Meylita Sari

PENERAPAN GENERALIZED LINEAR MODEL DALAM MENANGANI OVERDISPERSI PADA DATA PENGANGGURAN DI INDONESIA

Introduction

penerapan generalized linear model dalam menangani overdispersi pada data pengangguran di indonesia. Tangani overdispersi data pengangguran di Indonesia dengan Generalized Linear Model (GLM) & Poisson Generalized Inverse Gaussian Regression (PGIGR). Analisis faktor sosio-ekonomi utama.

Abstract

Unemployment remains a major problem in Indonesia. In 2023, the open unemployment rate was recorded at 5.32%, or approximately 7.86 million people. Low absorption of productive-age workers and limited job opportunities have led to a potential increase in unemployment in several regions. This situation requires in-depth analysis to reduce the increase in the open unemployment rate. Various socio-economic factors in Indonesia influence the unemployment rate and can be analyzed using the Generalized Linear Model (GLM) framework. Because unemployment data is count data, the approach used is Poisson Regression. This model has the basic assumption of equidispersion, meaning that the variance is equal to the mean. However, observations indicate that the variance is greater than the mean, resulting in overdispersion. To address this, a GLM development with an additional dispersion parameter, namely Poisson Generalized Inverse Gaussian Regression (PGIGR), was used. This model was chosen because it can capture greater data variation and represent factors that influence the unemployment rate. The results of this study indicate that the number of unemployed between provinces in Indonesia is influenced by the variables of Provincial Minimum Wage, GRDP Growth Rate on a Constant Basis, Literacy Rate, and TPAK, with an AIC value of 33,577.45.

Review

This study presents a timely and relevant analysis of unemployment data in Indonesia, a persistent socio-economic challenge. The authors effectively identify the inherent characteristics of unemployment figures as count data and appropriately initiate their analysis within the Generalized Linear Model (GLM) framework using Poisson Regression. A significant strength of this work is the explicit recognition and methodological response to the common problem of overdispersion in count data, where the observed variance exceeds the mean. By moving beyond the restrictive assumptions of standard Poisson models, the paper proposes and implements Poisson Generalized Inverse Gaussian Regression (PGIGR), which is stated to be capable of capturing greater data variation, thereby offering a more robust and accurate modeling approach. Methodologically, the choice to address overdispersion is commendable and crucial for deriving reliable inferences from count data. The utilization of Poisson Generalized Inverse Gaussian Regression (PGIGR) represents an interesting and potentially powerful advancement over more standard techniques like the Negative Binomial model, particularly if it indeed offers superior flexibility in handling diverse dispersion patterns. However, the abstract could benefit from a brief justification comparing PGIGR to other established overdispersion models within the GLM family, clarifying its specific advantages or theoretical underpinnings that make it uniquely suited for this application. Furthermore, given the analysis is conducted on inter-provincial data, the potential presence of spatial autocorrelation, which could influence the dependencies between provincial unemployment figures, is an aspect that an expert reviewer would expect to be considered or at least acknowledged, even if not directly modeled in this initial study. The findings from this research are impactful, highlighting that Provincial Minimum Wage, GRDP Growth Rate, Literacy Rate, and Labor Force Participation Rate (TPAK) are significant factors influencing the number of unemployed across Indonesian provinces. These insights offer valuable guidance for policymakers aiming to mitigate unemployment. The reported AIC value serves as a measure of model fit, and while it indicates a quantitative assessment, a discussion on the interpretation of the magnitude and direction of the effects of these variables, alongside possibly comparing the AIC with alternative models (e.g., a standard Negative Binomial or Quasi-Poisson model), would further strengthen the results. Future research could expand upon this foundation by exploring the temporal dynamics of these factors, incorporating spatial modeling techniques to account for geographical dependencies, and investigating additional socio-economic or demographic variables that might influence provincial unemployment rates.

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