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
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.
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.
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
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
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
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
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
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.
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
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
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
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.
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.
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
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
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
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 %
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
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,
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
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,
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.
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
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
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-
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
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
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
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
(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
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
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.
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
