We study the effects of network externalities on a unique matching protocol for faculty in a large U.S. professional school to offices in a new building. We collected institutional, web, and survey data on faculty`s attributes and choices. We first identify the different layers of the social...

We study the effects of network externalities within a protocol for matching faculty to offices in a new building. Using web and survey data on faculty's attributes and choices, we identify the different layers of the social network: institutional affiliation, coauthorships, and friendships. We...

The focus of this paper is the endogenous formation of peer groups. We study a model in which agents choose their peers prior to making decisions on multiple issues. Agents differ in how much they value the decision outcomes on one issue relative to another. While each individual can collect...

This paper uses a new data set on domestic child adoption to document the preferences of potential adoptive parents over born and unborn babies relinquished for adoption by their birth mothers. We show that adoptive parents exhibit significant biases in favor of girls and against...

We study a dynamic matching environment where individuals arrive sequentially. There is a tradeoff between waiting for a thicker market, allowing for higher-quality matches, and minimizing agents' waiting costs. The optimal mechanism cumulates a stock of incongruent pairs up to a threshold and...

We study a dynamic matching environment where individuals arrive sequentially. There is a trade‐off between waiting for a thicker market, allowing for higher‐quality matches, and minimizing agents' waiting costs. The optimal mechanism cumulates a stock of incongruent pairs up to a threshold...

This paper uses a new data set on domestic child adoption to document the preferences of potential adoptive parents over born and unborn babies relinquished for adoption by their birth mothers. We show that adoptive parents exhibit significant biases in favor of girls and against...