Working Paper: CEPR ID: DP13312
Authors: Laura Veldkamp; Alessandra Fogli
Abstract: Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do these differences come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks, the effect can be positive, negative, or zero. The reason is that networks diffuse ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment needlessly compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.
Keywords: growth; development; technology diffusion; economic networks; social networks; pathogens; disease
JEL Codes: E02; O1; O33; I1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| high-diffusion networks (D85) | income (E25) |
| high-diffusion networks (D85) | technology diffusion (O33) |
| technology diffusion (O33) | economic growth (O49) |
| high-diffusion networks (D85) | economic growth (O49) |
| high-disease environments (I12) | economic output (E23) |
| low-disease environments (I12) | economic growth (O49) |
| social networks (Z13) | disease transmission (I12) |
| disease transmission (I12) | economic output (E23) |