Multidimensional Procurement Auction with Loss-Averse Workers in Online Labor Markets
Service procurement auction involving multidimensional bids - typically a proposal and a price - are ubiquitous in online labor markets. One interesting component, referred to as all-pay, of this procurement process is the worker must invest effort and time upfront preparing a proposal, regardless of whether the worker eventually wins the contract. If a worker loses the auction, the cost of developing the proposal is lost and cannot be recovered. It is well known that individuals are loss averse (i.e. they are more sensitive to loss than to gains for the same magnitude of monetary incentive). Intuitively, loss aversion is important to our setting particularly due to our focal interest in the all-pay component. We study the impact of loss aversion by the use of game theoretical analysis. We show, in the unique symmetric equilibrium of this setting, that worker's loss aversion behavior always decreases their own equilibrium expected utility, but can increase or decrease the buyer’s equilibrium expected utility, depending on the degree of loss aversion and how the buyer values the worker's proposal. We also find that loss aversion can be more detrimental for the buyer when bidding competition is more intense. Two reimbursement policies that are commonly used in practice and studied in literature - the percentage and the flat reimbursement policy - are investigated as a mitigation strategy to the potential negative impact of loss aversion in the procurement auction. We show that both reimbursement policies can improve the social welfare without hurting either side. In addition, we find that the percentage reimbursement policy is preferred when the effect of loss aversion is detrimental enough (i.e., the degree of loss aversion and diminishing return of the quality value function are both high)
Year of publication: |
[2022]
|
---|---|
Authors: | Wu, Jason Xianghua ; Li, Shan ; Chen, Kay-Yut |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (67 p) |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 4, 2022 erstellt |
Other identifiers: | 10.2139/ssrn.4050013 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013296607
Saved in favorites
Similar items by person
-
Trust and Trustworthiness : Experiments with Artificial Intelligence (AI) Agents
Wu, Jason Xianghua, (2022)
-
Wu, Jason Xianghua, (2023)
-
The Behavioral Traps in Making Multiple, Simultaneous, Newsvendor Decisions
Chen, Kay-Yut, (2019)
- More ...