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Methodology, measurement and data
There is no national consensus on how school districts calculate high school achievement disparities between students who experience homelessness and those who do not. Using administrative student-level data from a mid-sized public school district in the Southern United States, we show that commonly used ways of defining which students are considered homeless can yield markedly different estimates of the homelessness-housed student high school graduation gap. The key distinctions among homelessness definitions relate to how to classify homeless students who become housed and how to consider students who transfer out of the district or drop out of school. Eliminating housing insecurity-related achievement disparities necessitates understanding the link between homelessness and educational achievement; how districts quantify homelessness affects measured gaps.
We show that fade out biases value-added estimates at the teacher-level. To do so, we use administrative data from North Carolina and show that teachers' value-added depend on the quality of the teacher that preceded them. Value-added estimators that control for fade out feature no such teacher-level bias. Under a benchmark policy that releases teachers in the bottom five percent of the value-added distribution, fifteen percent of teachers released using traditional techniques are not released once fade out is accounted for. Our results highlight the importance of incorporating dynamic features of education production into the estimation of teacher quality.
International assessments are important to benchmark the quality of education across countries. However, on low-stakes tests, students’ incentives to invest their maximum effort may be minimal. Research stresses that ignoring students’ effort when interpreting results from low-stakes assessments can lead to biased interpretations of test performance across groups of examinees. We use data from the Programme for International Student Assessment (PISA), a low-stakes test, to analyze the extent to which student effort helps to explain test scores heterogeneity across countries and by gender groups. Our results highlight the importance of accounting for differences in student effort to understand cross-country heterogeneity in performance and variations in gender achievement gaps across nations. We find that, once we account for differential student effort across gender groups, the estimated gender achievement gap in math and science could be up to 12 and 6 times wider, respectively, and up to 49 percent narrower in reading, in favor of boys. In math and science, the gap widens in most countries, even among some of the top 20 most gender-equal countries. Altogether, our effort measures on average explain between 36 and 40 percent of the cross-country variation in test scores.
School district consolidation is one of the most widespread education reforms of the last century, but surprisingly little research has directly investigated its effectiveness. To examine the impact of consolidation on student achievement, this study takes advantage of a policy that requires the consolidation of all Arkansas school districts with enrollment of fewer than 350 students for two consecutive school years. Using a regression discontinuity model, we find that consolidation has either null or small positive impacts on student achievement in math and English Language Arts (ELA). We do not find evidence that consolidation in Arkansas results in positive economies of scale, either by reducing overall cost or allowing for a greater share of resources to be spent in the classroom.
Researchers often include covariates when they analyze the results of randomized controlled trials (RCTs), valuing the increased precision of the estimates over the potential of inducing small-sample bias when doing so. In this paper, we develop a sufficient condition which ensures that the inclusion of covariates does not cause small-sample bias in the effect estimates. Using this result as a building block, we develop a novel approach that uses machine learning techniques to reduce the variance of the average treatment effect estimates while guaranteeing that the effect estimates remain unbiased. The framework also highlights how researchers can use data from outside the study sample to improve the precision of the treatment effect estimate by using the auxiliary data to better model the relationship between the covariates and the outcomes. We conclude with a simulation, which highlights the value of using the proposed approach.
Virtual charter schools provide full-time, tuition-free K-12 education through internet-based instruction. Although virtual schools offer a personalized learning experience, most research suggests these schools are negatively associated with achievement. Few studies account for differential rates of student mobility, which may produce biased estimates if mobility is jointly associated with virtual school enrollment and subsequent test scores. We evaluate the effects of a single, large, anonymous virtual charter school on student achievement using a hybrid of exact and nearest-neighbor propensity score matching. Relative to their matched peers, we estimate that virtual students produce marginally worse ELA scores and significantly worse math scores after one year. When controlling for student mobility during the outcome year, estimates of virtual schooling are slightly less negative. These findings may be more reliable indicators of the independent effect of virtual schooling if matching on mobility proxies for otherwise unobservable negative selection factors.
We estimate the longer-run effects of attending an effective high school (one that improves a combination of test scores, survey measures of socio-emotional development, and behaviours in 9th grade) for students who are more versus less educationally advantaged (i.e., likely to attain more years of education based on 8th-grade characteristics). All students benefit from attending effective schools. However, the least advantaged students experience the largest improvements in high-school graduation, college-going, and school-based arrests. These patterns are driven by the least advantaged students benefiting the most from school impacts on the non-test-score dimensions of school quality. However, while there is considerable overlap in the effectiveness of schools attended by more and less advantaged students, it is the most advantaged students that are most likely to attend highly effective schools. These patterns underscore the importance of quality schools, and the non-test score components of quality schools, for improving the longer-run outcomes for less advantaged students.
We examine U.S. children whose parents won the lottery to trace out the effect of financial resources on college attendance. The analysis leverages federal tax and financial aid records and substantial variation in win size and timing. While per-dollar effects are modest, the relationship is weakly concave, with a high upper bound for amounts greatly exceeding college costs. Effects are smaller among low-SES households, not sensitive to how early in adolescence the shock occurs, and not moderated by financial aid crowd-out. The results imply that households derive consumption value from college and household financial constraints alone do not inhibit attendance.
At least sixteen US states have taken steps toward holding teacher preparation programs (TPPs) accountable for teacher value-added to student test scores. Yet it is unclear whether teacher quality differences between TPPs are large enough to make an accountability system worthwhile. Several statistical practices can make differences between TPPs appear larger and more significant than they are. We reanalyze TPP evaluations from 6 states—New York, Louisiana, Missouri, Washington, Texas, and Florida—using appropriate methods implemented by our new caterpillar command for Stata. Our results show that teacher quality differences between most TPPs are negligible—.01-.03 standard deviations in student test scores—even in states where larger differences were reported previously. While ranking all a state’s TPPs may not be possible or desirable, in some states and subjects we can find a single TPP whose teachers stand out as significantly above or below average. Such exceptional TPPs may reward further study.
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.