What predicts a school's ties to outside organizations

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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.