We develop a model in which connections between individuals serve as social collateral to enforce informal insurance payments. We show that: (i) The degree of insurance is governed by the expansiveness of the network, measured with the per capita number of connections that groups have with the rest of the community. “Two-dimensional” networks—like real-world networks in Peruvian villages—are sufficiently expansive to allow very good risk-sharing. (ii) In second-best arrangements, insurance is local: agents fully share shocks within, but imperfectly between endogenously emerging risk-sharing groups. We also discuss how endogenous social collateral affects our results. (JEL D85, G22, O15, O17, Z13)

We decompose the beauty premium in an experimental labor market where “employers” determine wages of “workers” who perform a maze-solving task. This task requires a true skill which we show to be unaffected by physical attractiveness. We find a sizable beauty premium and can identify three transmission channels: (a) physically attractive workers are more confident and higher confidence increases wages; (b) for a given level of confidence, physically attractive workers are (wrongly) considered more able by employers; (c) controlling for worker confidence, physically attractive workers have oral skills (such as communication and social skills) that raise their wages when they interact with employers. Our methodology can be adopted to study the sources of discriminatory pay differentials in other settings.

This paper builds a theory of trust based on informal contract enforcement in social networks. In our model, network connections between individuals can be used as social collateral to secure informal borrowing. We define network-based trust as the largest amount one agent can borrow from another agent and derive a reduced-form expression for this quantity, which we then use in three applications. (1) We predict that dense networks generate bonding social capital that allows transacting valuable assets, whereas loose networks create bridging social capital that improves access to cheap favors such as information. (2) For job recommendation networks, we show that strong ties between employers and trusted recommenders reduce asymmetric information about the quality of job candidates. (3) Using data from Peru, we show empirically that network-based trust predicts informal borrowing, and we structurally estimate and test our model.

The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naïve learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent’s social influence in this generalized DeGroot model is essentially proportional to the degree-weighted share of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We show information aggregation preserves “wisdom” in the sense that initial signals are weighed approximately equally in a model of network formation that captures the sparsity, clustering, and small-world properties of real-world networks. We also identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. Simulating the modeled learning process on a set of real-world networks, we find that there is on average 22.4 percent information loss in these networks. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real-world network data show that with clustered seeding, information loss climbs to 34.4 percent. (JEL D83, D85, Z13)

We conducted online field experiments in large real-world social networks in order to decompose prosocial giving into three components: (1) baseline altruism toward randomly selected strangers, (2) directed altruism that favors friends over random strangers, and (3) giving motivated by the prospect of future interaction. Directed altruism increases giving to friends by 52% relative to random strangers, whereas future interaction effects increase giving by an additional 24% when giving is socially efficient. This finding suggests that future interaction affects giving through a repeated game mechanism where agents can be rewarded for granting efficiency-enhancing favors. We also find that subjects with higher baseline altruism have friends with higher baseline altruism.

Community Size and Network Closure by Hunt Allcott, Dean Karlan, Markus M. Möbius, Tanya S. Rosenblat and Adam Szeidl. Published in volume 97, issue 2, pages 80-85 of American Economic Review, May 2007