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This study explores the relationship between rising student absenteeism and changes in student achievement since the beginning of the COVID-19 pandemic. We conduct a mediation analysis using detailed administrative data on middle school students in North Carolina to estimate how increased post-pandemic absenteeism has affected test scores. In 2021-22, math achievement remained 0.24-0.27 standard deviations (SDs) below pre-pandemic levels, while absence rates were 2.8 percentage points higher. We find that this increase in absences accounts for a 0.04-0.06 SD decline in achievement, explaining 16-23% of the total change in test scores. Further, increased absenteeism may have also widened racial/ethnic and socioeconomic achievement gaps. These results suggest attendance recovery efforts can play a key role in learning recovery.
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.
Mounting evidence supporting the advantages of a diverse teacher workforce prompts policymakers to scrutinize existing recruitment pathways. Following four cohorts of Maryland public high-school students over 12 years reveals several insights. Early barriers require timely interventions, aiding students of color in achieving educational milestones that are prerequisites for teacher candidacy (high school graduation, college enrollment). While alternative pathways that bypass traditional undergraduate teacher preparation may help, current approaches still show persistent racial disparities. Data simulations underscore the need for race-conscious policies specifically targeting or differentially benefiting students of color, as race-neutral strategies have minimal impact. Ultimately, multiple race-conscious policy solutions addressing various educational milestones must demonstrate significant effects—approximately 30% increases—to reshape the teacher workforce to align with student body demographics.
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.
We examine the state of the U.S. K-12 teaching profession over the last half century by compiling nationally representative time-series data on four interrelated constructs: occupational prestige, interest among students, the number of individuals preparing for entry, and on-the-job satisfaction. We find a consistent and dynamic pattern across every measure: a rapid decline in the 1970s, a swift rise in the 1980s extending into the mid 1990s, relative stability, and then a sustained decline beginning around 2010. The current state of the teaching profession is at or near its lowest levels in 50 years. We identify and explore a range of hypotheses that might explain these historical patterns including economic and sociopolitical factors, education policies, and school environments.
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.
Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers’ expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers’ utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.
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.
We study the extent and consequences of biases against immigrants exhibited by high school teachers in Finland. Compared to native students, immigrant students receive 0.06 standard deviation units lower scores from teachers than from blind graders. This effect is almost entirely driven by grading penalties incurred by high-performing immigrant students and is largest in subjects where teachers have more discretion in grading. While teacher-assigned grades on the matriculation exam are not used for tertiary enrollment decisions, we show that immigrant students who attend schools with biased teachers are less likely to continue to higher education.
This paper describes a 12-week cluster randomized controlled trial that examined the efficacy of BookNook, a virtual tutoring platform focused on reading. Cohorts of first- through fourth-grade students attending six Rocketship public charter schools in Northern California were randomly assigned within grades to receive BookNook. Intent-to-Treat models indicate that students in cohorts assigned to BookNook outperformed their control-group peers by roughly 0.05 SDs. Given the substantial variability in usage rates among students enrolled in BookNook cohorts, we also leveraged Treatment-on-the-Treated approaches. These models suggest that students who completed 10 or more BookNook sessions experienced a reading advantage of 0.08 SDs, while those who completed 20 or more sessions—the recommended dosage—experienced a 0.26 SD developmental advantage.