Public Sector Compensation: An Application of Robust and Quantile Regression

S. A. Guajardo
{"title":"Public Sector Compensation: An Application of Robust and Quantile Regression","authors":"S. A. Guajardo","doi":"10.1177/0886368720939406","DOIUrl":null,"url":null,"abstract":"This study assesses whether the theoretical compensation framework used to explain differences in public sector pay among full-time federal and state employees may also explain differences in pay at a local government level. In doing so, this study uses ordinary least squares (OLS) regression to test the application of the theoretical framework to a specific local government. Robust and quantile regression models are used subsequently to validate the findings obtained by the OLS model. The findings reveal that the covariates used to explain differences in compensation among full-time federal and state employees have similar effects at a local governmental level. While the OLS statistical model explains 26% (R2 = .26) of the variance, the robust regression model explains 39% (R2 = .39) of the variance. The percentage of variation explained by the quantile statistical models ranges from 14% (pseudo-R2 = .14) to 50% (pseudo-R2 = .50).","PeriodicalId":79838,"journal":{"name":"Compensation and benefits review","volume":"1 1","pages":"59 - 74"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Compensation and benefits review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0886368720939406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

This study assesses whether the theoretical compensation framework used to explain differences in public sector pay among full-time federal and state employees may also explain differences in pay at a local government level. In doing so, this study uses ordinary least squares (OLS) regression to test the application of the theoretical framework to a specific local government. Robust and quantile regression models are used subsequently to validate the findings obtained by the OLS model. The findings reveal that the covariates used to explain differences in compensation among full-time federal and state employees have similar effects at a local governmental level. While the OLS statistical model explains 26% (R2 = .26) of the variance, the robust regression model explains 39% (R2 = .39) of the variance. The percentage of variation explained by the quantile statistical models ranges from 14% (pseudo-R2 = .14) to 50% (pseudo-R2 = .50).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
公共部门薪酬:稳健和分位数回归的应用
本研究评估了用于解释全职联邦和州雇员之间公共部门薪酬差异的理论薪酬框架是否也可以解释地方政府层面的薪酬差异。为此,本研究使用普通最小二乘(OLS)回归来检验理论框架在特定地方政府中的应用。随后使用稳健和分位数回归模型来验证OLS模型获得的结果。研究结果表明,用于解释联邦和州全职雇员薪酬差异的协变量在地方政府层面上具有相似的影响。OLS统计模型解释了26% (R2 = 0.26)的方差,而稳健回归模型解释了39% (R2 = 0.39)的方差。分位数统计模型解释的变异百分比范围从14%(伪r2 = 0.14)到50%(伪r2 = 0.50)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.50
自引率
0.00%
发文量
0
期刊最新文献
Exploring Pay Transparency and Communication: An Introduction to the First of Two Special Issues on the Topic of Pay Transparency The Impact of Linking Three Different Incentive Methods to Specific, Challenging Goals Variable Pay Transparency in Organizations: When are Organizations More Likely to Open Up About Pay? Pay Transparency and Pay Communication Access to Employer Benefits and Financial Insecurity Among Frontline Healthcare Workers
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1