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.

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.

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 networkbased 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.

Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing.

Advances in communication and transportation technologies have the potential to bring people closer together and create a "global village." However, they also allow heterogeneous agents to segregate along special interests, which gives rise to communities fragmented by type rather than by geography. We show that lower communication costs should always decrease separation between individual agents even as group-based separation increases. Each measure of separation is pertinent for distinct types of social interaction. A group-based measure captures the diversity of group preferences that can have an impact on the provision of public goods. While an individual measure correlates with the speed of information transmission through the social network that affects, for example, learning about job opportunities and new technologies. We test the model by looking at coauthoring between academic economists before and during the rise of the Internet in the 1990s.