What predicts a school's ties to outside organizations

The eight organizations: parents' groups, civic groups, local businesses, churches, social services, mental-health providers, juvenile justice agencies, and the police.

Choose which schools to look at

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Every percentage is weighted with the survey's own weights, because unweighted percentages of SSOCS do not describe US schools. The unweighted number of schools behind each figure is shown beside it, and a selection under 50 schools is suppressed rather than drawn.

No regression is recomputed here. The coefficients on the findings pages come from the paper's pipeline, with standard errors clustered on the sampling stratum within wave.

⬇ Download all 13,167 schools (CSV)

$ data & code run real R in this tab no install · R, Python, curl, JSON, citation

static files · CORS open · no key · no rate limit

This is a real R session, running in your browser through webR. Nothing is sent anywhere. The survey is already loaded as schools, a data frame of 13,167 rows, with the survey weight in w. The first run downloads R itself, about 30 MB.

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# the analytic file: 13,167 school-years, six surveys, 33 columns
base <- "https://school-connectivity.pages.dev/data/"
schools <- read.csv(paste0(base, "school_connectivity.csv"))

# every statistic is weighted with the survey weight; raw percentages are wrong
weighted.mean(schools$police, schools$w)      # 0.809
weighted.mean(schools$deg, schools$w)         # 4.00

# the paper's largest coefficient, rebuilt: the three standing arrangements as one index
z <- function(x) as.vector(scale(x))
schools$capacity <- z(z(schools$security) + z(schools$parent_process) + z(schools$equipment))
coef(lm(deg ~ capacity + z(crime_num) + factor(wave) + factor(level) + factor(urban),
        data = schools, weights = schools$w))["capacity"]
# +0.489. The paper reports +0.477 for the same index: it also holds school size, and
# uses both crime questions averaged rather than the one asked about the school's area.

# standard errors cluster on sw, the stratum crossed with the survey: 365 clusters
length(unique(schools$sw))                    # 365

# pandas
import pandas as pd
base = "https://school-connectivity.pages.dev/data/"
schools = pd.read_csv(base + "school_connectivity.csv")
edges   = pd.read_csv(base + "edges.csv")      # one row per school and organization

# weighted, always
w = schools["w"]
(schools["police"] * w).sum() / w.sum()        # 0.809
(schools["deg"] * w).sum() / w.sum()           # 4.00

# the published coefficients, with their clustered standard errors and caveats
import json, urllib.request
json.load(urllib.request.urlopen(base + "coefficients.json"))

curl -s https://school-connectivity.pages.dev/data/school_connectivity.csv -o schools.csv
curl -s https://school-connectivity.pages.dev/data/tie_rates.json | jq .overall
curl -s https://school-connectivity.pages.dev/data/coefficients.json | jq .capacity_index

# everything the site serves, static, CORS open, no key
data/school_connectivity.csv    # the analytic file: 13,167 school-years, 33 columns
data/school_connectivity.json   # the same, as records
data/explorer.csv               # the lean subset this page parses
data/edges.csv                  # the two-mode structure: one row per school and organization
data/codebook.json              # every column, its SSOCS variable, and the six coding traps
data/tables.json                # all 19 generated tables, rows and notes
data/coefficients.json          # the headline coefficients, each with its caveat
data/tie_rates.json             # weighted shares of the eight, overall and by survey

# READ codebook.json FIRST. Yes/no items are 1 and 2, the crime items are reversed,
# security staffing has a seam at 2015-16, and the cluster variable is sw, not strata.

# the source files are free from NCES; this site redistributes a subset
https://nces.ed.gov/surveys/ssocs/data_products.asp

# The working paper is under review and its authors are not listed here yet.
# Write to ron.levi@utoronto.ca for the current draft and how to cite it.
# To cite this site and its data:
@misc{school_connectivity_site,
  author = {{Community Safety and Policing Research Program, Global Justice Lab}},
  title  = {What predicts a school's ties to outside organizations},
  year   = {2026},
  note   = {University of Toronto},
  url    = {https://school-connectivity.pages.dev}
}

# and the source data
National Center for Education Statistics. School Survey on Crime and Safety (SSOCS),
public-use data files, 2005-06 through 2021-22. U.S. Department of Education.