- Sean P. Corcoran
Search for EdWorkingPapers here by author, title, or keywords.
Sean P. Corcoran
We provide a descriptive analysis of within-school and neighborhood similarity in high school applications in New York City. We depart from prior work by examining similarity in applications to specific schools rather than preferences for school characteristics. We find surprisingly low similarity within schools and neighborhoods, but substantial variation by race and prior achievement. White and Asian students are more likely to have choices in common relative to Black and Hispanic students, a difference that persists after controlling for achievement and location. Likewise, higher-achieving students are more likely to have choices in common, conditional on other student characteristics and location. An implication is that students’ likelihood of attending high school without any peers from their middle school or neighborhood varies by student background.
New York City’s universal pre-kindergarten program, which increased full-day enrollment from 19,000 to almost 70,000 children, is ambitious in both scale and implementation speed. We provide new evidence on the distribution of pre-K quality in NYC by student race/ethnicity, and investigate the extent to which observed differences are associated with the spatial distribution of higher-quality providers. Relative to other jurisdictions, we find the average quality of public pre-K providers is high. However, we identify large disparities in the average quality of providers experienced by black and white students, which is partially explained by differential proximity to higher-quality providers. Taken together, current racial disparities in the quality of pre-K providers may limit the program’s ability to reduce racial achievement gaps.
Estimates of teacher “value-added” suggest teachers vary substantially in their ability to promote student learning. Prompted by this finding, many states and school districts have adopted value-added measures as indicators of teacher job performance. In this paper, we conduct a new test of the validity of value-added models. Using administrative student data from New York City, we apply commonly estimated value-added models to an outcome teachers cannot plausibly affect: student height. We find the standard deviation of teacher effects on height is nearly as large as that for math and reading achievement, raising obvious questions about validity. Subsequent analysis finds these “effects” are largely spurious variation (noise), rather than bias resulting from sorting on unobserved factors related to achievement. Given the difficulty of differentiating signal from noise in real-world teacher effect estimates, this paper serves as a cautionary tale for their use in practice.