Bayesian implementation and rent extraction in a multi-dimensional procurement problem
Fabian Herweg () and
Klaus M. Schmidt
International Journal of Industrial Organization, 2020, vol. 70, issue C
Abstract:
We consider a multi-dimensional procurement problem in which sellers have private information about their costs and about a possible design flaw. The information about the design flaw is necessarily correlated. We solve for the Bayesian procurement mechanism that implements the efficient allocation at the lowest cost under the constraint that sellers are protected by limited liability. We show that the rents obtained from reporting costs truthfully can be used to reduce the rents sellers must get for reporting the flaw. We compare the efficient Bayesian mechanism to the efficient ex post incentive compatible mechanism studied by Herweg and Schmidt (2019) that is informationally less demanding.
Keywords: Auction; Correlated types; Inefficient renegotiation; Multidimensional screening; Procurement (search for similar items in EconPapers)
JEL-codes: D44 D47 D82 H57 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167718719300438
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Bayesian Implementation and Rent Extraction in a Multi-Dimensional Procurement Problem (2018) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:indorg:v:70:y:2020:i:c:s0167718719300438
DOI: 10.1016/j.ijindorg.2019.06.003
Access Statistics for this article
International Journal of Industrial Organization is currently edited by P. Bajari, B. Caillaud and N. Gandal
More articles in International Journal of Industrial Organization from Elsevier
Bibliographic data for series maintained by Catherine Liu ().