Abstract: Developmental education (dev-ed) aims to help students acquire knowledge and skills necessary to succeed in college-level coursework. The traditional prerequisite approach to postsecondary dev-ed—where students take remedial courses that do not count toward a credential—appears to stymie progress toward a degree. At community colleges across the country, most students require remediation in math, creating a barrier to college-level credits under the traditional approach. Corequisite coursework is a structural reform that places students directly into a college-level course in the same term they receive dev-ed support. Using administrative data from Texas community colleges and a regression discontinuity design, we examine whether corequisite math improves student success compared with traditional prerequisite dev-ed. We find that corequisite math quickly improves student completion of math requirements without any obvious drawbacks, but students in corequisite math were not substantially closer to degree completion than their peers in traditional dev-ed after 3 years.

Abstract: Student placement test records, course enrollments, and other student-level data collected by community colleges are vital for evaluating the outcomes of students in developmental education (dev-ed) courses. Researchers and policymakers rely on this information to examine the impact of existing programs and assess ongoing reforms to dev-ed – the accuracy of state administrative data is critical to those tasks. In this study, we examine math placement records in a statewide administrative data set to understand how test records provided by colleges in the state aligned with student course enrollment patterns. We highlight systematic data reporting problems, where many students lacked test scores and test exemption records necessary for policymakers and researchers to determine if they enrolled in the appropriate coursework for their needs. We also found that a non-negligible proportion of students enrolled in dev-ed math – 10% – did not require remediation due to exemption status or passing placement test scores. We conclude with a discussion of the pressing need for accuracy in data reporting, as up-to-date, high-quality student-level data are essential to evaluate ongoing reforms to developmental education.

To improve outcomes in math, many Texas colleges are adopting mathematics pathways, which accelerate developmental math and tailor math courses to different majors instead of requiring all students to take algebra. This study examines whether students participating in Dana Center Mathematics Pathways (DCMP) developmental courses enroll in and pass college-level math courses at higher rates than students who take traditional developmental math courses. It employs regression analysis controlling for student characteristics using student-level data compiled by the state from the more than 20 Texas community colleges that implemented the DCMP model in 2015 and 2016.

Results from this study are encouraging. They suggest that DCMP compressed prerequisite developmental courses are effective at accelerating community college students through their math requirements. Yet this study also found systematic sorting of students into DCMP by race/ethnicity, which could exacerbate educational inequalities.

Abstract: Women remain underrepresented in STEM careers that offer high average pay, and recruiting and retaining women in STEM careers is major public- and private-sector goal. The efficacy of such policies and their implications for the distribution of earnings and job satisfaction depend on the treatment effects of working in STEM for STEM-marginal women. This study uses administrative employment and survey data to shed light on the drivers of mid-career entry into and exit from STEM. We use difference-in-difference approaches to document three main findings. Our first finding is that women entering STEM professions see their earnings rise by 17 and also experience gains in non-pecuniary career satisfaction. Our second finding is that while earnings effects are approximately symmetric for STEM exiters, career satisfaction effects are not: earnings fall by 14 for women who leave STEM, but non-pecuniary job satisfaction weakly increases. Third, we find suggestive evidence that job satisfaction in STEM declines over time, indicating that exit behavior could be driven by compensating differentials. Our results highlight the possibility that improving STEM workplace amenities may reduce STEM career exits for women.

Abstract: The Good Jobs Challenge is a $500 million U.S. Department of Commerce initiative that provides federal funding for local workforce development programs. In this paper, we evaluate the effectiveness of GJC programs by comparing the outcomes of GJC participants to the outcomes of comparable workers facing similar labor market conditions. Applying a nearest-neighbor matching approach to rich Census data, including administrative earnings records, we match GJC participants to other workers with similar demographic characteristics and employment and earnings histories in order to assess the impact of a national workforce development effort on worker wages and success transitioning from low-wage to higher-wage jobs.

Abstract: Little is known about the pervasiveness or determinants of within-school ability tracking in the US. To fill this gap, we use detailed administrative data to estimate the extent of tracking in Texas public schools for grades 4 through 8 over the years 2011-2019. Strikingly, we find that ability tracking across classes within schools overwhelms sorting by ability across districts and schools, as well as sorting by race/ethnicity or economic disadvantage. We also examine how schools operationalize tracking as well as the local characteristics that predict tracking. Finally, we explore how exposure to tracking (and the bundle of associated practices) relates to achievement gains, finding that, on average, tracking increases inequality by slightly improving test scores of higher-achieving students without harming those of lower-achieving students.

Abstract: I propose a hypothesis test for clustered samples. This test is exact in samples with few clusters, few ever-treated clusters, cluster size outliers, or treatment intensity outliers; these features cause previous tests to over- or under-reject true hypotheses. I derive my test by inverting the distribution of the test statistic under a standard assumption about the errors, so that critical values can be selected from a distribution that matches the test statistic. I use Monte Carlo simulations to demonstrate where this adjustment is most impactful in achieving exact tests compared to previous hypothesis tests, and I apply my test to an empirical setting from the literature.

If you would like to implement my test in R, you can use my package "clubsoda", available on github.

install.packages("devtools")

devtools::install_github("akiva-yonah-meiselman/clubsoda")

?p.value.meis

?conf.interval.meis

Abstract: This paper studies the long-run effects of disruptive peers in disciplinary schools on educational and labor market outcomes of students placed at these institutions. The existing literature documents that students who are removed from their regular instructional setting and placed at disciplinary schools tend to have significantly worse future outcomes. We provide evidence that the composition of peers at these institutions plays an importantrole in explaining this link. We userich administrative data of high school students in Texas which provides a detailed record of each student’s disciplinary placements, including their exact date of placement and assignment duration. This allows us to identify the relevant peers for each student based on their overlap at the institution. We leverage within school-year variation in peer composition at each institution to ask whether a student who overlaps with particularly disruptive peers has worse subsequent outcomes. We show that exposure to peers in highest quintile of disruptiveness relative to lowest quintile when placed at a disciplinary school increases students’ subsequent removals (5-8% per year); reduces their educational attainment —lower high-school graduation (6%), college enrollment (7%), and college graduation (17%); and worsens labor market outcomes—lower employment (2.5%) and earnings (6.5%). Moreover, these effects are stronger when students have a similar peer group in terms of the reason for removal, or when the distribution of disruptiveness among peers is more concentrated than dispersed around the mean. Ourpaper drawsattention to an unintended consequence of student removal to disciplinary schools, and highlights how brief exposures to disruptive peers can affect an individual’s long-run trajectories.