Analysis of Workers’ Compensation Using Multiple Linear Regression Model: A Case Study in an Estates and Mills Company

Abstract

Fair and open compensation is a critical component of employees’ happiness and satisfaction. To provide competitive and equitable pay, an analysis of current compensation is necessary. Therefore, the main goal of this paper is to investigate the relationship between possible factors and compensation among workers who work in mills and estates sector.  This paper also aims to identify the differences in compensation between different job types and races. The result of the Multiple Linear Regression model showed that only five factors (total working days, race, gender, worker type, and job type) are significantly associated with worker’s compensation. This paper also revealed that the monthly pay for the four job types held worker which consist of general worker, harvester, mill worker and rubber tapper in estates and mills were differed through Kruskal-Wallis Test. To conclude, the findings of this paper could support the strategic goals of the business, benchmark compensation, enhance compensation decision-making, and provide a more equitable compensation structure for workers.