DISPARITAS EFISIENSI TEKNIS ANTAR SUB SEKTOR DALAM INDUSTRI MANUFAKTUR DI INDONESIA (1988-1998): Sebuah Analisis Makro dengan Penerapan Matode DEA - Perpustakaan UGM, i-lib ([Yogyakarta] : Universitas Gadjah Mada, 2002)
ABSTRACT This research uses one of many tools of analysis in empirical researches in economics, about Data Envelopment Analysis (DEA). DEA has been known in research methodology since years ago. This methode is used to develop of relatively levels of technical efficiency by macro. It is used to a ttemting technological progress or total factor productivity (TFP) with approach malmquist inclêx. To Analyzing disparity level of efficiency among sub-sectors this study uses coefficient variation ((CV) indicator. Finally, this study also employs. Pearson's correlation model in order to construct a correlation formula between efficiency and level and TFP. Estimation outcome shows that average growth of technical efficiency level in manufacture's industrial sectors, both with assumption of CRS and VRS or assumption with scale efficiency, as long as observation period, tends to stable. However, the growth of disparity of technical efficiency among sub-sectors tends to be positive. This indicator was signed with more and more declined in coefficient of variation in the beginning observation than the last observation. In another side, comparative level of the average growth of total factor productivity (TFP) in industrial manufacturing sectors didn't differ between two period of, analysis. This is evidence that the growth of TFP per sub-sectors as long as two period of analysis didn't change in the composition and level of growth. In while, the result of correlation method analysis shows the proof that in the beginning observation period didn't find correlation between the growth level of efficiency per sub-sector with the index of TFP growth. On the contrary, the last observation period, found a proof that there is a positive correlation significantly. Keywords: Technical efficiency -- disparity -- manufacturing industry, data