Analyzing the Impact of Company Location, Size, and Remote Work on
Entry-Level Salaries a Linear Regression Study Using Global Salary Data
Dublin Core
Title
Analyzing the Impact of Company Location, Size, and Remote Work on
Entry-Level Salaries a Linear Regression Study Using Global Salary Data
Entry-Level Salaries a Linear Regression Study Using Global Salary Data
Subject
Entry-Level Salary, Company Location, Company Size, Remote Work, Linear Regression
Description
This research explores the key factors influencing entry-level salaries in the global labor market of 2024, emphasizing the roles of company location, organizational
size, and the extent of remote work in shaping compensation levels. Drawing on the Global Salary 2024 dataset from Kaggle, which comprises over 5,600
observations across multiple industries and geographic regions, the study applies a multiple linear regression model executed in Python via Google Colab to
quantitatively examine salary disparities. The results indicate that company location and size significantly affect entry-level earnings, underscoring how regional
economic contexts, cost-of-living variations, and organizational capacity continue to drive wage formation. Conversely, the remote work ratio exhibits a negligible
and statistically insignificant effect, implying that flexibility in work arrangements has yet to translate into measurable financial value for early-career professionals.
Furthermore, introducing job title as a control variable enhances the model’s explanatory power, reaffirming the influence of individual skill specialization and job
function in determining compensation outcomes. These findings reinforce human capital theory while extending it by incorporating contextual and organizational
dimensions relevant to the digital labor economy. For job seekers, the study offers data-driven insights to guide career decisions and salary expectations across
regions, while employers may utilize the results to formulate fair and competitive pay strategies in an increasingly interconnected workforce. Ultimately, this study
provides a comprehensive understanding of how structural and individual factors interact to shape entry-level salary dynamics in the modern digital era
size, and the extent of remote work in shaping compensation levels. Drawing on the Global Salary 2024 dataset from Kaggle, which comprises over 5,600
observations across multiple industries and geographic regions, the study applies a multiple linear regression model executed in Python via Google Colab to
quantitatively examine salary disparities. The results indicate that company location and size significantly affect entry-level earnings, underscoring how regional
economic contexts, cost-of-living variations, and organizational capacity continue to drive wage formation. Conversely, the remote work ratio exhibits a negligible
and statistically insignificant effect, implying that flexibility in work arrangements has yet to translate into measurable financial value for early-career professionals.
Furthermore, introducing job title as a control variable enhances the model’s explanatory power, reaffirming the influence of individual skill specialization and job
function in determining compensation outcomes. These findings reinforce human capital theory while extending it by incorporating contextual and organizational
dimensions relevant to the digital labor economy. For job seekers, the study offers data-driven insights to guide career decisions and salary expectations across
regions, while employers may utilize the results to formulate fair and competitive pay strategies in an increasingly interconnected workforce. Ultimately, this study
provides a comprehensive understanding of how structural and individual factors interact to shape entry-level salary dynamics in the modern digital era
Creator
Joe Khosa1,*, Daniel Mashao2
, Fajar Subekti3
, Fajar Subekti3
Source
https://ijiis.org/index.php/IJIIS/article/view/215/145
Publisher
Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa
Date
1 september 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Joe Khosa1,*, Daniel Mashao2
, Fajar Subekti3
, “Analyzing the Impact of Company Location, Size, and Remote Work on
Entry-Level Salaries a Linear Regression Study Using Global Salary Data,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9713.
Entry-Level Salaries a Linear Regression Study Using Global Salary Data,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9713.