Networks and Economics 2

on YouTube ↗01h 04m

" summary

Uses Facebook data on 72 million users (25–44, 21 billion friendships, ~84% U.S. coverage) to impute incomes and define economic connectedness as share of friends above median income. Finds county-level economic connectedness strongly predicts upward mobility (raw correlation ~0.64–0.67), while clustering and generic spectral splits do not. Releases aggregated ZIP/county/high‑school measures (noise added, no individual IDs). Theorizes via DeGroot learning: homophily raises the second eigenvalue and slows consensus, with block‑model bounds linking homophily to convergence time.

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" chapters (25)

1. 0:00Start
2. 0:07Economic Background
3. 1:42Kerosene and Rice Sharing Network
4. 4:27The Gatsby Curve
5. 5:25Genie Index of Inequality
6. 6:46Geography of Upward Mobility in the United States
7. 7:23Geographic Patterns
8. 7:56Native American Reservations
9. 15:31Economic Homophily
10. 15:52Economic Connectedness
11. 17:54The Bias of Being on Facebook
12. 21:31Does Economic Connectedness Predict Um Household Income Rank
13. 23:27Clustering Ratio
14. 24:03Correlations between Upward Mobility and these Measures of Capital
15. 24:36Multivariate Regression
16. 25:54Ethnic Composition of the Area
17. 29:59Basic Opportunities
18. 30:24Job Context
19. 30:41Basic Learning
20. 32:45Referrals Mattering
21. 35:51Group Model
22. 41:58Example of Non-Convergence
23. 46:19Speed of Convergence
24. 54:23Consensus Time
25. 56:50Eigenvalue versus Edge Expansion Ratio