Discrimination in Dynamic Procurement Design with Learning-by-doing

Klenio Barbosa and Pierre Boyer

International Journal of Industrial Organization, 2021, vol. 77, issue C

Abstract: We study the long-run impact of procurement discrimination on market structure and future competition in industries where learning-by-doing makes incumbent firms more efficient over time. We consider a sequential procurement design problem in which local and global firms compete for public good provision. Both firms benefit from learning-by-doing if they provide the public good in the previous period, but global firms only may be able to transfer learning-by-doing from different markets. We show that the optimal procurement has to be biased in favor of the local firm even when all firms are symmetric with respect to their initial cost distribution.

Keywords: Discrimination; Dynamic Procurement; Local versus Global Firms; Learning-by-doing (search for similar items in EconPapers)
JEL-codes: D44 H57 H70 H87 (search for similar items in EconPapers)
Date: 2021
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/S0167718721000473
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Discrimination in Dynamic Procurement Design with Learning-by-doing (2016)
Working Paper: Discrimination in Dynamic Procurement Design with Learning-by-doing (2012)
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:77:y:2021:i:c:s0167718721000473

DOI: 10.1016/j.ijindorg.2021.102754

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 ().