Executive compensation: a calibration approach

Ivilina Popova and Joseph Haubrich
Additional contact information
Ivilina Popova: Krannert Graduate School of Management, Purdue University, West Lafayette, IN 47907-1310, USA

Economic Theory, 1998, vol. 12, issue 3, 581 pages

Abstract: We use a version of the Grossman and Hart principal-agent model with 10 actions and 10 states to produce quantitative predictions for executive compensation. Performance incentives derived from the model are compared with the performance incentives of 350 firms chosen from a survey by Michael Jensen and Kevin Murphy. The results suggest both that the model does a reasonable job of explaining the data and that actual incentives are close to the optimal incentives predicted by theory.

Keywords: Calibration; ·; Executive; compensation; ·; Principal-agent. (search for similar items in EconPapers)
JEL-codes: C60 G30 J33 (search for similar items in EconPapers)
Date: 1998-10-13
Note: Received: August 12, 1997; revised version: October 27, 1997
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.de/link/service/journals/00199/papers/8012003/80120561.pdf (application/pdf)
Access to the full text of the articles in this series is restricted

Related works:
Working Paper: Executive compensation: a calibration approach (1994)
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:spr:joecth:v:12:y:1998:i:3:p:561-581

Ordering information: This journal article can be ordered from
http://www.springer. ... eory/journal/199/PS2

Access Statistics for this article

Economic Theory is currently edited by Nichoals Yanneils

More articles in Economic Theory from Springer, Society for the Advancement of Economic Theory (SAET) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().