Search for EdWorkingPapers here by author, title, or keywords.
This article reviews the development of my thesis that the California Supreme Court's Serrano decisions, which began in 1971 and sought to disconnect district school spending with local property taxes, led to the fiscal conditions that caused California voters to embrace Proposition 13 in 1978, which radically undermined the local property tax system. I submit that my thesis is most likely true because of Proposition 13’s durability and the absence of alternative explanations that account for its longstanding power over California politics. The article then circles back to John Serrano himself. I want to respectfully suggest that John’s views about the role of public education and my own have more in common than might be suspected. At the very least I want to correct the impression that John supported Proposition 13, which was suggested by the title of my last full article about this topic.
‘QuantCrit’ (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories are not natural; 4) voice and insight (data cannot ‘speak for itself); and 5) a social justice/equity orientation (Gillborn et al, 2018). The approach has quickly developed an international and interdisciplinary character, including applications in medicine (Gerido, 2020) and literature (Hammond, 2019). Simultaneously, there has been ferocious criticism from detractors outraged by the suggestion that numbers are anything other than objective and scientific (Airaksinen, 2018). In this context it is vital that the approach establishes some common understandings about good practice; in order to sustain rigor, make QuantCrit accessible to academics, practioners, and policymakers alike, and resist widespread attempts to over-simplify and pillory. This paper is intended to advance an iterative process of expanding and clarifying how to ‘QuantCrit’.
The COVID-19 pandemic upended the U.S. education system and the economy in ways that dramatically affected the jobs of K-12 educators. However, data limitations have led to considerable uncertainty and conflicting reports about the nature of staffing challenges in schools. We draw on education employment data from the Bureau of Labor Statistics (BLS) and State Education Agencies (SEA) to describe patterns in K-12 education employment and to highlight the limitations of available data. Data from the BLS suggest overall employment in the K-12 labor market declined by 9.3 percent at the onset of the pandemic and remains well below pre-pandemic levels. SEA data suggest that teachers have not (yet) left the profession in mass as many predicted, but that turnover decreased in the summer of 2020. We explore possible explanations for these patterns including (1) weak hiring through the summer of 2020 and (2) high attrition among K-12 instructional support staff. State vacancy data also suggest that schools are facing substantial challenges filling open positions during the 2021-22 academic year. Our analyses illustrate the imperative to build more timely, detailed, and nationally representative data systems on the K-12 education labor market to better inform policy.
In very low-income settings, how much does family demand matter for child learning? In rural Gambia, caregivers with high aspirations for their children’s future education and career, measured before children start school, invest substantially more than other families in their children’s education. Despite this, essentially no children are literate or numerate three years later. In contrast, in villages receiving a highly impactful, teacher-focused supply-side intervention, children of high-aspirations caregivers are 25 percent more likely to achieve literacy and numeracy than other children. In such settings, greater demand can map onto developmentally meaningful learning differences, but only with adequate complementary inputs.
The 2020-2021 academic year was a trying year for teachers. We use a nationally representative sample of teachers from the RAND American Teacher Panel to document that teachers’ stated consideration of leaving the profession increased during the pandemic. We also study factors associated with teachers’ consideration of leaving the profession and high levels of job burnout during the pandemic. Approaching retirement age (being 55 or older), having to change instruction modes, health concerns, and high levels of job burnout all appear to be important predictors of the probability of considering leaving or retiring from teaching. Hybrid teaching increased consideration of leaving the profession because of COVID. Health concerns and switching instruction modes are all associated with higher levels of concern about job burnout. Interestingly, those approaching retirement ages do not present higher levels of concern about job burnout than younger teachers. Although increased consideration of leaving and concern about burnout do not yet appear to have materialized into higher attrition rates so far, higher levels of job dissatisfaction could affect teacher effectiveness and could harm student academic progress.
In this paper, I study the effect of winning the public school choice lottery on public school enrollment. In particular, I look at how different outside options affect how sensitive students are to receiving their first choice in the public school lottery, focusing on three measures of outside options: ability to afford private schools, geographic convenience of private schools, and zoned-school quality. Using rich administrative data from applications submitted through a centralized enrollment system in Tulsa Public Schools (TPS), I find that overall, students who do not get assigned to their top choice school in a public school choice program are 15 percentage points more likely to leave the public school system entirely than those who do get an offer at their top choice. This effect is driven by higher-income students: these students, who are more likely to be able to afford private schools, are 33 percentage points more likely to leave the public school system if they do not get an offer at the public school they rank first than those who do get a spot. Geographic convenience of private schools and zoned-school quality do not differentially affect students’ enrollment decisions once they receive a school assignment. These effects are important to understand as districts undergo efforts to increase participation in school choice programs, while seeking to maintain district enrollment. They also provide useful insights about how attrition may affect estimates of the impact of choice schools on student outcomes.
In this paper, we show that the election of a new school board member causes home values in their neighborhood to rise. This increase is identified using narrowly-decided contests and is driven by non-Democratic members, whose neighborhoods appreciate about 4% on average relative to those of losing candidates. We find that student test scores in the neighborhood public schools of non-Democratic winners also relatively increase, but this effect is driven by changing student composition, including via the manipulation of attendance zones, rather than improvements in school quality (as measured by test score value-added). Notably, we detect no differential changes when comparing neighborhood or scholastic outcomes between winning and losing Democratic school board candidates. These results suggest that partisan affiliation is correlated with private motivations for seeking public office.
The COVID-19 pandemic initially resulted in an unanticipated and near-universal shift from in-person to virtual instruction in spring 2020. During the 2020-21 school year, schools began to re-open and families were faced with decisions regarding the instructional mode for their children. We leverage administrative, survey, and virtual-learning data to examine the determinants of family learning-mode choice and the effects of virtual education on student engagement and academic achievement. Family preference for virtual (versus face-to-face) instruction was most highly associated with school-level infection rates and appeared relatively uniform within schools. We find that students who were assigned a higher proportion of instructional days in virtual mode experienced higher rates of attendance, but also negative student achievement growth compared to students who were assigned a higher proportion of instructional days in face-to-face mode. Students belonging to marginalized groups experienced more positive associations with attendance but were also more likely to experience lower student achievement growth when assigned a greater proportion of instructional days in virtual mode. Insights from this study can be used to better understand family preference as well as to target and refine virtual learning in a post-COVID-19 society.
Many studies rely on public sector employees’ reported career intentions instead of measuring actual turnover, but research does not clearly document how these variables relate to one another. We develop and test three ways in which measures of employee intentions and turnover might relate to one another: (a) intention may measure the same underlying construct as turnover; (b) intention may be distinct from but strongly related to turnover; or (c) intentions may be distinct from turnover. Using nationally representative data on 102,970 public school teachers, we conduct a descriptive and regression analysis to probe how teachers’ turnover intentions are and are not associated with attrition. While there is some variation across measures of intent, we find evidence most consistent with the second scenario; intention is distinct from, but strongly related to, turnover. We offer recommendations for how researchers should use public sector employee intentions in research.
An important subgroup of English learner-classified (EL) students immigrate to the U.S., entering U.S. schools upon their arrival. Using growth models and statewide data, this study asks first, how newcomers’ English proficiency status and growth compare to those of non-newcomer EL students; and second, what characteristics are associated with differences in English language growth patterns among newcomers. We find that newcomers enter school at earlier stages of English proficiency compared to their non-newcomer peers, but grow faster, especially in their first two years. We also find variation in growth patterns suggestive that schools play an important role in fostering growth.