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Neighborhood context, achievement & UC admissions

Each dot is a California public high school. The x-axis describes the school's context — its surroundings (American Community Survey measures for the census tract where the school sits) or its student body (unduplicated high-need share, and the grade-11 shares socioeconomically disadvantaged and English learner). The dot's color is its students' measured achievement (CAASPP grade 11, A–G completion). The y-axis is the admissions outcome at the selected UC campus. Drag a band on the rail below the chart to hold neighborhood roughly fixed, and see how much achievement still separates schools inside it.

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Click a dot (or a twin pair) for details.
How to read this — and important caveats
  • The question. The main explorer shows that a school's measured achievement relates to its UC admissions outcomes. This page asks the follow-up: is that relationship its own thing, or is it mostly the neighborhood? Hold context fixed conditions directly (brush a band of similar contexts and look at the spread inside). Local correlation formalizes the band: it traces the conditional correlation function — a kernel-weighted correlation of achievement with the outcome computed at every point of the context distribution. Twin schools makes it concrete (near-identical surroundings, very different achievement — do outcomes differ?). What explains more? summarizes with a variance decomposition, campus by campus.
  • Neighborhood = the school's tract, not its students. Each school is assigned the American Community Survey (ACS) 5-year profile of the census tract around its current campus location (CDE coordinates → Census Geocoder). Students may commute across boundaries, transfer, or attend charters far from home, and a tract (≈4,000 residents) is not an attendance zone. Read the x-axis as "the kind of place the school sits in."
  • Timing. Neighborhood and test measures are taken from each entering class's grade-11 year — the spring before application — matching the main explorer's CAASPP convention. 2025 classes use ACS 2024 (the latest 5-year release), and where a class's grade-11 join is unavailable (there are no 2020 entity files, affecting classes entering in 2021) the nearest available year substitutes. ACS 5-year estimates are rolling averages and move slowly; tract-level estimates carry sampling error, and top-coded medians ($250k+ income, $2M+ home value) are kept at the cap.
  • The SES index is a plain z-score composite — income, adult BA share, home value, minus poverty, minus unemployment — standardized each year over all California public high schools, so 0 is the average school's surroundings and ±1 is one standard deviation.
  • School-level student measures. Three context variables describe the student body rather than the tract. UPP is the CALPADS unduplicated share of grade 9–12 students who are FRPM-eligible, English learners, or foster youth — taken, as in the main explorer, from the entering class's senior year. % SED and % EL are the shares of the school's grade-11 CAASPP-reported enrollment that are socioeconomically disadvantaged (FRPM-eligible or neither parent a HS graduate) and English learners — taken from the class's grade-11 year like the test scores. These are enrollment counts, not test outcomes, so they exist even in low-participation years; they start in 2016 (the 2015 files do not report per-group enrollment), and classes entering 2016 use their senior-year value. A school with no reported group row counts as 0%.
  • Reading the local-correlation curve. Schools are ranked by the context variable (percentile scale, so skewed dollars behave); at each grid point the correlation between achievement and the outcome is computed with Gaussian weights (bandwidth in percentile points — the slider). The blue ribbon is a 95% interval using the kernel's effective sample size; the gray band is a no-variation envelope: the middle 95% of curves obtained when schools' (achievement, outcome) pairs are randomly shuffled against their context ranks 200 times — the wiggle pure noise would produce if the association were the same everywhere. Both are pointwise: judge the curve's overall shape, not one bump. "Linear moderation β" is the interaction coefficient from a standardized regression of the outcome on achievement, context percentile, and their product — the one-number linear summary of how the association trends across the distribution. Near the edges the kernel is one-sided; the grid stops at the 5th/95th percentiles.
  • Statistics are school-level and unweighted (each school counts once), computed on schools passing the min-applicants filter. Pooled periods use ratio-of-sums for funnel rates, as in the main explorer, with the same reliability gate for suppression-distorted rates. Partial correlation and the R² decomposition use ordinary least squares with one neighborhood and one achievement variable at a time — a lens, not a full causal model. "Shared" variance is explanatory overlap that the data cannot attribute to either factor alone.
  • This is an observational, school-level (ecological) analysis. It cannot say what happens to an individual student, and no partial correlation here identifies a causal effect in either direction.
  • Coverage. 1,464 of 1,518 schools in the main explorer have tract context (the rest are mostly closed or relocated campuses without current coordinates). CAASPP is missing for classes entering in 2021 (spring-2020 test cancelled) and 2022 (spring-2021 test non-representative); those periods show context-only dots in gray.
  • School composition variables. % SED and % EL are grade-11 shares from the CAASPP research files (2016+). The racial/ethnic composition variables are CDE Census Day enrollment shares for grades 9–12 (2014+; Asian and Filipino are separate CDE categories, combined here; "URG" follows UC's underrepresented-group definition: Hispanic/Latino, Black or African American, American Indian). All are aligned to each entering class's grade-11 year, like SED/EL, and describe the school's students — not its applicants to any campus.