Research Article

Predictive Insights: Using Macro and Micro Models for Wage Growth Forecast in Malaysia

Authors

  • NABILAH BINTI AHMAD Employment Services Division, Employment Insurance System Office, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • MOHAMAD FAHRURRAZI BIN KAMALUDIN Employment Information and Analysis Services Division, Employment Insurance System Office, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • MOHAMAD AMRIZAD BIN RUSLIN Disability Management Department, Prevention, Medical & Rehabilitation Division, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • EE MEE SIN Employment Services Division, Employment Insurance System Office, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • Azirruan Bin Arifin Deputy Chief Executive Office, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • GAYATHRI VADIVEL Employment Services Division, Employment Insurance System Office, Pertubuhan Keselamatan Sosial, Putrajaya, Malaysia
  • Mohammed Azman Bin Aziz Mohammed Group Chief Executive Officer Office, Pertubuhan Keselamatan Sosial, Kuala Lumpur, Malaysia

Abstract

The Malaysian government has implemented the Progressive Wage Policy (PWP) to accelerate wage growth and address the low contribution of employee compensation (CE) to Gross Domestic Product The objective, as outlined in the Twelfth Malaysian Plan (RMKe-12), is to achieve a median wage of RM2,700 per month by 2025 and attain an annual productivity growth rate of 3.7% from 2021 to 2025[22]. In line with this policy, Social Security Organization (PERKESO), an organization under the Ministry of Human Resource, has taken proactive measures to analyze and model wage growth forecasting for the upcoming years. This paper aims to develop a forecasting model by examining the relationship between wages and various macroeconomic and microeconomic variables, including the unemployment rate. The methodology employs both Phillips Curve and Artificial Intelligence Model to predict wage increments, covering the period from 2016 to 2023. The approach ensures the development of a robust model supported by big data. This study establishes a predictive relationship within a stylistic framework of wage bargaining, indirectly fostering dynamic ecosystems between the prevailing economic conditions and employers' market trends in the Macro Model. The model considers the institutional structure of the current economic condition and employers' market trends, incorporating factors based on economic indicators and contributions. Additionally, a Machine Learning Gradient Boosting Regressor Model is utilized to predict the output from micro models. This enhances the overall reliability of the model. Significantly, the methodological innovation revolves around the integration of Macro and Micro Models, utilizing detailed data from job placements and monthly contributions spanning from 2020 to 2023 for the wage forecast framework. This distinct approach facilitates forecast development through model averaging techniques customized to maximize the accuracy of wage increase and estimated salary predictions.

Article information

Journal

Journal of Economics, Finance and Accounting Studies

Volume (Issue)

7 (3)

Pages

01-08

Published

2025-04-30

How to Cite

NABILAH BINTI AHMAD, MOHAMAD FAHRURRAZI BIN KAMALUDIN, MOHAMAD AMRIZAD BIN RUSLIN, EE MEE SIN, Azirruan Bin Arifin, GAYATHRI VADIVEL, & Mohammed Azman Bin Aziz Mohammed. (2025). Predictive Insights: Using Macro and Micro Models for Wage Growth Forecast in Malaysia. Journal of Economics, Finance and Accounting Studies , 7(3), 01-08. https://doi.org/10.32996/jefas.2025.7.3.1

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Keywords:

WAGE FORECAST, MICROMODEL, MACROMODEL, ECONOMY, LABOUR MARKET