The Poverty Index Mapping in the Barlingmascakeb Area in 2019
Poverty is one of the serious problems in the national development process in Indonesia. Development basically aims to lead to a better situation and create a just and prosperous society. In the concept of Population-Centered Development, populations are not only treated as “objects” but also “subje...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN FR |
| Publicado: |
EDP Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a5aef17ddbf14761979226d6fbc6f178 |
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| Sumario: | Poverty is one of the serious problems in the national development process in Indonesia. Development basically aims to lead to a better situation and create a just and prosperous society. In the concept of Population-Centered Development, populations are not only treated as “objects” but also “subjects” of development. However, the incessant development carried out in various regions has not fully brought the welfare of the population. One of them is Central Java province which is a poverty pocket in Indonesia with a poverty percentage of 10.80 in 2019. Especially in the Barlingmascakeb area, namely Banjarnegara, Purbalingga, Banyumas, Cilacap, Kebumen, in the same year the percentage of poverty was relatively higher, respectively 14.76 percent, 15.03 percent, 12.53 percent, 10, 73 percent, and 16.82 percent. This study aims to examine the poverty index spatially in the Barlingmascakeb area in 2019 and find out the dominant indicators that determine the poverty index as one of the evaluation materials for the development process in the region. The data used to measure the poverty index is to use indicators on several dimensions of the Population-Centered Development Index (PCDI). The participation dimension is represented by the EPR (Employment to Population Ratio) indicator and the level of labor force participation rate; the dimension of partiality represented by the percentage of RREB (Regional Revenue and Expenditure Budget) for poverty reduction and other indicators related to poverty, namely income per capita. The spatial distribution of the poverty index is known through Geographic Information System (GIS) software. |
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