EconPapers    
Economics at your fingertips  
 

Optimal Ratcheting in Executive Compensation

Iny Hwang (hiny72@snu.ac.kr), Youngsoo Kim (ykim@culverhouse.ua.edu) and Michael K. Lim (milim@snu.ac.kr)
Additional contact information
Iny Hwang: SNU Business School, Seoul National University, Seoul 08826, Republic of Korea
Youngsoo Kim: Culverhouse College of Business, University of Alabama, Tuscaloosa, Alabama 35487
Michael K. Lim: SNU Business School, Seoul National University, Seoul 08826, Republic of Korea

Decision Analysis, 2023, vol. 20, issue 2, 166-185

Abstract: Recent empirical studies point out that the firms do not fully incorporate the managers’ past performance when revising future contractual terms. This study offers a theoretical perspective on the firm’s executive compensation strategy that supports such latest empirical findings. Using a two-period principal-agent model, we examine firm’s compensation schemes with ratchet principle taking into account key factors such as informational rent, capability uncertainty, and performance noise. After characterizing the optimal incentive rates for a given degree of ratcheting, we examine the efficacy of ratcheting contract in executive compensation. We also explore the optimal degree of ratcheting that strikes a fine balance between informational rent and ratchet effect. We find that the capability gap-performance noise ratio plays a critical role in determining the optimal degree of ratcheting.

Keywords: ratcheting; uncertainty; performance noise; informational rent; commitment (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/deca.2023.0467 (application/pdf)

Related works:
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:inm:ordeca:v:20:y:2023:i:2:p:166-185

Access Statistics for this article

More articles in Decision Analysis from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher (casher@informs.org).

 
Page updated 2024-12-28
Handle: RePEc:inm:ordeca:v:20:y:2023:i:2:p:166-185