There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen based on the kind of selection on unobservables it allows. Consequently, researchers can assess the plausibility of any exogeneity assumption by studying the distributions of treatment given the unobservables that are consistent with that assumption. We use this approach to study two common exogeneity assumptions: quantile and mean independence. We show that both assumptions require a kind of nonmonotonic relationship between treatment and the potential outcomes. We discuss how to assess the plausibility of this kind of treatment selection. We also show how to define a new and weaker version of quantile independence that allows for monotonic selection on unobservables. We then show the implications of the choice of exogeneity assumption for identification. We apply these results in an empirical illustration of the effect of child soldiering on wages.
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Homophily and Community Structure at Scale: An Application to a Large Professional Network
Professional networks affect labor market outcomes, efficiency, and knowledge diffusion. We study a large business card exchange network from Eight, a contact and career management app popular in Japan. Our empirical analysis is guided by a structural model of equilibrium network formation, with observable and unobservable heterogeneity, estimated via a two-steps approach that reduces computational challenges. In the first step, we recover the unobservable types; in the second step, we estimate the structural parameters, conditioning on estimated unobservables. Our results highlight the role of shared contacts and homophily in observables and unobservables in shaping the network of professional contacts.
more » « less- Award ID(s):
- 1951005
- PAR ID:
- 10552165
- Publisher / Repository:
- AEA Papers and Proceedings
- Date Published:
- Journal Name:
- AEA Papers and Proceedings
- Volume:
- 113
- ISSN:
- 2574-0768
- Page Range / eLocation ID:
- 156 to 160
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1257/pandp.20231094
