Analisis Pengaruh Belanja Pendidikan, Belanja Kesehatan, Tingkat Pendidikan, Derajat Kesehatan, dan Pengangguran Terhadap Indeks Keparahan Kemiskinan Kab/Kota Di Provinsi Jambi Tahun 2017-2021 - Gusmardi Gusmardi; Amri Amir; Muhammad Syafri (Universitas Batanghari, 2023)
This study aims to analyze the development of education spending, health spending, education level, health degree, unemployment and poverty severity index in regencies/cities in Jambi Province during 2017-2021, secondly to analyze the influence of education spending, health spending, education level, degree health, and unemployment on the poverty severity index in districts/cities in Jambi Province during 2017-2021, and thirdly to analyze policy implications related to the poverty severity index in districts/cities in Jambi Province during 2017-2021. The data used in this study is secondary data with a combined panel data type between Cross sections 11 Regencies/Cities in Jambi Province with Time series data for 2017-2021, research data originating from the Central Bureau of Statistics and the Ministry of Finance. This study uses panel data regression analysis and uses the eviews 12 analysis tool. Based on the results of a descriptive analysis of the variables Education Spending, Health Spending, Education Level, Health Degree, Unemployment and Poverty Severity Index, it was concluded that developments during the 2017-2021 period have fluctuated from year to year. Based on individual quantitative analysis, the variables of education level, health degree, and unemployment have a significant effect on the poverty severity index, while the education expenditure and health expenditure variables do not have a significant effect on the poverty severity index. Based on the quantitative analysis carried out, it was found that together the independent variables in the study had a significant effect on the independent variables, namely the poverty severity index with a probability of 0.0000 and R2 of 0.871104, which means that the poverty severity index can be explained by the model of 87.11 % while 12.89% is influenced by variables outside the model.