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Executive Job Matching : = Estimates...
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Huang, Ruoyan.
Executive Job Matching : = Estimates from a Dynamic Model.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Executive Job Matching :/
其他題名:
Estimates from a Dynamic Model.
作者:
Huang, Ruoyan.
面頁冊數:
1 online resource (93 pages)
附註:
Source: Dissertation Abstracts International, Volume: 78-03(E), Section: A.
Contained By:
Dissertation Abstracts International78-03A(E).
標題:
Finance. -
電子資源:
click for full text (PQDT)
ISBN:
9781369128055
Executive Job Matching : = Estimates from a Dynamic Model.
Huang, Ruoyan.
Executive Job Matching :
Estimates from a Dynamic Model. - 1 online resource (93 pages)
Source: Dissertation Abstracts International, Volume: 78-03(E), Section: A.
Thesis (Ph.D.)
Includes bibliographical references
I evaluate how firms' and CEOs' learning about their fit with one another affects CEO turnover and compensation decisions. Building and estimating a dynamic model of the executive labor market, I find that learning and selection eliminate low-quality matches and provide explanatory power for the excess skewness of CEO compensation in the data after controlling for firm and CEO characteristics. I further establish that learning generates a hump-shaped hazard rate curve of CEO turnover conditional on CEO tenure. Using a hand-collected dataset of CEO turnover, I discover that the speed and precision of learning determine the level and length of the "discovery phase" of the conditional hazard rate curve. I also find that CEO compensation demonstrates a firm's evaluation of the match quality and is predictive of the expected future tenure of its CEO. In short, I show the importance of learning and selection in explaining the relations between CEO compensation, CEO turnover, and firm performance.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369128055Subjects--Topical Terms:
559073
Finance.
Index Terms--Genre/Form:
554714
Electronic books.
Executive Job Matching : = Estimates from a Dynamic Model.
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Estimates from a Dynamic Model.
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2016.
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I evaluate how firms' and CEOs' learning about their fit with one another affects CEO turnover and compensation decisions. Building and estimating a dynamic model of the executive labor market, I find that learning and selection eliminate low-quality matches and provide explanatory power for the excess skewness of CEO compensation in the data after controlling for firm and CEO characteristics. I further establish that learning generates a hump-shaped hazard rate curve of CEO turnover conditional on CEO tenure. Using a hand-collected dataset of CEO turnover, I discover that the speed and precision of learning determine the level and length of the "discovery phase" of the conditional hazard rate curve. I also find that CEO compensation demonstrates a firm's evaluation of the match quality and is predictive of the expected future tenure of its CEO. In short, I show the importance of learning and selection in explaining the relations between CEO compensation, CEO turnover, and firm performance.
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click for full text (PQDT)
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