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Tiebout theorizes that local public services are provided more efficiently if costs are paid out of local revenues rather than by inter-governmental grants. But if local politics is not as pluralistic as Dahl has argued, citizens of higher socio-economic status will exercise greater influence, resulting in higher inequalities in service provision. We use administrative data to estimate the impacts of local revenue shares on individual performance of a nationally representative sample of over 140,000 U.S. eighth graders in math and reading. Causal effects are estimated with geographic discontinuity models and 2SLS models that use change in housing prices as an instrument. For every 10 percent increase in local revenue share, students perform about 0.05 standard deviations higher. Gains from local funding are less for disadvantaged students. Local financing affords better education for all but widens achievement gaps.
We use close tax elections to estimate the impact of school district funding increases on operational spending and education outcomes across seven states. The analysis indicates that districts where tax levies passed spent $400-$500 more annually per pupil through 5-7 years after the election. They directed most of these funds toward increasing instructor salaries. These spending increases correspond to student test score gains of approximately 0.1 of a standard deviation and gains in graduation rates of approximately 3-4 percentage points. There is some evidence of diminishing returns, as these effects are driven by districts below the median in spending per pupil.
Public support for school improvement policies can increase the success and durability of those reforms. However, little is known about public views on turnaround. We deployed questions and embedded experiments in a nationally representative 2017 survey (n=4,214) to uncover opinions regarding (a) which level of government should lead on turnaround and (b) state takeover of troubled districts. We find a large plurality prefers states play the greatest role in identifying and fixing failing schools. However, a substantial share prefers local governments increase their role. We find high levels of support for state takeover, yet support is greater in cases of financial mismanagement than academic underperformance. Those most likely to be directly affected express the least support for state takeover.
Recent attempts to measure schools’ influence on students' SEL show differences across schools, but whether these differences measure the true effect of schools is unclear. We examine the stability of school-by-grade effects on students' SEL across two years using a large-scale survey. Correlations among effects in the same grades across different years are positive but lower than those for math and English. Schools in the top or bottom of the effect distribution have more persistent impacts across years than those in the middle. Overall, the results suggest that these school effects measure real contributions to students' SEL. However, their low stability draws into question whether including school value-added measures of self-reported SEL in school performance systems would be beneficial.
We examine whether virtual advising – college counseling using technology to communicate remotely – increases postsecondary enrollment in selective colleges. We test this approach using a sample of approximately 16,000 high-achieving, low- and middle-income students identified by the College Board and randomly assigned to receive virtual advising from the College Advising Corps. The offer of virtual advising had no impact on overall college enrollment, but increased enrollment in high graduation rate colleges by 2.7 percentage points (5%), with instrumental variable impacts on treated students of 6.1 percentage points. We also find that non-white students who were randomly assigned to a nonwhite adviser exhibited stronger treatment effects.
We provide novel evidence on the causal impact of student absences in middle and high school on state test scores, course grades, and educational attainment using a rich administrative dataset that includes the date and class period of each absence. Our identification strategy addresses potential endogeneity due to time-varying student-level shocks by exploiting the fact that in a given year, there exists within-student, between-class variation in absences. We also leverage information on the timing of absences to show that absences that occur after the annual window for state standardized testing do not appear to affect test scores, which provides a further check of our identification strategy. We find that absences in middle and high school harm contemporaneous student achievement and longer-term educational attainment: On average, missing 10 math classes reduces math test scores by 7% of a standard deviation, math course grades by 19% of a standard deviation, the probability of on-time graduation by 8%, and the probability of immediate college enrollment by 7%. Similar results hold for absences in English Language Arts classes. These results suggest that absences in middle school and high school are just as harmful, if not more so, than absences in elementary school. Moreover, the timing of absences during the school year matters, as both the occurrence and the impact of absences are dynamic phenomena.
Use of education finance data is ubiquitous. Yet, because the academic calendar circumscribes two calendar years, researchers have linked the Consumer Price Index to three different dates: the Fall, Spring and academic fiscal years. We demonstrate that linking the CPI to these different academic year results in identifying different trends in U.S. educational spending during the Great Recession. Descriptive inferences should not be sensitive to researcher discretion about merge years. We provide an easy-to-use software package to facilitate implementation of NCES guidelines in the hope that future analyses of education finance data will explicitly and consistently apply inflation adjustments.
This paper describes and evaluates a web-based coaching program designed to support teachers in implementing Common Core-aligned math instruction. Web-based coaching programs can be operated at relatively lower costs, are scalable, and make it more feasible to pair teachers with coaches who have expertise in their content area and grade level. Results from our randomized field trial document sizable and sustained effects on both teachers’ ability to analyze instruction and on their instructional practice, as measured the Mathematical Quality of Instruction (MQI) instrument and student surveys. However, these improvements in instruction did not result in corresponding increases in math test scores as measured by state standardized tests or interim assessments. We discuss several possible explanations for this pattern of results.
Researchers commonly interpret effect sizes by applying benchmarks proposed by Cohen over a half century ago. However, effects that are small by Cohen’s standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In this paper, I present five broad guidelines for interpreting effect sizes that are applicable across the social sciences. I then propose a more structured schema with new empirical benchmarks for interpreting a specific class of studies: causal research on education interventions with standardized achievement outcomes. Together, these tools provide a practical approach for incorporating study features, cost, and scalability into the process of interpreting the policy importance of effect sizes.
In this paper we investigate the impact of a statewide program aimed at better aligning K-12 to higher education and improving college readiness. We replicate an earlier study focused on the effects of this program at one campus by employing detailed administrative data on the census of California students that enroll at all twenty-three campuses of the California State University (CSU) system. We evaluate whether the program has reduced remediation rates at CSU statewide and investigate whether program effects differ by student background. We find that participation in the Early Assessment Program reduces the average student’s probability of needing remediation at California State University by about 2-3 percentage points overall. Investigating heterogeneous treatment effects, we find the program effects are largely concentrated among students at the margin of remediation risk.