Modeling of Regency/City Unemployment Rates in Java Island Using Multilevel Binary Logistic Regression

Dhea Dewanti; Kristuisno Martsuyanto Kapiluka; Febryna Sembiring; Ajeng Bita Alfira; Anang Kurnia

doi:10.20956/j.v21i1.35584

Authors

Dhea Dewanti IPB University
Kristuisno Martsuyanto Kapiluka IPB University
Febryna Sembiring IPB University
Ajeng Bita Alfira IPB University
Anang Kurnia IPB University

DOI:

https://doi.org/10.20956/j.v21i1.35584

Keywords:

Unemployment Rate, Binary Logistic Regression, Multilevel, Hierarchical Data

Abstract

Multilevel binary logistic regression analysis is a development of logistic regression for hierarchical data structures. Hierarchical data is data from a population that has levels. This research examines the relationship model of Life Expectancy, Mean Years of Schooling, Expected Years of Schooling, Regency/City Minimum Wage as explanatory variables at level 1 (Regency) and Gross Regional Domestic Income (GRDP) as an explanatory variable at level 2 (Provincial) against Unemployment Rate (UR) as a response variable. The research results show that Life Expectancy and Minimum Wage at level 1 and GRDP at level 2 have a significant influence on district/city TPT on Java Island in 2022

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