Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new randomized sales mechanism for such environments. Bidders can "buy-it-now" at a posted price, or "take-a-chance" in an auction where the top d > 1 bidders are equally likely to win. The randomized allocation incentivizes high valuation bidders to buy-it-now. We analyze equilibrium behavior, and apply our analysis to advertiser bidding data from Microsoft Advertising Exchange. In counterfactual simulations, our mechanism increases revenue by 4.4% and consumer surplus by 14.5% compared to an optimal second-price auction.
We develop a model in which connections between individuals serve as social collateral to enforce informal insurance payments. We show that: (1) 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. (2) 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 D02, D31, D70)
Social learning is a rapidly growing field for empirical and theoretical research in economics. We encounter social learning in many economically important phenomena, such as the adoption of new products and technologies or job search in labor markets. We review the existing empirical and theoretical literatures and argue that they have evolved largely independently of each other. This suggests several directions for future research that can help bridge the gap between both literatures. For example, the theory literature has come up with several models of social learning, ranging from naïve DeGroot models to sophisticated Bayesian models whose assumptions and predictions need to be empirically tested. Alternatively, empiricists have often observed that social learning is more localized than existing theory models assume, and that information can decay along a transmission path. Incorporating these findings into our models might require theorists to look beyond asymptotic convergence in social learning.
We present a simple auction mechanism which extends the
second-price auction with reserve and is truthful in expectation.
This mechanism is particularly effective in private
value environments where the distribution of valuations are
irregular. Bidders can “buy-it-now”, or alternatively “takea-
chance” where the top d bidders are equally likely to win.
The randomized take-a-chance allocation incentivizes high
valuation bidders to buy-it-now. We show that for a large
class of valuations, this mechanism achieves similar allocations
and revenues as Myerson’s optimal mechanism, and
outperforms the second-price auction with reserve.
In addition, we present an evaluation of bid data from
Microsoft’s AdECN platform. We find the valuations are
irregular, and counterfactual experiments suggest our BINTAC
mechanism would improve revenue by 11% relative to
an optimal second-price mechanism with reserve.
We conduct a field experiment in a large real-world social network to examine how subjects expect to be treated by their friends and by strangers who make allocation decisions in modified dictator games. While recipients’ beliefs accurately account for the extent to which friends will choose more generous allocations than strangers (i.e. directed altruism), recipients are not able to anticipate individual differences in the baseline altruism of allocators (measured by giving to an unnamed recipient, which is predictive of generosity towards named recipients). Recipients who are direct friends with the allocator, or even recipients with many common friends, are no more accurate in recognizing intrinsically altruistic allocators. Recipient beliefs are significantly less accurate than the predictions of an econometrician who knows the allocator’s demographic characteristics and social distance, suggesting recipients do not have information on unobservable characteristics of the allocator.
We conduct 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 percent relative to random strangers, while future interaction effects increase giving by an additional 24 percent 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 de ne network-based trust as the highest 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, while loose networks create bridging social capital that improves access to cheap favors like information. (2) For job recommendation networks, we show that strong ties between employers and trusted recommenders reduce asymmetric information about the qualityof 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.
We show the existence of a pure strategy, symmetric, increasing equilibrium in double
auction markets with correlated, conditionally independent private values and many
participants. The equilibrium we find is arbitrarily close to fully revealing as the market
size grows. Our results provide strategic foundations for price-taking behavior in large
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 una®ected by physical attractiveness. We find a sizable beauty premium and can identify three transmission channels. (1) Physically-attractive workers are more confident and higher confidence increases wages. (2) For a given level of confidence, physically-attractive workers are (wrongly) considered more able by employers. (3) 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 adapted to study the sources of discriminatory pay differentials in other settings.
This paper examines a simple model of competing auction sites to give some insights into the concentration of auction markets. In our model, there are B ex-ante identical buyers, each with unit demand, and S sellers, each with a single unit of the good to sell and a reservation value of zero. At the start of the model, buyers and sellers simultaneously choose between two possible locations. Buyers then learn their private values for the good, and a uniform-price auction is held at each location. This is a very stark model, but we believe that it provides some useful insights, and that it serves as a benchmark case for richer and more realistic models.
Advances in communication and transportation technologies have the potential to bring people closer together and create a ‘global village’. However, they also allow heterogenous agents to segregate along special interests which gives rise to communities fragmented by type rather than 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. 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.