One of the key goals of the Indonesian government is to lower the proportion of poor people in the country. The government has tried to accelerate the poverty rate with several empowerment programs to increase productivity to increase the economic capacity of the community. Low-income levels that make it impossible to achieve essential living standards like health and education are what constitute poverty. Efforts to reduce poverty are in line with one of the Sustainable Development Goals (SDGs), which is a global agenda to improve people’s economic welfare. The first goal of the SDGs is to end poverty in all its forms everywhere, which means that overcoming the problem of poverty is the main goal of other goals that have been agreed. Based on the Law of the Republic of Indonesia Number 25 of 2000 concerning national development programs, the government’s efforts to reduce poverty in Indonesia are divided into two main parts, namely, protecting groups of people experiencing temporary poverty and helping people experiencing chronic poverty by empowering them and preventing new poverty. The government has made various efforts to reduce poverty in Indonesia.
However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. By comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike’s information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. So, the results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.
Author: Suliyanto, Drs., M.Si.
Journal: Poverty Modeling in Indonesia: A Spatial Regression Analysis
