Facebook Social Connectedness Index (SCI)

The Social Connectedness Index (SCI) is Meta’s measure of how strongly two places are linked by Facebook friendships. For a pair of locations, scaled_sci is proportional to the relative probability that a Facebook user in one is friends with a user in the other (friendship links between the two, divided by the product of their user counts), built from a snapshot of anonymized, aggregated friendships. It is distributed openly through the Humanitarian Data Exchange (HDX). Used in, for example, Rehbein & Rother (2025), where county-pair SCI proxies the social ties along which bank lending travels.

• Cost: free, public.
• API key: none required (bulk CSV download from HDX).
• Coverage: several geography levels, each its own file: country-country, sub-national admin level 1 and 2 (GADM), US counties, US ZCTAs, and European NUTS regions. A cross-section per release (the index has been re-snapshotted across several vintages), not a time series.
• Home: https://data.humdata.org/dataset/social-connectedness-index

Access

Each geography level is a separate file on the HDX dataset page. The country-country and US-county files are direct CSV downloads; some larger files (GADM2, US ZCTA) are served from Google Drive links listed on the same page.

# Country-to-country SCI (small, ~0.5 MB):
curl -sL "https://data.humdata.org/dataset/e9988552-74e4-4ff4-943f-c782ac8bca87/resource/652cf9c9-541f-47de-8d53-ff818062bd0c/download/country.csv" -o sci_country.csv

# US county-to-county SCI (245 MB, ~10.3M rows):
curl -sL "https://data.humdata.org/dataset/e9988552-74e4-4ff4-943f-c782ac8bca87/resource/97dc352f-c9c5-47d6-a6ef-88709e14006c/download/us_counties.csv" -o sci_us_counties.csv

Every file has the same five columns: user_country, friend_country, user_region, friend_region, scaled_sci. In the country file the region columns repeat the country code; in the US-county file user_region/friend_region are five-digit FIPS county codes.

Load in Python

import pandas as pd

sci = pd.read_csv("sci_us_counties.csv")            # FIPS-pair rows
sci = sci[sci.user_region != sci.friend_region]     # drop the diagonal
# undirected: keep each unordered pair once
sci = sci[sci.user_region < sci.friend_region]

Gotchas (the ones that bite pipelines)

• scaled_sci is a rescaled relative measure, not a count or a probability. Within a release the values are rescaled to integers with the single most-connected pair set to 1,000,000,000. Only the ordering and ratios within one file are meaningful; the absolute number is arbitrary. Do not read it as a number of friendships.
• Do not compare scaled_sci across files or vintages. Each release and each geography file is rescaled independently to its own max of 1e9, so a value in one vintage’s county file is not comparable to one in another vintage or in the country file. Normalize within the single file you use.
• It is symmetric and both directions are stored. (i, j) and (j, i) carry the same scaled_sci, so the file double-counts unordered pairs (the US-county file is roughly 3,200 squared = 10.3M rows). Deduplicate to one direction for pairwise analysis.
• The diagonal is included and dominates. Self-pairs (user_region == friend_region) measure within-area connectedness and are far larger than any cross-area value (within-county friendships dominate). Drop the diagonal for between-area work.
• Region codes differ by file. US counties are five-digit FIPS; sub-national files use GADM codes; the European file uses NUTS; ZCTAs are US ZIP-code tabulation areas. The user_country/friend_country columns are still present in sub-national files (both US in the county file), so do not assume those columns vary.
• It is a snapshot, not a panel. Each release is built from Facebook friendships at one point in time; releases are occasional re-snapshots, not a continuous series. Treat a file as a single cross-section and record which vintage you pulled.
• Built from anonymized, aggregated data with privacy protection. The index comes from de-identified, aggregated friendship counts; very thin pairs can be floored or noised. It reflects Facebook’s user base, which is not a uniform sample of the population, so connectedness is conditioned on who uses the platform.
• The US-county file is large. 245 MB and about 10.3M rows; read it filtered or in chunks rather than loading the raw symmetric table whole.

Reference

Field	Value
Host	Humanitarian Data Exchange: data.humdata.org/dataset/social-connectedness-index
Files	country.csv, gadm1.csv, gadm2.zip, US us_counties.csv, us_zcta.zip, nuts_2024.zip, geoboundaries variants
Columns	user_country, friend_country, user_region, friend_region, scaled_sci
US region code	Five-digit FIPS (county file)
Scaling	Integer, rescaled so the max pair = 1,000,000,000 within each file
Structure	Symmetric, both directions stored; diagonal included
Key required	No

Citation

Cite Bailey, Cao, Kuchler, Stroebel & Wong, “Social Connectedness: Measurement, Determinants, and Effects,” Journal of Economic Perspectives 32(3), 2018, 259-280, together with the specific SCI release and geography level, the retrieval URL, and the access date. Record the vintage, since the index is rescaled per release and the values are not comparable across versions.