Senior Project
Department of Economics
“Minority Returns to Community
College Degrees”
Amanda Eddie
December 2014
Advisor: Dr. Francesco Renna
Abstract
This paper analyzes the effect of minority individuals receiving a community college
education. I use data from the National Education Longitudinal Study of 1988, 2000 follow-up.
The data used allows for a broad analysis of many education-related effects on income. I find
through switching regression models that community college attendance helps bridge the wage
gap between minority and non-minority individuals. Previous research has not effectively
analyzed the effects of community college education for minority individuals. This paper aims to
supplement current literature which has used incomplete and outdated data.
1
Table of Contents
Introduction
3
Literature Review
4
Theoretical Model
11
Model Specification and Data
12
Model Specification
12
Data Sources and Expected Signs
13
Results
15
Minority Included
16
Minority Separated
17
Conclusion
20
References
22
Appendix
23
2
Introduction
Community colleges enroll more than seven million students each year in the United
States (Forbes 2014). These institutions attract students from low-income homes because of their
comparably low cost to four-year institutions. Two-year community colleges and technical
vocational schools can be a less costly alternative to the typical four-year institutions. Students
should be able to obtain a sufficient income to compensate for the opportunity costs of gaining a
community college education. Even though they may charge much less, few students actually
complete a community college degree.
The largest minority group in the United States is Hispanics, closely followed by blacks.
They remain the least educated and most economically vulnerable. According to the Kaiser
Family Foundation, 33% of all Hispanics and 35% of African-Americans live below the poverty
line1. The major cause of this impediment is their lower levels of post-secondary education.
Because of their potential low cost, two-year colleges can be a practical postsecondary option of
minorities. Many minority students are not prepared to enter four year institutions because of
prior racial discrimination and poor quality schools and academics preparation. Open enrollment
policies of community colleges can allow students to improve their academic standing at a lower
cost before transferring to four-year institutions.
Investment in human capital is a major way to increase earning potential. This research
tests the impact that Hispanic and black students, grouped as simply “minorities”, obtain from
investing in a community college education. How does an individual’s level of achieved
education affect their income? Many previous studies describe the relationship between
investment in human capital through formal post-secondary education and an individual’s
economic returns; however, there are few that specifically test labor market returns of
1
http://kff.org/other/state-indicator/poverty-rate-by-raceethnicity/
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community college education within minorities. There is a need for more research on this
subject. Many studies use outdated and incomplete data, therefore telling the same general story
that two-year college attendees have lower income than four-year college attendees.
Literature Review
Studies of the effects of community college education on income prior to 1995 have
limited relevance because they relied upon Census data from 1970 and 1980. While the Census
data has information on years of schooling completed, there is no distinction between 2- and 4year students and degree recipients. Also the nature of community colleges has changed greatly
since the 60s and 70s. As time progresses, the need for new, innovative data increases. For this
reason, many recent authors have used different and more recent data sets outside of Census
data. This analysis is based on this later research by Elizabeth Monk-Turner (1994), Thomas
Kane and Cecilia Rouse (1995), Grubb (1997), Marcotte, Bailey, Borkoski, and Kienzl (2005),
Arturo Gonzales and Michael Hilmer (2006), and Christopher Jepsen, Kenneth Troske, and Paul
Coomes (2012).
In 1994, Monk-Turner (1994) was able to use the National Longitudinal Survey (NLS).
NLS included a large sample of individuals who graduated from high school that had been out of
high school for 10 years, were working full-time, and had IQ scores listed. Considering income
ten years after high school allows time for education to have an effect (Cohn 1979). However, it
also means that it is a young sample so there may not be a large wage difference shown. The
average age of respondents in this analysis is 27 years old. The model in this research is that the
natural log of an individual’s hourly wage is a function of their race, gender, IQ score, socioeconomic background, region, metropolitan area, work experience, marital status, type of college
entered, years of education completed, and education goals. Region is a dummy variable which
4
takes on a value of 1 if the respondent lives in the South. Work experience for men is calculated
as ‘age - (years of education + 5)’. For women, work experience is calculated as ‘age – (years of
education + 5) – non work experience’. Non-work experience is the amount of time lapse since
the completion of schooling. This difference in calculation stems from the idea that, at the time,
it was not as necessary that women work outside of the home.
When education, IQ scores, and socioeconomic status are controlled, this analysis
determines it is significant where a student begins their post-secondary education. A student who
enters a 4-year institution has a 2.5% higher income at age 27 than a student that entered a 2-year
college. From this analysis, the author argued that a student was better off obtaining a 4-year
degree than by saving their money and obtaining a 2-year degree. She states the opportunity
costs for any given individual are similar, so an individual might as well push to obtain the 4year education, which is a potentially controversial issue.
Kane and Rouse (1995) developed a much referenced study from the National
Longitudinal Study of 1972, 1986 follow up (NLS 72) and the National Longitudinal Survey of
Youth of 1979 (NLSY). Together, NLS72 and NLSY fill the gaps previously left by Census data.
NLS72 has extensive family background variables and transcript information and were last
contacted in 1986, 14 years after their completion of high school. NLSY follows a more recent
cohort, has more complete schooling information, and better labor force information. Wages are
measured as log hourly wages, as well as log annual earnings. The authors point out that
selection bias is a large challenge in estimating the effect of education on wage differentials
because there may be innate differences between people which lead them to put themselves into
certain types of education. Selection bias is controlled through a series of standardized test
scores, high school class rank, and family income. A natural experiment is also set when
5
controlling for background and ability by using distance to 2- and 4- year institutions and tuition
costs.
Results when controlling for background and ability are similar to the OLS estimates.
The results show that for the time period from 1972 to 1986, on average, an individual that
attends a 2- year college earns roughly 10% more than an individual without any college
education. The wage differentials are small and possibly economically insignificant. The authors
suggest that if individuals were more informed about the benefits and costs of their own human
capital investment there may be an increase in wage differential. An individual whom is
uncertain about the return on investment in education then returns will be similar with or without
the education.
Grubb (1997) uses a possibly better data set than Kane and Rouse’s combined NLSY and
NLS72 data set because it is more recent as well as being a more complete single dataset
includes getting degree. Data in this research is obtained from Survey of Income and Program
Participation (SIPP) of 1984, 1987, and 1990. SIPP is
a household-based survey designed as a continuous series of national panels…SIPP is a
source of data for a variety of topics and provides for the integration of information for
separate topics to form a single, unified database. This allows for the examination of the
interaction between tax, transfer, and other government and private policies. Government
policy formulators depend heavily upon SIPP for information on the distribution of income
and the success of government assistance programs. SIPP collects information for assistance
received either directly as money or indirectly as in-kind benefits. SIPP data provide the most
extensive information available on how the nation’s economic well-being changes over time,
which has been SIPP’s defining characteristic since its inception in 1983
Kane and Rouse had to combine two data sets to continue their research, but SIPP is substantial
on its own. The model used is that earnings are a function of race, ethnicity, unionized jobs,
regional variables, marital status, disability, various levels of schooling completed, and how
much post-secondary education was completed (if not all).
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Grubb’s results show effects of 2-year post-secondary degrees compared to just high
school are higher for women at 23.4% than for men at 21.5%. This means that women have more
wage benefit than men for improving their human capital. However, in terms of mean annual
earnings, men have a 54.2% higher income than women. So, although women improve their
earning potential by improving their human capital, there is still a huge gender wage gap. This
large variation may be attributed to students not finding employment related to their field. The
best strategy seems from the results to be for a student to enter a program with high returns then
find related employment after finishing their degree. In conclusion, the authors suggest that more
information about the income effects of education should be provided to students as well as state
and federal policy emphasizing education completion and placement rates.
Research by Marcotte, Bailey, Borkoski and Kienzl (2005) seeks to update our
knowledge of the effects of community college education by using a different and more recent
data set, the National Education Longitudinal Study (NELS) for 1988 which looks at earnings in
1999. The authors are also concerned about the large potential for unobserved heterogeneity
between those enrolled and those not enrolled in post-secondary education because of selfselection bias. NELS data has a variety of measures to control for unobserved heterogeneity. The
model used in the research is that log yearly salary is a function of full-time equivalent years of
enrollment, earned degrees, experience, urban or rural area, race, and family income when the
respondent was in 8th grade. Marcotte et al. also included standardized math and reading test
scores, parental education, and parental expectation of education to control heterogeneity.
With these controls for unobserved heterogeneity, the result on community college
education is still significant. A male student with only a high school diploma or GED decreases
their income by 2.6% compared to a male student with community college enrollment. A female
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student with only a high school diploma or GED decreases their income by 14%. Women have
more to gain by receiving a community college degree. Women see larger returns to associate’s
degrees than men by 11.1%. The authors suggest there are important differences in education and
economic experiences between men and women. The mean income of respondents as a whole
was $26,028 in 2000. However, respondent’s mean income with no college enrollment was 22%
lower than the mean. Respondents with sub-baccalaureate degrees have an income only 10%
lower than the sample mean. This means that the high incomes of baccalaureate degrees are
skewing the income mean. The “sheepskin effect” may also be important to consider here. This
is the hypothesis that students obtaining a certificate of associate’s degree earn more than if they
had only completed the number of credits required to earn those credentials. This means that
students must complete their education and receive a degree. Individuals who do not graduate
will not see the same returns as an individual who did graduate.
The goal of the work by Gonzales and Hilmer (2006) is to examine the effect of 2-year
college attendance on the education outcome of a group of minority students, in this caseHispanic students compared to white students. They also argue that students with lower
perceived mental ability and income disadvantages enroll in 2-year colleges because of the costs
they associate with 4-year colleges. The authors use data from the High School and Beyond
(HSB) longitudinal study. HSB
“describes the activities of seniors and sophomores as they progressed through high
school, postsecondary education, and into the workplace. The data span 1980 through
1992 and include parent and teacher information, high school transcripts, student
financial aid records, and post-secondary transcripts in addition to student questionnaires
and interviews.” 2
2
HSB is a product of the National Center for Education Statistics which also publishes the NELS
data
8
To deal with the problem of selection bias, the authors use the Instrumental Variable (IV) of
distance as a way to control for unobserved heterogeneity, suggested by Kane and Rouse (1995).
This variable takes advantage of the fact that proximity and costs are not likely to be correlated
with unobserved factors affecting educational attainment. In this research there are three
mutually exclusive attendance categories: no college experience, 2- year college freshman, and
4-year college freshman. The hypothesis is that probability of attending a 2 year vs. 4-year is
decided by distance and tuition. The closer a student lives to an institution increases the
probability of attending that institution and decreasing the probability of attending the
alternative. If a student lives close to a 2- year college, they are more likely to attend that
institution than a 4- year institution which is further away.
The effects of distance are much larger for Hispanics and this may be caused by lower
socio-economic factors causing Hispanic students to live at home. Hispanic students decrease
their likelihood of attending a 2-year college by 2.4% for every 10 miles further out. White
students decrease their likelihood of attending a 2-year college by 0.8% for every 10 miles
further out. The IV model suggests that there is more research needed since Hispanic students
gain more than any other ethnic group.
Jepsen, Troske, and Coomes (2012) is one of the first papers to estimate labor market
returns on more detailed aspects of community college education outside of associate’s degrees,
using data from Kentucky for community colleges. Most studies focus solely on associate’s
degrees but most community colleges offer diplomas and certificates are offered that provide
many in-demand skills. The nature of the Kentucky Community and Technical College System
(KCTCS) dataset lends itself to a difference in difference estimator through “preferred student
fixed effects model”. This means that the data used differs along two dimensions: time and
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award. Earnings over time and between individuals with awards are compared with individuals
without awards. The problem with the data is that it is only one state and only those attending
community college. In their analysis, they begin by eliminating respondents younger than 17
years old, older than 60 years old, those that transfer to 4-year institutions, and those not seeking
a degree because these individuals are unlikely to be employed after graduation. The first
equation used is simply earnings as a function of the award received. In a more extensive
equation, earnings are a function of award received, enrollment period relative to analysis,
changing demographic variables, and students’ intentions.
As expected, associate’s degree and diplomas have much larger returns than certificates.
Compared to less than a year of college; men with an associate’s degree see an 11.2% increase in
income and women see a 19.3% increase. Although women have higher returns than men to a
community college education, their average earnings are still lower. Excluding students that
transfer out of two-year colleges may understate returns to associate’s degrees because of the
potential higher mental ability of those going onto four-year institutions. A dummy variable is
used to account for that potential bias. The author’s research suggests that the gender-wage
differential may be attributed to differences in fields of study. Associate’s degree in healthrelated fields have the highest returns, but there is a sizeable gender-wage gap. Women in healthrelated fields increase income 4.1% annually; men in a similar field increase income 7.3%, more
than an individual without college education. Their more detailed model suggests investment in
human capital through community colleges have large labor market returns but there is
substantial variation when analyzed separately by field of study and degree obtained.
There is a need for more research on this subject. The results, using data from 1972 until
1988, tell the same general story that two-year college attendees have lower income than four-
10
year college attendees. However, the magnitude of change on some variables differs greatly
between studies. Monk-Turner (1994) found that four-year college attendees have a 2.5% wage
gap over two-year college entrants. However, Kane and Rouse (1995) found that the wage gap
between two-year college graduates and high school graduates is 10%. It’s understandable that
income increases with level of income, but the magnitude of wage increase between the studies
seems unusual. Grubb (1997), Marcotte et al (2005) and Jepsen, Troske and Coomes (2012) all
report that women see larger returns to two-year college degrees than men. Grubb reports a
21.5% income increase for men from community college and a 23.4% income increase for
women. Marcotte et al report that men have a 2.6% income increase from community college,
and Jepsen, Troske and Coomes report an 11.2% increase for the same variable. Marcotte et al
report that women have a 14% income increase from community college, and Jepsen, Troske and
Coomes report a 19.3% increase for the same variable. Marcotte et al report that black
respondents see a 24.7% of income and Hispanic respondents see a 1.9% decrease of income
compared to white respondents. Monk-Turner reports that non-white respondents see a 4.4%
decrease in income compared to white respondents. Although the publications were published
years apart, analysis of log earnings should be more similar or it may be that more recent data
will yield more applicable results. The community college has changed as much as the world it
exists within since these studies were published.
Theoretical Model
Coursework
years
Type of
degree
Field of degree
Income
11
Demographic
variables
Figure 1: Theoretical Model
This paper demonstrates the effect of minority students receiving a community college degree on
labor market returns measured by income. In this model, the major theory is that of human
capital (Becker 1975). Most notably, human capital investment comes from education but it can
also occur in job training. The value of the human capital investment comes from how much the
skill can earn in the labor market. Average earnings of full-time workers will rise with the level
of education. The question is not whether education increases income: the question is how much
income increases with additional education and if different racial groups see different effects. In
this case, the variables used relate only to those with community college education so that
income is a function of field of degree plus demographics and standardized test scores.
Model Specification and Data
Model Specification
This study will be using a log-linear model developed by Marcotte, et al (2005).
lnwi = β0 + Xiβ1 + HDiβ2 + FTEiβ3 + ϵ,
(1)
where lnwi is the log of individual's labor earnings, Xi is a vector of standard demographic
controls, HDi is a vector of dummy variables measuring highest PSE award attained, and FTEi is
a measure of full-time equivalent years of PSE. There is expected multi co-linearity between
variables within the vector measuring highest post-secondary award attained. Therefore, I run
two models: one which includes type of degree obtained and another which measures only on
community college attendance.
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Data Sources and Expected Signs
The data used in this research is obtained from the National Education Longitudinal
Study of 1988 (NELS 88) published by the National Center for Education Statistics. This
household-based data collection was designed to address a wide range of education policy topics.
These topics include, but are not limited to, student learning, predictors of dropping out, and
school effects on students’ access to programs and opportunity for learning. Students reported on
a wide range of topics about school, work, home life, education resources, parent’s education,
neighborhood, future aspirations, extra-curricular activities, and achievement test scores. This
panel data set began collecting information from 8th grade students, their parents, and school
records in 1988 and continued with follow-ups in 1990, 1992, 1994, and 2000.
I have chosen to use the 2000 follow-up because it will show the best returns on
investment. By this time, many students have completed postsecondary education and joined the
workforce. As previously mentioned, I will be using variables for income, demographics, as well
as type and field of degree (see Table 1). Income is the respondent’s total income from all
sources before taxes. This includes salaries, wages, pensions, grants, financial aid, scholarships,
government assistance, dividends, interest and all other income. The vector of demographic
variables only includes gender and race. Race is used to determine if a respondent is in a
minority respondent; Black or Hispanic. If a respondent is in a minority then I expect their
income to decrease. Gender is used to determine whether respondent is male or female. Previous
research on the gender wage gap has shown that women see higher returns to a community
college education so I also expect women to see larger returns than men. Type of degree is used
to determine what type of degree was obtained by the respondent. This study will only be using
respondents who had obtained an associate’s degree, certificate, or just a high school diploma.
13
The reference group for this analysis is the white female respondent with only a high school
education; therefore respondents with education beyond an associate’s degree were removed.
This can be a potential limitation because of the large number of respondents that are removed.
There are near 12,000 respondents in the original NELS dataset; this research only uses 1391
respondents. Associate’s degrees should have the highest returns, followed by certificates then
high school diplomas. Field of degree is used to determine if the student received an engineering,
teaching, or nursing degree. The field of degree variables should have positive effects as
specialization of education should increase income. Engineering degrees are typically not going
to be possible to obtain through community colleges or other 2-year programs. However,
engineering technology degrees are available. I expect nursing degrees to have a large effect on
income because of their high demand. Previous research by Jepsen, et al (2012) mentions that
women in associate’s degrees in nursing have some of the highest returns overall. Full-time years
of post-secondary education will be used to test the human capital theory. The composite score
of standardized reading and math tests are used to determine the mental potential of a
respondent. As standardized test scores increase, income should increase. Parent years of
education are used to determine the amount of education a respondent’s parent received. The
more education a parent receives, the more they can teach a child. Income increases along with
education. Parental expectations are used to determine the amount of education a parent thinks
their child should receive. Expectations may have a positive or negative effect on income.
Expectations may increase education. Education may increase income. However, expectations
may be too intense, therefore causing a negative effect on income. Talk after is used to determine
if a student speaks with a counselor or teacher about future education plans after school. This
should have a positive effect on education which will have a positive effect on income.
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Results
Minority Included
The initial log-linear OLS regressions do not separate minority respondents from nonminority respondents; instead minority is input as a zero/one dummy variable. The reference
group for this study is the white, non-minority female with only a high school diploma. In the
first model (see Table 2:1) which does not include the type of degree obtained; minority
respondents, compared to non-minorities, see a 4% decrease in income. Male, compared to
female, have a 23% increase in income. Male is a highly significant variable at the 99% level.
Community college attendance increases income by 5% and is significant at the 10% level. Field
of degree obtained, and years of education are insignificant variables. The R-squared for this
model is 0.0688. Even though less than 7% of variation in this model is explained, the variables
have the signs expected.
The second model (see Table 2:2), which still does not separate for minority respondents
but uses associates degrees and certificates instead of community college attendance has many
results similar to the first model. Minority, male, and years of education are significant variables
at the minimum 90% level. Minority respondents, still included as a dummy variable, see a 4%
decrease in income. Male respondents increase income 23%. As years of education increase by
1, then income increases by 1%. Obtaining an associate’s degree or certificates are insignificant
variables, as well as the field of degree. The R-squared reduced to 0.0676 compared to the first
model. Yet again, less than 7% of variance is explained by the models used. Nevertheless, not all
of the variables have their expected outcomes. The two forms of the model have very similar
results
15
Minority Separated
The following results use the previous models and OLS, but are now sorted by whether
the respondent identified as a non-minority or minority to specifically analyze the effect of
education on the separate racial groups. The next model (see table 3:3) is for non-minorities with
the variable for attending a community college used instead of the type of degree obtained.
Unfortunately, the results do not show an effect for education. The white, non-minority male
respondent has a 25% higher income than the white, non-minority female respondent that is
significant at the 99% level. Community college attendance, field of degree and years of
education are insignificant. Although coefficients are similar as before, only male is a significant
variable at the 99% level; an R-squared for this regression of 0.0716 is understandable.
The same model (see table 4:5) uses the same education variables, but now is analyzing
for minority respondents compared to other minority respondents. Male respondents, who
identify as either black or Hispanic, have a 20% higher income than female minority
respondents. Male is the only significant variable at the 99% level. Community college, field of
degree, and years of education are insignificant variables. An R-squared of 0.0649 still means
less than 7% of variance is explained. As before though, only male is a significant variable so the
results mean very little.
The next model (see table 3:4) replaces the community college variable used previously
with associate’s degrees and certificates and looks at non-minority respondents. A white, nonminority male respondent in this model has an income 24% higher than a white woman. Male is
the only variable significant at the 99% level. Aggregate associate’s degrees, certificates, field of
degree, and years of education, are insignificant. An R-squared value of 0.0703 means 7% of the
variance is explained.
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The model in table 4:6 resembles the model from column 2 of table 3, but now is
analyzing for minority respondents. Now male and years of education are significant variables
above the 90% level. Male respondents, who identify as either black or Hispanic, have a 19%
higher income than female minority respondents. Years of education increase income by 2%.
Associate’s degrees, certificates, and field of degree are insignificant variables. An R-squared of
0.0689 still means less than 7% of variance is explained.
Overall, the models which included associate’s degrees and certificates seem to have a
slightly better result. When community college attendance was analyzed instead of type of
degree-more variation was explained and most of the variables had the expected signs. However,
the only. variables that were consistently significant at the minimum 90% level were male and
minority. The variation between non-minority and minority respondents may be due to the lack
of minority respondents. There were 959 white, non-minority respondents and only 432 black or
Hispanic, minority respondents in a total sample of 1,391. Though more of the variation was
explained in the non-minority models, there were more than double the observations.
Switching Regression
I use a switching regression model essentially because my previous OLS estimates are
inconclusive with few variables having significance even at the 90% level. OLS estimates also
cannot account for self-selection bias. There is a non-random sampling of minorities into certain
types of education; therefore there is likely unobserved bias within the sample. The first part of
the switching regression model determines, by a probit model, whether a student will attend a
community college or just high school. Self-selection bias is solved by correcting by non-random
sampling as well as incidental truncation. Non-random sampling can occur when dependent or
17
independent variables lack complete data due to an incomplete survey. Leaving respondents out
of analysis may lead to bias within the estimators. Incidental truncation is when the dependent
variable is not detected because of the outcome of another variable. The second part of the
switching regression model determines the income effects of those that finish their education at
community college, and those that finish their education at high school.
Education Choice
The first and second equations determine the probability that a student will graduate a
community college or just high school; respectively, based on personal characteristics.
Pr(Z=1) = β0+β₁(Xi)+(ui)
Pr(Z=0) = β0+β₁(Xi)+(ui)
Income Effect
From the education choice model, two equations are created to measure the income
effects of the education. The first predicts the income of a respondent who graduated a
community college. γλ is the probability from the respective education choice model.
Y1=β0+β1(X1)+γλ`+(u1)
The second equation predicts the income of a respondent who graduated only high
school.
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Y2=β0+β1(X1)+γλ0+(u1)
In the first part of the switching regression, the minority respondent is more likely than a
non-minority respondent to attend a community college and is significant at the 99% level. A
male respondent is also more likely than a female respondent to attend a community college. An
increase in standardized test scores increases probability of completing a community college
degree compared to completing high school education. As a respondent’s parent’s years of
education increase by 1 year, then a respondent increases likelihood to attend a community
college. When a respondent’s parent expects an additional year of education from their child,
then they are more likely to attend a community college. If a respondent talks to a teacher or
counselor after school about future education plans then they increase their likelihood of
attending a community college.
Looking at the probability of just completing high school, a non-minority respondent (see
Table 7) is less likely than a minority respondent to stop their education after receiving a high
school diploma. The male respondent is more likely than a female respondent to end education
after a high school diploma. Standardized test scores increasing, increases likelihood of finishing
education after high school.
In the second part of the switching regression, there are non-significant results of most
significant community college degrees on income. Most of the results which are significant, are
significant at least the 90% level. A minority community college graduate (see table 6) sees only
a 14% wage decrease compared to a non-minority community college graduate and is significant
at the 99% level. The male community college graduate increases their income by 28% over a
female community college graduate. A minority high school graduate (see table 8) has less 35%
19
less income compared to a non-minority high school graduate. A male high school graduate
increases their income by 6.8% over a female respondent.
Comparing minority to non-minority respondents, a minority individual is less likely to
stop their education after high school. A minority individual with a community college degree
has 14% less income than a non-minority individual with the same education. A minority
individual with only a high school diploma has 35% less income than a non-minority individual
with the same education. Therefore, the minority individual has less of a wage penalty if they
attend community college, as the minority individual with a community college education
increases their income by 20% over the minority individual with only a high school diploma.
Conclusion
This research set out to analyze the effect of community college education on minority
students. From my results, you can conclude that attending a community college has a positive
effect on minority income. Minorities are more likely to just complete high school, however if
they attend community college, they will have a 20% less wage penalty. Many OLS models did
not show statistically significant effects of community college education on income. In table 2:1,
community college education is significant at the 90% level. The switching regression results
have a much more positive result. Comparing minority to non-minority respondents, a minority
individual has less wage penalty if they attend community college. The minority respondent with
a community college degree has 13% less income than a non-minority female, the minority
respondent with only a high school diploma has 32% less income.
There are limitations to this research. NELS 88 is more concentrated with non-minority
respondents. There are high standard errors in this research which may stem from a lack of
20
minority respondents. High standard errors cause results to vary greatly from the mean estimate.
In a sample size of 1391, only 432 identify as black or Hispanic.
Another limitation of this research is that the coefficients of the first step of the switching
regression model cannot be interpreted. There is a non-linear relationship of the coefficients to
the probability of completing a community college degree. Currently, only the signs can be
interpreted in the first step. Since all of the variables have a positive effect, in order to continue
research, it would be necessary to modify code to be able to figure which most affects
probability.
The policy implications of this research are important. Currently minority individuals are
given large amounts of financial aid and grants to continue education beyond high school simply
because of their minority status. Many of these individuals waste their money by immediately
continuing education at a more difficult four-year institution and drop out. While it is likely still
possible for a minority individual to obtain labor market returns from a four-year institution,
two-year institutions clearly offer returns as well. There should be stipulations or at least strong
suggestions put on minority financial aid that the money be used for community college
education.
This research would be interesting to continue by analyzing fields of study in more depth,
as well as using a new data set. There are more fields of study offered at community colleges
than just engineering technology, teaching, and nursing. Jespsen et al (2012) used data was from
a single administrative area. Observations differed along time and difference and award type,
allowing for the “preferred student fixed effect model” to be used. NELS 88 data did not lend
itself to the same type of analysis. The reason for this analysis was outdated, incomplete data.
21
Using even more recent data from new data sources would lead to more complete research.
References
Gonzales Arturo, Hilmer Michael. “The role of 2-year colleges in the improving situation of
Hispanic postsecondary education” Economics of Education Review, 2006, pages 249257.
Grubb, W. Norton. “Returns to Education in the Sub-Baccalaureate Labor Market 1984-1990.”
Economics of Education Review. Vol 16 (3), June 1997.
Jepsen Christopher, Troske Kenneth, Coomes Paul. “The Labor-Market Returns to Community
College Degrees, Diplomas, and Certificates.” Discussion paper 6902, Institute for the
Study of Labor, October 2012.
Kane Thomas, Rouse Cecilia. “Labor-Market Returns to Two- and Four- Year College.” The
American Economic Review, June 1995, Volume LXXV, pages 600-614.
Marcotte Dave, Bailey Thomas, Borkoski Carey, Kienzl Greg. “The Returns of a Community
College Education: Evidence from the National Education Longitudinal Survey.”
Education Evaluation and Policy Analysis, 2005, Volume XXVII, pages 157-175.
Monk-Turner, Elizabeth. “Economic Returns to Community and Four-year College Education.”
The Journal of Socio-Economics, 1994, Volume XXIII, pages 441-447.
Weinstein, Michael. “Why We Must Prize Community College Success.” Forbes. April 2014.
22
Appendix
Table 1: Variable Definition and Descriptive Statistics
Variable
Definition
Income, all
sources, of
respondents
Respondent
Minority
identifies as black
or Hispanic
Respondent is
Male
male
Respondent
Certificate
receives a
certificate as
highest CC award
Respondent
Associate
receives an
associate’s degree
as highest CC
award
Respondent
Comc
attended a
community
college
Respondent
Dnursing
received a nursing
degree
Dengineering Respondent
received an
engineering
degree, all
disciplines
Respondent
Dteach
received a
teaching degree
Years of PSE
Yearseduc
attained by
respondent
Respondent’s
Stantest
score on a
reading/ math
standardized tests
Income
Mean
Expected sign
25998.34
Standard
deviation
17445.77
0.31
0.46
-
0.53
0.49
+
0.12
0.33
+
0.53
0.49
+
0.70
0.45
+
0.007
0.08
+
0.01
0.09
+
0.01
0.12
+
2.85
1.52
+
51.64
10.13
+
23
Respondent’s
1.73
.64
+
parent’s years of
PSE education
Respondent’s
2.61
1.36
+/Expectation
parental
expectations of
years of PSE
completed
Whether a student 0.38
0.03
+
Talkafter
talks after school
about future
education plans
with a teacher or
counselor
Source: National Education Longitudinal Study of 1988 , 2000 follow-up
Parentyears
Table 2: Pooled OLS Regression Results, including minority
(absolute values of t-values in parenthesis)
Variable
1
2
9.85 (284.24)*** 9.89 (285.83)***
Intercept
-0.04 (1.84)*
-0.04 (1.80)*
Minority
0.23 (9.69)***
0.23 (9.58)***
Male
-0.01 (0.45)
Associate
0.03 (0.95)
Certificate
0.05 (1.90)*
Comc
0.13 (1.13)
0.13 (1.10)
Dengineering
0.08 (0.14)
0.03 (0.36)
Dteach
0.08 (0.61)
0.02 (0.21)
Dnursing
0.01 (1.58)
0.01 (1.72)*
Yearseduc
R²/ Adj. R²
0.0688
0.0676
Observations
1391
*** 99% significance
** 95% significance
24
* 90% significance
Table 3: Pooled OLS Results, Minority=0 (white)
Variable
Intercept
Male
Associate
Certificate
Comc
Dengineering
Dteach
Dnursing
Yearseduc
R²/ Adj. R²
Observations
3
4
9.86 (234.72)*** 9.89 (237.27)***
0.25 (8.36)***
0.24 (8.30)***
0.003 (0.10)
0.02 (0.54)
0.04 (1.28)
0.27 (1.44)
0.26 (1.39)
0.02 (0.20)
0.03 (0.34)
-0.01 (0.08)
-0.06 (0.35)
0.008 (0.85)
0.008 (0.90)
0.0716
0.0703
959
Table 4: Pooled OLS Results, Minority=1 (black and Hispanic)
Variable
Intercept
Male
Associate
Certificate
Comc
Dengineering
Dteach
Dnursing
Yearseduc
R²/ Adj. R²
Observations
5
6
9.78 (165.83)*** 9.84 (166.88)***
0.20 (4.84)***
0.19 (4.57)***
-0.05 (1.12)
0.05 (0.91)
0.06 (1.43)
0.05 (0.33)
0.07 (0.48)
-0.001 (0.01)
0.02 (0.10)
0.19 (0.92)
0.14 (0.67)
0.02 (1.56)
0.02 (1.91)*
0.0649
0.0689
432
25
Table 5: Community College Probit
Parameter-Probit
Comc.intercept
Comc.minority
Comc.male
Comc.stantest
Comc.parentyears
Comc.expectation
Comc.talkafter
Estimate (t-value)
1.7211 (16.43)***
0.4931 (4.09)***
0.0182 (5.21) ***
0.0133 (1.98) **
0.0645 (1.93)*
0.0747 (1.78) *
0.4358 (2.11) **
Table 6: Community College Switching Regression OLS
Parameter-OLS
lnincome.intercept
Lnincome.minority
Lnincome.male
Lnincome.dbusiness
Lnincome.deducation
Lnincome.dengineering
Lnincome.dnursing
Lnincome.dteach
Lnincome.stantest
Lnincome.lambda
Estimate (t-value)
7.2613 (12.94)***
-0.1462 (3.21) ***
0.2845 (4.54)***
0.0131 (0.74)
0.0087 (0.28)
0.0055 (0.59)
0.912 (0.85)
0.0054 (0.61)
0.0927 (1.51)
-0.6852 (2.96)***
26
Table 7: High School Probit
Parameter-Probit
Comc.intercept
Comc.minority
Comc.male
Comc.stantest
Comc.parentyears
Comc.expectation
Comc.talkafter
Estimate (t-value)
-1.7211 (16.43)***
-0.4931 (4.09)***
-0.0182 (5.21)**
-0.0133 (1.98)
-0.0645 (1.93)*
-0.0747 (1.78)
-0.4358 (2.11)
Table 8: High School Switching Regression OLS
Parameter-OLS
Lnincome.intercept
Lnincome.minority
Lnincome.male
Lnincome.stantest
Lnincome.lambda
Estimate (t-value)
11.8742 (7.17)***
-0.3546 (3.98)***
0.0684 (2.66)***
0.0723 (1.51)
-0.5366 (3.14)***
SAS Code
27
Data seniorproject;
Lnincome=log(income);
If sex=1 then male=1;
Else sex=0;
If degree=1 then certificate=1;
Else certificate=0;
If degree=2 then associate=1;
Else associate=0;
if field =140 then Dnursing=1;
else dnursing=0;
if field =521 then Dengineering=1;
if field =522 then Dengineering=1;
if field=523 then Dengineering=1;
if field=524 then Dengineering=1;
else Dengineering=0;
if field =15 then Dteach=1;
else Dteach=0;
if field =96 then dbusiness=1;
else dbusiness=0;
if field =300 then dteach =1;
else dteach=0;
set seniorproject;
run;
proc means;
run;
proc reg;
model lnincome= minority male comc dengineering detach dnursing yearseduc;
model lnincome= minority male associate certificate dengineering detach dnursing yearseduc;
run;
proc reg;
model lnincome= male comc dengineering detach dnursing yearseduc;
model lnincome= male associate certificate dengineering detach dnursing yearseduc;
sort by minority;
run;
prog qlim data = seniorproject;
prob: model comc=minority male stantest parentyears expectation talkafter/ discrete
(d=probit);
prob: model lnincome=minority male dbusiness deducation dengineering dnursing dteach
stantest / select(comc=1);
run;
28
proc qlim data=seniorproject;
prob: model comc=minority male stantest parentyears expectation talkafter/ discrete
(d=probit);
prob: model lnincome= minority male dbusiness deducation dengineering dnursing dteach
stantest / select(comc=0);
run;
29