District-Level School Choice and Racial/Ethnic Test Score Gaps

The rapid expansion of school choice is restructuring public education in the United States. This study examines associations between charter and magnet school enrollment, White-Black and White-Hispanic segregation, and test score gaps at the district level from 2009 to 2015 in third to eighth grade using the Stanford Education Data Archive and the U.S. Department of Education's Common Core of Data. Robust findings indicate that higher charter school enrollment is associated with larger White-Black test score gaps and this effect is mediated by White-Black segregation. There is also evidence that magnet school enrollment is associated with White-Hispanic test score gaps. Overall, this study suggests that the expansion of school choice may have negative implications for structural education equity.

Test Score Gaps in Higher Order Thinking Skills: Exploring Instructional Practices to Improve the Skills and Narrow the Gaps

Today’s economy demands higher order thinking skills (HOTS), and the public education system has a critical role in supporting students’ acquisition of HOTS. Yet, numerous studies documented inequity in access to higher quality instruction that promotes HOTS, which could result in wide test score gaps in HOTS. In this study, I examined test score gaps in HOTS and explored instructional practices associated with HOTS, particularly among low-performing students, using large-scale international assessment data from the 2015 Trends in Mathematics and Science Study. I found wide test score gaps in HOTS in mathematics between the lowest and highest socioeconomic status students and between White students and students of color. Instructional practices such as the same ability group work, asking students to work on problems with teacher guidance, and working on problems with no immediately obvious method of solution were found positively associated with the test scores.

Ordinal Approaches to Decomposing Between-Group Test Score Disparities

The estimation of test score “gaps” and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate.

Gender Test Score Gaps Under Equal Behavioral Engagement

Girls tend to do better than boys academically, in part because they are more engaged in school. What if they weren’t? Using nationally representative data, I examine how equal starting points and trajectories of behavioral engagement in elementary school could change gender test score gaps. I find that equal engagement patterns could entirely reverse girls’ average leads over boys in fifth-grade reading test score achievement and could more than triple the average math test score gender gap currently favoring boys. These findings call into question narratives about favoritism towards girls in schools, instead highlighting educational advantages boys may enjoy despite being typically far less engaged in school than girls.

Average Gaps and Oaxaca–Blinder Decompositions: A Cautionary Tale about Regression Estimates of Racial Differences in Labor Market Outcomes

Using a recent result from the program evaluation literature, the author demonstrates that the interpretation of regression estimates of between-group differences in wages and other economic outcomes depends on the relative sizes of subpopulations under study. When the disadvantaged group is small, regression estimates are similar to the average loss for disadvantaged individuals. When this group is a numerical majority, regression estimates are similar to the average gain for advantaged individuals. The author analyzes racial test score gaps using ECLS-K data and racial wage gaps using CPS, NLSY79, and NSW data, and shows that the interpretation of regression estimates varies substantially across data sets. Methodologically, he develops a new version of the Oaxaca–Blinder decomposition, in which the unexplained component recovers a parameter referred to as the average outcome gap. Under additional assumptions, this estimand is equivalent to the average treatment effect. Finally, the author reinterprets the Reimers, Cotton, and Fortin decompositions in the context of the program evaluation literature, with attention to the limitations of these approaches.

Gender Achievement Gaps in U.S. School Districts

We estimate male-female test score gaps in math and English language arts (ELA) for nearly 10,000 U.S. school districts using state accountability data from third- through eighth-grade students in the 2008–2009 through 2015–2016 school years. We find that the average U.S. school district has no gender achievement gap in math, but there is a gap of roughly 0.23 standard deviations in ELA that favors girls. Both math and ELA gaps vary among school districts; some districts have more male-favoring gaps and some more female-favoring gaps. Math gaps tend to favor males more in socioeconomically advantaged school districts and in districts with larger gender disparities in adult income, education, and occupations; however, we do not find strong associations in ELA.