0.089 0.089
Non or less competitive 2
0.047 2
0.042 0.028
0.028 Very competitive
0.081 0.079
0.030 0.030
Highly or most competitive 0.151
0.131 0.049
0.050 Net family income
0.030 0.008
Adjusted R-squared 0.86
0.89 0.89
Number of observations 4977
4977 4977
Notes : 1 Standard errors are in parentheses. 2 Specifications 2 and 3 include dummy variables for industry and occupation, and
missing AFQT and net family income, not shown. 3 Significant: at the 1 level; at the 5 level; at the 10 level.
4. Results
I begin by regressing the log of hourly wages against experience, experience squared, tenure, tenure squared,
a dummy if the individual is male and a dummy if the individual is white.
6
There are the usual concave increases in log wages over experience and tenure. Males
earn significantly higher earnings than females, and there appears to be no significant difference in earnings among
white and non-white college graduates. These results are presented in specification 1 of Table 4.
Including AFQT scores, institutional characteristics, and industry and occupation dummies results not shown
6
White is defined here as those respondents identified as non- black, non-Hispanic; non-white is defined as those respondents
identified as black or Hispanic.
for industry and occupation among the regressors, speci- fication 2 of Table 4 does not significantly change the
quadratic returns to experience and tenure, nor the male premium. The sign of the coefficient on the white
dummy variable is now negative, and significantly differ- ent from zero. The omitted variable in this specification
is non-white, female, competitive, private, liberal arts college graduate. There is a positive and significant
return to academic ability, as measured by the age- adjusted AFQT score. Additionally, graduates from pub-
licly controlled institutions earned 4.5 less than gradu- ates from privately controlled institutions.
7
On the other
7
I am following convention and a rough approximation by referring to the coefficient on a dummy variable in a natural
logarithm regression as the percentage wage effect. A more accurate measure of the percentage change would be expb
2 1.
286 J. Monks Economics of Education Review 19 2000 279–289
hand, graduates from graduate degree granting research institutions earned approximately 14 more than gradu-
ates from liberal arts colleges.
8
This result is consistent with the findings of Dowd 1998 and Pascarella and
Terenzini 1991, both of whom found significant differ- ences across college versus university graduates. Gradu-
ates from specialized institutions earned approximately 19 more than liberal arts college graduates.
There is strong evidence of a positive and significant relationship between wages and college quality, as meas-
ured by Barron’s selectivity measure. Even after con- ditioning on AFQT score, gender, race, experience, ten-
ure, control of institution, classification of institution, and industry and occupation, graduates from more selec-
tive colleges and universities earn more than graduates from less selective institutions. In particular, graduates
from non or less competitive institutions earned approxi- mately 5 less than graduates from competitive insti-
tutions; graduates from very competitive institutions earned 8 more; graduates from highly or most competi-
tive colleges and universities earned 15 more than competitive college graduates.
Specification 3 of Table 4 includes the respondent’s 1979 net family income in order to control for differ-
ences in familial resources which may influence the col- lege selection process Loury Garman, 1995. While
net family income appears to have a positive and sig- nificant impact on log wages, its inclusion among the
regressors does not significantly alter the coefficients on the other individual or institutional characteristics. Indi-
viduals from more competitive institutions and larger research institutions earn significantly more than their
counterparts from less selective institutions and liberal arts colleges.
A number of alternative specifications were also tested. The college selectivity dummies were interacted
with experience to test for differences in the profile of earnings over time by college quality. No significant pat-
tern emerged. Additionally, the AFQT score and college selectivity were interacted to see if individual academic
ability and institutional quality were complements. Again, no significant pattern emerged. Finally, both the
public control dummy and Carnegie classification dummies were interacted with quality, but neither
proved significant.
Table 5 presents the results of separate regression analyses by gender. A Chow test that the relationship
between log hourly wages and the regressors is the same for males and females fails to reject the null at even the
10 significant level. While the overall returns to insti-
8
Separate dummy variables for masters, doctoral and research universities were initially included, but an F-test that
their coefficients were equal could not be rejected at the 5 level.
tutional characteristics are qualitatively the same for both males and females, there are some noteworthy differ-
ences in the magnitude of the individual effects. Specifi- cally, female graduates of specialized institutions earn
significantly more than their male counterparts. On the other hand, while both men and women graduates from
graduate degree granting institutions earn more than lib- eral arts college graduates, males receive a larger pre-
mium for attending graduate degree granting and research institutions than females. Females also have a
higher return to AFQT score and a larger non-white pre- mium, although these differences are not significantly
different from zero.
The final analysis Table 6 examines differences in the returns to individual and institutional characteristics
by race. In this case the Chow test rejects the null, at the 1 significance level, that the relationship between
log hourly wages and the regressors is the same for whites and non-whites. While the coefficients on insti-
tutional quality are not significantly different from zero for non-whites, the magnitude of these effects is larger.
In particular, non-white graduates of highly or most com- petitive institutions earn a larger premium than whites.
This result is consistent with earlier studies that explicitly examine returns to institutional quality across
racial groups. For example, Behrman et al. 1996a find greater returns to college quality among non-whites than
whites, and Loury and Garman find a larger return to college selectivity among blacks than whites.
The returns to graduating from a masters, doctoral or research university are not substantially different for
whites and non-whites. The returns to public institutions are different across racial groups. White graduates of
publicly controlled institutions earn significantly less than white graduates of privately controlled institutions,
while the earnings of non-white graduates of publicly controlled institutions are not significantly different from
the earnings of non-white graduates of private insti- tutions. Additionally, non-white graduates of specialized
institutions earn a premium that is not realized by white students.
Studies that fail to adequately address the sometimes substantial differences in the returns to both individual
and institutional characteristics across gender and racial groups are obfuscating the true relationship between
these interactions. It may be further interesting to per- form separate regression analyses within both racial and
gender groups, however there were too few observations for non-white females and non-white males.
While the underlying causes of the varying returns to institutional characteristics across demographic groups is
beyond the scope of this paper and the limitations of the data set, these results raise important questions concern-
ing the educational experiences on campus of different groups, labor market treatment of different groups from
similar institutions, and the potential interaction of indi-
287 J. Monks Economics of Education Review 19 2000 279–289
Table 5 Returns to college characteristics. Dependent variable: log hourly wages
Males Males
Females Females
Intercept 1.642
1.324 1.581
1.325 0.047
0.085 0.046
0.124 Experience
0.095 0.088
0.057 0.055
0.009 0.009
0.009 0.009
Experience squared10 2
0.030 2
0.027 2
0.007 2
0.007 0.005
0.005 0.005
0.005 Tenure
0.026 0.025
0.050 0.047
0.007 0.007
0.008 0.008
Tenure squared10 2
0.017 2
0.014 2
0.035 2
0.034 0.006
0.006 0.008
0.008 White
0.032 2
0.035 0.040
2 0.096
0.040 0.039
0.040 0.045
Armed Forces Qualification Test 0.079
0.132 0.029
0.032 Public institution
2 0.054
2 0.021
0.037 0.039
Masters, doctoral or research 0.177
0.081 0.043
0.045 Specialized institution
0.037 0.427
0.105 0.158
Non or less competitive 2
0.050 2
0.032 0.038
0.039 Very competitive
0.078 0.080
0.040 0.043
Highly or most competitive 0.138
0.139 0.065
0.073 Net family income
0.032 0.027
0.010 0.013
Chow statistic df: 34, 4909 0.35
Adjusted R-squared 0.87
0.91 0.85
0.88 Number of observations
2514 2514
2463 2463
Notes : 1 Standard errors are in parentheses. 2 The second specifications for both males and females include dummy variables for
industry and occupation, and missing AFQT and net family income, not shown. 3 Significant: at the 1 level; at the 5 level; at the 10 level.
vidual and institutional characteristics in signaling ability.
The higher returns to quality realized by non-white graduates found in this study appear consistent with a
model of affirmative action in hiring developed by Kol- pin and Singell 1997, where individuals from a pre-
ferred group earn a premium not realized by other indi- viduals with comparable institutional affiliations. If this
is indeed the case, then minorities have an added incen- tive to gain admission to highly selective institutions.
Furthermore, recent judicial and policy decisions restricting consideration of an applicant’s race in admis-
sions are likely to reduce the number of minority gradu- ates from top institutions, and may further raise the earn-
ings premium for minority graduates from highly selective
institutions. Clearly,
additional analyses
designed to explicitly explore the underlying causes of the differences across demographic groups in the returns
to institutional quality found in this and other studies is warranted.
5. Conclusion
