Your friends determine your economy

What is it that distinguishes economies that take advantage of new products from those that don’t?

Matthew Jackson visited Princeton last week and gave a seminar on “Information and Gossip in Networks”. It was sadly lacking in any good gossip (if you have any, please send it to me), but he gave an excellent talk on how a village’s social network directly affects its economy.

He was able to collect data from a microfinance institution in India that began offering credit in 75 villages in Kerala. Yet despite being relatively homogeneous – they were all small, poor, widely dispersed villages in a single Indian state – there was a large amount of variability in how many people in each village participated in the program. What explains this?

Quite simply, the social connections do. When the microfinance institution entered the village, they did so by approaching village leaders and told them about the program, about its advantages and why they should participate. These village leaders were then responsible for informing the people in their village about the program.

social network

Jackson’s team was able to compile the complete social networks of everyone in these villages. They knew who went to temple with whom, who they trusted enough to lend money to, who they considered their friends, and so on. It is quite an impressive bit of work; unfortunately I cannot find any of his examples online anywhere. They found, for instance, incredible segregation by caste (not surprising, but nice that it falls out so naturally out of the data).

What determined the participation rate was how connected the leaders were to the rest of their village. Not just how many friends they had, but how many friends their friends had, and so on. To get an even better fit, they modeled the decision as a diffusion from the leaders out to their friends. They would slowly, randomly tell some of their friends, who would tell some of their other friends, and so on.

network diffusion

Jackson said that he got a rho^2 of 0.3 looking at traditional centrality measures and 0.47  (50% improvement) if you use his new model. The main difference with his new model (‘diffusion centrality’) appears to be time, which makes sense. When a program has been in a village for longer, more people will have taken advantage of it; people do not all rush out to get the Hot New Thing on the first day they can.

Village leaders are not the only people that they could have told. It would be nice if they could find more central individuals – people even better connected than the leaders. Impressively, they find that they can simply ask any random adult who would be the best person in the village to tell? And there is a good chance that they would know. This is exciting – it means people implicitly know about the social network structure of their world.

The moral of the story is that in order to understand economic processes, you need to understand the structure of the economy and you need to understand the dynamics. Static processes are insufficient – or at least, are much, much noisier.


Banerjee, A., Chandrasekhar, A., Duflo, E., & Jackson, M. (2014). Gossip: Identifying Central Individuals in a Social Network SSRN Electronic Journal DOI: 10.2139/ssrn.2425379

Banerjee A, Chandrasekhar AG, Duflo E, & Jackson MO (2013). The diffusion of microfinance. Science (New York, N.Y.), 341 (6144) PMID: 23888042


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