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K-12 Education
A survey targeting education researchers conducted in November, 2020 provides both short- and longer-term predictions of how much achievement gaps between low- and high-income students in U.S elementary schools will change as a result of COVID-related disruptions to schooling and family life. Relative to a pre-COVID achievement gap of 1.00 SD, respondents’ median forecasts for increases in achievement gaps in elementary school by spring, 2021 were very large – from 1.00 to 1.30 and 1.25 SD, respectively, for math and reading. Researchers forecast only small reductions in gaps between spring 2021 and 2022. Although forecasts were heterogeneous, almost all respondents predicted that gaps would grow during the pandemic and would not return to pre-pandemic levels in the following school year. We discuss some implications of these predictions for strategies to reduce learning gaps exacerbated by the pandemic as well as the mental models researchers appear to employ in making their predictions.
Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers. Using nearly 1,000 word-to-word transcriptions of 4th- and 5th-grade English language arts classes, we apply novel text-as-data methods to develop automated measures of teaching to complement classroom observations traditionally done by human raters. This approach is free of rater bias and enables the detection of three instructional factors that are well aligned with commonly used observation protocols: classroom management, interactive instruction, and teacher-centered instruction. The teacher-centered instruction factor is a consistent negative predictor of value-added scores, even after controlling for teachers’ average classroom observation scores. The interactive instruction factor predicts positive value-added scores. Our results suggest that the text-as-data approach has the potential to enhance existing classroom observation systems through collecting far more data on teaching with a lower cost, higher speed, and the detection of multifaceted classroom practices.
In this paper we study the effects of three large, nearly-simultaneous coal-fired power plant closures on school absences in Chicago. We find that the closures resulted in a 7 percent reduction in absenteeism in nearby schools relative to those farther away following the closures. For the typical elementary school in our sample, this translates into around 372 fewer absence-days per year in the aggregate, or around 0.71 fewer annual absences per student. We find that reductions in absences were larger in schools where pre-closure exposure to coal-fired power plants was more intense: namely, schools with low levels of air conditioning, schools more frequently in the wind path of the plants, and non-magnet (i.e., neighborhood) schools where students were more likely to live nearby. To explore potential mechanisms responsible for these absence reductions we investigate the effects of the closures on housing values and children’s respiratory health. We do not find statistical evidence of endogenous migration into neighborhoods near the coal-fired power plants following the closures but do find declines in emergency department visits for asthma-related conditions among school-age children.
Despite calls for more evidence regarding the effectiveness of teacher education practices, causal research in the field remains rare. One reason is that we lack designs and measurement approaches that appropriately meet the challenges of causal inference in the context of teacher education programs. This article provides a framework for how to fill this gap. We first outline the difficulties of doing causal research in teacher education. We then describe a set of replicable practices for developing measures of key teaching outcomes, and propose causal research designs suited to the needs of the field. Finally, we identify community-wide initiatives that are necessary to advance effectiveness research in teacher education at scale.
We study the effect of exposure to immigrants on the educational outcomes of US-born students, using a unique dataset combining population-level birth and school records from Florida. This research question is complicated by substantial school selection of US-born students, especially among White and comparatively affluent students, in response to the presence of immigrant students in the school. We propose a new identification strategy to partial out the unobserved non-random selection into schools, and find that the presence of immigrant students has a positive effect on the academic achievement of US-born students, especially for students from disadvantaged backgrounds. Moreover, the presence of immigrants does not affect negatively the performance of affluent US-born students, who typically show a higher academic achievement compared to immigrant students. We provide suggestive evidence on potential channels.
The growing phenomenon of private tutoring has received minimal scholarly attention in the United States. We use 20 years of geocoded data on the universe of U.S. private tutoring centers to estimate the size and growth of this industry and to identify predictors of tutoring center locations. We document four important facts. First, from 1997-2016, the number of private tutoring centers grew steadily and rapidly, more than tripling from about 3,000 to nearly 10,000. Second, the number and growth of private tutoring centers is heavily concentrated in geographic areas with high income and parental education. Nearly half of tutoring centers are in areas in the top quintile of income. Third, even conditional on income and parental education, private tutoring centers tend to locate in areas with many immigrant and Asian-American families, suggesting important differences by nationality and ethnicity in demand for such services. Fourth, we see little evidence that prevalence of private tutoring centers is related to the structure of K-12 school markets, including the prevalence of private schools and charter or magnet school options. The rapid rise in high-income families’ demand for this form of private educational investment mimics phenomena observed in other spheres of education and family life, with potentially important implications for inequality in student outcomes.
Many states mandate districts or schools notify parents when students have missed multiple unexcused days of school. We report a randomized experiment (N = 131,312) evaluating the impact of sending parents truancy notifications modified to target behavioral barriers that can hinder effective parental engagement. Modified truancy notifications that used simplified language, emphasized parental efficacy, and highlighted the negative incremental effects of missing school reduced absences by 0.07 days compared to the standard, legalistic, and punitively-worded notification—an estimated 40% improvement over the standard truancy notification. This work illustrates how behavioral insights and randomized experiments can be used to improve administrative communications in education.
During the past fifteen years, immigration enforcement increased dramatically in the U.S. interior. There is a growing recognition that immigration enforcement in the U.S. interior has spillover effects onto U.S. citizens. I examine the impacts of a type of partnership between Immigration and Customs Enforcement (ICE) and local law enforcement, 287(g) programs, on school engagement within North Carolina. In North Carolina, nine counties were approved to establish 287(g) programs, and another fifteen applied but were not approved to participate. I use a triple difference strategy in which I compare educational outcomes for different groups of students in these two sets of counties before and after activation of 287(g) programs. I find that 287(g) programs decrease school engagement by decreasing attendance. This effect appears to be driven by increases in chronic absenteeism (missing 15 or more days per year). In contrast, I observe no effect of 287(g) programs on achievement.
Jackson, Wigger, and Xiong (2020a, JWX) provide evidence that education spending reductions following the Great Recession had widespread negative impacts on student achievement and attainment. This paper describes our process of duplicating JWX and highlights a variety of tests we employ to investigate the nature and robustness of the relationship between school spending reductions and student outcomes. Though per-pupil expenditures undoubtedly shifted downward due to the Great Recession, contrary to JWX, our findings indicate there is not a clear and compelling story about the impact of those reductions on student achievement. Moreover, we find that the relationship between K-12 spending and college-going rates is likely confounded with contemporaneous higher education funding trends. While we believe that K-12 spending reductions may have negative impacts on student outcomes, our results suggest that estimating generalizable causal effects remains a significant challenge.
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.