ANALISIS PENGARUH KETIMPANGAN DISTRIBUSI PENDAPATAN TERHADAP JUMLAH PENDUDUK MISKIN DI PROVINSI JAWA TENGAH PERIODE 2000 – 2007

Abstract

Poverty has always been a problem in every developing country, even in developed countries too well. In Indonesia, the high number of poor people influenced by various factors, including the existence of inequality in distribution of income between one province to another province. This study aims to analyze the effect of inequality income distribution, measured using the Gini Index and Index Williamson, of the number of poor people in Central Java from 2000 to 2007. One of the main causes of poverty in the Province Central Java is due to the imbalance in the distribution of income in each district / city, to reduce poverty levels The then Government of Central Java Province and Local Government should bias are working together to minimize differences in distribution gap income not to be too high from one district / city with a other. The type of data used are secondary data obtained from the Agency Statistics Center of Central Java Province. Tool that uses regression analysis linear regression and log linear with Number of Poor People Java Province Central as the dependent variable and two independent variables namely, the Gini Index and Williamson Index as measure inequality of income distribution. After being tested the deviation of the classical assumption test Multicollinearity, autocorrelation and Test Test heterocedastity, turn out indicate normally distributed data. The results show the value of data processing F amounted to 16.686 (probability = 0.006) and coefficient of determination (adjusted R2) amounted to 0.870. Through regression can be obtained from the results that the Total Population Poor province of Central Java have been significantly affected by the level of income inequality of income distribution as measured using the Index Williamson (with a value of 1.834) and the Gini index (with a value of 0.477). Then Williamson concluded that the Index is more influential than Gini Index.