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K-12 Education
Community schools are an increasingly popular strategy used to improve the performance of students whose learning may be disrupted by non-academic challenges related to poverty. Community schools partner with community based organizations (CBOs) to provide integrated supports such as health and social services, family education, and extended learning opportunities. With over 300 community schools, the New York City Community Schools Initiative (NYC-CS) is the largest of these programs in the country. Using a novel method that combines multiple rating regression discontinuity design (MRRDD) with machine learning (ML) techniques, we estimate the causal effect of NYC-CS on elementary and middle school student attendance and academic achievement. We find an immediate reduction in chronic absenteeism of 5.6 percentage points, which persists over the following three years. We also find large improvements in math and ELA test scores – an increase of 0.26 and 0.16 standard deviations by the third year after implementation – although these effects took longer to manifest than the effects on attendance. Our findings suggest that improved attendance is a leading indicator of success of this model and may be followed by longer-run improvements in academic achievement, which has important implications for how community school programs should be evaluated.
Youth voter turnout remains stubbornly low and unresponsive to civic education. Rigorous evaluations of the adoption of civic tests for high school graduation by some states on youth voter turnout remain limited. We estimate the impact of a recent, state-mandated civics test policy—the Civics Education Initiative (CEI)—on youth voter turnout by exploiting spatial and temporal variation in the adoption of CEI across states. Using nationally-representative data from the 1996-2020 Current Population Survey and a Difference-in-Differences analysis, we find that CEI does not significantly affect youth voter turnout. Our null results, largely insensitive to a variety of alternative specifications and robustness checks, provide evidence regarding the lack of efficacy of civic test policies when it comes to youth voter participation.
How much does family demand matter for child learning in settings of extreme poverty? In rural Gambia, families with high aspirations for their children’s future education and career, measured before children start school, go on to invest substantially more than other families in the early years of their children’s education. Despite this, essentially no children are literate or numerate three years later. When villages receive a highly-impactful, teacher-focused supply-side intervention, however, children of these families are 25 percent more likely to achieve literacy and numeracy than other children in the same village. Furthermore, improved supply enables these children to acquire other higher-level skills necessary for later learning and child development. We also document patterns of substitutability and complementarity between demand and supply in generating learning at varying levels of skill difficulty. Our analysis shows that greater demand can map onto developmentally meaningful learning differences in such settings, but only with adequate complementary inputs on the supply side.
How far is the world away from ensuring that every child obtains the basic skills needed to be internationally competitive? And what would accomplishing this mean for world development? Based on the micro data of international and regional achievement tests, we map achievement onto a common (PISA) scale. We then estimate the share of children not achieving basic skills for 159 countries that cover 98.1% of world population and 99.4% of world GDP. We find that at least two-thirds of the world’s youth do not reach basic skill levels, ranging from 24% in North America to 89% in South Asia and 94% in Sub-Saharan Africa. Our economic analysis suggests that the present value of lost world economic output due to missing the goal of global universal basic skills amounts to over $700 trillion over the remaining century, or 11% of discounted GDP.
School districts in the United States often borrow on the municipal bond market to pay for capital projects. Districts serving economically disadvantaged communities tend to receive lower credit ratings and pay higher interest rates. To remedy this problem, 24 states have established credit enhancement programs that promise to repay district debt when a district cannot do so, thereby enhancing the district’s credit rating. I rely on cross- and within-district variations to estimate the effect of receiving state credit enhancement on district bond interest rate, per-pupil capital spending, and student performance. State enhancement reduces district bond interest rates by 6% and increases per-student capital spending by 6% to 7%. It also reduces the disparity in interest rate and capital spending across districts serving lower and higher income families, with no discernible effect on test scores. I find no evidence that the amount of enhanced school debt is associated with significant changes in interest rates paid by state governments. Districts in states without such programs could have achieved cost savings in the range of $383 million to $1 billion from 2009 to 2019 had the states adopted similar programs.
There has been an explosion of special education research and new policies surrounding the topic of racial and ethnic disproportionality. Some recent research shifts focus to whether school context moderates findings (e.g., is a Black student less likely than a White student to receive special education services as the proportion of a school’s Black students increases?). We extend this emerging literature using eight years of elementary student-and school-level data from NYC public schools, examining more school contextual moderators, expanding racial categories, and distinguishing between cross-sectional and over-time differences. We find that student racial/ethnic composition, teacher racial composition, school size, concentration of poor students, and teachers’ perceptions of school climate all moderate disproportionality. These school context factors appear to be particularly salient for the classification of Black students, and most of these associations are driven by differences across schools, suggesting new avenues for researchers and levers for policy.
The distribution of teaching effectiveness across schools is fundamental to understanding how schools can address disparities in educational outcomes. Research and policy have recognized the importance of teaching effectiveness for decades. Five stylized facts predict that teachers should be differentially allocated across schools such that poor, Black and Hispanic students are taught by less qualified and less effective teachers. Yet, research is unclear whether these predictions have empirical support. Our purpose is to better understand whether there are meaningful differences in teacher effectiveness among schools. We find that poor, Black and Hispanic students are more likely to be taught by novice teachers when they live in more segregated MSAs. Moreover, the geographic nature of segregation varies across MSAs. Differentiating segregation within urban districts and segregation between urban districts and outlying districts in the same MSAs is essential to understanding poor students’ exposure to novice teachers and policies that address these disparities. We find that poor, Black and Hispanic students are 50 percent more likely to be exposed to at least one novice teacher during elementary school compared to their more affluent white peers. These results raise questions regarding the enforcement of ESSA’s requirements on the distribution of teacher qualifications and quality.
The COVID-19 pandemic drew new attention to the role of school boards in the U.S. In this paper, we examine school districts' choices of learning modality -- whether and when to offer in-person, virtual, or hybrid instruction -- over the course of the 2020-21 pandemic school year. The analysis takes advantage of granular weekly data on learning mode and COVID-19 cases for Ohio school districts. We show that districts respond on the margin to health risks: all else equal, a marginal increase in new cases reduces the probability that a district offers in-person instruction the next week. Moreover, this negative response is magnified when the district was in-person the prior week and attenuates in magnitude over the school year. These findings are consistent with districts learning from experience about the effect of in-person learning on disease transmission in schools. We also find evidence that districts are influenced by the decisions of their peers.
Student absenteeism is often conceptualized and quantified in a static, uniform manner, providing an incomplete understanding of this important phenomenon. Applying growth curve models to detailed class-attendance data, we document that secondary school students' unexcused absences grow steadily throughout a school year and over grades, while the growth of excused absences remain essentially unchanged. Importantly, students starting the school year with a high number of unexcused absences, Black and Hispanic students, and low-income students accumulate unexcused absences at a significantly faster rate than their counterparts. Lastly, students with higher growth rates in unexcused absences consistently report lower perceptions of all aspects of school culture than their peers. Interventions targeting unexcused absences and/or improving school culture can be crucial to mitigating disengagement.