This paper studies a stability notion and matching processes in the job market with incomplete information on the workers' side. Each worker is associated with a type, and each firm cares about the type of her employee under a match. Moreover, firms' information structure is described by...

This paper studies a stability notion and matching processes in the job market with incomplete information on the workers' side. Each worker is associated with a type, and each firm cares about the type of her employee under a match. Moreover, firms' information structure is described by...

In this paper, we extend the stability notion and Bayesian efficiency notion of Liu (2020) to local ones, as well as his result—that under certain intuitive conditions, stable matchings are Bayesian efficient—to an analogous one for local notions. Furthermore, the extended stability notion,...

This paper studies a matching algorithm which is termed incomplete-information deferred acceptance (DA). We show that DA is finite and that it produces a stable market state. We also compare DA with algorithms in the literature, including the complete-information Gale-Shapley algorithm, the...

This paper extends the Bayesian stability notion of Liu (2020) to define the Bayesian stability of a market state , which consists of a matching outcome and an information structure. Here, the information structure can be arbitrarily heterogeneous among agents. We first establish that Liu’s...

A principal who values an object allocates it to one or more agents. Agents learn private information (signals) from an information designer about the allocation payoff to the principal. Monetary transfer is not available but the principal can costly verify agents' private signals. The...

This paper studies a stability notion and matching processes in the job market with incomplete information on the workers' side. Each worker is associated with a type, and each firm cares about the type of her employee under a match. Moreover, firms' information structure is described by...

We identify necessary and sufficient conditions under which a coarse data set can be coarsely rationalized by a linear order (or weak order). The conditions are easy to check, and efficient algorithms are provided. We apply our theory to investigate the observable restrictions of several...