Dynamic Panel Data Modeling Indonesian Poverty Level

Abstract

Poverty is one of several issues that require the government's attention in Indonesia. This is because it has an impact on society's social economy. This study was conducted to identify and analyze inconsistencies or endogenous and exogenous variables that influence poverty levels in Indonesia. A descriptive quantitative analytic methodology was applied using data from secondary sources that was gathered from the Indonesian Central Statistics Agency Indonesia over ten years, from 2013 to 2022. The model used in this study is dynamic panel data regression analysis based on Arellano-Bond's Generalized Method of Moment (GMM) method. First-Difference (FD) GMM and System (SYS) GMM are the two best models. To ascertain both immediate and long-term consequences, GMM develops a model that is impartial, reliable, and effective. The study's conclusions indicate that the short- and long-term relationships between poverty levels and the index of human development have been significantly affected negatively. Exports significantly lower poverty rates both immediately and over time. Both the short- and long-term levels of poverty are significantly improved by imports. The prob value or p-value significance level alpha ( ) = 5% indicates that this has a significant effect. It is hoped that the research findings will be used as reference material for future macroeconomic developments in order to overcome poverty alleviation by focusing on research weaknesses and limitations.