Identifikasi peubah penciri rumah tangga miskin dan rumah tangga yang sedikit di atas garis kemiskinan [J.Il.Pert.Indo]

Abstract

Poverty still becomes main problem in this country. The categorization of the poor or not poor household based on the poverty line is difficult to be performed in practice. Therefore, it is needed to find other variables that could be used to characterize poor household. In addition, because the households that almost poor could become poor easily, it is also needed to analyze the probability of these household become poor household. This research use Susenas Data Kor 2003 from Badan Pusat Statistik for Jawa Barat province which includes explanatory variables: house physical condition, protein consumption, type of fuel/energy, ownership of asset, and also head of household characteristic. Result from logistic regression analysis shows some poor household characteristics: floor area per capita <= 8 m2, there's no closet, final place of feces exile is not tank, closet type is not goose neck, do not consume food with high protein, don't have motor vehicle or saving, electrics do not use gauge, head of household is a woman, amount of household members >= 5, head of household's age > 55 years. For the urban area, another characteristics are: don't have farmland, do not use gas, do not use electrics from PLN, using firewood, head of household's work status is erratic, head of household's education maximum is elementary school. For rural area, another characteristics are: house is not property of them selves; most of wall not made by cement; don't have precious goods, store, or productive asset; do not use kerosene. Ordinal logistic regression obtain model that explain relation between household status and its independent variables. However this model can not explain probability of almost poor household become poor household, because the household exactly have higher opportunity to be categorized as not poor. Probability of almost poor household categorized as poor household only 9.59% for urban area and 11.79% for rural area.