Social capital I: measurement and associations with economic mobility

Sample construction

This section describes the methods used to generate the data analysed in this paper. A server-side analysis script was designed to automatically process the raw data, strip the data of personal identifiers, and generate aggregate results, which we analyzed to produce the conclusions in this paper. The script then promptly deleted the raw data generated for this project (see the Privacy and Ethics section).

We work with privacy-protected data from Facebook. Survey data show that more than 69% of the US adult population used Facebook in 2019, and about three-quarters of those individuals did so every day. The same survey also found that Facebook usage rates are similar across income groups, education levels and racial groups, as well as among urban, rural and suburban residents; they are lower among older adults and slightly higher among women than men.

Starting from the raw Facebook data as of 28 May 2022, our primary analysis sample was constructed by limiting the data to users aged between 25 and 44 years who reside in the United States, were active on the Facebook platform at least once in the previous 30 days, had at least 100 US-based Facebook friends and had a ZIP code. Our final analysis sample consists of 72.2 million Facebook users who constitute 84% of the US population between ages 25 and 44 years (based on a comparison to the 2014–2018 American Community Survey (ACS)). We focus on the 25–44-year age range because previous work has documented that its Facebook usage rate is above 80%, higher than for other age groups. In addition, the ACS publicly releases demographic data for certain age groups, one of which is ages 25–44 years, which enables us to compare our sample with the full population as well as to use ACS aggregates to predict SES (‘Variable definitions’).

We do not link any external individual-level information to the Facebook data. However, we use various publicly available sources of aggregate statistics to supplement our analysis, including data on median incomes by block group from the 2014–2018 ACS, data on economic mobility by Census tract and county from the Opportunity Atlas, and measures of county-level and ZIP-level characteristics, such as the share of the population by race and ethnicity and the share of single parents, from the ACS and the Census. We describe those data in detail in Supplementary Information A.5.

Variable definitions

We construct the following sets of variables for each person in our analysis sample. We measured these variables on 28 May 2022.

Friendship links

The data contain information on all friendship links between Facebook users. We focus only on friendships within our analysis sample; that is, we exclude friendships with people aged below 25 years or above 44 years, people who live outside the United States or people who do not satisfy one of our other criteria for inclusion in the analysis sample.

Facebook friendship links need to be confirmed by both parties, and most Facebook friendship links are between individuals who have interacted in person. The Facebook friendship network can therefore be interpreted as providing data on people’s real-world friends and acquaintances rather than purely online connections. Because individuals tend to have many more friends on Facebook than they interact with regularly, we also verify that our results hold when focusing on an individual’s ten closest friends, where closeness is measured on the basis of the frequency of public interactions such as likes, tags, wall posts and comments.

Locations

Following prior work, we use location data to construct statistics at various geographical levels. Every individual is assigned a residential ZIP code and county based on information and activity on Facebook, including the city reported on Facebook profiles as well as device and connection information. Formally, we use 2010 Census ZIP code tabulation areas (ZCTAs) to perform all geographical analyses of ZIP-code-level data. We refer to these ZCTAs as ZIP codes for simplicity. According to the 2014–2018 ACS, there are 219,214 Census block groups, 32,799 ZIP codes and 3,220 counties, with average populations of 1,488, 9,948 and 101,332 in each respective geographical designation.

Socioeconomic status

We construct a model that generates a composite measure of socioeconomic status (SES) for working-age adults (individuals between the ages of 25 and 64 years) that combines various characteristics. We construct our baseline SES measure in three steps, which are described in greater detail in Supplementary Information B.1.

First, for Facebook users who have location history (LH) settings enabled, we use the ACS to collect the median household income in their Census block group. LH is an opt-in setting for Facebook accounts that allows the collection and storage of location signals provided by a device’s operating system while the app is running. We observe Census block groups from individuals in the LH subsample. By contrast, we can only assign ZIP codes to individuals who do not have LH enabled. If an individual subsequently opts out of LH, their previously stored location signals are not retained.

Second, we estimate a gradient-boosted regression tree to predict these median household incomes using variables observed for all individuals in our sample, such as age, sex, language, relationship status, location information (ZIP code), college, donations, phone model price and mobile carrier, usage of Facebook on the Internet (rather than a mobile device), and other variables related to Facebook usage listed in Supplementary Table 4. We use this model to generate SES predictions for all individuals in our sample.

Finally, individuals (including the LH users in the training sample) are assigned percentile ranks in the national SES distribution on the basis of their predicted SES relative to others in the same birth cohort.

We do not use any information from an individual’s friends to predict their SES, which ensures that errors in the SES predictions are not correlated across friends, which would bias our estimates of homophily by SES. We also do not use direct information on individuals’ incomes or wealth, as we do not observe these variables at the individual level in our data. However, we show below that our measures of SES are highly correlated with external measures of income across subgroups.

The algorithm described above is one of many potential ways of combining a set of underlying proxies for SES into a single measure. To verify that our findings are not sensitive to the specific variables or algorithm used to predict SES, we show that our results are similar when we use a simple unweighted average of z-scores of the underlying proxies or when we directly use ZIP code median household incomes for all users, eschewing the prediction model and other proxies entirely (Supplementary Table 5).

Parental SES

We link individuals in our primary analysis sample to their parents (who may not be in the analysis sample themselves) to construct measures of family SES during childhood. To link individuals to their parents, we use self-reported familial ties, a hash of user last names, and public user-generated wall posts and major life events (see Supplementary Information A.2 for details). We then use the SES of parents, constructed using the algorithm described above, to assign parental SES to individuals. Finally, we assign individuals a parental SES rank on the basis of their predicted parental SES, ranking individuals on the basis of parental SES relative to others in the same birth cohort. We are able to assign parental SES ranks for 31% of the individuals in our primary analysis sample.

High school friendships

To identify friendships made in high school, we first use self-reports to assign individuals to schools. For people who do not report a high school, we use data on their friendship networks to impute those groups (see Supplementary Information A.3 for details). For the 3.3% of users who report multiple high schools, we select the school in which the user has the largest number of friends. This process produces information on high schools for 74.9% of individuals in our analysis sample. Finally, if an individual and one of their friends attended the same high school within three cohorts of each other, we identify them as high school friends.