PEMODELAN JUMLAH PENDUDUK MISKIN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION
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Date
2021-01
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Abstract
Poverty is a condition of an individual’s inability to meet his basic needs. The number
of poor people in Indonesia in September 2019 was 24,79 million people or around
9,22%. This study uses a geographically weighted negative binomial regression model
to determine the factors that affect the amount of poverty in Indonesia in 2019.
Modeling the number of poor people in Indonesia using Poisson regression is
overdispersed, so to overcome it using geographically weighted negative binomial
regression. Based on the regression model used, the results show that the significant
variables are the open unemployment rate, the percentage of households that occupy
non-private houses, the percentage of illiteracy, and gross regional domestic product at
current prices per capita.
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Keywords
Poverty, geographically weighted negative binomial regression, Poisson regression, overdispersion, negative binomial regression