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Infant sex ratios that differ from the biological norm provide a measure of gender status inequality that is not susceptible to social desirability bias. Ratios may become less biased with educational expansion through reduced preference for male children. Alternatively, bias could increase with education through more access to sex-selective medical technologies. Using National Vital Statistics data on the population of live births in the U.S. 1969-2018, we examine trends in infant sex ratios by parental race/ethnicity, education, and birth parity over 5 decades. We find son-biased infant sex ratios among Chinese and Asian Indian births that persist in recent years and regressions suggest son-biased ratios among births to Filipino and Japanese mothers with less than high school education. Infant sex ratios are more balanced at higher levels of maternal education, particularly when both parents are college educated. Results suggest greater equality of gender status with higher education in the U.S.
We design a commitment contract for college students, "Study More Tomorrow," and conduct a randomized control trial testing a model of its demand. The contract commits students to attend peer tutoring if their midterm grade falls below a pre-specified threshold. The contract carries a financial penalty for noncompliance, in contrast to other commitment devices for studying tested in the literature. We find demand for the contract, with take-up of 10% among students randomly assigned a contract offer. Contract demand is not higher among students randomly assigned to a lower contract price, plausibly because a lower contract price also means a lower commitment benefit of the contract. Students with the highest perceived utility for peer tutoring have greater demand for commitment, consistent with our model. Contrary to the model's predictions, we fail to find evidence of increased demand among present-biased students or among those with higher self-reported tendency to procrastinate. Our results show that college students are willing to pay for study commitment devices. The sources of this demand do not align fully with behavioral theories, however.
The pursuit of multiple educational outcomes makes teaching a complex craft subject to potential conflicts and competing commitments. Using a dataset in which teachers were randomly assigned to students paired with videotapes of instruction, we both document and unpack such a tradeoff. Upper-elementary teachers who excel at raising students’ math test scores often are less successful at improving student-reported engagement in class (and vice versa). Further, the teaching practices that improve math test scores (e.g., cognitively demanding content) can simultaneously decrease engagement. At the same time, paired quantitative and qualitative analyses reveal two areas of practice that support both outcomes: active mathematics with opportunities for hands-on participation; and established routines and procedures to proactively organize the classroom environment. In addition to guiding practice-based teacher education, our mixed-methods analysis can serve as a model for rigorously studying and identifying dimensions of “good” teaching that promote multidimensional student development.
The COVID-19 pandemic has been a trying period for teachers. Teachers had to adapt to unexpected conditions, teaching in unprecedented ways. As a result, teachers' levels of stress and burnout have been high throughout the pandemic, raising concerns about a potential increase in teacher turnover and future teacher shortages. We use administrative data for the state of Arkansas to document the effects of the COVID-19 pandemic on teachers’ mobility and attrition during the years 2018-19 to 2021-2022. We find stable turnover rates during the first year of the pandemic (2020-2021) but an increase in teacher mobility and attrition in the second year (2021-2022). Teacher mobility and attrition increased by 2 percentage points (10% relative increase) this second year but with heterogeneous effects across regions and depending on the teacher and school characteristics. Our results raise concerns about increased strain in areas already experiencing teacher shortages and a potential reduction in the diversity of the Arkansas teacher labor force.
We examine the dynamic nature of student-teacher match quality by studying the effect of having a teacher for more than one year. Using data from Tennessee and panel methods, we find that having a repeat teacher improves achievement and decreases absences, truancy, and suspensions. These results are robust to a range of tests for student and teacher sorting. High-achieving students benefit most academically and boys of color benefit most behaviorally. Effects increase with the share of repeat students in a class suggesting that classroom assignment policies intended to promote sustained student-teacher relationships such as looping may have even larger benefits.
School finance reforms are not well defined and are likely more prevalent than the current literature has documented. Using a Bayesian changepoint estimator, we quantitatively identify the years when state education revenues abruptly increased for each state between 1960 and 2008 and then document the state-specific events that gave rise to these changes. We find 108 instances of abrupt increases in state education revenues across 43 states; about one-quarter of these changes had been undocumented. Half of the abrupt increases that occurred post-1990 were preceded by litigation-prompted legislative activity, and Democrat-party control of a state increases the probability of a changepoint occurring by 8 percentage points.
Can public university honors programs deliver the benefits of selective undergraduate education within otherwise nonselective institutions? We evaluate the impact of admission to the Honors College at Oregon State University, a large nonselective public university. Admission to the Honors College depends heavily on a numerical application score. Nonlinearities in admissions probabilities as a function of this score allow us to compare applicants with similar scores, but different admissions outcomes, via a fuzzy regression kink design. The first stage is strong, with takeup of Honors College programming closely following nonlinearities in admissions probabilities. To estimate the causal effect of Honors College admission on human capital formation, we use these nonlinearities in the admissions function as instruments, combined with course-section fixed effects to account for strategic course selection. Honors College admission increases course grades by 0.10 grade points on the 0-4 scale, or 0.14 standard deviations. Effects are concentrated at the top of the course grade distribution. Previous exposure to Honors sections of courses in the same subject is a leading potential channel for increased grades. However, course grades of first-generation students decrease in response to Honors admission, driven by low performance in natural science courses. Results suggest that selective Honors programs can accelerate skill acquisition for high-achieving students at public universities, but not all students benefit from Honors admission.
A significant share of education and development research uses data collected by workers called “enumerators.” It is well-documented that “enumerator effects”—or inconsistent practices between the individual people who administer measurement tools— can be a key source of error in survey data collection. However, it is less understood whether this is a problem for academic assessments or performance tasks. We leverage a remote phone-based mathematics assessment of primary school students and survey of their parents in Kenya. Enumerators were randomized to students to study the presence of enumerator effects. We find that both the academic assessment and survey was prone to enumerator effects and use simulation to show that these effects were large enough to lead to spurious results at a troubling rate in the context of impact evaluation. We therefore recommend assessment administrators randomize enumerators at the student level and focus on training enumerators to minimize bias.
Knowing how policy-induced salary schedule changes affect teacher recruitment and retention will significantly advance our understanding of how resources matter for K-12 student learning. This study sheds light on this issue by estimating how legislative funding changes in Washington state in 2018-19—induced by the McCleary court-ordered reform—affected teacher salaries and labor market outcomes. By embedding a simulated instrumental variables approach in a mixed methods design, we observed that local collective bargaining negotiations directed new state-level funding allocations toward certificated base salaries, particularly among more senior teachers. Variability in political power, priorities, and interests of both districts and unions led to greater heterogeneity in teacher salary schedules. Teacher mobility rate was reduced in the first year of the reform, and subsequently new hiring rate was reduced in the second year. Suggestive evidence indicates that a $1,000 salary increase would have larger effects on junior teachers’ hiring and their transfers between districts to a greater extent than late-career teachers.
This study introduces the signal weighted teacher value-added model (SW VAM), a value-added model that weights student-level observations based on each student’s capacity to signal their assigned teacher’s quality. Specifically, the model leverages the repeated appearance of a given student to estimate student reliability and sensitivity parameters, whereas traditional VAMs represent a special case where all students exhibit identical parameters. Simulation study results indicate that SW VAMs outperform traditional VAMs at recovering true teacher quality when the assumption of student parameter invariance is met but have mixed performance under alternative assumptions of the true data generating process depending on data availability and the choice of priors. Evidence using an empirical data set suggests that SW VAM and traditional VAM results may disagree meaningfully in practice. These findings suggest that SW VAMs have promising potential to recover true teacher value-added in practical applications and, as a version of value-added models that attends to student differences, can be used to test the validity of traditional VAM assumptions in empirical contexts.