NORTHWESTERN UNIVERSITY

HIGHER EDUCATION ALTERNATIVES FOR DISADVANTAGED STUDENTS

B.A. THESIS SUBMITTED TO

THE FACULTY OF THE DEPARTMENT OF MATHEMATICAL METHODS IN THE

SOCIAL SCIENCES (MMSS)

IN PARTIAL OF FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF BACHELOR OF ARTS

ANDREA MARCOS HADJÓPULOS

EVANSTON, ILLINOIS

MAY 15, 2012

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HIGHER EDUCATION ALTERNATIVES FOR DISADVANTAGED STUDENTS Abstract

The research question that lead this thesis was the following: What is the most rewarding

educational degree for the economically and academically disadvantaged population of

students to pursue after graduating from high school? Using two different statistical methods

that accounted for selection bias, multivariate regression analysis and propensity score

matching, the economic return of a bachelor’s degree, an associate’s degree, and a certificate

were estimated for the sub-population of interest. The results indicate that the returns to a

bachelor degree might not be worth the effort for the typical disadvantaged student to attain

without considering the possibility to obtain a scholarship. The return of an associate’s degree

is also not that clear without taking the student’s intended field of study into account. After

accounting for the average real and opportunity costs involved, this thesis concludes that a

lack of post-secondary degree alternatives exist for the average disadvantaged student to

pursue after high school.

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Acknowledgements

This research project would not have been possible without the support of many individuals.

The author wishes to extend her sincere gratitude to her thesis advisor, Prof. James

Rosenbaum, for his continuous guidance and shared insight in the whole process. Deepest

gratitude is also due to the MMSS and Sociology department thesis advisors, Prof. Joseph

Ferry and Prof. Carolyn Chen, respectively. Other key individuals were the group of teacher

assistants, Christopher Carroll, Kelly Becker, and Aanchal Jain, whose knowledge and

support made this study successful. Special thanks to all her classmates in both departments

for their shared enthusiasm and support.

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Table of Contents Introduction…………………………………………………………………………………….1

Literature Review………………………………………………………………………………2

Methods and Hypothesis………………………………………………………………...……..8

Limitations and Student Sample Demographics………...............................................11

Variable Specification………………………………………………………………...13

Data Analysis

Method 1) Multivariate regression analysis…………………………………………..17

Method 2) Propensity score matching .........................................................................24

Discussion and Implications for Future Research and Policy………………………………..36

Conclusion……………………………………………………………………………………45

Endnotes………………………………………………………………………………………47

References…………………………………………………………………………………….49

Appendix A……………………………………………………………………………….......52

Appendix B…………………………………………………………………………………...53

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List of Tables

Table 1: Return of educational degrees based on short-term yearly income (logged) among the

different sub-populations……………………………………………………………..20

Table 2: Return of educational degrees based on probability of attaining a professional or

managerial position in the short-run among the different sub-populations…………..30

Table 3: Probit regressions reporting marginal effects of educational credentials on perceived

PSE impact among different sub-populations………………………………………...34

Table 4: Propensity score matching estimates using B.A. as dependent variable, ln(Income) as

outcome………………………………………………………………………..……...53

Table 5: Propensity score matching estimates using A.A. as dependent variable, ln(Income) as

outcome……………………………………………………………………………….54

Table 6: A summary of results of matching estimates of effect of each dependent variable

(B.A. or A.A.) on yearly income……………………………………………………..55

HIGHER EDUCATION ALTERNATIVES FOR DISADVANTAGED STUDENTS

Introduction

Extensive research has been done to analyze the return of postsecondary education

based on graduates’ employment outcomes in the United States (Beach 2009, Dale & Krueger

2002, Gill & Leigh 2000, Grubb 1993, 1997, 2004, Mishel et al. 2007, Paulsen 2001, Stern et

al 1995). Nonetheless, findings from current research have failed to determine the best

options of postsecondary education for the population of low-achieving high school graduates

from low-income backgrounds, who fail to see the benefits that a college degree can offer

them. It has been shown that more education does, on average, lead to higher earnings and

more stable employment (Brand and Halaby 2006; Goodman 1978; Iwamura 1996; Monk-

Turner 1994) but this does not apply to all individuals. Socioeconomic status, race, gender

and academic markers mediate the actual benefits of vocational credentials (Thomas Bailey

2004), which are the ones that low-achieving, low-income students usually pursue after high

school. It may be the case that for some low-income students, an associate’s degree may have

a greater payoff than a bachelor’s degree in certain occupations after accounting for the

differences in costs between a two-year community college and a four-year college. Future

research is needed to determine the most rewarding options of postsecondary education for

disadvantaged youth to increase their possibility for upward social mobility.

This paper will start with a broad look at the overall body of literature on the returns to

higher education and then narrow the focus on previous findings that specifically look at

economically and academically disadvantaged students. Then, the two-method analysis that

composes this study will be explained, followed by a discussion of potential limitations that

must be taken into account when completing this research on the return of educational

attainment. Finally, the results of both statistical methods on the return of post-secondary

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education for disadvantaged students will be described and discussed along with its policy

implications.

Literature Review

Social reproduction is understood as the processes that perpetuate the current socio-

economic structure. The theory arises from the belief that there exists racial/ethnic, cultural

and institutional rooted barriers in society that impede any type of mobility among members

in different socio-economic groups. There is a prevalent notion that a college degree is a

necessary prerequisite for employment in America today. However, even though most high

school students aspire to achieve a college degree, educational institutions do not seem to be

giving disadvantaged students the necessary tools to successfully complete a college degree

(Bowles & Gintis 2002; Jencks 1998; Rist 1970; Roderick & Nagaoka 2008; Rosenbaum

2001). On the other hand, research findings suggest there is an unclear pay-off for

academically and economically disadvantaged high school graduates from achieving post-

secondary education (Bailey, Kienzl, and Marcotte 2004; Beach 2009; Grubb and Lazerson

2004; Jere R. Behrman 1996). This inconsistency leaves disadvantaged students without a

proper higher education alternative to pursue and, furthermore, imply that post-secondary

institutions are not serving their function as mediums for an improvement in lifestyle to occur

among low socio-economic status (SES) families.

Research (Behrman et al, 1996) suggests that there are important demographic

differences in the wage benefits of college. “The estimated wage benefits from higher college

quality and more time in college tend to be highest for nonwhite males, next for nonwhite

females and then white females, and least for white males” (Behrman et al, 1998). The

estimated net gains for these different populations suggest that there appear to be incentives

for nonwhites to increase time in college but not so much for whites, which contrasts with

common interpretations of earnings equations. Therefore, there seems to be unrealized

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potential for taking advantage of these expected net benefits from more time in college or

attending higher-quality colleges, especially for minorities and women. This can be due to

several factors such as poor information about college, lack of knowledge on financial aid

options, discriminatory admissions or poor quality credentials, as the authors point out.

Behrman’s study, however, does not account for college selectivity and its potential impact in

future earnings.

Further research has looked at an institution’s level of selectivity as a determinant for

future earnings. This is an especially important issue to look at since many unobservable

attributes that allow students to be accepted in selective colleges may be the same attributes

that influence future earnings. This selection bias has been very problematic for past studies

that have used Ordinary Least Squares (OLS) regressions to account for differences in student

attributes that may be correlated with future earnings. Correlation of these unobserved student

variables with future earnings tend to produce biased OLS estimates that overstate the payoff

of attending a selective institution. Dale and Krueger (2002) complement previous studies that

try to control for this selection bias and look at the effect of school selectivity on earnings

using the College and Beyond data set and the National Longitudinal Survey of the High

School Class of 1972. Their findings suggest that students who graduated from more

selective colleges earned about the same as those students with the same qualifications who

attended less selective colleges. An interesting explanation that the authors give for this lack

of return to school selectivity is that students get a better return in terms of future earnings if

they rank higher in less selective institutions. Dale and Krueger go a step further to test this

assumption and they find that students who would attend a more selective school (100

average SAT points higher) would be ranked about 5 to 8 percentile points lower in their rank.

Moreover, when class rank is added to the wage equations, the authors find that students who

graduate 7 percentile points higher in their class had higher expected earnings of about 3

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percent, which may suggest that the advantages of class rank offset any advantages of

attending an elite college on earnings. An interesting finding, however, is that school

selectivity does seem to matter for the low-income population. When the authors add an

interaction term between the school average SAT score (measuring selectivity) and the log of

parental income, the coefficient is significant and negative. This indicates that for a student of

low-income background, the payoff of attending a more selective college (200 SAT points

higher) is higher (8 percent), compared to the insignificant payoff on earnings for a person

with mean income attending a college with the same level of selectivity (Dale & Krueger

2002:1518). The college’s environment or influential peer effects of higher ability students

may be some of the factors behind this correlation between a school’s selectivity and a low-

income student’s expected earnings, as some studies have pointed out (Goethals 2001,

Gordon & Zimmerman 2003). Given the difficulty of measuring peer effects in such exposed

environments, this outlier relationship deserves further study.

Another body of literature looks more specifically at two-year or sub-baccalaureate

credentials and their role in the labor market. The increasing enrollment at the sub-

baccalaureate level has lead researchers to test whether occupational fields offer students

good opportunities or whether these lead them into occupations that restrict their possibilities

for upward social mobility. Many authors’ studies support the idea that a sub-baccalaureate

credential can lead to increases in earnings (Bailey, Kienzl, and Marcotte 2004). Stern et al.

(1995) estimated the monetary impact on earnings of an associate’s degree and found that its

value is between $1,000 and $2,000 more a year than a high school diploma. Furthermore, the

earnings differential was greatest for women with a vocational degree and insignificant for

males (Beach 2009: 32). Paulsen (2001) also reviewed the literature on the returns to

investment in sub-baccalaureate education and his findings suggest that “one year of

community college credit, independent of earning a degree can lead to average increased

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earnings between 5 percent and 8 percent, and two years of credit can lead from 10 percent to

16 percent increased earnings. The effect of a sub-baccalaureate credential, either a certificate

or an associate’s degree, can lead to earnings increases between 15 percent and 27 percent”

(Gerber and Cheung 2008: 304). Moreover, the average earnings potential was higher for

women and low-income graduates, although it may be the case that results were skewed for

the higher earnings of nursing in particular. Even though these studies suggest that two-year

college degrees may be beneficial to high school graduates from low-SES backgrounds, the

results for entry-level earnings are not the same across all industries and are often quite

mixed.

In Working in the Middle, Grubb (1996) shows the returns to certificates, associate

degrees, and baccalaureate degrees by field of study. Two-year courses on technical, business,

or health related industries yielded positive returns on earnings while the rest of the areas did

not seem to have a significant effect on earnings. Men have an average return to vocational

certificates of about 15 percent for 1987, probably influenced the most by the effects of

engineering, computer, and health-related certificates. For women in 1987, only health-related

certificates had significant returns, and business and vocational/technical as well in 1990.

Men had clearer returns to associates degrees in engineering and computer fields in 1987 and

in business in 1990. For women, business and health-related occupations had constant

significant returns but failed to obtain economic returns in other fields, probably due to

gender dominated employment patterns. For baccalaureate degrees, returns in most areas were

significant and gender consistent (SEE Grubb 1996 Working in the Middle, Table 3.3 p. 95).

It is therefore evident, that the field of study an individual decides to enter matters

significantly at the sub-baccalaureate level. Grubb separates the earning potential based on

gender and field of study, but fails to account for differences in students of different academic

ability and socioeconomic background.

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In The Education Gospel: The Economic Power of Schooling, Grubb (2004) follows

up on the previous study by comparing the private economic pay-off of the different degrees

of schooling based on March 2000 data on earnings from Current Population Survey.

Furthermore, apart from looking at the empirical differences in earnings across postsecondary

degrees, Grubb considers the internal rate of return on education, which includes direct and

indirect costs of schooling, like actual tuition, and opportunity costs, like the earnings

foregone while enrolled in a postsecondary institution. His findings show that these rates of

return have increased from 15% in 1942 to 16.5% for 1978. When looking at the variation of

rates of return between occupations and genders, Grubb’s findings suggest key insights for

further research.

“At the postsecondary level, the economic benefits of one-year certificates are high for women in business and in health occupations, while fields like child care, engineering, and computer-related fields for women, and craft occupations for men, have low benefits or even no benefit. Among two-year associate degrees, only those in business, engineering, and computer-related fields and health (dominated by nursing) are substantially more valuable than other, while those in education (largely child care), public service (like fire and police protection), and various craft occupations yield no greater benefits than a high school degree (Grubb, 1997b, 2002a). At the baccalaureate level, graduates in engineering and health enjoy the highest benefits, followed by business and science/mathematics majors, those with degrees in the humanities, the social sciences, and education rank at the bottom” (Grubb 2004).

These findings are a sound basis to provide a sense of direction to those individuals who

increasingly decide to enroll in sub-baccalaureate education. Grubb’s study however, uses

data before the 1980s and much has changed since then. Moreover, even though his study

shows that economic benefits exist for the average individual to continue higher education,

the pay-off of a postsecondary degree are not clear for many subgroups of the American

population.

More recent evidence exists that supports Grubb’s findings and extends on his

research by looking at the differences in returns to a sub-baccalaureate education based on

gender and race. Bailey et al. (2004) focus their research further by assessing the returns of

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the sub-population of academically and economically disadvantaged students using the NELS

1988 data set. The authors’ findings show that most students do receive economic benefit

from sub-baccalaureate education, although the effects are different by gender and degree

completion. The average economic return for low-achieving women who attain an associates

degree are positive and significant, roughly 44 percent greater than women with only a high

school degree. However, for women that do not complete their associate’s degree, the returns

are none or even negative. Therefore, women should have a strong incentive in completing a

sub-baccalaureate degree. Unlike women, low-achieving men experience positive economic

returns from both, earning an associate degree and occupational coursework, even when it

does not lead to a credential. Interestingly however, both academically low-achieving men

and women who complete certificates do not show an increase in their earning potential as

compared to similar high school graduates. In sum, the study finds that academically

disadvantaged women gain economic benefit from earning a certificate or an associate’s

degree, yet no significant return exists when a credential is not attained. On the other hand,

academically disadvantaged men benefit from an occupational education whether they earn an

associate’s degree or not.

When looking at the economically disadvantaged sub-population, the study shows

positive earning advantages for men and women that are statistically different from the

earnings of high school graduates. Economically disadvantaged men and women experience

about a 26-28 percent return after achieving an associate’s degree and a 20 percent return

from achieving a certificate. Even though these percentages seem overestimated, the

attractiveness of completing an occupational education rather than a bachelor’s degree for

disadvantaged high school graduates, seems to be leading this sub-population in the right

direction. However, since Bailey et al. do not break down the differences in returns based on

professions, the variability of these returns of sub-baccalaureate education among occupations

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for disadvantaged students cannot be seen. Given current research on the significant

differences in the returns to sub-baccalaureate education based on occupations, these returns

cannot be used to guide policy strategies and allocation of financial resources among

occupational programs. Moreover, the finding that women do not benefit from a community

college education without earning a degree as opposed to men who do is unsettling and

requires further exploration.

Extensive research has been done that attempts to model the return of higher education

but most studies suffer various limitations. Overall, the literature on the returns of higher

education is polluted by selection bias and each study uses a different method to account for

this critical issue. Additionally, few studies have concentrated on sub-baccalaureate education,

specifically on certificates and associate’s degrees. Furthermore, little attention has been

given to sub-groups of disadvantaged populations. Given such limitations of the wider body

of literature on the economic returns in higher education, further investigation is required to

test the many emerging but often varied results of recent research on employment outcomes

of sub-baccalaureate graduates from academically and economically disadvantaged

backgrounds. This study will therefore extend on the economic and sociological literature on

higher education by demonstrating how educational attainment translates in the labor market

for different groups of the disadvantaged sub-population. Apart from specifically focusing on

the sub-population of disadvantaged individuals, the analysis will consist on how the different

sources of such disadvantages, economic and academic, interact in the labor market.

Methods and Hypothesis

To study the labor outcomes of sub-baccalaureate education on the lives of

economically and academically disadvantaged youth1, this study examined the educational

1 For the purposes of this study, academically disadvantaged students are defined as those with average grades of Ds and Cs in high school, and economically disadvantaged students are those from the bottom two quartiles of

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track and employment outcomes of a national sample of students. Specifically, the returns of

a certificate, an associate’s degree (A.A.) and a bachelor’s degree (B.A.) were compared

among different sub-populations of high school students based on three types of employment

outcomes: 1) yearly income 2) prestigious occupations and 3) perceived impact of post-

secondary degree attainment. As the demand and need for higher education in the United

States grows, the need for alternative options to completing a four-year degree also grows,

especially for those academically and economically disadvantaged high school students.

Given the growing demand for sub-baccalaureate labor (Rosenbaum 2006), the hypothesis

that I want to test is whether economically and academically disadvantaged graduates who

achieve an A.A. in two-year colleges get a higher return than what they could have obtained

from achieving a B.A. degree, considering the estimated real and opportunity costs of

completing a four-year college for this sub-population of students.

In order to test the hypothesis, the National Educational Longitudinal Survey of 1988

(NELS) was used since it contains extensive data on a national sample of high school students

followed for eight years after their high school graduation. This database allowed me to look

at information on key pre-college characteristics of respondents and their employment

outcomes collected in the spring of 2000. Specifically, information on the students’

socioeconomic status (SES) and high school grades was used to compare the employment

outcomes of economically and academically disadvantaged students that completed a sub-

baccalaureate education with those similarly disadvantaged students who achieved bachelor’s

degrees. The analysis used employment variables from the NELS such as earnings,

occupation2 and perceived post-secondary (PSE) degree impact. These outcomes are variables

their reported socioeconomic status (SES). The term “disadvantaged” is used to categorize academically low-performing students since they are at a disadvantage to obtain a scholarship to pay for post-secondary education, and are therefore limited to complying with the standards of a loan if needed. 2 Managerial and professional positions often define middle-class status, which indicate potential for upward social mobility. These occupations will be considered as outcome variables and serve as indicators for upward social mobility among the disadvantaged student sub-population.

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that were taken out of the fourth and last follow-up wave in 2000, at the time where

respondents were around 26 and 27 years of age and had graduated from high school in 1992.

The yearly earnings were logged. The occupation of students was broken down mainly into

blue-and white-collar jobs. The white-collar jobs however, were further categorized into

specific areas such as professional and managerial, engineering, computer science and IT,

medical services, clerical, education, and other white-collar, in order to make comparable

categories to the ones mentioned in the findings on the return of education mentioned above.

A more detail explanation of how this process was completed can be found in the appendix.

To account for the problem of selection bias when completing research on the return

of education, the analysis consisted of a two-method approach. First, a probit model was used

to estimate the rate of return of different levels of education for different sub-populations.

Four separate linear regressions were run using the short-term income as the outcome variable,

and three independent dummy variables for bachelor’s degree (1 if responded completed a

B.A. degree and 0 otherwise), associate’s degree, and certificate degree attainment. The

coefficients of these three variables were compared in significance and magnitude to examine

differences in the return to educational attainment among four sub-populations of students: 1)

low-SES and low-achieving, 2) low-SES and high-achieving, 3) high-SES and low-achieving,

4) and high-SES and high-achieving. A wide range of independent variables were included in

this model that tried to capture the influence of pre-college characteristics, such as

demographic background, college aspirations, parent’s expectations, and peer, teacher and

school effects. These variables were chosen to be included in the model because of past

literature that suggests these are influential variables in the process of attaining higher

education (Sewell & Shah 1967). The process through which these variables were created is

explained in the Variable Specification section.

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Even though the return of educational attainment for the disadvantaged sub-population

of interest could be examined through the comparative analysis previously explained, another

approach was taken to complement these findings. As mentioned above, most literature that

tries to model the rate of return of higher education is polluted due to the issue of selection

bias. It is therefore necessary to control for the possibility that students in educational

institutions may self-select and students who actually obtain a certain type of degree may not

be random. In order to account for this problem of selection bias, similar students were

grouped together through a statistical method known as propensity score matching (PSM).

Covariates such as race, gender, SES, parental education, high school Math and English

grades and test scores, peer influence, teacher and parent support, and individual college

aspiration were considered in order to group the sample into subsets of respondents who share

similar pre-college characteristics. Through this method, predicted probabilities were obtained

of completing a B.A. degree for the entire sample of disadvantaged students who did and did

not attain a degree. Thus, this approach allowed for a more unbiased comparison of

employment outcomes among respondents in the sample, independent of their actual degree

attainment. A more detailed explanation and comparison of the two methods used, multi-

variate regression and propensity score matching, is included in the Endnotes section.

As will be discussed below, there are important limitations to consider when

generalizing over the return of achieving a degree for different sub-populations. Regardless of

such limitations, however, the NELS dataset is still the most extensive and valid source of

information to study the return of baccalaureate and sub-baccalaureate education for

disadvantaged high school graduates.

Limitations and Student Sample Demographics

Although the NELS contains rich information on educational tracks and economic

returns of a nationally representative sample of respondents, the data have important

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limitations. First, the information collected after 1994 is not as extensive as what was

collected prior to that year. Second, earnings data is provided for up to eight years after high

school graduation and six years after scheduled graduation from a two-year program, the

earning data for those with B.A. degrees can only be observed four years after scheduled

graduation. Thus, it is worthy to mention that the 1999 earnings reported in the last NELS

follow-up are not ideal since it may take young people some time to settle into their long-term

careers that would best reflect their earning potential. Most respondents who achieve a

bachelor’s degree are around 26 years old and have just recently graduated from college.

Moreover, a final limitation that is related to this final observation and that various studies

(Jere R. Behrman 1996; Monk-Turner 1994) mention, is the importance of controlling for the

number of years of education acquired in calculating the economic returns to educational

degrees. Unfortunately, however, most respondents in the NELS legitimately skipped the

question that attempted to capture the years of full-time post-secondary coursework.

Therefore, this study fails to statistically control for the years of post-secondary education.

Nonetheless, a discussion on the real and opportunity costs of the different degrees will be

included in the analysis of the results that takes into account the average differences in years

of post-secondary coursework.

Before analyzing and comparing the educational paths and employment outcomes of

the NELS student sample, it was necessary to restrict the data that was used in the regression

analysis. For obvious purposes, I only kept the respondents who received their high school

diploma, which is about 85 percent of the sample or 10,417 students. Additionally, since I

was interested in the employment outcomes of the sample, I only used the respondents who

were not enrolled in a postsecondary institution at the time of the last follow-up. I also

eliminated the respondents who reported earning a graduate or professional degree. These last

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restrictions left me with a total sample of 7,675 students, where 34 percent of these enrolled

in a sub-baccalaureate program after high school and 33 percent in a baccalaureate program.

Even though the reported enrollment rates are fairly high, there seems to be an

achievement gap since relatively few respondents reported a certificate or AA as their highest

degree. Only 7.9 percent of the entire sample reported that their highest degree earned was a

certificate and 7.3 percent reported having completed an associate’s degree. Interestingly, 30

percent of the entire sample reported having some post-secondary education but no degree

attained by 2000. On the other hand, about 30 percent of the sample reported having

completed a bachelor’s degree. I will extend the discussion on the various credentials and

those attending college and not completing a credential in the analysis of the data. Despite

these limitations, NELS is still the best education data set to produce authoritative and valid

findings.

Variable Specification

The variables from the NELS had to be recoded in order to fit the purposes of this

analysis. Six key groups of covariates were created which were included in both sections, the

section that examined the likelihood of disadvantaged students to achieve a bachelor’s degree

(B.A.) and the section on the differences in the return of postsecondary degree attainment

using propensity score matching. The categories were defined as 1) general demographics, 2)

parents’ academic expectations for their children while in high school; 3) academic

achievements and individual academic aspirations 4) peer influence on college attainment; 5)

school environment and 6) teacher influence on the respondent’s education path. A

description of these variables is required prior to the interpretation of the models followed

below.

The demographic variables were composed of a dummy variable for gender,

socioeconomic status (SES), race, and whether the respondent had attended a public or private

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high school. The dummy variable for gender equaled 1 if the respondent was a female and 0 if

the respondent was a male. The SES variable estimated socioeconomic status based on the

parent questionnaire data in the base year, first follow-up and second follow-up which

included both parent’s highest level of education attained, occupation and 1992 household

income. The racial categories included 1) Asian, Pacific Islander, 2) Hispanic, 3) Black, not

Hispanic, 4) White, not Hispanic, and 5) American Indian and Alaskan. The category (4) of

non-Hispanic whites was left as a base group and dummy variables were included for all the

rest. Finally, a dummy variable was included to indicate whether the respondent had attended

a private or public high school (Private=1). It is worthy to note here that the large majority of

the student sample attended a public high school (84.3 percent) where as only 6 percent of the

attended a private high school and the rest attended a catholic high school.

The variable for parent’s academic aspirations for the students was created as follows.

A variable for the father and mother’s college expectation was coded as 1 if the respondent

answered the following questions as 8, 9, or 10:

How far in school father (mother) wants respondent to go?

0) Does not apply – 5.6 %

1) Less than HS – 0.4 %

2) HS only – 3.3 %

3) Less 2 years/school – 0.7 %

4) 2yrs more/school – 1.5 %

5) Trade school degree – 3.0 %

6) Less 2yrs college – 0.6 %

7) More 2yrs college - 5.7 %

8) Finish college – 29.9 %

9) Master’s degree – 12.1 %

10) Ph.D., M.D., Other – 12.3 %

11) Don’t know – 6.9 %

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Then the variable for parent’s college expectation was created by the average sum of the

bivariate variables created for each parent’s expectations and coded as “pacollexp”.

The category of academic achievement included the respondent’s Math and English

grades and test scores delivered by the NELS. The high school grades used were the average

grades in Math and English of the respondent while in high school. The variable for the Math

and English grade was a composite of the average in which ‘1.00’ represented the highest

grade, comparable to an A+, and the ‘12.01-13.00’ represented the lowest grade, comparable

to an ‘F’. These variables were reverted so that the scale went from the lowest grades (12.01-

13.00) to the highest grades (1.00). Then, the variable for average grades was created as the

average of the Math and English grades and recoded as ‘AvgGrades’. The test scores that

were also considered in the academic achievement category were the math and reading

standardized score of the respondent during the second follow-up of the NELS. These were

coded as ‘ReadTest’ and ‘MathTest’.

The dummy variables that captured the individual academic aspirations was created

using the respondent’s answers to the following questions:

1) Highest level of education expected (in senior year)?

2) Is it important getting good education?

3) Has respondent taken the College Board SAT test (by their senior year)?

4) Has respondent taken the ACT test (by their senior year)?

The dummy variable from question (1) was coded 1 if the answer was college or higher and

named as ‘collorhigher’. A dummy variable for question (2) was coded 1 if answer was “very

important” and 0 otherwise, and named ‘Collimp’. The answers to questions (3) and (4) were

coded as SATACT=1 if the respondent had answered, “yes already took” either of the tests.

This extra dummy variable was created as a better proxy for academic attainment aspirations

since most high school seniors respond that they aspire to complete a B.A. degree (69%

percent) but few students take the necessary steps to even apply to college. Only 29 percent of

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the sample had already taken the ACT and 36 percent had already taken the SAT by spring

term of their senior year of high school in 1992. Some studies confirm and have noted the

existence of this aspirations-achievement gap in the enrollment process of high school

graduates into postsecondary institutions (Schneider & Stevenson 1999, Roderick & Nagaoka

2008). It was therefore considered as an equally valid (if not more valid) proxy of the

respondent’s individual postsecondary aspirations.

The category for peer influence on college attainment was composed based on the

following questions:

1) Friend’s desire for respondent after high school

2) Among friends, how important is it to study?

3) Among friends, how important is it to finish high school?

4) Number of friends to attend a 4 year school?

A variable ‘PeerDesire’ was created and coded as 1 if the respondent answered “to go to

college” for question (1) above and 0 otherwise. A variable ‘StudyImp’ was created and

coded as 1 if the respondent answered “very important” to question (2) above or 0 otherwise.

A variable ‘FinishHS’ was created and coded as 1 if the respondent answered “very important”

to question (2) above or 0 otherwise. Lastly, a variable ‘Attend4yr’ was created and coded as

1 if respondent “most of them” or “all of them” to question (4) above or 0 otherwise. No

weighted average was created for this category as a whole, but rather each variable was

included as a separate dummy to see whether particular peer influences existed on the

student’s likelihood to complete a B.A. degree.

The category for school environment was composed of three dummy variables that

attempted to capture particular aspect of the respondent’s high school during the base year (8th

grade) survey. These characteristics were the school spirit of the respondent’s high school,

whether the high school had strict rules, and whether the respondent perceived that the

17

discipline was fair. Three variables were coded as 1 if the student answered, “strongly agree”

or “agree” to the following questions:

1) There is real school spirit

2) Rules for behavior are strict

3) Discipline is fair

The variables ‘SchoolSpirit’, ‘StrictRules’ and ‘FairDiscipline’ were created from the

questions above and included separately in the first stage model for the likelihood of B.A.

attainment.

Lastly, the category of variables that tried to measure the influence of teachers on the

respondent’s education attainment was created from the following questions.

1) Favorite teacher’s desire for respondent after high school

2) Teachers praise my effort

3) Most of my teachers listen to what I say

The variable ‘TeacherDesire’ was created and coded as 1 if the respondent answered “go to

college” for question (1) above and 0 otherwise. The variable ‘PraiseEffort’ was created and

coded as 1 if the respondent answered, “strongly agree” or “agree” to the question (2) above

and 0 otherwise. The variable ‘TeachersListen’ was created and coded as 1 if the respondent

answered “strongly agree” or “agree” to question (3) above. Same as in the previous

categories, these three bivariate variables were included as distinct factors in the model to see

their independent effects, if any, on the likelihood of educational attainment among

disadvantaged students.

Data Analysis Method 1: Multivariate Regression analysis

To answer the leading research question about whether the return of a B.A. degree is

worth it or not for the economically and academically disadvantaged population of interest, it

18

was necessary to compare the coefficients of educational credentials among different sub-

populations. The same model was used to measure the different impact of educational

attainment on employment outcomes (yearly income, occupation, PSE impact) among four

distinct sub-populations: 1) low-SES, low-achieving students, 2) low-SES, high-achieving

students, 3) high-SES, low-achieving students, and 4) high-SES, high-achieving students.

Low-achieving students (or those referred to as “academically disadvantaged”) were those

students whose average high school grades were equivalent to Ds and Cs (ranging from D- to

C+). In turn, high-achieving students were the ones with high school grades equivalent to B’s

and A’s. Students who had F’s were not included in the analysis. A continuous variable for

SES was also included to see whether different levels of SES within each sub-population had

an effect on the short run potential of yearly income. Additionally, a continuous variable for

average Math and English high school grades (similar to high school GPA) was included in

the model to see whether there was an effect of specific grade changes within each student

cohort.

Three dummy variables were included to measure the impact of attaining educational

credentials on employment outcomes: bachelor’s degree (BachDeg), associate’s degree

(AssocDeg) and certificate (CertDeg). As mentioned before, those respondents who achieved

more than a bachelor’s degree were dropped as well as those who did not graduate from high

school. It is important to note that a fourth dummy was also included in the model for

clarification purposes that controlled for the respondent’s who had skipped the NELS

question of highest degree attained by 2000. The number of respondents who skipped was too

significant (n= 2,533) to simply include in the base group without a clear way to interpret this

group of respondents. Thus, this left high school graduate students who had some post-

secondary experience but who had not attained a degree by 2000 as the base group. In sum,

19

the coefficients of interest here are those of a B.A. degree, an A.A degree and a certificate,

and how they compare among the different sub-populations.

Before discussing the results, an understanding of what group of students composes

the base group among the different sub-populations is worthy of attention. The variable for

gender is a dummy variable for female (Female=1 if the respondent is a female and 0 if the

respondent is a male). Dummy variables were also included for different racial categories:

American Indian or Alaskan native (NativeAmer), Asian or Pacific Islander (Asian), non-

Hispanic Black (Black), and Hispanic or Latino (Hispanic). Additionally, a dummy variable

labeled ‘SkipDeg’ was included in the model since these were the respondents who skipped

the question of “highest degree attained by 2000” (NELS 2000). Since the question was only

directed to students who had indicated that they had some postsecondary education

experience, these are probably high school graduates who may not have had any

postsecondary experience and who did not complete a degree nonetheless. In order to avoid

misinterpreting the results, this dummy variable was included in the regressions and those

respondents who had specifically indicated that they had some PSE experience, but had

attained no credential, were left as the base group for educational attainment. Therefore, the

base group within each sub-population is non-Hispanic white males who graduated from high

school and who are in the same SES (top or bottom) and academic quartiles (low-or– high) in

high school. The results are displayed in the following Table 1.

20

Table 1 Return of educational degrees based on short-term yearly income (logged) among the different sub-populations

ln( Income) Low-SES Low-Grades

Low-SES High-Grades

High-SES Low-Grades

High-SES High-Grades

BachDeg 0.32*** 0.14** 0.16*** 0.31***

(7.46) (2.39) (5.08) (8.27)

AssocDeg 0.11** 0.03 -0.03 0.17*

(3.01) (0.54) (-0.61) (2.4)

CertDeg -0.05 0.02 0.02 -0.1

(-1.39) (-0.22) (0.54) (-1.06)

SkipDeg 0.03 -0.19 0.06 0.03

(0.92) (-2.45) (1.14) (0.3)

Female -0.38*** -0.24*** -0.30*** -0.25***

(-15.79) (-6.17) (-11.35) (-9.13)

SES 0.08** 0.07 0.02 0.01

(2.7) (1.49) (0.48) (0.38)

NativeAmer -0.35*** -0.38 0.12 -0.12

(-3.18) (-1.26) (0.82) (-0.46)

Asian 0.09 0.09 0.04 0.04

(1.32) (1.38) (0.44) (0.79)

Black -0.13*** 0.1 -0.13** 0.14

(-3.67) (1.24) (-2.79) (1.64)

Hispanic 0 0.06 0.05 -0.17**

(0.01) (0.94) (1.03) (-2.62)

AvgGrades 0.01 0.04* 0.02 0.01

(0.68) (2.04) (1.94) (0.98)

Constant 10.2 9.92 10.12 10

(196.48) (63.08) (171.36) (91.23)

n() 2112 1,030 1982 2551 R^2 0.152 0.061 0.079 0.067

Numbers in parenthesis are t ratios. *p<.05.

**p<.01 ***p<.001 (two-tailed tests).

Using the annual income (logged) of respondents as the dependent variable, the

differences in the coefficients of the different educational attainments (B.A., A.A., and

certificate) among the sub-populations of interest show how the pay-offs of educational

attainment differ greatly for each population. The unstandardized coefficients for the dummy

variables can be interpreted as percentage increases in earnings when compared to the base

21

group population. This section will interpret and describe the findings and these will later be

discussed in the following section.

In the low-SES, low-achieving population (n=2,112), both the coefficients for a

Bachelor’s degree (BachDeg) and an Associate’s degree (AssocDeg) are statistically

significant with magnitudes of 0.32 and 0.11, respectively. This implies that for a low-SES,

low-achieving student, attaining a B.A. degree would increase his or her short-term earnings

by 32 percent compared to a similar student in the same economically and academically

disadvantaged sub-population. The coefficient for an A.A. degree is interpreted similarly,

increasing the short-term yearly earnings of a student within this sub-population about 11

percent on average.

In the low-SES, high-achieving population (n=1,030), the return of a bachelor’s

degree is statistically significant but surprisingly smaller than the return for the low-achieving

students in the same SES interval. The magnitude of this coefficient is (0.14) which indicates

a return of 14 percent higher earnings than a low-SES, high-achieving high school graduate

without a postsecondary degree. Therefore, a bachelor’s degree increases annual earnings

31.6 percent for the average low-SES, low achieving student, an additional increase of 14.2

percent from the high-achieving population. This also applies to the return of an A.A., since

the coefficient for this variable is insignificant for low-SES, high-achieving students, but has

a significant return of 11 percent for low-SES and low achieving students. This finding may

lead a high-achieving student, who can get into a selective four-year college, to attain a B.A.

degree rather than an A.A. degree in a two-year college. This point and the differences in

returns between the low-achieving and high-achieving students will be further discussed in

the Discussion and Implications section.

Looking at the top two quartiles of the SES student sample distribution, the returns for

a bachelor’s degree (B.A.) are still positive and significant and these even increase in

22

magnitude for the high performing students, contrary to the lower return of a B.A. for the

high-achievers in the low-SES population as mentioned above. A B.A. increases the annual

earnings of high-SES, low-achieving students (n=1,982) about 16 percent and 31.2 percent for

the high-SES, high achieving students (n=2,551), which is the B.A. same return for the low-

SES, low-achieving students. Therefore, among students whose family’s SES is in the top two

quartiles, those with lower grades receive a smaller pay-off from attaining a bachelor’s degree

than those with higher grades while in high school. Additionally, an associate’s degree still

exhibits a positive and significant result for the high-SES, high-achieving population (16.6

percent increase in earnings), while the coefficient is not significant (and it is actually

negative) for the high-SES, low-achieving students. Therefore, for those low-achieving

students who could probably pay for most of a four-year college tuition, it does not make

sense for them to get an associate’s degree when they can get a bachelor’s degree.

There were other variables that were statistically significant across the different sub-

populations and others that differed among the groups, which can shed some insight into the

factors that influence the employment outcomes of disadvantaged high school graduates. A

socioeconomic (SES) continuous variable was included in each group to measure the effect of

SES among the different sub-populations. Interestingly, the only sub-population where the

SES coefficient was significant was among the population of low-SES, low-achieving

students. The coefficient for this variable was 0.076, which indicates that for every unit

increase in SES, the low-SES, low-achieving student receives an increase of 7.6 percent in his

short-run yearly salary. This may indicate the subtle but critical existence of social

reproduction in the bottom quartiles of the SES distribution.

Interesting results can also be seen in the differences across the race dummies between

the different sub-populations. The Hispanic variable is only significant in the high SES, high-

achieving population. This may be due to the fact that there are more observations in this sub-

23

population (n=2,551) of fulltime workers than in the other ones. For whichever the reason or

due to a combination of factors, high achieving Hispanics in the upper half of the SES tend to

suffer from their racial identity. Additionally, among those with low grades, the Black

dummy variable becomes significant with a negative coefficient of -0.13 for both low-SES

and high SES. In other words, the low-achievement of Blacks in high school attenuates the

racial discrimination that they may face in the labor market. This could be related to racial

discrimination among the jobs that require less intellectual capacity and are more dependent

on manual labor, but such a hypothesis cannot be determined from these findings due to the

lack of an occupation specification. Nonetheless, it is of no surprise that low high school

grades can negatively affect the job salary of some populations more than others, especially in

the short run.

Lastly, an expected result that was consistent among all the different populations was

the statistically significant and negative effect of the female dummy variable on the yearly

income of respondents. The magnitude of the coefficients does not have a wide range of

fluctuation (0.14 difference points) but it is interesting to see where the effect is the greatest.

The female coefficient of highest magnitude was found in the sub-population of low-SES,

low achievers (-0.38), followed by the high-SES, low achievers (-0.30), then by the high-SES,

high achievers (-0.25) and finally had the least effect within the low-SES, high-achieving

population (-0.24). These results cannot be taken as literally but it is interesting to see that

overall, the effect of being female is more detrimental with the low achieving half of the sub-

populations.

In the above multivariate analysis, the key assumption to achieve consistent estimation

of the coefficients on educational attainment was that by conditioning on a sufficiently

exhaustive set of pre-college characteristics, college attendance would be randomized. An

additional assumption to achieve consistent estimation of the coefficients was to assume that

24

the covariates that measure these pre-college characteristics are uncorrelated with what is left

in the error term. Even though an exhaustive set of covariates was used that attempted to

capture such pre-college characteristics, few were statistically significant and they left the

majority of the variation unexplainedi. This is the reason we turn to propensity score matching

(PSM). Propensity score matching requires weaker assumptions to obtain unbiased

coefficients. Mainly, PSM is not affected by possible non-linearity effects of the covariates,

which do affect regression analysis. The main difference between the methods, however, lies

in the interpretation of the coefficients of the degrees attained. In regression analysis,

coefficients on the educational attainment variables represent the average treatment effects

within the sub-populations of interest. As it will be explained below (and more extensively in

the Endnotes section), propensity score matching is a stronger method to use since it breaks

down this treatment effect between the treated (those who attained a particular degree) and the

untreated.

Method 2: Propensity Score Matching

As its name implies, propensity score matching (PSM) creates matches for students

who share similar pretreatment conditions, such as educational aspirations, family

background, peer and teacher influence, and high school environment. PSM assumes that the

potential outcomes (e.g. income level) are independent of the treatment status (e.g.

completing a B.A. degree), given this set of observable covariatesii. Additionally, for each

value of covariates, there is a positive probability of the respondent attaining an educational

degree or notiii. This second assumption allows respondents who share the same set of

covariates to be given the same (or very similar) propensity score of achieving the educational

degree of interest, regardless whether the respondents actually completed that type of degree

or not. Therefore, by calculating probabilities for each outcome, propensity score matching

estimates two potential outcomes for each student; one outcome that arises if the student had

25

completed the treatment and another one if the same student did not complete the treatmentiv.

The algorithm chosen was kernel matchingv since it uses weighted averages of all individuals

in the control group to estimate the counterfactual outcomes. Matching estimates were thus

derived by comparing mean levels of the respondents’ short-term income among respondents

who shared similar pretreatment covariates but achieved different educational degrees.

Unfortunately, the number of observations was not enough to be able to directly

compare the return of a B.A. versus an A.A. among the disadvantaged population of interest.

However, a propensity score matching was completed using B.A. as the treatment for only the

disadvantaged sub-population of interest. In other words, within the low-SES and low-

performing students, similar students were grouped together based on the pre-college

characteristics already mentioned and assigned a propensity to attain a B.A. degree. In total,

1,683 observations were used, where 218 had completed a B.A. by 2000 and 1,184

respondents had completed either an A.A., a certificate, or had had some PSE experience but

had attained no degree. The same method was used to test an A.A. as the treatment within this

sub-population as well, but without including those who had attained a B.A. in order to have a

clear base group for comparative purposes. The number of respondents who had attained an

A.A. within this sub-population was 222 respondents, and the other 1,232 were respondents

who had less PSE experience but no B.A.

The results for the propensity score matching using a bachelor’s degree and an

associate’s degree as the dependent variables suggest that a B.A. degree generates significant

returns for the disadvantaged sub-population of interest, while the A.A. degree does not. The

return of the treated is referred as the “average treatment effect of the treated” or ATT. The

return of that degree for the control is the “average treatment effect of the untreated” or ATU.

As shown in Table 4, the B.A. coefficient was 0.12 and the T-statistic (1.71) was significant

26

at the 95 percent level.3 The causal effect of completing a B.A. was higher for those who did

not complete the degree (ATU) than for those who had completed a B.A. (ATT) by the time

of the last follow-up in 2000. This means that the disadvantaged students who are not

completing a B.A. would get a higher return than the ones who did. Therefore, it can be

concluded that the pay-off of a B.A. degree for this disadvantaged sub-population does

increase their yearly income in the short term. However, the real and opportunity costs of

students in specific circumstances should be taken into consideration when estimating the net

benefits of the possible educational paths available to them. This will be discussed further in

the Discussion and Implications for Future Research and Policy section.

The coefficients that were significant in influencing the outcome variable (yearly

income) through the attainment of a B.A. were SES, individual and parental academic

expectations, English grades while in high school, and whether the respondent’s friends in

high school were planning on attending a four year-college (R2=0.226). The coefficients on

the variables for individual expectation (collorhigher) and parental expectations (pacollexp)

were significant (p<0.001) and had the highest magnitudes, 1.55 and 1.05, respectively. The

variable for average English grades while in high school was statistically significant

(p<0.001) and had a magnitude of 0.36. The coefficient of whether the respondent always did

the homework on time (AlwaysHW=1) was not statistically significant at the 95 percent level

(p=0.054) but it was at the 90 percent level, and its large magnitude of 0.395 is worth noting.

Finally, the variable that indicated that most or all of the respondent’s friends were planning

on attending a four-year college by the spring term of their senior year in college was also

significant (p<0.05) and of magnitude 0.403. These findings of factors that influence

employment outcomes through college completion are supported by previous studies

3 Table used to see p-value was the Student Table in < http://www.statsoft.com/textbook/distribution-tables/> with infinite number of observations.

27

(Coleman 1961, Farkas 2000, Rosenbaum 2001, Sewell & Shah 1967) and should be

considered for further research and educational policy discussion.

Similarly, the same propensity score matching was completed treating the associate’s

degree as the treatment. The base group composed all the other low-SES, low achieving

respondents who had lower PSE experience. The total number of observations was 1,525 with

222 of these with an A.A. degree. Using the log of income as the outcome variable and the

A.A. completion as the dependent variable, the observed coefficient of an A.A. degree was

0.14 but the T-statistic (1.52) was not significant at the 95 percent level. This indicated that

the completion of an A.A. would not bring a significant return to the average economically

disadvantaged, low-achieving high school graduate. A summary of these results can be found

in Table 6 in Appendix B.

Nonetheless, the coefficients that were significant are worthy to note. The female

coefficient was significant in the case of A.A. as the dependent variable with a negative

coefficient of -0.40, indicating that females are much less likely to pursue an A.A. after high

school. The SES coefficient was also significant in this case and somewhat smaller (than

using B.A. as the dependent variable) but with a similar magnitude (0.07). The Asian,

Hispanic and Native American coefficients were also significant and positive in magnitude

(0.81, 0.47 and 2.16 respectively). The variable for English grades was the only one of the

academic variables that was significant and its magnitude (0.14) was about two-thirds smaller

than the coefficient for the same variable when using B.A. as the dependent variable. Lastly,

the coefficient for always doing homework became statistically significant (p<0.05) and with

a higher magnitude (0.56). As could be expected, the variable that captured whether the

respondent’s peers were attending four-year colleges was not significant in this model, but the

variable that captured whether the respondent’s friends desired for him or her to go to college

was significant and had a magnitude of 0.71. These results for the propensity score matching

28

using a bachelor’s degree and an associate’s degree as the dependent variables are shown in

detail in Tables 4 and 5 in Appendix B.

These matching results complement the regression estimates previously mentioned in

a sense that by only looking at the return of a B.A. versus an A.A., a B.A. seems to generate

higher salaries than the A.A. for the average disadvantaged student if given the choice. Note

that the matching estimated coefficient for having attained a B.A. was almost three times

smaller (0.12) than the one from the regression estimations above (0.32). However, by

definition, the average disadvantaged student probably does not have the economic means or

past academic achievement to complete a B.A. degree successfully (Roderick and Nagaoka

2008; Rosenbaum 2001). Considering the disadvantaged sub-population only, the matching

and regression B.A. estimates are within the range of 0.12 to 0.32, respectively, and 0.11 to

0.14 for the return of an A.A. degree. These coefficients do not seem as appealing for the

disadvantaged students as the ones previously mentioned in the Literature Review. Moreover,

these should not be taken literally because of the lack of specific information on the real and

opportunity costs that limited this study (and is also quite ambiguous for the average

disadvantaged student pursuing higher education). Further context is needed to interpret these

results and to identify the specific factors and strategies that might better inform and prepare a

disadvantaged high school graduate considering post-secondary education. These will be

discussed in the next section.

In order to complement the returns of the educational degrees under study, two other

outcomes were used to evaluate the return of educational attainment: 1) likelihood of attaining

a managerial or professional occupation and 2) perceived post-secondary impact. Using a

probit model as with the income variable, but using professional and managerial occupation

as the outcome, the coefficients for the different educational degrees were compared across

the same sub-populations as before. Similarly, the coefficients of the probit model can be

29

interpreted as the marginal effects of the variables of interest, in this case educational

attainment, on the likelihood to obtain a professional or managerial degree. It is important to

mention that the magnitudes of the coefficients by themselves are not so much of interest as

much as how these compare among the different sub-populations. The results can be seen in

Table 2.

30

Table 2 Return of educational degrees based on probability of attaining a professional or managerial position in the short-run among the different sub-populations

Professional &

Managerial

Low-SES

Low-Grades

Low-SES

High-Grades

High-SES

Low-Grades

High-SES

High-Grades

BachDeg 0.104*** 0.125*** 0.137*** 0.039

(3.88) (4.03) (6.58) (1.87)

AssocDeg 0.048* 0.151*** 0.068* 0.068

(1.97) (3.43) (2.38) (1.69)

CertDeg -0.005 0.114* -0.023 -0.023

(-0.22) (2.10) (-0.74) (-0.41)

SkipDeg -0.031 -0.033 -0.020 -0.004

(-1.58) (-0.65) (-0.55) (-0.06)

Female 0.018 -0.005 -0.018 -0.053**

(1.21) (-0.21) (-1.09) (-3.45)

SES 0.012 0.007 0.078*** -0.031

(0.64) (0.22) (3.81) (-1.89)

NativeAmer 0.067 0.500 0.05 0.199

(0.89) (1.93) (0.50) (1.50)

Asian -0.524 0.019 0.134** 0.068**

(-1.30) (0.47) (2.95) (2.61)

Black 0.027 -0.03 0.293 -0.039

(1.26) (-0.57) (0.96) (-0.88)

Hispanic 0.001 -0.043 -0.023 -0.061

(0.07) (-1.17) (-0.81) (-1.80)

AvgGrades 0.003 0.04*** -0.003 -0.001

(0.43) (3.37) (-0.40) (1.39)

Private -0.164* 0.258** -0.047 0.041

(-2.12) (-2.64) (-1.43) (-1.82)

n() 2958 1516 2889 3672 R^2 0.01 0.03 0.03 0.01

Numbers in parenthesis are z ratios. *p<.05. **p<.01 ***p<.001 (two-tailed tests).

It is interesting to see that the coefficients of the B.A. dummy increase in magnitude

from the economically disadvantaged populations to the high-SES, low-achieving population.

It seems as though coming from the higher-SES half increases the chances of attaining those

positions of higher prestige. Interestingly, however, the B.A. coefficient ceases to be

31

significant for the high-SES, high achieving individuals. Having attained a B.A. degree

increases the chances of obtaining a professional or managerial position 10.4 percent for the

low-SES, low-achieving students, 12.5 percent for the low-SES, high achieving students, and

13.7 for the high-SES, low-achieving students. The reason for the insignificant coefficient of

a B.A. degree in increasing the chances to attain a professional or managerial position for the

high-SES, high achieving students may be related to the valuable social network that this sub-

population is already composed of, making the potential to network with colleagues in college

less important to attain such a prestigious occupation. An important consideration to take into

account when looking at these occupations is that it usually takes more than two years to

attain a professional or managerial occupation, if we assume a standard four-year time period

to complete a college degree.

Similarly, the A.A. coefficient is also significant in all the populations except in the

high-SES, high-achieving sub-population. Attaining an A.A. degree is more influential for the

low-SES, high-achieving student population; 15.1 percent increase in the likelihood of

attaining a professional or managerial position in the short-run. While for the other sub-

populations, the coefficients are much smaller (4.8 percent in the low-SES, low-achieving and

6.8 percent in high-SES, low-achieving). Interestingly, the coefficient of a certificate degree

was only significant for the low-SES, high achievers with a magnitude of 11.4 percent. If one

compares the coefficients of a certificate, an A.A. and a B.A. in this low-SES, high achieving

sub-population, the A.A. coefficient is the greatest in magnitude (15.1 percent) followed by

the B.A. coefficient (12.5 percent) and lastly by the certificate coefficient (11.4 percent). This

may be due to the fact that an A.A. takes, on average, half as much time as a B.A., which

makes the A.A. coefficient more influential for increasing the chances of obtaining a

professional or managerial position in the short-run since it allows the graduate more time to

32

work and thus advance his or her position in a company. Due to the lack of time variable to

complete the respective PSE degree, this hypothesis cannot be verified.

Other significant coefficients are worthy to mention in the regressions using

professional or managerial occupations as the outcome variable. The female coefficient was

only significant in the high-SES, high achieving population and had a negative magnitude of -

0.053. This indicates that females are 5.3 percent less likely to attain a professional or

managerial occupation within the high-SES, high achieving population in the short-run. This

makes sense since the employment outcomes being considered are the ones that respondents

had when they were 26 to 27 years of age, which is a common time period when women start

getting pregnant and forming families. A plausible reason this female coefficient is only

significant in this sub-population is that the only women who are in a position to attain such

occupations are the high-SES, high-achieving women. Further research is needed to

determine the forces influencing the attainment of these positions among women.

Additionally, other variables were significant which seem to exhibit an interaction

amongst them taking place. The SES coefficient was significant only in the high-SES, low-

achieving sub-population with a magnitude of 0.078. This indicates that a one-unit increase in

a respondents SES can increase the chances of the average high-SES, low achieving student

to attain a professional or managerial position in the short-run about 7.8 percent. This may

suggest that an individual’s SES can help increase a low-achieving student’s chances to attain

a prestigious job in the short-run, probably due to family relations. Similarly, being Asian can

also increase the average high-SES individual’s likelihood to attain a professional or

managerial position in the short-run. For the high-SES, low-achieving population, the effect

of being Asian was 13.4 percent while the high-SES, high-achieving population, had an

equally significant coefficient but of lesser magnitude (6.8 percent). The coefficient for

average grades was also only significant for the low-SES, high achieving sub-population with

33

a positive magnitude of 0.04. This indicates that a rise in a unit of the average Math and

English grades of the average low-SES, high-achieving student in high school increases the

likelihood of this individual to obtain a professional or managerial position in the short run of

about 4 percent. This coefficient can serve as proof of the positive impact that grades can

have for the low-SES population in the labor market, simply in making respondents stand out.

Lastly, this coefficient may be related to the coefficient of going to a private high school,

which is also significant only in this sub-population. The coefficient of whether the

respondent attended a private high school is positive and significant with a magnitude of

0.258. This suggests that if the average low-SES, high-achieving respondent goes to a private

high school, this increases the individual’s chances of attaining a professional or managerial

occupation in the short run about 25.8 percent. This coefficient was the largest in magnitude

compared with all the rest of the variables in any sub-populations and is supported by other

studies (Brand & Halaby 2005). Therefore, looking at the previous two variables together, for

the average low-SES, high achieving student, increasing his average high school grades about

a letter grade and attending a private high school, increases the student’s chances of attaining

a professional or managerial position in the short run about 30 percent. This is a significant

impact in the average low-SES, high achieving student, but few of these students actually get

to attend a private high school, precisely due to their low-SES background.

Finally, since no one can judge the pay-off of educational attainment better than

respondents themselves, the perceived impact of post-secondary attainment was used as the

outcome variable only for the disadvantaged sub-population of interest. Post-secondary

impact was based on the respondents’ answers in two questions: 1) Would you say that your

schooling after high school has provided you with the opportunities for better jobs than you

could have gotten had you not attended? 2) Would you say that your schooling after high

34

school has allowed you to earn higher salaries? (NELS Codebook). The results of the

combined answers as the dependent variables are shown in the probit model below.

Table 3 Probit regressions reporting marginal effects of educational credentials on perceived PSE impact among different sub-populations.

PSE Impact Low-SES

Low-Grades

Low-SES

High-Grades

High-SES

Low-Grades

High-SES

High-Grades

BachDeg 0.36*** 0.26*** 0.32*** 0.26***

(10.32) (10.58) (16.15) (17.86)

AssocDeg 0.31*** 0.12*** 0.17*** 0.05***

(9.74) (4.99) (7.39) (4.63)

CertDeg 0.26*** 0.06* 0.17*** 0.03

(8.72) (2.10) (7.33) (1.49)

SkipDeg -0.65*** -0.57*** -0.62*** -0.07*

(-21.21) (-10.79) (-12.07) (-2.33)

Female 0.03 -0.06** -0.02 0.00

(1.52) (-2.79) (-0.97) (0.12)

SES -.02 0.04 0.02 0.03***

(-0.93) (1.54) (0.80) (3.53)

NativeAmer 0.15 0.07 0.02 -0.01

(1.34) (0.41) (0.24) (-0.19)

Asian -0.13* -0.06 -0.03 -0.02

(-2.20) (-1.80) (-0.65) (-1.27)

Black -0.04 0.06 -0.00 -0.06*

(-1.25) (1.50) (-0.05) (-2.34)

Hispanic -0.03 0.08** 0.12*** 0.04*

(-1.14) (2.75) (4.28) (2.39)

AvgGrades 0.00 0.01 0.02 0.00

(0.06) (0.50) (1.93) (1.58)

Private -0.14 0.01 0.00 0.02

(-1.10) (0.10) (0.09) (1.54)

n() 3023 1546 2948 3762 Pseudo R^2 0.34 0.31 0.22 0.19

Numbers in parenthesis are z ratios. *p<.05. **p<.01 ***p<.001 (two-tailed tests).

Note: PSE impact is based on the respondents’ perceived impact of their attained postsecondary degrees on the likelihood of getting “better jobs” and “higher salaries” (NELS 88:00).

There are a few interesting figures to point out from the results on the perceived post-

secondary impact of educational attainment on employment of disadvantaged students. First,

it is interesting to note the upward increasing trend of educational attainment on improving

35

jobs and salaries, based on the perceived impact of the disadvantaged sub-population. A

certificate increased the perceived chances of obtaining better jobs or higher salaries 26

percent. The return of an A.A. increased this likelihood even more (31 percent), when

compared to those respondents in the base group who had some PSE experience. The

perceived return of the B.A. degrees was the highest, with 36 percent increase in the

likelihood to attain better jobs or higher earnings for this sub-population. It is interesting to

note here that perceived impact of an A.A. is almost identical to that of a B.A. Not

surprisingly, the SkipDeg category was significant and negative for post-secondary impact of

educational attainment, since these respondents were probably the ones who did not even

enroll in any post-secondary institution.

When comparing the educational degree coefficients among the different sub-populations,

other interesting observations can be noted. Interestingly, low-achieving students seem to

perceive a greater impact from attaining a B.A. than high-achievers (0.36 for low-SES and

0.32 for high-SES low-achieving students compared to 0.26 for both high-achieving sub-

populations). Specifically, low-SES and low-achievers value an A.A. the most in comparison

to the other sub-populations. Even more striking, is the highly valued certificate among the

low-SES, low-achieving students when compared to the rest of the sub-populations. Therefore,

members of this disadvantaged sub-population seem to really value higher education. The

problem therefore seems to lie in the process of completing the credential rather than on the

lack of perceived value.

Other coefficients that are interesting to note among the sub-population of interest are

the coefficients of the Asian and Black dummy variables, and the variable of private high

school. The significant and negative coefficients of these variables seem to indicate that

Asians, Blacks, and students who went to private high schools have much higher expectations

of job occupations and salaries, and are therefore not satisfied with the benefits from the level

36

of education they attained. In other words, it may be the case that Asians and Blacks are

disappointed of the returns of educational credentials in increasing their chances for upward

social mobility. A similar effect seems to occur on the attitude of employment outcomes for

students who attend private high schools. The coefficient is statistically significant and

negative, meaning that students who attend private schools are taught to believe that they will

get better jobs than the ones they actually obtain in the short-run after achieving their degree.

Further research is needed to shed some light in how these perceptions differ among groups

and what factors influence their expectations of employment outcomes.

Lastly, one other variable that is worthy to mention in the advantaged sub-group is the

continuous SES variable. Within the high-SES, high-achievers, there is a perceived increase

in the value of educational degrees in terms of employment outcomes as the SES increases.

This can be due to the signaling effect that an educational credential has among the middle

and upper classes. It can also suggest the increasingly common perspective of an educational

degree as a luxury good among those who can afford a degree (Menand, 2011).

These results on the perceived post-secondary education impact among disadvantaged

students seem to complement the previous findings of the positive and significant returns of a

B.A. and A.A. degree among the disadvantaged sub-population of students as compared to the

others. Therefore, returning to the main question leading this research, what is the best

educational path for this disadvantaged sub-population to take? Further context and

discussion is needed to address this question.

Discussion and Implications for Future Research and Policy

The college-for-all norm is highly valued in American society given its appeal to the

moralistic theory of justice (Jencks 1988) and equal opportunity that characterizes a “good”

society. As mentioned above, the disadvantaged and the advantaged sub-populations were the

ones that exhibited the highest return on a B.A. and of equal magnitude. This finding was

37

surprising, in the sense that 4-year college seemed to have a higher pay-off for the sub-

populations in the extremes of the economic and academic distribution, while the pay-off of

the same degree was much lower for the mixed groups. As suggested by these clear

differences in returns of educational degrees among the various sub-populations,

heterogeneity in the quality of the post-secondary education must exist that generates these

differences in economic returns for the same types of degrees. In the matching estimates,

however, the coefficients on the return of educational degrees for the disadvantaged

population of interest were much lower for the B.A. and even insignificant for the A.A. How

then should these varied and somehow mixed results be interpreted? The following discussion

will try to combine the findings of employment outcomes, professional & managerial

occupations, and perceived PSE impact focusing on disadvantaged students.

This study examined the pay-off of different educational degrees among different sub-

populations. However, the returns to attaining a degree should not be directly compared.

Further assumptions are needed to level the returns of a B.A. degree versus an A.A. based on

the real and opportunity costs involved in completing each one. For example, if one considers

the current average tuition of $32,976 that four-year public colleges charge and compares it to

the average two-year tuition of $5,926 that a student would pay in a community college, the

real return to a B.A. degree does not seem so attractive anymore (College Board). As seen

above, the returns of a B.A. were almost three times the return of an A.A. degree. However,

when real costs are taken into account, the costs of completing a four-year public college are

more than five times the cost of the two-year community college. The return of attaining a

B.A. seems even less appealing when one considers the average four-year tuition that a

student would have to pay in a private nonprofit college, which is around $114,000 or almost

20 times more than the tuition for an A.A. (College Board website).

38

These comparisons are not even accounting for the opportunity costs of college, which

can be estimated from the foregone income that could be potentially earned by the student

during the two additional years of fulltime coursework that college requires. If one assumes a

standard 40-hour workweek, 50 workweeks per year, and an average $10 hour wage, the

additional opportunity cost of college would mount to $52,976 for public four-year colleges

($20,000 + $32,976 = $52,976) and $134,000 for private non-profit four-year colleges

($20,000+114,000 = $134,000). The three-times greater benefit (at the most) of attaining a

B.A. than an A.A. for this disadvantaged sub-population would seem rather bleak after

accounting for the higher costs of a four-year college degree, which are between 10 to 22

times greater than the costs to attain an A.A. Over a lifetime, the economic return of attaining

a B.A. degree may be worth it among the disadvantaged sub-population. However,

extrapolating these results would have to take into account the respondents’ years of fulltime

work, whether they had families to maintain, and other factors influencing their level of

income that would be almost impossible to take into account. It is worthy to note that above

analysis does not consider the possibility to earn scholarships either. Given the low-

achievement of the sub-population of interest, these respondents would be even more likely to

obtain a loan, thus increasing their cost of college even more. Moreover, as tuition continues

to rise at public four-year universities, which is much higher than the average costs in most

countries, “students don't want to gamble on a bachelor's degree that may leave them in debt,

with no job” (Gonzales 2012). In sum, it is easy for economically and academically

disadvantaged students to deviate from the possibility of attaining a B.A. when they get to the

point of applying to a four-year college as Roderick and Nagaoka’s (2008) study clearly point

out.

The central question that drove this research was whether a B.A. degree is worth the

struggle for disadvantaged students when real and opportunity costs are taken into account.

39

After estimating the returns of a B.A. degree with two different methods and taking into

account the average real and opportunity costs for a disadvantaged student, this does not seem

to be the case, specially without considering the possibility to attain a scholarship. Being

realistic, if the average low-SES student reaches senior year of high school and has a C-

average grade or below, it is quite unlikely that this student will be able to obtain a

scholarship. Rosenbaum (2001) shows that high school seniors with poor grades (Cs or lower)

with plans of completing college degrees only have a 14 percent chance of achieving these

plans even 10 years after high school. To these students, the ideal of equality of opportunity

seems to be just that, an ideal.

The regression estimate of the A.A. coefficient (0.11) indicates that community

colleges can serve as a rewarding alternative for this sub-population of students. However, as

the past body of literature on the return of sub-baccalaureate education shows, the returns of

A.A. vary widely among fields of study. As previously mentioned, Grubb’s (1996) study

suggests that attaining an A.A. in business and technical fields, and some health fields for

females can significantly increase a graduate’s income level in the labor market, but this

economic return is not as rewarding in other fields. The matching estimate for the return of an

A.A. among the disadvantaged sub-population, which was insignificant at the 95 percent level,

also leaves a lot of this variability unexplained. This second estimate seems to suggest that the

goal to pursue an A.A. degree does not seem to be the ideal option for the average

disadvantaged student either in most fields of study.

Not surprisingly, a lot of variation in economic returns is left unexplained even after

using two different methods that accounted for selection bias. Two alternate measures of

employment outcomes, the likelihood of attaining a professional or managerial occupation

and the perceived PSE impact, were added to complement the findings on the returns based

on income. Taken altogether, these findings support the argument that equality of opportunity

40

for the disadvantaged sub-population is far from being a reality. Completing a B.A. or A.A.

degree would only increase the chances of the average disadvantaged student in attaining a

professional or managerial occupation 10 percent and 5 percent, respectively. Even though

these occupations are usually attained at a later stage in the average graduate’s life (at 35-40

years of age), the low percentage increase and low explanation of variability do not seem to

provide an incentive alone to encourage this sub-population of students to complete a B.A. or

A.A. degree. Interestingly, the significant positive coefficients on the impact of PSE

credentials on the labor market seem to suggest that the value of post-secondary education is

actually perceived the highest by the disadvantaged sub-population. The actual attainment of

an educational credential, however, is a different story.

Given the implications of low-grades for low-SES students on their higher education

alternatives, the most obvious solution to the lack of opportunities for upward social mobility

for this sub-population seems to point towards increasing their academic performance in high

school. Higher grades could open alternatives to better colleges and higher wages for the

average disadvantaged student as previously mentioned studies have suggested (Behrman

1996; Dale & Krueger 2002). If average grades are taken as an indicator of academic effort

rather than as innate cognitive ability, can these disadvantaged low-performing students be

motivated to do better in school and take their grades more seriously? As some studies have

pointed out, the effort that students put into their coursework during high school is related to

how feasible they perceive college attainment to be, how educational dependent their planned

careers are, and how their identities align with such educational paths (Farkas 2000; Mesmin

2012). The effort in their schoolwork, therefore, is a necessary pre-requisite for this sub-

population of students to be prepared to enroll in a four-year college and actually receive the

benefits of completing a B.A. The inherent problem is that educational attainment is not

necessarily seen as the path to future earnings and identity formation for this disadvantaged

41

sub-population (Mesmin 2012). Even thought this research pointed at the positive perceived

value in PSE degrees among disadvantaged students, some research has suggested that low-

SES students perceive that schooling is irrelevant to their future jobs, which in turn, causes

peer pressure that discourages many from working hard in school (Ainsworth-Darnell and

Downey 1998).

Another reason for the lack of opportunities for upward social mobility among low-

SES high school students may also have to do with the acknowledged but often ignored

system of tracking that is established in most public schools since elementary (Oakes 1986,

Rosenbaum 1978). Rosenbaum’s (1978) study mentions the aspirations-achievement gap

exhibited in interviews of random samples of general-track and non-college-track students.

Basically, non-college-track students had the same college aspirations than the rest of the

students but were not being directed towards a college degree. Moreover, the author suggests

that schools “minimize pressure for upward track changes while allowing students to make

free, but misinformed, decisions” about their educational plans after high school (Rosenbaum

1978, 276). On a similar note, Oakes (1986) argues that tracking reinforces inequality among

already economically disadvantaged students, thus increasing the gap among students as they

go through the standard K-12 education. Furthermore, she argues that students’ expectations

are inherently lowered in the lower level courses due to identity formation and behavior

problems. As implied by these studies, an invisible structure seems to be in place that

determines the post-secondary expectations that students make, limiting their opportunities

after high school without their awareness. Once economically and academically

disadvantaged students reach their senior year of high school, they have been sorted into

categories of those who will most likely attain a B.A. degree from those who are less likely to

complete such a degree through an institutionalized tracking system.

42

Finally, most research and educational policy do not take into account the different

learning styles of individuals, and how some types of students may be advantaged over others

by certain teaching methods. For students who prefer practice to theory, vocational programs

in community colleges may make more sense than the liberal arts model of four-year colleges.

Research does seem to point to the direction of fostering a connection between vocational

colleges and employers through job allocation services and by providing incentives to

teachers to pair students in internships (Rosenbaum 2006, Lerman & Pouncy 1990). This

research examines the value that vocational colleges can have on the disadvantaged sub-

population of students, which complements this study’s finding that a vocational program

may make more sense given the employment returns from an A.A. versus a B.A. degree after

accounting for the costs of both.

The recurring problem mentioned in research lies in the inherent education-job market

gap. It may be the case that the U.S. does have postsecondary choices but apparently the

information of these is not getting to this disadvantaged subpopulation. There is no system of

accountability that provides the direct incentives of people running schools to ensure that

students receive their proper training for the job market. Past research has pointed at the

successful apprenticeship system in Germany as one way in which to link the high school

education system to the workplace (Stevenson & Stigler 1992, Lerman & Pouncy 1990). In

Germany, about 70 percent of young people enter the job market through the apprenticeship

system and 68 percent of high school graduates work in the occupation for which they were

trained (Lerman & Pouncy 1990). A similar type of apprenticeship system has also been

successful in Japan where high schools provide access to jobs through long-standing

relationships with certain employers (Rosenbaum 2001). Although the United States and

Japan have similar proportions of high school graduates who directly enter the workforce

(about 40 percent), “Japanese high schools help over 75 percent [of their students get a job]

43

while American high schools help only 10 percent of their students (Rosenbaum 2001: 13). A

similar school-to-work model, once in place, would be able to encourage students through

teachers and even street knowledge about these placement services, to exert more effort in

high school and work sites given the perceived pay-off to academic success. Taking into

account the consideration that some students find certain ways of learning more effective than

others, apprenticeships that provide practical learning students on-site training can even

increase their interest to further advance their education. Additionally, students would be

exposed to a different social context in which employers can motivate them and even serve as

mentors and professional role models; examples to follow that their SES background

currently lacks. A high school-to-labor market apprenticeship would help open the

opportunities of disadvantaged youth in advancing into the mainstream of economic and

social life. The strong appeal of having opportunity for all in the U.S., however, may have

impeded such a system to formalize on a national level.

The ideal of equal opportunity makes policy makers want to give all students a choice

to decide for themselves what higher education path to take, rather than to formalize school-

to-work linkages. This decision however, may not be perceived as a choice (even if it may be

for a few) by most economically and academically disadvantaged students because of their

inability to get a scholarship or their failure to be prepared for college by their senior year of

high school. Also, strong believers of the opportunity for all approach would argue that

accommodating most disadvantaged youth into vocational programs can restrict their

occupational mobility into less rewarding, nonprofessional careers (Lerman & Pouncy 1990).

However, research has shown that for students in vocational programs, school-employer

linkages raises wages both right after graduation and then years later, and it raises wages even

more than family contacts (Rosenbaum 2001: 130). Moreover, if one considers the different

learning styles of individuals, a counter argument for the previous viewpoint is that the more

44

practical training that characterizes vocational programs can even increase a low-performing

student’s motivation while still at high school and in pursuing higher education. Regardless,

given the feeble returns after accounting for the costs of a four-year college education, more

alternatives are needed that provide practical training for disadvantaged students to be better

prepared once they enter the job market.

Such an overarching apprenticeship system may seem daunting at first but existing

post-secondary programs seem to be starting with a similar apprenticeship model that

connects vocational training institutions and the labor market through practical short-term

training. Just to offer an example, Code Academy is a Chicago based start-up founded by two

Northwestern University graduate students that offers intensive courses on web development

and design for a reasonable fee. In their three-month course, students are paired up with a

mentor to help them create their idea of a web page or application. Additionally, managers

and professionals of web companies come to give talks once a week and develop relationships

of trust among the students. After this workshop, students learn a practical skill that they can

show through an effective product (web application) to employers and thus be more prepared

for the job market in a matter of weeks. There are, of course, certain limitations with this

model in that it works specially well in the coding community because of the ease of

conveying the practical skills and knowledge that a student knows to employers and the high

need of coding as a skill in many occupational areas in the current job market. Nonetheless,

community colleges could implement more practical vocational training to lessen the PSE-to-

labor market gap that currently exists in the United States. By fostering an employment-to-

education linkage program, the informational gaps that currently inhibit students from making

informed decisions would decrease and with this, the education system could better guide the

student’s employment outcomes and even increase their educational motivation since high

school.

45

Conclusion

This study attempted to offer guidance on post-secondary education alternatives for

disadvantaged high school graduates. For a more holistic approach on the subject, it attempted

to illustrate the possible methods to correct for selection bias by comparing regression

estimates to matching estimates of educational attainment on employment outcomes of a

nationally representative sample of students. Few studies in the main body of literature had

attempted to isolate the effects of educational attainment on employment outcomes for the

sub-population of interest. It is important to note that although the study attempted to capture

as much pre-college covariates as possible to isolate the effects of educational attainment,

these characteristics were mainly observable. Both, the regression and matching models, left

most of the variation to predict employment outcomes unexplained (around 80 percent). This

is mainly because various factors come into play along an individual’s transition from

educational institutions into the labor market. Further research is needed to determine how

students’ family background, individual and parental expectations, peer influence, teacher

attention and school environment interact to produce the observable and not so obvious

attitudes and differences in the return of educational degrees for this disadvantaged sub-

population.

The task of isolating the effects that influence employment outcomes for

disadvantaged students from their time in high school until they reach the labor market is very

challenging given the inter-correspondence of factors that occur together in a single individual

(Bowles and Gintis 2002, Oakes 1986). Moreover, the real return of a college degree for the

disadvantaged sub-population of students is unclear, especially after accounting for the costs

involved. Even though the benefits of completing a post-secondary degree may seem

worthwhile, even more so in the long run, the actual costs of higher education impede

disadvantaged students to follow this path after college. The lack of post-secondary

46

educational alternatives for this sub-population of students is a growing concern but the

“college-for-all” approach has been hugely destructive for their personal development. The

increasing need to complete a college degree is not being complemented with proper guidance

for those disadvantaged students to actually complete post-secondary education. The ideal of

an opportunity for all has turned into a kind of self-fulfilling prophecy that places students

into categories that determine their opportunities since early on in their lifetimes. It is an

appealing ideal but one that ignores the social context of the reality of disadvantaged students.

Further research should be done for institutions to better guide the disadvantaged sub-

population of students as a whole, both in their personal and professional development.

Endnotes

i To test these results, the same regressions were repeated for the four sub-populations outlined above adding all the pre-college covariates previously mentioned. The overall significance and magnitude of the educational attainment coefficients did not change significantly for each sub-population when adding the extra variables, which included academic goals, academic achievements, parental expectations, school environment, and peer and teacher effects of the respondents before attending college. Some variables changed in

47

significance in each sub-population but are not worthy to mention given the focus on the academically and economically disadvantaged sub-population. Two additional variables were added to the regressions in order to clarify these findings. A variable for the date to attain the degree completed was included, which significantly lowered the number of observations due to the high percentage of missing answers. This variable was not significant for the low-SES populations because of the lack of observations and it was significant with negative magnitudes for the top SES quartiles. This indicates that delayed degrees reduce educational attainment pay-offs in terms of yearly salary. However, due to the lack of observations for the low-SES quartiles the findings cannot be extended over the sub-population of interest. Also, test scores for Reading and Math for the respondents’ senior year were also included in the regressions. Unfortunately, the coefficients for all sub-populations were not significant, thus providing no further insight into the reasons for the difference in returns. Given the ambiguity and difficulty to examine the issue of the return on education (as seen in the literature review), another method was employed to complement or challenge the results mentioned above. ii This first necessary assumption is called the unconfoundness or selection on observables since it assumes that the differences among the observations are accounted for in order to reduce selection bias (Heinrich et al. 2010). iii This second assumption is called the common support condition or overlap condition because it ensures that there exists sufficient overlap in the characteristics of the treated and untreated units of observation to find adequate matches (Heinrich et al. 2010). iv As with any method, there are advantages and disadvantages in using this method. Matching can only be completed for the treated and control units if these are within the areas that have enough covariates to estimate the matches for both outcomes. These areas are called common support, which are “regions of covariate values where both treatment and control units have positive frequency in the sample” Brand, J. and C. Halaby. 2006. "Regression and matching estimates of the effects of elite college attendance on educational and career achievement☆." Social Science Research 35:749-770. Therefore, sample units are in the common support area when they have units with a comparable pretreatment condition who are from the opposite type (i.e., treatment or control). Through this method, “observation-specific counterfactuals are created for each treated unit, thereby avoiding bias due to misspecification of the functional form” as in regression analysis ibid.. In sum, the main advantage of using propensity score matching to improve causal inference of educational attainment on employment outcomes is that is does not assume linear selection as is assumed through OLS regression analysis. The limitation of this method is that units that do not have a comparable unit in the opposite group, are placed in the off-support and are discarded in the analysis. Therefore, this decreases the amount of observations and thus the reliability of the analysis. Nonetheless, the advantages of using propensity score matching to improve the causality of educational attainment on employment outcomes outweigh the limitations. v The reason kernel matching was chosen was to use as most information as posible. While other algorithms use only a few observations to estimate the counterfactual outcomes, kernel matching uses all the individuals in the control group to calculate these estimates. The drawback of using this method lies in the possibility of using some observations that can be bad estimators. The weights that are used to calculate the counterfactual outcomes dependo n the distance between wach individual from the control group and the participant observation for which the counterfactual was estimated. For more information see Caliendo, Marco. and

48

Sabine. Kopeinig. 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching." IZA:32.

49

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52

Appendix A: Categories of job occupations

The variables for occupations were categorized as follows:

Category Occupations

Professional and Managerial N = 2,921

Financial Services Professionals Legal Professionals Medical practice professionals Medical licensed professionals Human services professionals Scientist, statistician professionals Computer systems/related professionals Managers-executive Managers-midlevel Managers-supervisory, office, other Admin

Clerical N = 1,271

Secretaries and receptionists Cashiers, tellers, sales clerks Clerks, data entry Clerical other

White Collar N = 2,849

Personal services Cooks, chefs, bakers, cake decorators Business, financial support services Sales/purchasing Customer service Legal support Editors, writers, reporters Performers/artists Health/recreation services

Medical Services (Nursing) N = 451

Medical Services

Engineering/IT N= 633

Engineers architects software engineers Research assistants/lab technicians Technical/professional workers, other Computer programmers Computer/computer equipment operators

Education (Teacher Occupation) N = 875

Educators- K-12 teachers Educators- instructors other than K-12

Blue Collar (base group) N= 2,195

Farmers, foresters, farm laborers Laborers (other than farm) Mechanic, repairer, service technician Craftsmen Skilled operatives Transport operatives (not pilots) Protective services, criminal justice Military

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Appendix B: Propensity Score Matching Results Table 4 Propensity score matching estimates using B.A. as dependent variable, ln(Income) as outcome Logistic regression Number of obs = 1683 LR chi2(27) = 293.70 Prob > chi2 = 0.0000 Log likelihood = -501.93362 Pseudo R2 = 0.2263 --------------------------------------------------------------------------- BachDeg | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------- Female | -.105101 .1796022 -0.59 0.558 -.4571148 .2469129 SES | .8144905 .2344742 3.47 0.001 .3549296 1.274051 Asian | .2760911 .3912535 0.71 0.480 -.4907517 1.042934 Black | .3956494 .2429147 1.63 0.103 -.0804547 .8717534 Hispanic | -.3726194 .2389381 -1.56 0.119 -.8409296 .0956907 Private | 1.006676 .9416938 1.07 0.285 -.8390097 2.852362 collorhigh~p | 1.547867 .3054142 5.07 0.000 .9492661 2.146468 Collimp | -.4065872 .2718702 -1.50 0.135 -.9394429 .1262685 SATACT | .2824728 .212722 1.33 0.184 -.1344547 .6994003 ReadTest | -.0356775 .012651 -2.82 0.005 -.060473 -.010882 MathTest | .054292 .0155041 3.50 0.000 .0239045 .0846794 EngGrades | .3623672 .0625602 5.79 0.000 .2397514 .484983 MathGrades | -.0502572 .0655705 -0.77 0.443 .178773 .0782586 AlwaysHW | .3950017 .2047384 1.93 0.054 .0062782 .7962817 pamorehs | .0379873 .028412 1.34 0.181 .0176992 .0936739 paeducmiss~g | -.037897 .0294962 -1.28 0.199 .0957085 .0199145 pacollexp | 1.052729 .2431369 4.33 0.000 .5761899 1.529269 SchoolSpirit | .0118953 .1959717 0.06 0.952 .3722021 .3959927 StrictRules | -.1344497 .1762483 -0.76 0.446 .47989 .2109905 FairDiscip~e | -.2611156 .1809277 -1.44 0.149 .6157275 .0934962 PeerDesire | .0083633 .1867781 0.04 0.964 .3577149 .3744416 StudyImp | .316334 .1946741 1.62 0.104 .0652202 .6978883 FinishHS | .2725617 .2369568 1.15 0.250 .1918652 .7369886 Attend4yr | .4025959 .1826047 2.20 0.027 .0446972 .7604945 TeacherDes~e | .1522908 .2259602 0.67 0.500 .2905832 .5951647 PraiseEffort | -.2217535 .194415 -1.14 0.254 .6027999 .1592928 TeachersLi~n | .2036842 .2177439 0.94 0.350 .2230861 .6304544 _cons | -6.140077 .7884677 -7.79 0.000 -7.685446 4.594709 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Variable Sample | Treated Controls Difference S.E. T-stat ----------------------------+----------------------------------------------------------- LogIncome Unmatched | 10.0641573 9.917572 .146585301 .048048525 3.05 ATT | 10.0641573 9.96445893 .099698371 .058322561 1.71 ATU | 9.92593141 10.0432808 .117349408 . . ATE | .11460481 . . ----------------------------+---------------------------------------------- Note: S.E. does not take into account that the propensity score is estimated4. 4 Since tbe standard errors do not take into account the extra variation that the propensity score matching generates, the standard errors are not correct. These were corrected using a method called bootstrapping, which takes into account the variance that is added to the normal sampling variation from the procedure of calculating the propensity score and the common support. What bootstrapping does is that it re-estimates the results N times and then averages the standard errors of each bootstrapp to get the modified standard error. The underlying assumption here is that the distribution of the N estimated average treatment effects that come from the N bootstrapp samples approximate the sampling distribution (and thus the standard error) of the population mean. In this case, there were 50 replications. (see ibid.).

54

psmatch2: |psmatch2: Common Treatment | support assignment | Off suppo On suppor | Total -----------+----------------------+---------- Untreated | 281 1,184 | 1,465 Treated | 0 218 | 218 -----------+----------------------+---------- Total | 281 1,402 | 1,683 Bootstrap results Number of obs = 1683 Replications = 50 --------------------------------------------------------------------------- Observed Bootstrap Normal-based | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------- _bs_1 |.1015319 .060535 1.68 0.093 -.0171146 .2201783 --------------------------------------------------------------------------- Table 5 Propensity score matching estimates using A.A. as dependent variable, ln(Income) as outcome Logistic regression Number of obs = 1525 LR chi2(28) = 130.45 Prob > chi2 = 0.0000 Log likelihood = -567.5795 Pseudo R2 = 0.1031 --------------------------------------------------------------------------- AssocDeg | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------- Female | -.4004633 .1739777 -2.30 0.021 -.7414533 -.0594734 SES | .726597 .2129092 3.41 0.001 .3093025 1.143891 NativeAmer | 2.157582 .6093364 3.54 0.000 .9633044 3.351859 Asian | .8088834 .394173 2.05 0.040 .0363186 1.581448 Black | .0717463 .2552622 0.28 0.779 -.4285584 .5720509 Hispanic | .4655012 .2035266 2.29 0.022 .0665963 .864406 Multi | -.5916876 .5059162 -1.17 0.242 -1.583265 .3998899 collorhigh~p | .6770635 .1980434 3.42 0.001 .2889055 1.065221 Collimp | .3081397 .2643166 1.17 0.244 .2099114 .8261908 SATACT | .3496034 .1836261 1.90 0.057 -.0102971 .7095039 ReadTest | .0087228 .0122465 0.71 0.476 -.0152799 .0327256 MathTest | -.0057289 .0138821 -0.41 0.680 -.0329373 .0214795 EngGrades | .1358845 .0557702 2.44 0.015 .026577 .245192 MathGrades | .0462325 .0570336 0.81 0.418 -.0655514 .1580163 AlwaysHW | .5589169 .1964915 2.84 0.004 .1738006 .9440332 pamorehs | .004411 .0360775 0.12 0.903 -.0662996 .0751216 paeducmiss~g | -.0130976 .0373474 -0.35 0.726 -.0862972 .0601019 pacollexp | .0157348 .1916206 0.08 0.935 -.3598347 .3913044 SchoolSpirit | .0164542 .1814619 0.09 0.928 -.3392046 .372113 StrictRules | -.1629294 .1641407 -0.99 0.321 -.4846392 .1587804 FairDiscip~e | -.2470206 .1734955 -1.42 0.155 -.5870656 .0930243 PeerDesire | .7045957 .1738239 4.05 0.000 .3639071 1.045284 StudyImp |-.2835968 .1961997 -1.45 0.148 -.6681411 .1009475 FinishHS |-.1284329 .1949003 -0.66 0.510 -.5104304 .2535646 Attend4yr | .1141168 .173384 0.66 0.510 -.2257097 .4539432 TeacherDes~e | -.1126087 .1964077 -0.57 0.566 -.4975606 .2723432 PraiseEffort | .0941849 .1862177 0.51 0.613 -.2707951 .459165 TeachersLi~n | .2176962 .1993099 1.09 0.275 -.172944 .6083363 _cons |-3.406511 .6912007 -4.93 0.000 -4.761239 -2.051782 --------------------------------------------------------------------------- Variable Sample | Treated Controls Difference S.E. T-stat ----------------------------+---------------------------------------------- LogIncome Unmatched | 10.0044693 9.89075068 .113718568 .048279155 2.36

55

ATT | 10.0044693 9.92940665 .075062607 .049361469 1.52 ATU | 9.90130363 10.0508139 .14951025 . . ATE | .138143416 . . ----------------------------+---------------------------------------------- Note: S.E. does not take into account that the propensity score is estimated. psmatch2: | psmatch2: Common Treatment assignment | Off suppo On suppor | Total -----------+----------------------+---------- Untreated | 71 1,232 | 1,303 Treated | 0 222 | 222 -----------+----------------------+---------- Total | 71 1,454 | 1,525 Note: Here base group are respondents who had some PSE experience or a lower credential than an A.A. Respondents that completed a B.A. by last follow-up were dropped from base group when calculating these matching estimates. Bootstrap results Number of obs = 1525 Replications = 50 --------------------------------------------------------------------------- | Observed Bootstrap Normal-based | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------- _bs_1 | .0771808 .0478756 1.61 0.107 -.0166537 .1710153 ---------------------------------------------------------------------------    Table 6 Summary results of matching estimates of effect of each dependent variable (B.A. or A.A.) on yearly income. Outcomes Treatment Average

for Graduates (Treated)

Average for non-

Graduates (Control)

Difference between Treated

and Controls

Standard Errors

P< |z| n1 (number

not treated)

n() (number treated)

ln(Income) B.A. 10.064 9.964 0.114 0.061 0.093 1,184 218 ln(Income A.A. 10.005 9.930 0.138 0.048 0.107 1,232 222 Note: Results are based on kernel matching. Standard errors are bootstrapped with 50 replications.