Specific schoolsFull briefing
Two MS within a half-mile: one screens, one zones. Same neighborhood, different futures.
The longer form of the briefing for this story: the original thesis, the supporting findings the data team flagged, and the reporting directions a journalist could follow. For the short version and the data analysis, see the main story page.
Thesis
Pick a half-mile radius in any NYC neighborhood and find a screened middle school next to an unzoned/zoned one. The screened school's outcomes look better — but that's selection, not value-add. The interesting question: which kids are missing from each school and why?
Supporting findings
- Methodology: for each screened MS, find the nearest zoned/open MS within 1 mile and same district.
- Sample pairs: NEST+m (D2 screened) vs Hudson Square School (D2 zoned).
- Demographics gap: screened MS will skew higher-income, more Asian, fewer ELL/SWD.
- Outcomes gap: screened MS will have 80%+ on tests; matched zoned could be 50%.
- But peer-group comparison (within selective bucket) is more honest — does NEST+m outperform other selective MS, or is it middling-among-peers?
- Track 8th-grade SHSAT yields from each — does the screened MS funnel more kids to specialized HS?
- Track HS placements — even controlling for SHSAT, do screened-MS kids land in more selective HS?
- Talk to families who applied to BOTH the screened and zoned options — what drove the choice?
- Look at lottery yields — what's the acceptance rate at the screened MS, and who's getting rejected?
- Is there a 'gentle merit' middle ground (like De Blasio-era unscreened lottery in D15)?
Reporting directions
- Compute geographic-pair MS data for top 20 pairs.
- Profile one D15 pair (where unscreening happened in 2018) over 5 years before/after.
- Interview Christine Quinn-Miller (former D15 Community Education Council) on the unscreening experience.
- Compare with Chicago, Boston selective-enrollment data.