Data Envelopment Analysis DEA

ISBN : 978-602-17761-0-0 193 © 2013 Published by Center for Pulp and Paper through REPTech2012 and limitation of RD funds. Moreover uncertainty of business climate, dificulties in obtaining clear and clean HTI, licences, and others. Ministry of Industry [2]. Ministry of Forestry also found an imbalance between pulpwood demand 17,91 Million M 3 and pulpwood supply from industrial tree plantation 3,98 million M 3 IWGFF [3]. It causes capacity of the pulp industries increase slowly. The shortage of pulpwood supply approximately reached 12,68 million M 3 tonnes per annum. All of these factors could have effeced the overall performance of Indonesian pulp and paper industry. Eficiency and productivity indices have been accepted by the economists as a standard tool for evaluating the economic performance within irms or industries A comprehensive measurement of eficiency and productivity in the pulp and paper industry is of great importance to both policy makers and businessmen. Several past studies had investigated the e iciency and productivity in manufacturing industry. For instances, Nyrud [4] in Norwegia, Zheng [5] in China, Ismail [6] in Malaysia, Margono [7], Alviya [8], and Kadarsyah [9] in Indonesia. While past studies of performance in the pulp and paper industry for instance, Hseu and Shang [10] compared eficiency and productivity of the industry in OECD countries, while in Indonesia for instances, Situmorang [11] investigated demand and supply side, Putra [12] analysed structure, behaviour, and performance based on SCP theory, Primadhita [13] investigated impact of liberalization to the industry performance. To the best of our knowledge, analysis of eficiency and productivity have previously not been conducted on Indonesian pulp and paper industry. On this study we employ non-parametric method Data Envelopment Analysis DEA to estimate technical eficiency and Total Factor Productivity TFP Malmquist to estimate productivity change. We also use econometric approach to ind out determinants or factors that can contribute to the eficiency and productivity. This paper is organized into ive sections. The following sections are method, data, analysis of the results, conclusion and policy implications.

2. Method

The terms eficiency is referred to concept of Farrell [14]. He divided eficiency become technical eficiency and allocative eficiency. The technical eficiency deined as the ability of a production unit to produce output with minimum input input-oriented. While allocative eficiency deined as the using of inputs in optimum proportions for given prices and production technology. In this study we focus to technical eficiency to see how eficient irms of the industry in using their resources. Coelli et al. [15] deined productivity of a irm as the ratio of the output s that it produces to the input s that it uses. The terms eficiency and productivity often used interchangeably, but actually they are not precisely same things. Coelli et al.[15] concluded that a irm may be technically eficient but may still be able to improve its productivity by exploiting scale economies. Productivity is often referred to total factor productivity TFP that is a productivity measure involving all factor of production.

2.1 Data Envelopment Analysis DEA

In this study, we apply Data Envelopment Analysis DEA method of Charnes et al. [16] and Banker et al. [17] to estimate eficiency level. The DEA method is a non-parametric approach that not assume any production function. The DEA methodology yields estimate of a irm-speciic eficiency index in range 0 to 1, where a irm with an index of 1 means operate at its minimum input. The DEA model input oriented by Charnes et al.[16] is : Where k is irm being evaluated, Z k is eficiency of DMU, v and u each are weighted input and output value being estimated, j is the irst irm to -N N = number of irms, i is the irst input to - m m = number of input, and r is the irst output to - s s = number of output. The equation 1 is a DEA input oriented model with assume constant return to scale CRS condition the irms optimizely operate. The DEA- Variable Return to Scale VRS model is developed by Banker et al. [17] to accomodate condition where the irms does not optimizely operate. The DEA VRS model input oriented by Banker et al. [17] is: VRS model used to increase estimation validity of technical eficiency by scale eficiency SE. Scale eficiency calculated as ratio technical eficiency CRS to technical eficiency VRS.

2.2 TFP Total Factor Productivity Malmquist Index