Implementation of Negative Binomial Regression to Address Overdispersion in the Analysis of Unemployment Determinants in Sulawesi, 2023
DOI:
https://doi.org/10.24252/msa.v13i2.61738Keywords:
Unemployment, Overdispersion, Negative Binomial RegressionAbstract
This study focuses on the implementation of negative binomial regression as a solution to overcome the problem of overdispersion in the analysis of determinants of unemployment on the island of Sulawesi in 2023. Unemployment is not only viewed as a statistical phenomenon or economic issue, but also as an important indicator that reflects social welfare and the success of development in a region. Sulawesi Island, with its growth in the agricultural and industrial sectors, faces serious challenges in reducing unemployment rates, which have the potential to cause regional disparities if not addressed appropriately. This study aims to develop an appropriate negative binomial regression model to overcome overdispersion and identify the main factors that influence the unemployment rate. The method used is negative binomial regression analysis of district/city unemployment data in Sulawesi Island, which is discrete and shows symptoms of overdispersion. With significant variables including population size, Human Development Index (HDI), and the number of job placement or fulfillment services. These three factors have been proven to have a significant effect on the number of unemployed people in Sulawesi Island in 2023.
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