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Post-secondary education
Labor organization efforts grew following the pandemic in the United States at tech companies, automakers, and even higher education institutions. This brief examines unionization trends at private colleges and universities from 2007 to 2023, revealing staff as the main force behind unionization attempts, followed by contingent faculty. Major unions like the SEIU and the AFL-CIO play significant roles in representing college and university employees. This study underscores the importance of understanding historic unionization efforts, shedding light on often overlooked staff categories like maintenance and security.
Many public workforce training programs lead to industry-recognized, third-party awarded credentials, but little research has been conducted on the economic benefits of these credentials in the labor market. This paper provides quasi-experimental evidence on the labor market returns to industry-recognized credentials connected to community college workforce noncredit training programs. Based on novel data that includes approximately 24,000 working-age adults enrolled in noncredit workforce training programs at the Virginia Community College System, we employ a comparative individual-level fixed effects model to estimate earnings premia net of fixed attributes and earnings time-trends. Our results indicate that earning an industry-recognized credential, on average, increases quarterly earnings by approximately $1,000 and the probability of being employed by 2.4 percentage points, although there is substantial heterogeneity in economic return across different program fields. Back-of-the-envelope calculations suggest that the earnings gains associated with the industry credential obtained through the noncredit workforce training would exceed program costs in just over half a year on average.
As states incorporate measures of college readiness into their accountability systems, school and district leaders need effective strategies to identify and support students at risk of not enrolling in college. Although there is an abundant literature on early warning indicators for high school dropout, fewer studies focus on indicators for college enrollment, especially those that are simple to calculate and easy for practitioners to use. This study explores three potential indicators of college readiness that educational leaders may consider using as part of an early warning system for college enrollment. Using district administrative data, our analysis shows that an indicator based on attendance, grades, and advanced course-taking is slightly more effective at predicting college enrollment than indicators based on course failures or standardized test scores. However, the performance of these indicators varies across different student demographic and socioeconomic subgroups, highlighting the limitations of these measures and pointing to areas where they may need to be supplemented with contextual information. Through event history analysis, we demonstrate that the ninth grade is a particularly challenging year for students, especially those who are male, Black, Hispanic, or economically disadvantaged. These results suggest that educational leaders ought to consider identifying and targeting students at risk of not attending college with additional resources and support during the freshman year of high school.
Colleges have increasingly turned to data science applications to improve student outcomes. One prominent application is to predict students’ risk of failing a course. In this paper, we investigate whether incorporating data from learning management systems (LMS)--which captures detailed information on students’ engagement in course activities--increases the accuracy of predicting student success beyond using just administrative data alone. We use data from the Virginia Community College System to build random forest models based on student type (new versus returning) and data source (administrative-only, LMS-only, or full data). We find that among returning college students, models that use administrative-only outperform models that use LMSonly. Combining the two types of data results in minimal increased accuracy. Among new students, LMS-only models outperform administrative-only models, and accuracy is significantly higher when both types of predictors are used. This pattern of results reflects the fact that community college administrative data contains little information about new students. Within the LMS data, we find that LMS data pertaining to students’ engagement during the first part of the course has the most predictive value.
Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included – demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., are letters evaluated at face value across all students, or are they mostly compared to other letters from the same high school or counselor?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.
We provide evidence about college financial aid from an eight-year randomized trial where high school ninth graders received a $12,000 merit-based grant offer. The program was designed to be free of tuition/fees at community colleges and substantially lower the cost of four-year colleges. During high school, it increased students’ college expectations and low-cost effort, but not higher-cost effort, such as class attendance. The program likely increased two-year college graduation, perhaps because of the free college framing, but did not affect overall college entry, graduation, employment, incarceration, or teen pregnancy. Additional analysis helps explain these modest effects and variation in results across prior studies.
We estimate the education and earnings returns to enrolling in technical two-year degree programs at community colleges in Missouri. A unique feature of the Missouri context is the presence of a highly regarded, nationally ranked technical college: State Technical College of Missouri (State Tech). We find that enrolling in a technical program in Missouri increases the likelihood of associate degree attainment and post-enrollment earnings, but that the positive effects statewide are driven largely by students who attend State Tech. These findings demonstrate the potential for a high-performing community college to change students’ education and labor market trajectories. At the same time, they exemplify the potential for substantial institutional heterogeneity in the returns to postsecondary education.
This study investigates the role of college major choices in labor market outcomes, with a focus on racial minorities and immigrants. Drawing upon research on school-to-work linkages, we examine two measures, linkage, the connection between college majors and specific occupations in the labor market, and match, the alignment of workers’ occupations with their college majors. Analyzing data from the American Community Survey, 2013-2017, we show that linkage positively predicts earnings, particularly for workers in matched occupations, and negatively predicts unemployment. Notably, Black, Hispanic, and foreign-born workers in matched occupations benefit more from linkage strength than their White and U.S.-born counterparts. This advantage is more pronounced in states that are popular destinations for immigrants. Our findings suggest that earnings and unemployment disparities experienced among racial minorities and immigrants may diminish if they pursue majors closely tied to jobs in the labor market and secure jobs related to their college majors.
This paper studies the causal impacts of public universities on the outcomes of their marginally admitted students. I use administrative admission records spanning all 35 public universities in Texas, which collectively enroll 10 percent of American public university students, to systematically identify and employ decentralized cutoffs in SAT/ACT scores that generate discontinuities in admission and enrollment. The typical marginally admitted student completes an additional year of education in the four-year sector, is 12 percentage points more likely to earn a bachelor's degree, and eventually earns 5-10 percent more than their marginally rejected but otherwise identical counterpart. Marginally admitted students pay no additional tuition costs thanks to offsetting grant aid; cost-benefit calculations show internal rates of return of 19-23 percent for the marginal students themselves, 10-12 percent for society (which must pay for the additional education), and 3-4 percent for the government budget. Finally, I develop a method to disentangle separate effects for students on the extensive margin of the four-year sector versus those who would fall back to another four-year school if rejected. Substantially larger extensive margin effects drive the results.
This study found that the MATC Promise increased college attainment by encouraging Milwaukee high school students to access state and federal aid, and to consider matriculating to their local two-year college. The MATC Promise exemplifies the last-dollar model of college aid. If seniors at Milwaukee area public high schools complete academic milestones, apply for financial aid, qualify based on low family income, and matriculate to Milwaukee Area Technical College (MATC), then the Promise covers any remaining tuition charges. The message promoting free college was the program’s main element, since the funding support for eligible students came primarily from existing state and federal aid. We studied outcomes for the first four graduating classes after the Promise was launched, compared to the trend in Milwaukee for the previous six graduating classes. The rate of matriculation to MATC increased from 10 percent to 15 percent. There was no such increase in matriculation to other technical college districts around the state, suggesting that the increase was caused by the Promise. The increase in enrollment was larger among lower-income students and those in the urban Milwaukee Public Schools. Those students were more likely to apply for financial aid earlier, regardless of whether they ultimately qualified for the Promise, and their rate of matriculation to any college increased from 45 percent to 49 percent. There was no indication that attracting additional students to college led to lower graduation rates, though we were limited to examining credentials earned in two years or less.