Factors Influencing Labor Absorption in the Micro and Small Industry Sector in Banten Province for the 2019-2023 Period

This research seeks to investigate how the quantity of industries, Gross Regional Domestic Product, Production Value, Minimum Wage affects Labor Absorption in the micro and small industry sector in Banten Province. With 8 regencies and cities in Banten and a research timeframe from 2019 to 2023 utilizing quantitative methods, the analysis employing multiple linear regression demonstrates that the model possesses exceptional explanatory power. The R-squared value of 0.989332 reveals that approximately 98.93% of the variation in the dependent variable (labor force number/Y) is accounted for by the independent variables. According to the findings of the regression analysis, the Gross Domestic Product (GDP) variable positively affects the level of employment (labor). This suggests that there is statistically inadequate evidence to claim that alterations in the number of industries, productivity levels, or minimum wage directly influence labor absorption levels. This condition could arise from factors beyond the primary model, or the third variable's influence may be of an indirect nature. This model indicates that GDP could be a key element affecting labor absorption, whereas other factors have not yet played a substantial role.

Authors

Institutions

Publication Details

Journal
eCo-Buss
Published
2025-08-15
DOI
https://doi.org/10.32877/eb.v8i1.2950
Primary Topic
SMEs Development and Digital Marketing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Influencing Labor Absorption in the Micro and Small Industry Sector in Banten Province for the 2019-2023 Period

Tri Angreni, Vivin Hanitha, Hendra Hendra
eCo-Buss
SMEs Development and Digital Marketing
article

Factors Influencing Labor Absorption in the Micro and Small Industry Sector in Banten Province for the 2019-2023 Period

Tri Angreni, Vivin Hanitha, Hendra Hendra
article en

Abstract

This research seeks to investigate how the quantity of industries, Gross Regional Domestic Product, Production Value, Minimum Wage affects Labor Absorption in the micro and small industry sector in Banten Province. With 8 regencies and cities in Banten and a research timeframe from 2019 to 2023 utilizing quantitative methods, the analysis employing multiple linear regression demonstrates that the model possesses exceptional explanatory power. The R-squared value of 0.989332 reveals that approximately 98.93% of the variation in the dependent variable (labor force number/Y) is accounted for by the independent variables. According to the findings of the regression analysis, the Gross Domestic Product (GDP) variable positively affects the level of employment (labor). This suggests that there is statistically inadequate evidence to claim that alterations in the number of industries, productivity levels, or minimum wage directly influence labor absorption levels. This condition could arise from factors beyond the primary model, or the third variable's influence may be of an indirect nature. This model indicates that GDP could be a key element affecting labor absorption, whereas other factors have not yet played a substantial role.

eCo-BussVol. 8(1)
Universitas Dharmas Indonesia (ID)
Openalex Percentile: Top 61%
SMEs Development and Digital Marketing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Factors Influencing Labor Absorption in the Micro and Small Industry Sector in Banten Province for the 2019-2023 Period — Tri Angreni, Vivin Hanitha, et al. · eCo-Buss (2025) | TGRS Research Map | TGRS