The Impact of Student Employment During the First Two Years of College
Info: 5179 words (21 pages) Example Literature Review
Published: 1st Dec 2021
Nonis and Hudson (2006) eloquent state, “Students spending less time studying and more time working are two trends that all college and universities will have to confront. Lowering academic standards by rewarding minimum effort and achievement (expecting less) is certainly a short-term strategy but one that will have negative long-term consequences” (p. 151). This statement captures the conundrum that education researchers and practitioners wrestle with when the inevitable topic of the financial burden of higher education is intoduced. The dramatic rise in college cost has forced many students to divide their time spent studying and engaging in academic and cocurricular activities with working on and off campus jobs while attending college. The study of the impact of this ever-evolving reality is confounding at best and contradictory at worst.
This literature review will give a broad summary of the dominant studies on the topic of student work and its impact on student academic success. Though the primary sources are recent peer-reviewed research articles, I will provide additional context with references to some of the education literature’s historic works on the topics of student retention, academic success, and college student employment. I will also attempt to categorize the literature according to dominant themes that appear most often in the literature. This will include prominent theories and methodologies.
The goal is to pull together the findings of those who study the impact of working on academic success for the students and the results are not always negative. There are studies that indicate a positive correlation as well as those that find detrimental results for student retention and success. Earlier studies focused on student retention and success as they relate to socioeconomic status. They were mostly concerned with access. The study of working students has evolved to include other variables and student characteristics to better understand the factors that impact students’ retention.
Finally, by aggregating the findings of the included literature I hope to provide some implications for practice. As students continue to struggle with striking the right balance between funding their education goals and the future impact of incurring college debt, it is incumbent upon the academy, policy makers and community stakeholders to find new ways to help students overcome the growing financial challenges that only seem to increase at a record pace. These recommendations will also expose gaps in the research that may be addressed by future education researchers and/or practitioners who are interested in this topic.
The number of students working to pay for their education has continued to rise over the last forty years matching the escalating cost of postsecondary degree attainment. Riggert, Boyle, Petrosko, Ash, D., & Rude-Parkins (2006) state, “Student employment is no longer an isolated phenomenon; it is an educational fact of life” (p. 64). According the National Center for Education Statistics, approximately seventy-one percent of undergraduate students 16-24 were employed in 2012 while enrolled in college. Among them 20% work full-time, and of those working part-time, half work more than 20 hours per week. When disaggregated by gender, seventy percent of male undergraduate students were employed compared to seventy three percent of female students (NCES, 2012; Broton, 2016; Logan, J., Hughes, T., & Logan, B., 2016).
Not only are the numbers of working college students increasing, but the amount of time students are spending studying is decreasing. Growth in student employment began in the mid-1960s and continued until the percentage of employed full-time traditional-age students reached its peak at fifty-two percent in 2000 (Broton, 2016; Stern & Nakata, 1991; U.S. Department of Education, 2014). Couple this with the fact that financial aid at both the federal and state levels is decreasing, and the financial outlook for collegebound students is challenging.
Researchers for many years seemed to approach questions regarding student retention through the Tinto lens focused on retention or the Kuh lens with a keen eye toward student engagement. Later studies, such as Warren’s (2002) “Reconsidering the relationship between student employment and academic outcomes: A new theory and better data” broke new ground by proposing a working theory to help corral the many variables that impact student success in the context of student work.
Pascarella, Edison, Nora, Hagedorn, &Terenzini (1998) noted that only a modest body of empirical investigation has evaluated the impact of student employment on college outcomes.
(Riggert et al, 2006 pg. 70). During the time of Pascarella’s study there were few, if any, accepted theoretical models in use to define the relation between student employment and academic outcomes. This may have lead to the mixed findings we will discuss later in which some research supports a positive correlation between working and academic outcomes while other similar studies do not.
According to the latest NCES report, about 40% of undergraduate students identified themselves as working to pay for educational needs and they are referred to as Students Who Work. Twenty-one percent of the survey respondents were not employed. Students Who Work reported working an average of twenty-five hour per week (NCES, 2012). Broton, Goldrick-Rab, & Benson, (2016) found that students often work long hours “because their pay is low and college is expensive. The net price of college has grown as financial aid has lost “purchasing power” and sticker prices have risen” (p. 479).
Studying students who work inevitably comes back to academic performance and student retention. When addressing the impact of student employment on higher education, most studies have used either academic performance or student retention to determine the impact of employment (Riggert et al, 2006). Defining retention presents challenges. “There has been disagreement over whether to define retention as re-enrollment from year to year (e.g., fall to fall) or from semester to semester (e.g., fall to spring)” (p. 66). Also, it has been unclear how changes by students to part-time status, transfer to other educational institutions, and “stop out” (i.e., enrollment interruption with subsequent re-enrollment) should be managed (p. 66).
(Riggert et al, 2006) quotes (Tinto,1982), “While typically seen as negative, dropping out can be positive for both the student and the institution if the goals of the student and the institution are not consonant” (p. 4). Tinto (1983) suggested that dropping out might reflect a student’s mature recognition that the college experience has not met his or her needs. The student’s goals and intentions in coming to the educational institution may not have included graduation. Thus maintaining enrollment for its own sake can be counterproductive for both student and institution.
Theory of The Allocation of Time
The prevailing theories of student success and working frame the problem in two ways, resource allocation factors and socio-psychological factors (Baert et al., (2017). (Nonis et al 2006) examined the effect of both time spent studying and time spent working on academic performance. This represents the allocation framework which assumes that students’ academic performance correlates with the amount of time and energy they’re able to devote to their studies. Findings have been mixed in these studies. ?
To explain nonpositive associations , the literature has mainly relied on similar theory, The Zero-Sum theory. The key idea of this theory is that student employment crowds out time spent on activities that foster educational performance (Kalenkoski and Pabilonia, 2012; Baert, 2017). The Zero Sum model of student employment explains that any time that students direct toward paid employment takes away from the time that is available to study for school. Thus, the expectation is that the greater the number of hours that a student works, the lower his/her grades will be. The result of studies based on this assumption alone have been largely inconclusive.
Lang (2012) discusses Warren’s (2002) models, the zero-sum model and the primary orientation model. The work of Baert, 2017; Warren 2002; Kalenkoski and Pabilonia 2009, 2012 has shown that, “an (additional) hour spent working does not necessarily decrease the time spent on school-related activities proportionally, which, to some extent, impairs the validity of this theory” (Baert, 2017 p. 5). An alternative explanation for the nonpositive association between (hours of) student work and educational performance might be explained best by Warren’s (2002) primary orientation theory.
Primary orientation theory appears to better explain non positive relationships between hours spent working and student’s performance in class as we see in the Baert (2017) study, “ increasing the working time by one hour per week is associated with a decrease in the percentage of courses passed of 0.9 percentage points” (p. 5). This theory was developed by Warren (2002) who posited that students who are psychologically oriented toward school perform better academically than students who are oriented toward work when all factors are equal.
The primary orientation model of student employment, assumes that the intensity of paid employment only matters when it coincides with a disinterest in academics. As such, the expectation is that motivated students are generally able to balance paid work and their scholastic responsibilities (Warren, 2002; Baert, 2017). It suggests that this association is driven by socio-psychological factors, rather than by resource allocation. Plainly put,Warren (2002) argues that student employment is mainly detrimental for students with a primary orientation towards work (in contrast to students with a primary orientation towards school, who do not let their studies suffer from their employment).
Baert (2017) provides an assessment of the orientation theory with respect to the nonpositive association between student employment and academic performance reported in the literature. In line with our expectations, a negative relationship between hours worked and academic performance was only found for students with a primary orientation towards work (versus school).
Studying Students Who Work
Several recent studies examine student working behavior as a function of student’s response to the rising cost of higher education. As the net price of education which includes housing and food costs, available financial aid sources have not kept pace with the cost and many students must resort to self-financing their education costs. Broton, Goldrick-Rab, & Benson, (2016) sought to understand if grant aid might change work behaviors among college students. They studied a group of low-income students at a large public state university who were randomly assigned an offer of an additional need-based grant. They found that students worked less hours when given additional aid.
Broton 2016 studied the impact of grant aid on student’s work behavior. We consider average impacts on the percent of students working at all, working off-campus, and working extensively. We also estimate the average impact on the number of total hours worked, number of hours worked off campus, and hours worked in on-campus employment. Next, we examine if the grant offer affected the time of day students worked.
In the Balancing Work and Learning report, Carnevale, Smith, Melton, and Price (2018) report, “lower-income students are more likely to find minimum-wage service jobs that are unrelated to future goals and which don’t pay enough to outrun the costs associated with college” (p.3). When the Pell Grant was created in the early 1970s, it covered more than seventy-five percent of the cost of attending a public four-year college for low income students, whereas today, it covers just thirty percent.
Conversations about the disproportionate impact of working while learning on low income students is necessary, however, this may divert attention away from the study of other student characteristics which may yield more reliable data. Examples include students’ attitudes about college and work, how the job relates to the student’s career choice, the student’s time management skills, and the students orientations toward work and school (Warren, 2002; Baert, 2017)
Baert, Marx, Neyt, Van Belle. & Van Casternen, (2017) argue, “The peer-reviewed literature is inconclusive with respect to the significance and magnitude of the penalty of student employment in terms of educational performance (Kalenkoski & Pabilonia 2012).” Some studies, depending on the research methodology, report student employment having a significant negative impact om variables such as graduation rates and grade point averages (Brody et al., 2014; Darolia 2014; Triventi 2014). On the other hand, other studies on working students have found a neutral effect when using exam scores as a measure (Rothstein 2007).
The Baert study (2017) sought to empirically explore the validity of the primary orientation theory with respect to the association between student employment and academic performance for students in higher education . The researchers sampled 255 full-time students who were randomly selected and given a four-part questionnaire. The questionnaire identified the respondents’ demographic data, the average number of hours that students worked, as well as the students’ primary orientation toward school or work.
Logan, Hughes, and Logan (2016) as well as Lang (2012) sought to estimate the impact that employment may have on a student’s academic performance. Their studies correlate the number of hours worked with each student’s academic performance. Lang (2012)l states, “it can be seen that students who work many hours per week appear to be able to maintain the same grades, participate equally in co-curricular activities and have same amount of time to prepare for class as other students who work fewer hours per week.”
Choi (2018) studied how student employment affects college persistence and how these effects differ by individual likelihood of participating in student employment. Choi examines the relationship between college student employment and dropout using propensity score matching to address concerns over pretreatment heterogeneity. To this end, he analyzed data from the National Longitudinal Survey of Youth 1997 using propensity score matching and stratification-multilevel analysis. (Choi 2018) Thus, it is important to note that the causal effects of student employment on dropout have not been established, and the validity of findings of this study greatly depends on the plausibility of the covariates of the current model.
In a sample consisting of students at a 2-year, nonresidential community college, Halpin (1990) found the Tinto model to be effective in predicting persistence. Consistent with the Tinto model, results also suggested that academic integration issues had greater influence than social integration issues in the sample of 2-year, nonresidential students.
Logan 2016 recommends Estimated findings show that students should be discouraged from working over 20 hours in off-campus jobs in their first 2 years of college (Logan, 2016; Halpin, 1990; Tinto, Wilson 2016 ).
Broton’s (2016) study of the Wisconsin Grant Aid program paticipants looked at 1438 randomly selected participants who met the eligibility criteria: Wisconsin residents who attended and graduated from a state public high school or earned a Wisconsin High School Equivalency Diploma within 3 years of matriculating to one of the state’s 42 public colleges or universities, where they enrolled for at least 12 credits, completed the Free Application for Federal Student Aid (FAFSA), and qualified for a federal Pell Grant, while still possessing unmet need (excluding loans) of at least US $1.
Lang (2013) utilized existing data from the National Study of Student Engagement NSSE administered in 2008 to 794 randomly selected students observed a significant negative correlation between hours employed and GPA. Furthermore, their study also demonstrated that students employed primarily for financial reasons tend to receive lower grades than students working to obtain career-specific skills.
Lang (2012 pg. 8) Results show that there are not any discernable differences concerning students who work and those who do not work regarding their grades, time spent preparing for class, race, overall college experience, sex and other variables in the study. Lang 2012, In this study, it has been found that neither employment nor the number of hours worked per week affect the grades of college students when controlling for race, sex and the other variables in the study. These findings lend support to Warren's primary orientation model of student employment.
Riggert et al, 2006 “To summarize, there are differences of opinion regarding the impact of employment on college students’ performance. Some researchers view student employment as potentially harmful. Others regard it as neutral or even beneficial, and tacitly approve of unlimited work hours with no reduction of course load. Given the number of working students, the outcome of this empirical debate is of no small consequence.”
Riggert et al, 2006 67 In fact, students who did not work had a rate of enrollment interruptions similar to that of students working 16–34 hours weekly. At highest risk for nonpersistence were students working 35 or more hours per week. Working students reported limitations in choice of classes, number of available classes, times in which to schedule classes, and library access.
Greene & Maggs, (2015) took the approach of looking at the time trade-off students face when working as a student. They found more time spent on employment was linked to less time spent on academics across days and semesters whereas organized activities were associated with less time on academics at the daily level only. Whether or not this negatively impacted students’ academic performance was not supported in their findings.
An indicator of student success is the student grade point average. For those who seek to understand the impact of student work on academic success, grade point average is a standard measure. There are several studies that indicate lower g.p.a.’s for working students especially when their hours working off-campus exceed 15-20 hours per week (Logan et al, 2016). Ethniciti and gender show slightly significant relationship under the same conditions. There was no significant finding showing benefits to students living on-campus relative to those living off-campus (Loga et al 2016).
Overall, students working 20 or more hours per week at an off-campus job have GPAs that are relatively lower than students working fewer hours. This may relate more to students being less engaged while working than the actual hours spent working 0-20 hours per week (Mathis et l 2017 p. 147). The NCES (Horn & Malizio, 1998) found that students who worked 1–15 hours per week had the lowest risk for enrollment interruption, even when compared with students who did not work.
Choi (2018), found that engaging in “intense work has deleterious effects on college persistence. However, these negative effects vary significantly according to likelihood of participation in intense work.” The results indicate that employment has less negative impacts on completion for those most likely to participate in intense work, who are typically those from the most disadvantaged social backgrounds. This finding suggests that efforts to reduce the deleterious effects of intense work on persistence should be practiced with careful consideration for sub-populations that may have different reasons for and effects of student employment. High propensity students who do not engage in intense work already have extremely high dropout rates, and this can mask the effects of intense work on dropout. That is, regardless of work engagement, disadvantaged students often drop out for other reasons. This aligns somewhat with the Primary Orientation theory in that the study incorporates students’ attitude or feelings about working and school prior to attending college.
A challenge that I noticed is that there is relatively little focus on community college students and their working behavior. Greenes study (2015) on time use a large public flagship university who’s demographic profile indicated that a large portion of the student population is affluent. Greene suggests that r, students attending community colleges or commuter universities likely face unique time use demands and therefore may manage their time differently than students in the current sample.
(Choi 2018) Although job experience is considered to be an important requirement for labor market entrance, it can also threaten bachelor’s degree completion. Therefore, unless it is necessary for financial purposes for college retention, advantaged students should carefully consider their employment options when deciding to participate in student employment. Even though field-related and voluntary work experience helps graduates to realize a fast integration into the labour market, it is not linked to higher chances for entering a favourable class position or to higher wages in the long run. (Weiss, Klein, & Grauenhorst, 2014)
Second, although disadvantaged students are less penalized from engaging in intense work, participating can still be problematic for several reasons such as lack of training in nonability related skills such as time management and are also more likely to face issues outside the campus environment that may impact their retention.
Conclusions and Recommendations for Practice
In summary, the question of the impact of working on college students’ academic success is complex. There are no simple answers or direct cause and effect relationships to leverage for practice based on the studies presented. When examining subgroups, student backgrounda and environmental aspects of particular institutions we’re able to get closer to understanding the implications of the available research’s findings. What we do understand is that certain populations experience the burdens of the rising cost of education moreso than others resulting in an increased dependence on working to afford college and and college-related expenses.
There are several successful approaches that have yielded positive outcomes within the chosen body of literature. These are approaches that institutions could replicate with hopefully similar results. First, is the Wisconsin Scholars Grant studied by Broton, et al (2016). In a randomized experiment the researchers examined undergraduate students who were offered a private need-based grant (grant aid) to understand the impact on those student’s decisions to work and/or utilize student loans. Students offered the grant were less likely to work at all and reported working fewer hours. They also demonstrated less dependence on student loans.
Baert (2017) from a policy perspective, our results indicate that students should be discouraged from prioritizing their student job over their studies.
Baert, S. & Rotsaert, O.& Verhaest, D.& Omey, E. (2016). Student employment and later labour market success: No evidence for higher employment chances: Student Employment and Later Labour Market Success. Kyklos. 69. 401-425.
Baert, S., Marx, I., Neyt, B, Van Belle, E. & Van Casternen, J. (2017). Student employment and academic performance: an empirical exploration of the primary orientation theory, Applied Economics Letters, DOI: 10.1080/13504851.2017.1343443
Broton, K. M., Goldrick-Rab, S., & Benson, J. (2016). Working for college: the causal impacts of financial grants on undergraduate employment. Educational Evaluation and Policy Analysis, 38(3), 477–494.
Canabal, M. E. (1998). College student degree of participation in the labor force: Determinants and relationship to school performance. College Student Journal, 32(4), 597-605
Carnevale, A. Smith, N., Melton, M., Price, E. (2015). Learning while earning: The new normal (pp. 1-70, Rep). Georgetown University Center on Education and the Workforce McCourt School of Public Policy. Retreived from https://cew.georgetown.edu/wp-content/uploads/Working-Learners-Report.pdf
Choi, Y. (2018). Student employment and persistence: Evidence of effect heterogeneity of student employment on college dropout. Research in Higher Education, 59(1), 88-107. doi:http://dx.doi.org.
Greene, K. M., & Maggs, J. L. (2015). Revisiting the time trade-off hypothesis: Work, organized activities, and academics during college. Journal of Youth and Adolescence, 44(8), 1623-1637. doi:http://dx.doi.org.
Greene, K.M., & Maggs, J.L. (2017). Academic time during college: Associations with mood, tiredness, and binge drinking across days and semesters. Journal of Adolescence, 56(4), 24-33. Retrieved from: https://www-sciencedirect-com.
Halpin, R. L. (1990). An application of the tinto model to the analysis of freshman persistence in a community college. Community College Review, 17(4), 22–32. https://doi.org/10.1177/009155219001700405
Horn, L. J., & Malizio, A. (1998). Undergraduates who work: National postsecondary student aid study, 1996 (National Center for Education Statistics, U.S. Department of Education, Office of Educational Research and Improvement Publication No. NCES 98-137). Washington DC: U.S. Government Printing Office.
Kalenkoski, C.M., & Pabilonia, S.W. (2012). Parental transfers, student achievement, and the labor supply of dollege students. (BLS Working Paper 387). Journal of Population Economics, 23(2), 469–496.
Lang, B. (2012). The similarities and differences between working and non-working students at a mid-sized American public university. College Student Journal, 46(2), 24-255.
Logan, J., Hughes, T., & Logan, B. (2016). Overworked? An observation of the relationship between student employment and academic performance. Journal of College Student Retention: Research, Theory & Practice, 18(3), 250–262. https://doi.org/10.1177/1521025115622777
Mathis, E. L., Bullock-Yowell, E., Leuty, M. E., & Nicholson, B. C. (2017). Student congruence with academic major: Do hours worked and attitude affect satisfaction and success? Australian Journal of Career Development, 26(3), 142–152. https://doi.org/10.1177/1038416217727737
Neyt, B., Omey, E., Verhaest, D., & Baert, S. (2019). Does student work really affect educational outcomes? A review of the literature. Journal of Economics Surveys, 33(3), 896–921.
National Center for Education Statistics. (2012). Higher education: Gaps in access and persistence study. Washington, DC: U.S. Government Printing Office Retrieved from http://nces.ed.gov/pubs2012/2012046/chapter6_9.asp
Nonis, S. A., & Hudson, G. I. (2006). Academic performance of college students: Influence of time spent studying and working. Journal of Education for Business, 81(3), 151-159. Retrieved from http://www2.lib.ku.edu/login?url=https://search-proquest-com.
Nuñez, A., & Sansone, V.A. (2016). Earning and learning: Exploring the meaning of work in the experiences of first-generation Latino college students. The Review of Higher Education 40(1), 91-116. doi:10.1353/rhe.2016.0039.
Pascarella, E., Edison, M., Nora, A., Hagedorn, L., & Terenzini, P. (1998). Does Work Inhibit Cognitive Development during College? Educational Evaluation and Policy Analysis, 20(2), 75-93. Retrieved from www.jstor.org/stable/1164375
Riggert, S. C., Boyle, M., Petrosko, J. M., Ash, D., & Rude-Parkins, C. (2006). Student employment and higher education: Empiricism and contradiction. Review of Educational Research, 76(1), 63–92. https://doi.org/10.3102/00346543076001063
Rothstein, D. (2007). High school employment and youths' academic achievement. The Journal of Human Resources, 42(1), 194-213. Retrieved from www.jstor.org/stable/40057302
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press.
Vinichenko, M. V., Makushkin, S. A., Melnichuk, A. V., Frolova, E. V., & Kurbakova, S. N. (2016). Student employment during college studies and after career start. International Review of Management and Marketing, 6(5) Retrieved from http://www2.lib.ku.edu/login?url=https://search-proquest.com.
Warren, J. R. (2002). Reconsidering the relationship between student employment and academic outcomes: A new theory and better data. Youth & Society, 33(3), 366-393.
Wenz, M., & Yu, W. (2010). Term-time employment and the academic performance of undergraduates. Journal of Education Finance, 35(4), 358–374.
Weiss, F., Klein, M., & Grauenhorst, T. (2014). The effects of work experience during higher education on labor market entry: learning by doing or an entry ticket? Work, Employment and Society, 28(5), 788–807. Retrieved from https://doi.org/10.1177/0950017013506772
Wilson, S. D. (2016). Lack of persistence in college and the high-achieving, low-income student: a review of the literature. Community College Enterprise, 22(2), 42.
Cite This Work
To export a reference to this article please select a referencing stye below:
Related ServicesView all
Related ContentAll Tags
Content relating to: "Education"
Education is the process of teaching or learning, especially systematically during childhood and adolescence, in a school or college, or the knowledge that someone gains from this. Post study, education can mean the imparting or acquiring of specific knowledge or skills required for a task, or profession for example.
Impact of Web-Based Instruction (WBI) in Schools
The purpose of the study is to build a model to predict the level of diffusion and utilization of Web-Based Instruction in school....
Flow in A Second-Language Learning Classroom
As students who spend large amounts of time in a classroom working on different tasks and under different conditions, we have recently become interested in the idea of flow as a psychological state....
DMCA / Removal Request
If you are the original writer of this literature review and no longer wish to have your work published on the UKDiss.com website then please: