PENGARUH ANGKA HARAPAN HIDUP, ANGKA HARAPAN LAMA SEKOLAH DAN PERTUMBUHAN EKONOMI TERHADAP JUMLAH PENDUDUK MISKIN KABUPATEN/KOTA DI PROVINSI SUMATERA SELATAN TAHUN 2016-2020

This study discussed The Influence of Life Expectancy, Expected Length of Schooling and Economic Growth on the Number of Poor People in Regencies/Cities in South Sumatra Province in 2016-2020. The data collection technique used in this research was quantitative data and the type of secondary data was data obtained from the Central Statistics Agency (BPS). The analytical method used was Panel Data Regression. The results showed that the partial test for the variable life expectancy (X1) obtained a Tarithmetic value greater than T table or 3.371913 > 1.66388, then Ho was rejected, meaning that life expectancy (X1) significantly had a positive influence on the number of poor people (Y) and the variable of Expected Length of Shooling (X2) obtained a T count value smaller than T table or -3.663001 < -1.66388, then Ho was rejected, meaning that the Expected Length of Shooling (X2) had a significant negative influence on the Number of Poor Population (Y) and the variable Economic Growth (X3) obtained the value of T count was smaller than T table or -2.408051 < -1.66388, then Ho was rejected, meaning that Economic Growth (X3) had a significant negative influence on the Number of Poor Population (Y). Based on the simultaneous test, the calculated F value > F table or 5112.887 > 2.72, so it can be concluded that there was a significant influence of life expectancy (X1), Expected Length of Schooling (X2), economic growth (X3) on the number of poor people (Y) Districts/Cities in South Sumatra Province 2016-2020 simultaneously, in other words the regression model was correct. The value of the coefficient of determination obtained the Adjusted R-Squared value of 0.999136. This showed the percentage of the influence of Life Expectancy (X1), Expected Length of Schooling (X2), Economic Growth (X3) on the Number of Poor Population (Y) of 99.91% while the remaining 0.09% was influenced or explained by other variables which were not included in the research model.

Keywords : Life Expectancy, Expected Length of Schooling, Economic Growth, and Number of Poor Populations.