The simple economics of white elephants

Juan-José Ganuza and Gerard Llobet

Mathematical Social Sciences, 2020, vol. 106, issue C, 91-100

Abstract: This paper shows that the concession model discourages firms from acquiring information about the future profitability of a project. Uninformed contractors carry out good and bad projects because they are profitable in expected terms even though it would have been optimal to invest in screening them out according to their value. White elephants are identified as avoidable negative net present-value projects that are nevertheless undertaken. Institutional arrangements that limit the losses that firms can bear exacerbate this distortion. We characterize the optimal concession contract, which fosters the acquisition of information and achieves the first best by conditioning the duration of the concession to the realization of the demand and includes payments for not carrying out some projects.

Keywords: Concession contracts; Information acquisition; Flexible-term concessions (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165489620300317
Full text for ScienceDirect subscribers only

Related works:
Working Paper: The Simple Economics of White Elephants (2019)
Working Paper: The simple economics of white elephants (2019)
Working Paper: The Simple Economics of White Elephants (2018)
Working Paper: The Simple Economics of White Elephants (2017)
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:matsoc:v:106:y:2020:i:c:p:91-100

DOI: 10.1016/j.mathsocsci.2020.01.011

Access Statistics for this article

Mathematical Social Sciences is currently edited by J.-F. Laslier

More articles in Mathematical Social Sciences from Elsevier
Bibliographic data for series maintained by Catherine Liu ().