SPATIAL REGRESSION AND SPATIAL AUTOCORRELATION ANALYSIS OF THE DETERMINANTS OF POVERTY IN INDONESIA IN 2022
DOI:
https://doi.org/10.31092/jia.v7i2.2318Abstract
Poverty remains a solemn challenge in Indonesia although the country has made significant progress in recent decades. Even though the government's efforts and various poverty alleviation programs have been carried out, the majority of Indonesia’s population lives below the poverty line. This research aims to examine variables that influence Indonesia's poverty rate by Province in 2022 using spatial regression analysis and spatial autocorrelation. The secondary data used comes from 34 Provinces in Indonesia and is obtained based on the results of publications by the Central Statistics Agency (BPS). The dependent variable is the percentage of poor people (P0), while the independent variables include average lenght of school (RLS), life expectancy (AHH), open unemployment rate (TPT), and Gini Ratio. The analytical methods used include descriptive analysis, multiple linear regression analysis, and spatial regression analysis. It is hoped that this research can provide relevant policy recommendations for developing poverty alleviation policies in Indonesia.
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