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Finishing College: Sociodemographic Inequalities and Life Course Transitions


by Josipa Roksa — August 14, 2013

Combining literatures on sociodemographic inequalities and life course transitions, this study provides a new lens for understanding degree completion of traditional-age students, one that conceptualizes college enrollment in the context of the life course and examines how schooling interacts with other life course transitions. The findings indicate that life course transitions contribute to socioeconomic and gender inequalities in degree completion, and that attendance patterns are crucial for understanding the negative relationship between life course transitions and degree attainment.

While most high school graduates expect to earn a college degree and the majority of high school graduates make the transition into higher education, many leave college without a degree in hand. Only slightly over half of first-time college students who enter four-year institutions full time complete bachelor’s degrees within six years, and the percentage of students completing degrees is substantially lower at two-year institutions. Moreover, completion rates are very unequal, with students from different socioeconomic, racial/ethnic, and gender groups experiencing substantially different outcomes (NCES, 2011).


Although an extensive body of literature has examined sociodemographic inequalities in degree completion, previous studies have dedicated relatively little attention to considering the role of life course transitions in contributing to these patterns (for recent reviews, see Grodsky & Felts, 2009; Gamoran, 2001; Kao & Thompson, 2003). This omission is notable given that many young adults today do not focus exclusively or consistently on their studies, but instead combine schooling with transitions into work, marriage/cohabitation, and parenthood (Pallas, 2004). The first question addressed in this study is, thus, whether life course transitions contribute to sociodemographic inequalities in degree completion.


The second question explores whether and how enrollment patterns help to explain the negative relationship between life course transitions and degree attainment. While it may not be surprising that work, marriage/cohabitation, and parenthood are associated with degree completion, there is a dearth of evidence regarding the mechanisms linking life course transitions to educational outcomes. Drawing on the life course research and in particular insights about how individuals may manage competing roles, I examine whether and how students who make transitions into adult roles limit their investment in schooling.


Combining literatures on sociodemographic inequalities and life course transitions, this study provides a new lens for understanding degree completion of traditional-age students, one that conceptualizes college enrollment in the context of the life course and examines how schooling interacts with other life course transitions. The findings based on the National Longitudinal Survey of Youth of 1997 show that life course transitions contribute to socioeconomic and gender inequalities in degree completion. Moreover, attendance patterns are crucial for understanding the relationship between life course transitions and degree attainment. These findings highlight the importance of adopting the life course perspective for understanding degree completion of traditional-age students and for developing policies to improve their educational success.


PREVIOUS RESEARCH


Sociodemographic differences in degree completion are inevitably a product of myriad factors, from the resources students bring to higher education, such as academic preparation, to their experiences in the higher education system, including various dimensions of academic and social integration (for a comprehensive review of the literature see, Pascarella & Terenzini, 2005). However, extant literature on degree completion in general and sociodemographic differences in particular tends to focus on academic and schooling-related factors and dedicate less attention to the unique phase of the life course that coincides with entry into higher education for traditional-age students.


In recent decades, the ordering and timing of transitions has become increasingly variable (O’Rand, 2000) as well as “demographically dense” (Rindfuss, 1991), with multiple transitions occurring in a short amount of time. Young adults today move in and out of educational institutions and the labor market, and they, at times, combine those transitions with marriage and parenthood (Pallas, 2004). Consequently, most traditional-age students are not dedicating time solely to their studies, but are combining schooling with work, marriage/cohabitation, and/or parenthood. The extent to which these patterns vary across sociodemographic groups could contribute to inequalities in degree completion.


Although research on sociodemographic inequalities does not focus on life course transitions, higher education literature more broadly has shown that life course transitions are related to a range of educational outcomes. However, this literature is highly fragmented. For example, ample research on college employment of traditional-age students rarely considers the role of marriage and parenthood (for a review, see Pascarella & Terenzini, 2005; Riggert, Boyle, Petrosko, Ash, & Rude-Parkins, 2006). At the same time, studies of marriage and parenthood are not as concerned with employment (other than as a control variable) and tend to concentrate on non-traditional-age students, examining a return to schooling years after high school graduation (e.g., Bradburn, Moen, & Dempster-McClain, 1995; Deutsch & Schmertz, 2011) or why non-traditional-age students are less likely to complete educational credential (e.g., Jacobs & King, 2002; Taniguchi & Kaufman, 2005). What is thus missing is a comprehensive framework for understanding students’ trajectories through higher education—not just a study of employment or a study of parenthood as discrete events, but consideration of the unique phase of the life course that overlaps with entry into higher education for traditional-age students. By conceptualizing schooling in the context of the life course, this study contributes to understanding the role of different life course transitions in producing unequal patterns of degree completion across students from different socioeconomic, racial/ethnic, and gender groups.


Focusing on young adulthood as a unique time when individuals may pursue schooling while making other life course transitions also leads to the second contribution of this study: examining whether students’ attendance patterns explain the association between life course transitions and degree completion. Thinking about schooling as one component of young adults’ lives that may need to be coordinated with other transitions highlights the possibility of conflict between different social roles (e.g., Marini, Shin, & Raymond, 1989). As students aim to juggle life course transitions with schooling, they may try to manage competing roles by seeking educational pathways that are more conducive to combining multiple social roles. For example, if students transition into adult roles and find themselves struggling to meet all of the accompanying demands, they may try to reduce the role strain by pursuing more flexible enrollment patterns or, in general, limiting their investment in schooling by interrupting schooling, attending less frequently, or enrolling part time. Previous literature on the relationship between life course transitions and higher education outcomes has focused more on documenting the relationships than exploring possible mechanisms. However, one recent study noted that part-time enrollment is the primary reason for lower degree attainment of older students, who disproportionately combine work, marriage, and parenthood with schooling. The authors concluded that “the results clearly support the hypothesis that competing demands make it more difficult for older students to complete their studies” (Jacobs & King, 2002, p. 225; see also Taniguchi & Kaufman, 2005). Whether and how traditional-age students experience the same challenges and manage life course transitions by reducing their commitment to schooling is examined in this study.


DATA AND METHODS


This study relies on data from the National Longitudinal Survey of Youth of 1997 (NLSY97), a nationally representative sample of individuals born between 1980 and 1984. The baseline survey was administered in 1997 to 8,984 individuals in 6,819 households, who were selected using a multistage stratified random sampling design and re-interviewed annually. NLSY97 provides detailed information on respondents’ work patterns and transitions into marriage, cohabitation, and parenthood. Moreover, it includes measures of higher education enrollment and educational credentials completed. The sample for this study includes 4,704 traditional-age students who had valid information on degree completion.1


The key outcome of interest is bachelor’s degree completion. The probability of degree completion is typically modeled using logistic regression. However, standard logistic regression models estimate an outcome at one point in time, which creates two challenges for the present study: (a) It does not allow for inclusion of time-varying covariates and thus fails to capture the changing nature of students’ pathways through higher education over time, and (b) it estimates the probability as if everyone who will complete the degree has done so within the timeframe of the study, which may not be the case as some students may take longer to attain their credentials (i.e., right-censoring). To address these concerns, I rely on a discrete time event history model.


In order to estimate this model, the data file is organized in a person–month format. The person enters the dataset at the point of entry into higher education, and remains “at risk” until she either experiences the event (i.e., completes a bachelor’s degree) or exits the sample. The model is thus estimating the risk of bachelor’s degree completion in each month, called the hazard, which is the conditional probability that an individual would obtain a bachelor’s degree in time period j, given that she did not do so in an earlier time period. Background variables remain constant through time while variables representing transitions into roles typically associated with adulthood (employment, marriage/cohabitation, and parenthood) take on different values in different time periods.


The corresponding estimated discrete time hazard model can be described as follows:


logit h(tij) =SαDij + SbnSOCIODEMOGRAPHIC CHARACTERISTICSnij +


SbmLIFE COURSE TRANSITIONSmij+


SbpENROLLMENT PATTERNSpij + Sbqdqij


where Dij indexes dummy variables for distinct time periods. SOCIODEMOGRAPHIC CHARACTERISTICSij is a vector of parental education (coded as the highest of mother’s or father’s education and divided into four categories: high school or less (reference), some college, bachelor’s degree, and graduate/professional degree), parental income (divided into quartiles, with the first quartile serving as a reference), race/ethnicity (divided into four categories: White (reference), African American, Hispanic, and other non-White racial/ethnic groups), and gender (dummy variable indicating that a respondent is a female). LIFE COURSE TRANSITIONSij is a vector of transitions into marriage/cohabitation (dummy variable indicating if a respondent is married/cohabitating), parenthood (dummy variable indicating if a respondent has a child), and employment (divided into three categories of low (20 or fewer hours per week), medium (21–34 hours per week) and high (35 or more hours per week) intensity and coded cumulatively to reflect nonlinear patterns and consistency over time reported in previous research (e.g., Staff & Mortimer, 2007). ENROLLMENT PATTERNSij is a vector of variables capturing different dimensions of enrollment in higher education, including interrupted enrollment (dummy variable indicating whether a student stopped out for at least 6 months), continuous measure of the total months enrolled, and continuous measure of the percentage of time students enrolled part time. dij is a vector of control variables listed at the bottom of Table 1.2


RESULTS


INEQUALITIES IN DEGREE COMPLETION


The first model in Table 1 confirms the commonly observed patterns of inequality: Even after controlling for a range of factors listed at the bottom of the table, students from more advantaged family backgrounds are more likely to complete bachelor’s degrees than their less advantaged counterparts. Similarly, African American (p<0.10) and Hispanic students are less likely to complete bachelor’s degrees than White students. On the other hand, reflecting recent trends in higher education, women are more likely to complete college degrees than men.


The next two models address the first research question, which is whether life course transitions contribute to these observed inequalities in degree completion. Before considering this question, it is worthwhile to note that family transitions are highly consequential for degree attainment. Even net of controls, marriage/cohabitation decreases the odds of degree attainment by 40%, while having a child decreases them by 55%. Similarly, Model 3 suggests that transitioning into another role typically associated with adulthood—high-intensity employment (working full time, i.e., 35 or more hours per week)—is negatively associated with degree attainment. At the same time, medium-intensity employment is not related to degree completion, and low-intensity employment (20 or fewer hours per week) is positively related to this educational outcome. These findings are consistent with the previous research on college employment, suggesting that intense employment has a negative relationship to persistence and attainment, while limited employment has either no relationship or a positive relationship to educational outcomes (e.g., Bozick, 2007; Staff & Mortimer, 2007).


Considering the patterns of inequality, the results indicate that family formation does not explain much of the inequality in degree completion across different socioeconomic groups: After including family transitions in Model 2, the coefficients for parental education and income change very little. Employment, however, is much more consequential for this form of inequality. As Model 3 indicates, after controlling for employment patterns, the coefficients for parental education (bachelor’s and graduate/professional degrees) decrease by approximately two-thirds and are no longer statistically significant. The coefficient for the highest income quartile decreases by approximately 25%, although it remains statistically significant.3


Thus, even net of academic preparation and other controls, employment contributes to the lower likelihood of degree completion of students from less-advantaged backgrounds. While the present analysis cannot illuminate the mechanisms producing these differences—e.g., whether they may be related to different conceptions of life course transitions (Settersten & Ray, 2010) or differential access to family financial resources (Schoeni & Ross, 2005)—it highlights the importance of employment for understanding socioeconomic inequality in degree completion and developing policies to reduce these inequities.


Table 1 also indicates that life course transitions inform the patterns of gender inequality in degree completion. Controlling for family transitions in Model 2 amplified the gender difference, while controlling for employment in Model 3 decreased it. These patterns indicate that women are more likely to make transitions in family roles, which have a negative relationship to degree completion, but have a lower degree of participation in the labor market, which has positive consequences for degree completion (supplemental descriptive statistics confirm these patterns). Jointly, these analyses provide insights into the complex patterns of female advantage in degree completion and their relationship to life course transitions. While life course transitions provide insights into gender and socioeconomic inequalities in degree attainment, they do not explain the racial/ethnic gaps in degree completion as coefficients for African Americans and Hispanics remain relatively similar across models.


Table 1. Discrete Time Event History Analyses of Bachelor's Degree Completion, Coefficients, and Standard Errors (in Parentheses)

    
 

Model 1

Model 2

Model 3

 

Sociodemographic Characteristics

Family Transitions

Employment

Sociodemographic Characteristics

   

Parental Education (High school or less = reference)

   

   Some college

0.104

0.097

0.025

 

(0.090)

(0.089)

(0.088)

   Bachelor's degree

0.334**

0.268**

0.121

 

(0.092)

(0.092)

(0.092)

   Graduate/professional degree

0.305**

0.253**

0.096

 

(0.097)

(0.096)

(0.098)

Parental Income (First quartile = reference)

   

   Second quartile

0.075

0.028

0.033

 

(0.096)

(0.110)

(0.097)

   Third quartile

0.156

0.110

0.102

 

(0.099)

(0.098)

(0.101)

   Fourth quartile

0.386**

0.328**

0.298**

 

(0.101)

(0.101)

(0.106)

Race/Ethnicity (White = reference)

   

   African American

-0.173

-0.186

-0.233*

 

(0.094)

(0.096)

(0.099)

   Hispanic

-0.242*

-0.237*

-0.253*

 

(0.107)

(0.107)

(0.105)

   Other non-White

0.326**

0.233

0.094

 

(0.125)

(0.128)

(0.128)

Female

0.180**

0.269**

0.154*

 

(0.060)

(0.061)

(0.064)

Life Course Transitions

   

Family Transitions

   

   Married/cohabitating

 

-0.511**

-0.411**

  

(0.085)

(0.091)

   Child

 

-0.809**

-0.767**

  

(0.132)

(0.138)

Employment (cumulative)

   

   Low intensity (20 or fewer hrs per week)

  

0.023**

   

(0.003)

   Medium intensity (21–34 hrs per week)

  

0.004

   

(0.004)

   High intensity (35 or more hrs per week)

  

-0.021**

   

(0.003)

    

Control Variables

Yes

Yes

Yes

    

-2ll

14195.612

14066.577

13798.315

    

*p<0.05, **p<0.01.  

   

Notes. N= 4,704 persons and 206,535 person-months. All models are weighted and adjusted for clustering of individuals within families and include a series of time dummies estimating the baseline hazard.
Control variables include: age at entry into higher education, two-parent household, number of siblings, ASVAB test scores, high school GPA, high school track, and whether students entered two-year vs. four-year and public vs. private institutions.


LIFE COURSE TRANSITIONS AND ENROLLMENT PATTERNS


Presented results indicate that considering transitions into roles typically associated with adulthood is important for understanding socioeconomic and gender differences in degree completion. Students’ enrollment patterns is one mechanism that may help to explain the negative relationship between life course transitions and degree attainment and it can be examined using NLSY97 data. If transitioning into roles typically associated with adulthood poses role conflicts for students who are also attending higher education, it may be hypothesized that students will try to minimize the role conflict. One way to accomplish this is to limit investment in higher education through less consistent or part-time enrollment. Table 2 considers this proposition.


Table 2 follows the same logic as that employed in Table 1. The first model is the baseline model, which presents selected results from Model 3 in Table 1. Subsequent models include additional variables to evaluate the extent to which their inclusion alters the coefficients for the key predictors included in the baseline model, in other words, the extent to which enrollment patterns help to explain the relationship between life course transitions and degree completion. Model 1 adds a measure of interrupted enrollment (whether students had spells of non-enrollment lasting more than 6 months) and shows that students who interrupt their enrollment are less likely to complete bachelor’s degrees. However, including interrupted enrollment in the model does not alter the coefficients for life course transitions. This would imply that college entrants who transition into roles typically associated with adulthood are not necessarily more likely to interrupt their enrollment than other students.


Interrupted enrollment, however, is a relatively crude indicator of attendance patterns. A more nuanced indicator is the number of months students are enrolled, considered in Model 2. It is not surprising that the cumulative number of months enrolled is related to degree attainment. However, the crucial point is that after including this measure in the model, the coefficients for life course transitions decrease substantially and are no longer statistically significant at the conventional p<0.05 level. This indicates that students who transition into roles typically associated with adulthood spend less time in higher education. These students reduce their investment in higher education (i.e., they enroll for substantially fewer months) which leads to their lower likelihood of degree attainment.


The final model considers the role of part-time enrollment. The more time students spend attending higher education part time, the less likely they are to complete their degrees. Although the coefficients for life course transitions were already not statistically significant in the previous model, it is worthwhile to note that the coefficients for marriage, and especially parenthood, decrease even further after including part-time enrollment in the model. The coefficient for parenthood drops virtually to zero, indicating that part-time enrollment is particularly relied upon by individuals who are juggling parenthood with schooling (see also Jacobs & King, 2002).


Table 2. Selected Results for Life Course Transitions and Enrollment Patterns From Discrete Time Event History Analyses of Bachelor's Degree Completion, Coefficients and Standard Errors (in Parentheses)

     
 

Baselinea

Model 1

Model 2

 Model 3

Life Course Transitions

    

Family Transitions

    

   Married/cohabitating

-0.411**

-0.420**

-0.185

-0.134

 

(0.091)

(0.093)

(0.100)

(0.107)

   Child

-0.767**

-0.756**

-0.109

0.006

 

(0.138)

(0.143)

(0.153)

(0.150)

Employment (cumulative)

    

   Low intensity (20 or fewer hrs per week)

0.023**

0.020**

0.007

0.006

 

(0.003)

(0.003)

(0.004)

(0.004)

   Medium intensity (21-34 hrs per week)

0.004

0.007

-0.006

-0.005

 

(0.004)

(0.004)

(0.005)

(0.005)

   High intensity (35 or more hrs per week)

-0.021**

-0.020**

-0.003

0.003

 

(0.003)

(0.003)

(0.003)

(0.004)

     

Enrollment Patterns

    

   Interrupted enrollment

 

-1.222**

-0.306**

-0.273**

  

(0.080)

(0.093)

(0.105)

     

   Months enrolled, cumulative

  

0.123**

0.127**

   

(0.006)

(0.006)

     

   % part-time enrollment

   

-0.020**

    

(0.002)

     

Sociodemographic Characteristics

Yes

Yes

Yes

Yes

Control Variables

Yes

Yes

Yes

Yes

     

-2ll

13798.315

13483.660

12252.61

12076.600

     

*p<0.05, **p<0.01.  

    

Notes. N= 4,704 persons and 206,535 person-months. All models are weighted and adjusted for clustering of individuals within families and include a series of time dummies estimating the baseline hazard.


a Baseline model is Model 3 from Table 1.


CONCLUSION


President Barack Obama pledged in his first speech to a joint session of Congress in February 2009 that, “We will provide the support necessary for you to complete college and meet a new goal: by 2020, America will once again have the highest proportion of college graduates in the world.”4 While one much-emphasized strategy for reaching this goal is encouraging non-traditional-age students to enter or return to higher education, another strategy is to improve degree completion rates among young adults. Indeed, it is among the youngest cohorts that the U.S. has fallen the furthest behind other nations.


Much previous research on degree attainment of traditional-age students has focused on studying students’ academic preparation and trajectories. Similarly, many policy efforts in recent decades have been directed toward improving academic preparation as well as decreasing gaps among different sociodemographic groups (e.g., core curriculum, high school exit exams, and No Child Left Behind). While academic preparation is important, present analyses indicate that much broader and more comprehensive approaches are needed to improve bachelor’s degree attainment of young adults and reduce social inequality in this important outcome.


Presented results reveal the importance of life course transitions for educational success and inequality in educational outcomes. Transitions into adult roles contribute to understanding the gender gap in degree completion, and employment patterns, in particular, help to explain the socioeconomic inequality in higher education. Moreover, presented analyses indicate that the negative relationship between life course transitions and degree attainment emerges in large part from students limiting their investment in higher education by reducing overall enrollment as well as attending part-time. When students have to manage conflicting social roles of work, marriage/cohabitation, parenthood, and schooling, they limit their investment in the latter.


Incorporating insights from the life course perspective into the study of educational outcomes in higher education is crucial not only for developing more compelling explanations of social inequality but also for proposing more effective policies to facilitate degree completion. One possible strategy may involve attending to attitudes and messages transmitted to students during high school. While the vast majority of students in the U.S. expect to attain a bachelor’s degree, many do not have concrete plans for achieving that goal, nor do they have a clear understanding of the relationship between educational and occupational requirements (Schneider & Stevenson, 1999). Although it is feasible for young adults to complete a degree while working and raising a family, combining those roles is challenging. Strengthening high school advising to provide students with resources to think through, plan, and consider whether and how, in concrete terms, they will realize their goals may help them to develop more successful strategies leading toward degree completion.


College advising may also benefit from considering how schooling fits within the context of the life course. Much college advising, if it occurs at all, focuses on academics. Helping students think about education in the context of their lives, and providing information about different pathways through higher education as well as resources that may help students combine schooling with other life course transitions, may be particularly valuable. In addition, existing higher education policies and programs beyond advising may deserve review, evaluating the extent to which they can assist students in their efforts to combine schooling with other social roles.


Since individuals in the U.S. rely extensively on their families to facilitate the transition to adulthood, this leaves many individuals, particularly those from less advantaged circumstances, vulnerable and without a safety net, implying a need for developing more adequate supports for young adults as they navigate transitions into adult roles (Settersten & Ray, 2010). The extent to which higher education institutions can provide resources, targeted (such as assistance with childcare) as well as more general (such as better financial aid counseling and financial assistance), are worthy of consideration. Although developing specific policies requires a careful study of different approaches within specific higher education contexts, this study indicates that considering the challenges of combining multiple social roles is crucial for improving educational attainment of students for whom going to class is only one component of a complex journey toward a degree.


Acknowledgements


This project was supported by funding from the Spencer Foundation and the University of Virginia. The author is grateful to Richard Arum, Lynn Cooke, Mathew McKeever, Samuel Lucas, Aaron Pallas, participants in the Higher Education Policy Seminar at UVA and CREO Speaker Series at the University of Notre Dame for insightful comments on earlier versions of the manuscript.


Notes


1. “Young adults” and “traditional-age students” are used interchangeably and include students who entered higher education under the age of 24, which is the cut-off used to define dependent status for financial aid.

2. Missing data on sociodemographic characteristics and control variables was addressed using multiple imputation.

3. Since comparing coefficients across logistic regression models may lead to erroneous conclusions, I conducted supplemental analyses, including y-standardized coefficients and average marginal effects, which replicate the substantive findings reported herein.

4. Barack Obama, remarks prepared for the Joint Session of Congress in Washington D.C., February 24, 2009.


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Cite This Article as: Teachers College Record, Date Published: August 14, 2013
http://www.tcrecord.org ID Number: 17213, Date Accessed: 10/23/2017 6:20:01 PM

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About the Author
  • Josipa Roksa
    University of Virginia
    E-mail Author
    JOSIPA ROKSA is an associate professor of sociology and education at the University of Virginia. She studies social inequality in students’ experiences and outcomes in higher education. In addition to publishing in sociology and education journals, she is coauthor of Academically Adrift: Limited Learning on College Campuses.
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