Analisis Faktor-Faktor yang Mempengaruhi Upah Riil Tenaga Kerja Sektor Konstruksi Antar Provinsi di Indonesia Priode 2010-2023

Ramadhan Hibatur Rahman; Karin Angelika Putri; Ma’isyatur Rodhiyah; Novia Ardhana; Yossinomita Yossinomita

doi:10.61132/prosemnasproit.v2i2.182

Authors

Ramadhan Hibatur Rahman Universitas Dinamika Bangsa
Karin Angelika Putri Universitas Dinamika Bangsa
Ma’isyatur Rodhiyah Universitas Dinamika Bangsa
Novia Ardhana Universitas Dinamika Bangsa
Yossinomita Yossinomita Universitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.182

Keywords:

Upah Riil, Pengangguran, Data Panel, Fixed Effect Model (FEM), Random Effect Model (REM), Indonesia

Abstract

This study aims to analyze the factors affecting real wages of construction workers across provinces in Indonesia from 2010 to 2023 using panel data analysis. The independent variables include Provincial Minimum Wage (UMP), Consumer Price Index (CPI), Open Unemployment Rate (TPT), and Performance Pay (Balas Jasa). A panel dataset of 476 observations from 34 provinces over 14 years was analyzed using three model approaches: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The best model was determined through Chow Test, Hausman Test, and Lagrange Multiplier Test, which confirmed that the Fixed Effect Model (FEM) is the most appropriate for analyzing this research data. FEM estimation results show that simultneously, all independent variables (UMP, CPI, TPT, and Performance Pay) have a significant effect on real wages with an F-statistic value of 436,465.9 (p-value = 0.0000 < 0.05), indicating that the model as a whole is highly valid and capable of explaining the variation in real wages collectively. However, partial tests reveal that only the Real Wage variable has a positive and statistically significant effect on Performance Pay (coefficient = 106.3320; t-statistic = 1276.083; p-value = 0.0000), while UMP (p-value = 0.1472), CPI (p-value = 0.6460), and TPT (p-value = 0.6934) show no significant effects at the 5% significance level. The research model demonstrates very high predictive ability with an R-squared value of 0.999735 (99.97%), indicating that the variables studied can explain nearly all variation in real wages of construction workers at the provincial level. This research provides policy implications that improving real wages in the construction sector requires an integrated approach that focuses not only on minimum wage setting but also on regional inflation control, human capital quality improvement, and creating conducive labor market conditions through unemployment reduction

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