Suchergebnisse

Abstract: This study aims to analyze how the influence of infrastructure availability, socioeconomic conditions, and the effect of location on poverty levels. The descriptive analysis is used to give a general description of poverty by using thematic charts and maps. The poverty map is analyzed by spatial autocorrelation of poverty levels by using a Moran Scatterplot and the Local Indicators of Spatial Association (LISA) Map. The results of the study indicate the existence of spatial linkages to poverty. The Increasing of other variables outside the model in neighboring regions will increase the level of poverty in a region. The infrastructures of road extension, clean water infrastructure, economic growth, quality of education, and health have a significant influence on the level of poverty, while the percentage of satisfactory sanitation did not demonstrate to affect the significant effect on poverty. The conclusion is that the level of poverty in the provinces of Central Java and Yogyakarta has an irregular distribution and a clustered spatial pattern.

This study aims to examine the determinants of working children based on individual and environmental factors in urban and rural areas. The study draws on secondary data from various sources, including the March 2019 National Socio-Economic Survey (Susenas), the Indonesia Poverty Profile 2019, the Indonesian Education Statistics 2019, and the Indonesian Child Profile 2020. A multilevel binary logistic regression model was used as the analytical method. According to the study's findings, children have a greater proclivity to work in rural areas than in urban areas. The study's findings also show that children who smoke have a higher proclivity in both urban and rural areas. Further research indicates that individual factors, such as household size as measured by the number of household members, have a more significant influence on the determinants of children working in urban areas. Meanwhile, environmental factors significantly influence the determinants of children working in rural areas, precisely the percentage of children working in the informal sector and the school participation rate of 13-15 years old. The findings show that the determinants of working children in urban and rural areas are similar on an individual level but differ on an environmental level. Based on these findings, the government and related parties must encourage the reduction of smoking behavior in children in urban and rural areas to reduce the number of working children. Accelerating the decline in working children, particularly in rural areas, can increase school participation through improved educational quality and facilities.