Thursday, 26 April 2018

Facebook as a measure of social connectedness

Economists are often maligned for not recognising the importance of social relations in research. That is somewhat unfair, since the importance of social connections or networks is well recognised in the research on migration and trade, not to mention the growing literature on the importance of social capital. However, the biggest problem with including social connections in economics research is that they are notoriously difficult to measure. So, I was quite excited to read this 2017 NBER Working Paper (ungated version here) by Michael Bailey (Facebook), Ruiqing Cao (Harvard), Theresa Kuchler, Johannes Stroebel (both New York University), and Arlene Wong (Princeton). In the paper, the authors demonstrate a new Social Connectedness Index (SCI), derived from Facebook friends data:
Specifically, the SCI corresponds to the relative frequency of Facebook friendship links between every county-pair in the U.S., and between every U.S. county and every foreign country.
The paper then goes on to demonstrate the usefulness of the SCI:
We use these data to document important geographic patterns of social networks. We also show that the SCI data can be informative about the role of social connectedness for the large number of social and economic outcomes that can be measured at various levels of geographic aggregation, such as trade, migration, and patent citations. To facilitate further research along these dimensions, the SCI data can be made accessible to members of the broader research community...
We find that the intensity of friendship links is strongly declining in geographic distance, with the elasticity of the number of friendship links to geographic distance ranging from about -2.0 over distances less than 200 miles, to about -1.2 for distances larger than 200 miles. Conditional on distance, social connectedness is significantly stronger within states than across state lines. We also show that, conditional on geographic distance, the social connectedness between two counties is increasing in the similarity of these counties along important social and economic characteristics...
After aggregating the SCI to the state level to match available interstate trade data, we document that state-pairs with higher social connectedness see larger trade flows, even after controlling flexibly for geographic distance...
We also find that when counties are more connected, they are likely to have more cross-county patent citations...
Finally, we find that more connected county-pairs see more migration and labor flows, highlighting the potential of social networks to overcome frictions involved in moving across the United States...
Overall, the findings presented in this paper suggest that social connectedness plays a large role in explaining social and economic interactions, both within and across counties.
It seems to me that there is a huge amount of potential in using the SCI data. Better still, the dataset is available to researchers, as Bailey et al. note in a footnote:
Researchers are invited to submit a one-page research proposal for working with the SCI data to The data will be shared for approved research projects under the terms of an NDA between Facebook and approved researchers.
The Bailey et al. analysis suffers from being correlation rather than causal, but the depth and coverage of the SCI data means that there are a lot of research questions that it could be useful for, especially in studies of migration (where social networks matter in terms of migrants' or potential migrants' decisions about where to move), immigrant assimilation (where local social networks facilitate immigrants' adaptation to their new location), entrepreneurship (where social networks may impact on business success), idea or norms diffusion (where social networks are important mechanisms for promotion), and for any application where the measurement of social capital is important.

The biggest problem may be: is this dataset still available, given the current climate surrounding Facebook and data? There is no individual data in the SCI dataset (it is made up of county-level and country-level data only), so one would hope so.

[HT: Marginal Revolution, in July last year]

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