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

E-mail: thep_phd@yahoo.com

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 FrameworkSelected Variables Data Processing

Outputtotal sales

Inputs:labor, inventory,

fixed assets, capital

Z-variablesage, 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 (2000-2005) 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

No Classifications Sub-Classifications Number of companies

1 Basic Industry CementStone, Clay, Glass, Concrete productsMetal and Allied ProductsChemical and Allied ProductsPlastic and Glass ProductLumber and Wood ProductsPaper and Allied ProductsAdhesive

33

116

11553

Total Companies 47

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 :

0

io

n

jijj x

1

mi ........,2,1

ro

n

jrjj yy

1

sr ........,2,1

11

n

jj

0j nj ........,2,1

iox and roy are respectively the ith input and rth output for a DMUo under evaluation.

Each DMU has a set of s output measures, rjy ( sr ........,2,1 ), and a set of m input

measures, ijx ( mi ........,2,1 ).

Research ModelsDEA Approach

(1)

DEA Slack based model (Coelli et al., 2005)

Minimizeλ,OS,IS ),'1'1( ISNOSM

Subject to 0 OSQqi

,0,0,0

0

ISOS

ISXxi

OS is an 1Mx vector of output slacks, IS is a 1Nx vector of input slacks, and M1 and N1 are 1Mx and 1Nx vectors of ones, respectively. Note that in this second-stage LP, θ is not a variable, its value is taken from the first-stage results. This second-stage LP must be solved for each of the I firms involved.

(2)

Stochastic Frontier Analysis (SFA) Approach

The stochastic frontier production function for panel data can be written as (Battese and Coelli, 1995):

)exp( itititit UVxY (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

itx

itU itV

Research Models

itit UV

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 itU itV

)(lnln)ln(lnlnlnlnln 62

543210 itititititititit ILLKFILY

)(lnln)ln()(lnln)(lnln 102

987 ititititititit FIIKLFL 2

14132

1211 )ln()ln(ln)ln()(lnln itititititit KKFFKI

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):

itU

itU = itit Wz (5)

The technical inefficiency effects (5) in this study are assumed to be defined by (6):

itit WTimeperiod )(4 (6)

itWThe random variable is defined by the truncation of the normal distribution with zero mean and variance.

)()()( 3210 itititit eMarketsharSizeAgeU

DEA ResultsFirm Code crste vrste scale RTS

1 INTP 0.363 0.376 0.967 drs

2 SMCB 0.325 0.343 0.947 drs

3 SMGR 0.256 0.389 0.658 drs

4 ARNA 0.304 0.413 0.735 irs

5 IKAI 0.279 0.706 0.395 irs

6 MLIA 0.253 0.259 0.978 drs

7 ALMI 0.330 0.332 0.993 irs

8 BTON 0.213 0.978 0.218 irs

9 CTBN 0.298 0.304 0.979 irs

10 INAI 0.230 0.241 0.954 irs

11 JKSW 0.064 0.321 0.200 irs

12 JPRS 0.497 0.616 0.806 irs

13 LMSH 0.556 0.915 0.608 irs

14 LION 0.217 0.252 0.862 irs

15 PICO 0.221 0.266 0.831 irs

16 TBMS 1.000 1.000 1.000 crs

17 TIRA 0.280 0.289 0.968 irs

18 AKRA 1.000 1.000 1.000 crs

19 BUDI 0.717 0.754 0.951 irs

20 CLPI 1.000 1.000 1.000 crs

21 LTLS 0.362 0.367 0.986 drs

22 SOBI 0.658 0.683 0.962 irs

23 UNIC 1.000 1.000 1.000 crs

24 AKPI 0.400 0.412 0.969 irs

25 AMFG 0.227 0.230 0.985 irs

26 APLI 0.335 0.432 0.774 irs

27 BRNA 0.352 0.365 0.965 irs

28 DYNA 0.249 0.252 0.990 irs

29 FPNI 0.345 0.391 0.881 irs

30 LMPI 0.329 0.432 0.761 irs

31 LAPD 1.000 1.000 1.000 crs

32 SIMA 0.367 0.432 0.850 irs

33 SMPL 0.301 0.352 0.855 irs

34 TRST 0.593 0.640 0.926 irs

35 BRPT 0.187 0.190 0.986 drs

36 DSUC 0.353 0.353 0.998 irs

37 SULI 0.266 0.266 1.000 crs

38 SUDI 0.237 0.245 0.967 irs

39 TIRT 0.246 0.250 0.984 irs

40 FASW 0.614 0.616 0.995 irs

41 INKP 0.649 1.000 0.649 drs

42 TKIM 0.545 0.675 0.807 drs

43 SPMA 0.260 0.278 0.937 irs

44 SAIP 0.555 0.577 0.963 irs

45 DPNS 0.330 0.354 0.932 irs

46 EKAD 0.440 0.458 0.961 irs

47 INCI 0.381 0.568 0.671 irs

Mean 0.425 0.502 0.868

INPUT SLACKS OUTPUT SLACKS

Firm Code (L) (I) (F) (K) Sales 1 INTP 0.00 0.94 19.85 12.96 0.00 2 SMCB 0.14 0.75 15.26 6.04 0.00 3 SMGR 0.00 0.00 21.11 21.03 0.00 4 ARNA 0.00 0.00 7.51 0.52 0.00 5 IKAI 0.00 9.62 19.87 6.68 0.00 6 MLIA 4.19 0.67 13.13 0.00 0.00 7 ALMI 0.00 0.00 1.09 1.98 0.00 8 BTON 0.00 0.00 43.93 46.76 29.21 9 CTBN 0.00 0.00 0.00 11.58 0.00

10 INAI 8.70 0.00 0.00 0.00 0.00 11 JKSW 0.00 0.97 13.02 13.08 9.48 12 JPRS 0.00 14.42 0.00 17.42 0.00 13 LMSH 0.00 5.96 18.39 0.98 0.00 14 LION 1.16 0.00 0.00 3.24 0.00 15 PICO 0.00 0.88 9.31 0.00 0.00 16 TBMS 0.00 7.60 0.00 2.34 0.00 17 TIRA 3.11 0.00 1.98 0.03 0.00 18 AKRA 1.89 0.00 13.47 8.93 0.00 19 BUDI 0.00 0.00 26.67 0.00 0.00 20 CLPI 0.00 0.00 0.00 20.50 0.00 21 LTLS 0.77 0.00 0.44 0.00 0.00 22 SOBI 0.00 1.95 0.35 3.78 0.00 23 UNIC 0.00 0.00 3.86 7.91 0.00 24 AKPI 0.00 0.00 11.88 0.00 0.00 25 AMFG 0.00 0.00 0.85 3.98 0.00 26 APLI 0.00 0.00 9.99 0.00 0.00 27 BRNA 0.00 0.00 0.00 8.12 0.00 28 DYNA 0.27 0.00 0.34 2.05 0.00 29 FPNI 0.00 0.00 8.75 3.10 0.00 30 LMPI 0.00 8.94 8.45 1.05 0.00 31 LAPD 0.00 0.00 4.25 23.77 0.30 32 SIMA 0.00 0.00 0.00 23.54 0.00 33 SMPL 0.67 2.01 1.21 4.08 0.00 34 TRST 0.00 2.72 12.34 0.00 0.00 35 BRPT 0.49 1.21 0.00 5.62 0.00 36 DSUC 1.27 0.55 1.67 0.00 0.00 37 SULI 1.78 0.00 3.95 1.95 0.00 38 SUDI 0.00 0.00 3.53 2.41 0.00 39 TIRT 0.00 1.25 3.84 0.00 0.00 40 FASW 0.00 0.00 18.50 6.06 0.00 41 INKP 0.00 0.00 31.24 31.14 0.00 42 TKIM 0.00 5.17 19.22 8.43 0.00 43 SPMA 0.00 0.00 13.43 0.00 0.00 44 SAIP 0.00 0.00 25.78 0.00 0.00 45 DPNS 1.91 0.00 0.00 10.25 3.22 46 EKAD 0.00 0.00 0.00 10.62 0.00 47 INCI 0.00 0.00 8.81 42.31 0.00

Mean 0.56 1.40 8.88 7.96 0.90

*) 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, Ho LR ValueCritical value

*)Decision

(Cobb-Douglas function)

186.99 18.30 Reject

(no inefficiency effects)

9.031 13.40 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)

4,3,2,1,0ij

0543210

Variables Parameters Coefficient Estimates

t-ratio

Constant 0 2.5127 2.3689*)

ln L (Labor) 1 0.7384 1.6937

ln I (Inventory) 2 0.8547 2.3036*)

ln F (Fixedassets) 3 -0.1390 -0.2444

ln K (Capital) 4 -0.1730 -0.3738

(ln L)2

5 0.0286 0.9024 ln L x ln I

6 -0.1269 -2.5878*) ln L x ln F

7 0.2063 3.3068 *) ln L x ln K

8 -0.1869 -3.0260*) (ln I)2

9 -0.0484 -1.3501

ln I x ln F 10 -0.0918 -1.9464

ln I x ln K 11 0.2456 3.2337*)

(ln F)2 12 -0.0312 -1.3525

ln F x ln K 13 0.0028 0.0871

A. Production Frontier

(ln K)2 14 -0.0275 -1.2522

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)

Constant

0 0.5101 0.5746

Age 1 -0.0029 -0.6328

Size 2 -0.2195E-07 -0.4653

Market share 3 -0.2098 -0.8337

B. Inefficiency Effects

Time 4 0.0461 1.9753

222

uvs

0.2162 9.5357**) C. Variance Parameters

22 / su 0.4229 0.8717

Log-likelihood ratio 9.0313 ***) Mean TE (Technical Efficiency) 0.6106

Variables Parameters

CoefficientEstimates

t-ratio

***) Critical value is 13.40 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 0.8547. The average technical efficiency (mean TE) for the basic industry is 0.6106.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.