Analysis of Poverty Levels on the Island of Java Based on Districts in 2020–2023

Poverty on the island of Java is still a serious problem that is influenced by various dimensions, both education, health, as well as economic factors and regional development. This study aims to analyze the factors that affect the poverty rate in five provinces on the island of Java during the 2020–2023 period. The data used is secondary panel data from the Central Statistics Agency (BPS) covering 84 districts/cities. The analysis method used panel data regression with the Chow test and the Hausman test to determine the best model. The estimation results show that the Fixed Effects Model (FEM) model is the most suitable model. Simultaneously, the variables Life Expectancy Rate (AHH), Average School Length (RLS), Construction Cost Index (IKK), Building Village Index (IDM), Fiscal Independence Index (IKF), and Indonesian Disaster Risk Index (IRBI) have a significant effect on poverty levels. Partially, AHH, RLS, IDM, and IKF had a significant negative effect, while IKK and IRBI had no significant effect on poverty. These findings affirm the importance of improving the quality of health and education, sustainable village development, and regional fiscal independence in reducing poverty.

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Journal
SHILAP Revista de lepidopterología
Published
2026-02-01
Primary Topic
Economic Growth and Fiscal Policies
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article

Analysis of Poverty Levels on the Island of Java Based on Districts in 2020–2023

Sabrina Laura Sananta Sananta, Muhammad Arif
SHILAP Revista de lepidopterología
Economic Growth and Fiscal Policies
article

Analysis of Poverty Levels on the Island of Java Based on Districts in 2020–2023

Sabrina Laura Sananta Sananta, Muhammad Arif
article en

Abstract

Poverty on the island of Java is still a serious problem that is influenced by various dimensions, both education, health, as well as economic factors and regional development. This study aims to analyze the factors that affect the poverty rate in five provinces on the island of Java during the 2020–2023 period. The data used is secondary panel data from the Central Statistics Agency (BPS) covering 84 districts/cities. The analysis method used panel data regression with the Chow test and the Hausman test to determine the best model. The estimation results show that the Fixed Effects Model (FEM) model is the most suitable model. Simultaneously, the variables Life Expectancy Rate (AHH), Average School Length (RLS), Construction Cost Index (IKK), Building Village Index (IDM), Fiscal Independence Index (IKF), and Indonesian Disaster Risk Index (IRBI) have a significant effect on poverty levels. Partially, AHH, RLS, IDM, and IKF had a significant negative effect, while IKK and IRBI had no significant effect on poverty. These findings affirm the importance of improving the quality of health and education, sustainable village development, and regional fiscal independence in reducing poverty.

SHILAP Revista de lepidopterología
Muhammadiyah University of Surakarta (ID)
No poverty
Openalex Percentile: Top 68%
Economic Growth and Fiscal Policies
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