Analisis Pengaruh PDRB, UMP, Investasi, Pengangguran dan Pandemi Covid-19 Terhadap Tingkat Kemiskinan di Indonesia: Studi Data Panel 2015-2023

Poverty is a multi-layered and complex economic problem in Indonesia. This study aims to explore the factors that influence the poverty rate in 34 Indonesian provinces in the period from 2015 to 2023 by utilizing the static panel regression method. The independent variables analyzed include Gross Regional Domestic Product (GRDP), Provincial Minimum Wage (UMP), investment, and the open unemployment rate. Secondary data was collected from the Central Statistics Agency (BPS) and other relevant sources. The findings of this study show that GRDP and investment have a negative and significant impact on the poverty rate, indicating that economic growth and increased investment can serve to reduce poverty rates. In contrast, the open unemployment rate shows a positive relationship with poverty, indicating that increased unemployment worsens the poverty situation. Meanwhile, the results of the minimum wage vary in different provinces, depending on the dynamics of the labor market and local economic policies. The implications of this study emphasize the importance of developing policies that support inclusive economic growth, increasing investment that has a direct impact on job creation, and strategies to reduce unemployment rates to tackle poverty more effectively.

Authors

Institutions

Publication Details

Journal
Inisiatif Jurnal Ekonomi Akuntansi dan Manajemen
Published
2025-04-08
DOI
https://doi.org/10.30640/inisiatif.v4i2.4014
Primary Topic
Economic Growth and Fiscal Policies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analisis Pengaruh PDRB, UMP, Investasi, Pengangguran dan Pandemi Covid-19 Terhadap Tingkat Kemiskinan di Indonesia: Studi Data Panel 2015-2023

Alfina Putri Satyatama
Inisiatif Jurnal Ekonomi Akuntansi dan Manajemen
Economic Growth and Fiscal Policies
article

Analisis Pengaruh PDRB, UMP, Investasi, Pengangguran dan Pandemi Covid-19 Terhadap Tingkat Kemiskinan di Indonesia: Studi Data Panel 2015-2023

Alfina Putri Satyatama
article en

Abstract

Poverty is a multi-layered and complex economic problem in Indonesia. This study aims to explore the factors that influence the poverty rate in 34 Indonesian provinces in the period from 2015 to 2023 by utilizing the static panel regression method. The independent variables analyzed include Gross Regional Domestic Product (GRDP), Provincial Minimum Wage (UMP), investment, and the open unemployment rate. Secondary data was collected from the Central Statistics Agency (BPS) and other relevant sources. The findings of this study show that GRDP and investment have a negative and significant impact on the poverty rate, indicating that economic growth and increased investment can serve to reduce poverty rates. In contrast, the open unemployment rate shows a positive relationship with poverty, indicating that increased unemployment worsens the poverty situation. Meanwhile, the results of the minimum wage vary in different provinces, depending on the dynamics of the labor market and local economic policies. The implications of this study emphasize the importance of developing policies that support inclusive economic growth, increasing investment that has a direct impact on job creation, and strategies to reduce unemployment rates to tackle poverty more effectively.

Inisiatif Jurnal Ekonomi Akuntansi dan ManajemenVol. 4(2)
Muhammadiyah University of Surakarta (ID)
Openalex Percentile: Top 89%
Economic Growth and Fiscal Policies
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.