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Even though women have continuously caught up with men in education attainment and labor market participation since the 1970s, the wage gap between men and women still universally exists today. Do female college graduates still earn less than their male counterparts if men’s and women’s “profiles” of observed productivity-related characteristics are statistically adjusted to be equivalent? To answer this research question and better understand the current gender wage gap, I introduce a novel propensity score stratification method for gender wage gap decomposition. This new method overcomes certain limitations of the traditional Blinder-Oaxaca decomposition method, and provides an example of validly applying propensity score-based methods (mostly used in causal settings) to gender wage gap decomposition, a non-causal setting. Making use of this new method, I analyze a nationally representative sample from the Baccalaureate and Beyond Longitudinal Study, which represents the 1993 Cohort of U.S. college graduates. Through propensity score stratification, the observed productivity-related characteristics between men and women in the sample are statistically adjusted to be equivalent within each stratum of propensity score. After “equalizing” these characteristics, evidence shows the women-to-men wage ratio among this college educated population is still 87.4% at the tenth year after they graduated from college. This remaining gender gap cannot be explained by the observed gender differences in productivity-related characteristics, and is the evidence of a discriminatory wage gap possibly existing in the labor market. Additionally, the unexplained gender wage gap universally exists regardless whether these “profiles” of qualifications and labor market experience are stereotypically female or male. Even acknowledging that this research cannot account for all the gender differences in productivity due to data limitation, the results of this research will add to the empirical evidence of measuring the discriminatory wage gap that possibly exists in the labor market.
Panel or grouped data are often used to allow for unobserved individual heterogeneity in econometric models via fixed effects. In this paper, we discuss identification of a panel data model in which the unobserved heterogeneity both enters additively and interacts with treatment variables. We present identification and estimation methods for parameters of interest in this model under both strict and weak exogeneity assumptions. The key identification insight is that other periods' treatment variables are instruments for the unobserved fixed effects. We apply our proposed estimator to matched student-teacher data used to estimate value-added models of teacher quality. We show that the common assumption that the return to unobserved teacher quality is the same for all students is rejected by the data. We also present evidence that No Child Left Behind-era school accountability increased the effectiveness of teacher quality for lower performing students.
Guidance counselors provide the main source of college advising for low-income high school students, but are woefully understaffed in high-need schools. This paper evaluates an approach to school-based college advising that relies on teachers rather than counselors. Using a randomized control trial in sixty-two Michigan high schools, I estimate the effects of a college planning course for high school seniors on postsecondary enrollment, persistence, and degree receipt. The course teaches about postsecondary education opportunities, application processes, and strategies for persisting toward a degree. I find no effect of the course on the number of students entering college, but an increase in the number persisting and earning a degree, particularly among low-income students. This is due to a shift in the composition of enrollees toward higher-achieving students: the course increases enrollment among high-achieving, low-income students, who have relatively high persistence rates, and reduces enrollment among low-achieving students, who in the course’s absence would have enrolled and then quickly dropped out. The program’s main cost is potential learning loss from displaced time in other subjects, which is difficult to measure but appears small.
School closures induced by the COVID-19 pandemic led to concerns about student learning. This paper evaluates the effect of school closures on student learning in Uzbekistan, using a unique dataset that allows assessing change in learning over time. The findings show that test scores in math for grade 5 students improved over time by 0.29 standard deviation despite school closures. The outcomes among students who were assessed in 2019 improved by an average of 0.72 standard deviation over the next two years, slightly lower than the expected growth of 0.80 standard deviation. The paper explores the reasons for no learning loss.
Hardware requirements are a barrier to widespread adoption of digital learning software among low-income populations. We investigate the demand among smallholder-farming households for a simple, adaptive math learning tool that can be accessed by widely available ``brick'' phones, and its effect on educational outcomes. Over a quarter of invited households used the tool, with greater demand among households lacking electricity, radios, or televisions. Usage was highest when schools were out of session. Engagement lapsed without regular reminders to use the service. Using random variation in access to the service, we find evidence that the platform increased test scores, school attendance, and grade attainment. Interpretation of these estimates is complicated by potentially endogenous outcome observation.
Heightened concerns about the health of the teaching profession highlight the importance of studying the early teacher pipeline. This exploratory, descriptive paper examines preservice teachers' (PST) expressed motivation for pursuing a teaching career and its relationship with PST characteristics and outcomes. Using data from one of the largest teacher education programs in Texas, we use a natural language processing algorithm to categorize into topical groups roughly 2,800 essay responses to the prompt, "Explain why you decided to become a teacher.'' We identify 11 topics that largely reflect altruistic and intrinsic (though not extrinsic) reasons for teaching. The frequency of motivation topics varied substantially by PST gender, race/ethnicity, and certification area. While topics collectively explained little of the variance in PST outcomes, we found preliminary evidence that intrinsic enjoyment of teaching and prior experiences with adversity predicted higher performance during clinical teaching and lower attrition as a full-time K–12 teacher.
Given the importance of early literacy to long-term student success, by 2021, 41 states and the District of Columbia adopted early literacy policies to improve student literacy by the end of third grade. We use an event-study approach to examine the impact of these policies on high- and low-stakes test scores. Our results suggest that adopting an early literacy policy improves elementary students’ reading achievement on high-stakes assessments, particularly in third grade and in states with comprehensive early literacy policies and third-grade retention requirements. We also find suggestive evidence that early literacy policies reduce socioeconomic and racial high-stakes achievement gaps in reading and have positive spillover effects on math achievement. However, we find little evidence of significant gains in low-stakes test scores except in states with comprehensive policies. Our findings highlight the importance of content and incentives for early literacy policies.
There is growing concern that some nonprofit public service providers may be nonprofit in name but not in fact. We consider this concern in the context of nonprofit charter schools, which sometimes subcontract their daily operations to for-profit management organizations. We use unique data from Ohio to study how nonprofit charter schools’ reliance on for-profit operators affects student achievement and attendance. The results indicate that nonprofit charters that subcontract with for-profit operators tend to be more effective and equitable in promoting student achievement (but not attendance, a less salient outcome) than nearby traditional public schools serving similar students. However, nonprofit charters that subcontract with for-profit operators tend to be less effective (with regard to both achievement and attendance) and less equitable (with regard to attendance) than other nonprofit charters nearby. Further analysis comparing the administration and outcomes of for-profit and nonprofit operators suggests that the profit motive may help explain the inferior performance of nonprofit charters with for-profit operators. Our study offers theoretical insights for literatures on charter schools, contracting, performance monitoring, and sector boundaries, and it has immediate implications for education policy and management.
Fadeout is a pervasive phenomenon: post-test impacts on cognitive skills commonly decrease in the years following an educational intervention. Less is known, although much is theorized, about social-emotional skill persistence. The current meta-analysis investigated whether educational RCT impacts on social-emotional skills demonstrated greater persistence than impacts on cognitive skills among 87 interventions involving 59,237 participants and 443 outcomes measured at post-test and at least one follow-up. For post-test impacts of the same magnitude, persistence rates were similar (43% of post-test magnitude) across skill types for follow-ups occurring 6 to 12 months after post-test. At 1- to 2-year follow-ups, persistence rates were larger for cognitive skills (37%) than for social-emotional skills. Interestingly, smaller posttest impacts persisted at proportionately higher rates than larger impacts, which may benefit interventions measuring social-emotional outcomes given their smaller post-test impacts. Considered in whole, social-emotional and cognitive skills demonstrated similar patterns of fadeout.
The COVID-19 pandemic resulted in substantial unfinished learning for U.S. students, but to differing degrees for various subgroups. For example, students of color, from low-income families, or who attended high-poverty schools experienced greater unfinished learning. In this study we examined the degree of unfinished learning for students who went into the pandemic scoring in the top or bottom 10% in the math or reading achievement distributions. Our results show that students who scored at or below the 10th percentile grew less during the pandemic than their similarly-scoring, pre-COVID peers and, as of the end of the 2021 – 2021 school year, had yet to rebound toward pre-COVID levels of growth or achievement. Conversely, students who scored at or above the 90th percentile largely grew at rates closer to their pre-COVID peers. These students were harmed less academically and have recovered more quickly than their peers scoring at or below the 10th percentile.