Econometrica: Mar 2017, Volume 85, Issue 2

Robustness and Separation in Multidimensional Screening
p. 453-488

Gabriel Carroll

A principal wishes to screen an agent along several dimensions of private information simultaneously. The agent has quasilinear preferences that are additively separable across the various components. We consider a robust version of the principal's problem, in which she knows the marginal distribution of each component of the agent's type, but does not know the joint distribution. Any mechanism is evaluated by its worst‐case expected profit, over all joint distributions consistent with the known marginals. We show that the optimum for the principal is simply to screen along each component separately. This result does not require any assumptions (such as single crossing) on the structure of preferences within each component. The proof technique involves a generalization of the concept of virtual values to arbitrary screening problems. Sample applications include monopoly pricing and a stylized dynamic taxation model.

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Supplemental Material

Supplement to "Robustness and Separation in Multidimensional Screening"

This supplementary appendix contains additional materials for the paper “Robustness and Separation in Multidimensional Screening.” Section B contains proofs of auxiliary results not included in the main paper. Section C details how the generalized virtual values coincide with traditional (ironed) virtual values in the single-good monopoly problem. Theorems, equations, and sections in the main paper are referenced using the original numbering.

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Supplement to "Robustness and Separation in Multidimensional Screening"

This zip file contains replication files for the manuscript.

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