aAkademi Teknologi Industri Padang, Kementerian Perindustrian, Padang, Indonesia, [email protected]
bLembaga Penyelidikan Ekonomi dan Masyarakat, Fakultas Ekonomi-Universitas Indonesia, Jakarta, Indonesia, [email protected]
ABSTRACT
Pulp and paper industry is one of developing-industries in Indonesia. But the industry still faces issues, such as shortage of pulpwoods and waste papers, deterioration of machines performance in old mills, and others that may affect performance of Indonesian pulp and paper industry. This research aims to determine the technical
eficiency and productivity level (as performance indicators) of Indonesian pulp and paper industry, and to determine the factors that may affect the eficiency and productivity. This study use irm-level panel data of
large and medium industries survey by Central Bureau of Statistics (BPS) year 2005 to 2009. The Analysis
is carried out in two stages. Firstly, estimating technical eficiency of irms in using their resources by Data
Evelopment Analysis (DEA) method. While productivity level calculated by Total Factor Productivy (TFP)
Malmquist index, and subsequently decomposed into two components : eficiency change and technological change. Secondly, determining the factors that may affect eficiency and productivity by ixed-effect data panel regression method. The empirical results show the overall average of technical eficiency in range 71% to 79%. Averagely productivity change is slightly increased and driven by technical change rather than technical eficiency. The eficiency is negatively affected by amount of employees, irm age, and import. While productivity is positively affected by wage and technical progress, and negatively affected by irm age and market concentration.
Suggestions that implicate to policies are also made for development of Indonesian pulp and paper industry.
Keywords: eficiency, productivity, indonesian pulp and paper industry, DEA, TFP malmquist Introduction
Indonesian economic structure has undergone
signiicant change over the past decades, which
the contribution of manufacturing sector to Gross Domestic Product (GDP) had overed contribution of agriculture sector. Manufacturing sector is one of sectors that prioritized by Indonesia government to drive economics growth.
Pulp and paper industry is one of developing industries in Indonesia. The pulp industry ranked number 9 and paper industry ranked number 8 in the world level, both industry ranked number 3 in Asia level. Ministry of industry[1]. Export of pulp product increased from 2005 to 2007 but decreased in 2008 and 2009 due to global economic crisis, but increased
in 2010. Export growth decreased signiicantly from
2006 to 2009 but increased in 2010. The pulp industry was still imported raw material which the growth reached 6% (compared to export growth that only 1%). While, export of paper product increased from 2005 to 2007 but decreased in 2008 to 2010. Export growth was negative in range the years 2008 – 2010 and the average export growth from 2006 to 2009 was 10%. Ministry of industry[1]. Paper industry was also still imported raw material which average import growth reached 9 % in range the years 2006 - 2010. Most of raw material for pulp industry (> 95%)
are pulpwood such as acacia, pines and eucalyptus, the rest are agriculture wastes such as bagasses and straws. Woodpulp demand of pulp industry is
fulilled by industrial tree plantation (hutan tanaman
industri, or HTI), bridging material (land clearing of HTI), logging wastes and natural forest . These HTI
and other materials will produce short ibers as raw materials for the pulp industry. However, long ibers
are still imported from overseas approximately 2,5 million tonnes per annum. While raw material of paper industry mainly consist of virgin pulp and waste paper, which the average national composition approximately 40% : 60%. Up to 2010, there were 14 pulp mills and 79 paper mills in Indonesia with capacity each 7,9 ton and 12,17 tonnes per annum. Majority of the mills are located in Sumatera, Java and East Kalimantan. Ministry of Industry [1]
In spite of Indonesian pulp and paper industry has shown an upward trend, but there are issues that could be barrier to development of the industry. Ministry of Industry released a Road Map of Paper Industry in 2009 that explained the weaknesses of Indonesian pulp and paper industry, such as low performance of old machines, limitation of pulpwood supply from HTI, and using of alternative raw material have not optimum yet, shortage of domestic waste papers to
fulilled demand of paper industry, high dependency to foreign mainly machines and process equipments,
and limitation of R&D 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 M3) and
pulpwood supply from industrial tree plantation (3,98
million M3) IWGFF [3]. It causes capacity of the pulp
industries increase slowly. The shortage of pulpwood
supply approximately reached 12,68 million M3 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 eiciency 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.Productivityis 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, Zk 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
We apply the methods of Fare et al.[18] to estimate
TFP indices without a priori speciication of the
underlying technology and producer behaviour. TFP
Malmquist approach accounts for both the shift of best practice and the change in relevant position of irm
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along the spectrum of best practice. The Malmquist
productivity index can be fully decomposed into
technical change, eficiency and scale change, so that
we could have an insight into the factor.
Fare et al. [18] measured productivity change
(TFPCH) of the irms at time t+1 and time t as equation
3. Next, TFPCH divided into two components
eficiency change (EFFCH) and technological change (TECHCH) as Equation 4. The equations (4) could be combined became equation 5. Next, Fare et al. [18] divided TFPCH became equation 6. We use software
DEAP 2.1 that developed by Coelli [19] to compute
DEA eficiency score and TFP Malmquist index.
2.3 Empirical Speciication
We employ econometrics approach speciically
regression model to determine factors may affect
eficiency and productivity. We specify the empirical speciication for eficiency (equation 7) and productivity (equation 8).
We use double-log model to analyse determinants
of eficiency and productivity growth. Where lnTE denotes technical eficiency (CRS and VRS), lnEMP
is number of employees include production workers
and others, lnPROD is irm productivity that proxied by labor productivity, lnAGE is irm age, lnCON is market concentration based on Herindahl-Hirschman
Index, lnWPL is wage per labor, TP is technical progress that obtained by TFP decomposition, PIMP is proportion of import material to total material, DLOC
is dummy of irm location (1 : located in industrial
park, 0 : otherwise), DEXP is dummy of export status (1 : export, 0 : non-export), DFINV is dummy of
foreign irm status (1: foreign irm, 0: others), and are
parameters being estimated. We used econometrics software EVIEWS 6.0 to estimated these regression model.
3. Data
The yearly data from 2005 to 2009 is obtained from yearly surveys of medium and large size
manufacturing irms conducted by the Central Bureau
of Statistic (BPS) Indonesia. Due to poor availability of statistical data before 2005, the present study covers only year 2005 to 2009. Some observations had to be removed from the data set because of missing
variables and obvious outliers. Discarding irms with incomplete data left 88 irms that for each sub-sector
were : (ISIC 21011) pulp 5 ; (ISIC 21012) culture
paper 24 ; (ISIC 21014) speciic paper 11 ; (ISIC
21015) industry paper 15; (ISIC 21016) tissue paper 20 ; and (ISIC 21019) other papers 13. The sample accounted for 65% of the total gross output and 60%
of total amount of irms in 2009. The sample used as a
realistic proxy of the industry.
The sample include four inputs standard (labor, capital, material , and energy) and one output (gross
total output) for calculating eficiency. Labor is the
total number of employees is used instead of man hours due to the unavailability of the data, capital is
other expenses of irm that used as proxy for capital
due to unavailability of the data, material is the total
value of the material used by irms both import or
local material, and energy is total cost value include fuel, lubricants and electrical generator. While gross
total output is the total value of a irm’s output in a speciic year. All variables, except labor, are delated
in 2000 thousand rupiah price. While data of variables
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