{"title":"高管薪酬基准:探索美国大型企业首席执行官(CEO)薪酬驱动因素的新视角","authors":"Jooh Lee, Niranjan Pati","doi":"10.1108/bij-03-2024-0208","DOIUrl":null,"url":null,"abstract":"PurposeThis study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions.Design/methodology/approachThis research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation.FindingsThis research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages.Originality/valueThis research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.","PeriodicalId":502853,"journal":{"name":"Benchmarking: An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Benchmarking executive compensations: exploring fresh perspectives on chief executive officer (CEO) compensation drivers in major US corporations\",\"authors\":\"Jooh Lee, Niranjan Pati\",\"doi\":\"10.1108/bij-03-2024-0208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions.Design/methodology/approachThis research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation.FindingsThis research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages.Originality/valueThis research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.\",\"PeriodicalId\":502853,\"journal\":{\"name\":\"Benchmarking: An International Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Benchmarking: An International Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/bij-03-2024-0208\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Benchmarking: An International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/bij-03-2024-0208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的 本研究旨在从经济和非经济两方面的角度,通过调查管理权限因素之间的关系,为正在进行的高管薪酬评估做出贡献。设计/方法/途径本研究采用集合横截面普通最小二乘法(OLS)回归和带固定效应的最小二乘法(LSDV)模型来研究首席执行官(CEO)薪酬的决定因素。研究结果本研究发现,在 0.05 水平上,公司规模、业绩(通过 ROA 和托宾 Q 值)和首席执行官特征(年龄、任期、股票所有权、MBA 学位)是高管薪酬的重要决定因素。相比之下,教育机构的声望、博士学位、MBA 与短期业绩的相关性以及 CEO 的任期对薪酬的影响不大。此外,该研究还强调了行业类型(制造业与科技业)对薪酬影响的重要性,强调了公司指标和 CEO 资历在设计高管薪酬方案中的作用。通过整合普通最小二乘法(OLS)和最小二乘法虚拟变量(LSDV)模型,本研究解决了时变变量和不可观测异质性带来的挑战。传统的 OLS 模型在确定影响 CEO 薪酬的一系列经济和非经济因素时,其准确性一直受到这些问题的影响。这种新颖的方法框架极大地推动了对不可观测变量的研究,这些变量不仅可能因所选公司而异,也可能因时间段而异,从而提供了对首席执行官薪酬决定因素的更详细了解。
Benchmarking executive compensations: exploring fresh perspectives on chief executive officer (CEO) compensation drivers in major US corporations
PurposeThis study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions.Design/methodology/approachThis research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation.FindingsThis research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages.Originality/valueThis research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.