Abstract
This paper derives necessary and sufficient conditions for allocations to be incentive compatible in multidimensional screening problems that satisfy a generalized single crossing property. We then devise a numerical method based on these results to solve multidimensional screening problems. Importantly, our numerical method can be applied to multidimensional screening problems for which existing approaches cannot be applied. We apply this method to several numerical examples in the context of multidimensional optimal taxation. In addition to illustrating how to apply our theoretical results and implement our numerical method, our simulations highlight the importance of bunching in optimal multidimensional taxation. Finally, we prove that bunching must occur in multidimensional optimal taxation problems when the social planner has sufficiently redistributive preferences.
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The datasets analyzed in the current study are available from IPUMS at https://www.ipums.org/
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Acknowledgements
The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. Thank you to two anonymous referees and the editor, Nicholas Yannelis, for providing many helpful comments that improved the paper. Thanks to various seminar participants at Tulane University and Virginia Tech. Thanks to Stefano Barbieri for providing helpful comments on this paper. This paper also benefited significantly from conversations with Ilia Krasikov and Mike Golosov. Finally, a special thanks to Katy Bergstrom who has been immensely helpful in providing feedback on this project.
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Dodds, W. Solving multidimensional screening problems using a generalized single crossing property. Econ Theory 77, 1025–1084 (2024). https://doi.org/10.1007/s00199-023-01519-8
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DOI: https://doi.org/10.1007/s00199-023-01519-8