<![CDATA[This study aims to analyze and determine the effect of the number of BPJS participants, especially PBI, education, UMK, and per capita income, on poverty in six Cities/Regencies of the Former Kedu Residency in 2015 – 2020. The method used is quantitative, where data acquisition is secondary data through various sources in time series and cross-section data. The cross-section data consists of six cities/districts, while the time series data is from 2015 to 2020. Technically, the data is analyzed using panel data regression with the Fixed Effect Model approach as an excellent model to use. The results show that the variable number of BPJS participants, especially PBI, positively affects poverty in six Cities/Regencies of the Former Kedu Residency in 2015-2020; the UMK and income per capita variables harm poverty. Meanwhile, the education variable measured by the school participation rate parameter does not affect poverty.]]>
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