TECHNICAL EFFICIENCY IN THE BASIC INDUSTRY LISTED ON INDONESIA STOCK EXCHANGE ( IDX): A STOCHASTIC FRONTIER APPROACH 7th International Conference on Data Envelopment Analysis, 10 – 12 July, 2009 Philadelphia, USA Presented by T.Handono Eko Prabowo Faculty of Economics, Sanata Dharma University, Yogyakarta - Indonesia

Background of the Study The competitive environment is putting tremendous pressure on the basic industry. The manufacturing sector contributes the highest contribution to Indonesian GDP growth from the year 2001 to date. The Basic industry is one of the most important sectors listed on the IDX.

Objectives of the Study To model performance based on the DEA model on evaluating efficiency using firm’s traditional inputs and an output To determine the stochastic frontier association of total sales to labor, inventory, fixed assets, and capital. Test whether age, size, market share, and time period have effects to technical inefficiency of the basic industry.

Significance of the Study The study attempts to have significant and original contributions to the performance measurement field by modeling performance measurement for the IDX-listed basic industry for the first time. Applications: DEA, SFA, and Cobb Douglas. Previous studies are focus on SMEs, agriculture, Banking, and Private & Public sector. The test period covering 6 years (2000 – 2005), 47 basic industry firms listed on IDX (282 pooled data).

The result of this study will help the following in the performance of their functions: The SEC Investors (stock holders) Management Creditors The findings of the study open new areas of future research.

BASIC INDUSTRY LISTED ON JAKARTA STOCK EXCHANGE (JSX ) (47 firms) SFA DERIVED MODELS FOR PERFORMANCE MEASUREMENT OF INDONESIA BASIC INDUSTRY Conceptual Framework Selected Variables Data Processing Output total sales Inputs: labor, inventory, fixed assets, capital Z-variables age, size, market share, manufacturing classifications, and time period Derived DEA

Scope and Limitations of the Study The study covers 47 basic industry-firms listed on Indonesia Stock Exchange (IDX) from 2000–2005. The time period ( ) is chosen because of economic and political stability (interest rate, exchange rate, inflation).

Ho1: The technical efficiency of the basic industry- firm is constant over the period. Ho2: The returns to scale performance of each the basic industry-firm are the same over the test period. Ho3: There is no source of inefficiencies identified in the slack performance of the basic industry-firm. Ho4:There is no significant association of productive efficiency total sales to labor, inventory, fixed assets, and capital. Ho5:There is no significant effect of firm’s age, size, market share, and time period to technical inefficiency of the basic industry. Hypotheses

This research design is descriptive and quantitative in nature and focuses on 3 major aspects. To model performance based on the DEA model on evaluating efficiency using firm’s traditional inputs and an output To determine the stochastic frontier association of total sales to labor, inventory, fixed assets, and capital, and To test whether age, size, market share, and time period have effects to the technical inefficiency of the basic industry. RESEARCH METHODOLOGY

Table 1: BASIC INDUSTRY Classifications, Sub-Classifications & Number of Companies NoClassificationsSub-ClassificationsNumber of companies 1Basic Industry Cement Stone, Clay, Glass, Concrete products Metal and Allied Products Chemical and Allied Products Plastic and Glass Product Lumber and Wood Products Paper and Allied Products Adhesive Total Companies47 Source: Indonesian Capital Market Directory (ICMD), 2006.

DEA Variables The relative efficiency by which the basic industry firms utilize their inputs is reflected on the output factor (total sales) they have produced. These variables will be analyzed through the input- oriented DEA model. This study used four (4) inputs: labor, inventory, fixed assets, and capital (Kathuria (2001);Wei Koh, et al.(2004); and Mojo (2006)). The outputs used is total sales (Nakajima et al. (1998) and Chirwa (2001)). Data Variables

SFA Variables The stochastic is modeled by an output variable: total sales (Nakajima et al. (1998) and Chirwa (2001)) The inputs are: labor, inventory, fixed assets, and capital (Chirwa (2001); Wei Koh, et al.(2004); and Mojo (2006)) The study used firm-specific variables: age, size, market share, and time period (Lundvall & Battese (2000); Erzan and Filiztekin (2005))

These variables are chosen based on the assumption that firms’ performance is multidimensional in nature and that there exist a various indicators of firms’ performance. The study also considered the existing literatures especially in the basic industry. Data are adjusted for inflation, using Consumer Price Index (CPI) with base year as 1993 price, to obtain real values. Variables Con’t …

The VRS DEA model (input-oriented) can be written as: (Chen, 2004) Minimize : Subject to : Research Models DEA Approach (1)

DEA Slack based model (Coelli et al., 2005) Minimize λ,OS,IS Subject to (2)

Stochastic Frontier Analysis (SFA) Approach The stochastic frontier production function for panel data can be written as (Battese and Coelli, 1995): (3) Yit denotes the production at the t-th observation (t = 1,2, ……..,T) for the i-th firm (i = 1,2, ……,N); β is a vector of unknown parameters to be estimated; is a vector of values of known functions of inputs of production and other explanatory variables associated with the i-th firm at the t-th observation; =is non-negative random variable, = random error Research Models

This study adopts a trans-log production function to characterize the production frontier facing the basic industry listed on IDX. SFA con’t... The Equation (3) can be expressed in log-linear form to give: (4) Yit represents total sales of the basic industry firm i-th at the t-th year of observation. I = inventory, F = fixed Assets, K = capital, =is non-negative random variable, = random error

SFA con’t … This study follows the Battese and Coelli’s (1995) representation for technical inefficiency effects. The technical inefficiency effect,,in the stochastic frontier model (3) could be specified in the Equation (5): = (5) The technical inefficiency effects (5) in this study are assumed to be defined by (6): (6) The random variable is defined by the truncation of the normal distribution with zero mean and variance.

DEA Results

*) Critical values are obtained from the appropriate chi-square distribution, except for the test of hypothesis involving for technical inefficiency effects (Kodde and Palm, 1986) SFA Results Null Hypotheses, HoLR Value Critical value *) Decision (Cobb-Douglas function) Reject (no inefficiency effects) Accept Table 5: Generalized Likelihood-ratio tests of Null Hypotheses for Parameters in the Stochastic Frontier Production function for Total Sales: Basic Industry (2000 – 2005)

The Maximum-Likelihood Estimates of Parameters of the Stochastic Frontier Production function for Total Sales: Basic Industry (2000 – 2005) *) Significant at 5 % probability level (p< 0.05) **) Significant at 1 % probability level (p < 0.01)

VariablesParametersCoefficient Estimates t-ratio ***) Critical value is for 7 d.f as for Table 1 of Kode and Palm (Coelli and Battese, 1998) for technical inefficiency effects. *) Significant at 5 % probability level (p< 0.05) **) Significant at 1 % probability level (p < 0.01)

The study shows that technical efficiencies of the basic industry firms are constant and the returns to scale performance of each firm are the same over the test period is rejected. The study also indicates the existence of output slacks (output deficits) and input slacks (input wastages) in the basic industry’s operation. All four inputs appear to be the major determinants of the basic industry growth. Inventory is the single most important input with an input elasticity of The average technical efficiency (mean TE) for the basic industry is The combined approaches of parametric (SFA) and non-parametric (DEA) may lead to robust and bias-free analysis of the basic industry performance. Conclusion

RECOMMENDATIONS The SEC; Investors (stock holders); Management; and Creditors. The efficiency estimates should not be interpreted as being “definitive measures of inefficiency”. By contrast, a range of efficiency scores may be “developed and act as a signaling device” rather than as a conclusive statement. DIRECTIONS FOR FUTURE RESEARCH An extension this study could be to analyze “all sectors” or “other sectors” listed on Indonesia Stock Exchange (IDX). In redesigning the possible studies mentioned above, variables such as the market capitalization and total assets could be considered.