Public Sector Compensation: An Application of Robust and Quantile Regression

Source: Salomon Alcocer Guajardo, Compensation & Benefits Review, OnlineFirst, August 3, 2020
(subscription required)

From the 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).