Cost efficiency of Malaysian oil and fat industry: An empirical evidence. Abstract

Transcription

1 Sudan j.agric. res. (007), 9, ARC, Sudan, Cost efficiency of Malaysian oil and fat industry: An empirical evidence Mohamed O. A. Bushara 1 and Mohd. Ghazali Mohayidin Abstract This paper reports the application of the stochastic frontier approach, which comprises the estimation of the Battese and Coelli model specification (1993 and 1995) in which the inefficiency effects are directly influenced by a number of variables. The paper shows that the application of this stochastic frontier models can give meaningful results regarding the Cobb-Douglas cost function on balanced panel data. Results indicated that the Cobb-Douglas cost function, a homothetic cost structure, was found to be preferred over the trans-log functional form in Malaysian oil and fat industry. The industry appears to be characterized by Hics-neutral technical progress. The mean cost inefficiencies of Malaysian oil and fat industry in the 1 different years ( ) tended to increase from 1985 to 1988 then decreased slowly and slightly up to Among the oil and fat sub-sectors, palm oil relatively seemed to be the most economically inefficient while coconut oil sub-sector is the best of the peers. Results revealed that all factors in the cost inefficiency effect results had significant effects on cost inefficiency of the average firm except for caal, labour and energy productivities. The mean cost inefficiency appeared to decline over the sample period, which is this liely to be partially the result of an increase in excess capacity in the industry during this time. Introduction Many past and present analysis of efficiency in oil and fat industry involved the calculation of simple ratio measures, such as labour productivity (output per employee) or total factor productivity (output per unit of all inputs) (NPC, 1997). These measures could be very informative but could also be quite misleading, because each measure considered either a single input in isolation of all inputs without diagnosing them separately to now the cause and effect of these inputs (Zeitsch and Lawrence, 1996). Farrell (1957) proposed a method of measuring relative efficiency which could account for all factors of production simultaneously. He proposed measuring the technical efficiency of a firm in an industry by comparing its observed output to that output which could be produced by a fully efficient firm, given the same bundle of inputs. Many subsequent papers have applied and extended Farrell s ideas. This literature might be divided into two categories, according to the method chosen to estimate the production function or its dual cost function of the fully efficient firm which are now commonly nown as frontier functions. These two estimation methods were mathematical programming computation and econometric estimations, and the debate continued over which approach was the most appropriate method to use. The mathematical programming approach to frontier estimation has become nown as Data Envelopment Analysis (DEA) (Mohaydin and Bushara, (00). Charnes et al. (1978) coined the expression DEA in their use of the activity analysis model for efficiency gauging and described more fully in Charnes et al. (1985) and Grossopf (1986). Seiford and Thrall (1990) provided a thorough review of the DEA literature. The primary shortcomings of the DEA approach were that measurement errors could have a large influence upon the shape and position of the estimated frontier; and that one could not use standard statistical theory to test hypotheses regarding the cost parameters. 1 Gezira University Universiti Putra Malaysia

2 136 Mohamed O. A. Bushara and Mohd. Ghazali Mohayidin Most theoretical frontier functions did not explicitly formulate a model for the inefficiency effects. Empirical papers, in which the issue of the explanation of the inefficiency was raised, included Kalirajan and Shand (1989). A number of empirical studies, e. g., Pitt and Lee (1981), and Kalirajan (1989) have investigated the determinants of technical efficiency variation among firms in an industry by regressing the predicted inefficiencies, obtained from an estimated stochastic frontier, upon a vector of firm-specific factors such as firm size, age and education of manager, etc. in a second stage regression. There was, however, a significant problem with this two stage approach. In the first stage, the inefficiency effects were assumed to be independently and identically distributed, while in the second stage they were assumed a function of a number of firm specific factors, which implied that they were not identically distributed. Papers by Kumbhaar (1991) Kumbhaar etal (1991) and Reifschneider and Stevenson (1991) noted this inconsistency and specified stochastic frontier models in which the inefficiency effects were made an explicit function of the firm specific factors, and all parameters were estimated in a single-stage maximum lielihood procedure. Battese and Coelli (1995) extended this approach to accommodate panel data, which permitted the simultaneous investigation of both the determinants of technical inefficiencies, along with the degree of technical efficiency change and technical change over time. This model will be used to assess the efficiency of the Malaysian oil and fat industry which include coconut, palm oil and other fat sector in which the inefficiency effects were directly influenced by a number of variables, for the period 1985 to Materials and Methods Empirical Stochastic Trans-log Cost Function Model Specification: A trans-log stochastic frontier cost function was specified for Malaysian oil and fat industry. The cost of the output of the plant was assumed to be a function of the four inputs of caal, labour, energy and material after KLEM. Technical change was permitted to be non-neutral (the neutral was where the intercept was variable and the coefficients were constant; Kalirajan and Shand (1989). The error term was assumed to have two components, with properties as specified in Battese and Coelli (1993) and used by Coelli (1996). The trans-log cost function was assumed to be described by: C W F M IT β 0 β1 ( yit ) β β 3 β 4 β 5T = PIT W F M ( Y ) it 9 β β β β W F M β ( ) ( ) ( ) 10 yit β 11 yit β y 1 it W F W M β ( ) 13 yit T β14 β 15

3 Cost efficiency of Malaysian oil and fat industry: An empirical evidence 137 W F M F β T 16 β T 17 β 18 M β 19 T β T ε it 0 ε it = ν i t µ it ; i= 1,... N, t = 1,...., T, Where, the subscript i indicated that observation involved referred to i-th firm in the particular year t. C it = total cost value in Malaysian Ringgit at the prices of 1994; K = caal price; Y = total output value in Malaysian Ringgit at the prices of 1994; (Battese etal 1998); W = labour price; F = energy price; M = material price; T = a time trend; In = refers to the natural logarithm. The βs was unnown parameters to be estimated. The ν it were iid N (0, σ v ) random errors, and are assumed to be independently distributed. The µ it which were non negative random variables associated with cost inefficiency were assumed to be independently distributed, such that the distribution of µ it was obtained by truncation at zero of the normal distribution with mean m it and variance σ U. The cost inefficiency of the i-th firm was defined as the ratio of the observed production cost to the corresponding stochastic production cost. If the cost inefficiency effect, µ it, was zero, given the level of output and associated explanatory variables, under the assumptions of the frontier model (equation (1), the frontier cost will be as follows: C i * = ƒ (y i ; p i ; β ) and the cost efficiency (CE) of the i-th firm was expressed by: C E = C i / C i * =ƒ (y i ; p i ; β ) exp (µ i t ) /ƒ (y i ; p i ; β ) = Exp (µ it )...() The cost efficiencies were, thus, more than or equal to unity. If the cost inefficiency were absent from the model (equation 1), then the cost efficiency of all the firms would be unity. This implied that the firms were producing at the frontier cost, given the output values and the input prices. The Battese and Coelli (1993 & 1995) Inefficiency Effect Model Specification: To count for the time effect the following equation may be considered: µ it = Z it δ w it = & 0 & 1 ly it & lkpp it & 3 ll it & 4 lfpp it & 5 lm it & 6 t it & 7 t w (3) Where ly it = output as proxy for capacity utilization (Coelli 1996b); lkpp it = caal productivity; llp it = labour productivity; lfpp it = energy productivity; = size of installed unit (in Ringgit Malaysia) of the intermediate inputs i.e. lm it

4 138 Mohamed O. A. Bushara and Mohd. Ghazali Mohayidin Material, Coelli (1996b), Lundvall and Battese (1998), Maisom (1991), Berndt, and Wood (1975). t it = age of installed unit (in years); t it = age squared of the installed unit in (years), and w stands for error. The & i are unnown parameters to be estimated. The cost efficiency frontier models (equations 1 and 3) account for both technical change and time varying cost inefficiency effects. The year variable in the stochastic frontier 1 accounts for hics-neutral technological change. However, the year variable in the cost inefficiency model 3 specifies that the cost inefficiency effects may change linearly with respect to time. The distributional assumptions on the cost inefficiency effects permit the effects of technical change and time varying behaviour of the cost inefficiency to be identified. In addition to the intercept parameters β o and & 0, in the stochastic frontier and the cost inefficiency model. Battese and Coelli (1995) proposed the nd model, in which the cost inefficiency effects are as defined above in 3 where Z it is an 1 x M vector of explanatory variables associated with the cost inefficiency defined above; δ is an M x 1vector of unnown parameters to be estimated, and w it are unobservable random variables, which are assumed to be independently distributed, obtained by the truncation of the normal distribution with mean zero and unnown variance, σ, such that µ it is non-negative ( i. e., w it - z it δ). Given the specification of one in the cost-inefficiency effect model for panel data, proposed by Battese and Coelli (1993 and 1995), the null hypothesis that the cost inefficiency effects are not stochastic or random is expressed by Η 0: γ = zero, where γ is to be defined latter here. Further, the null hypothesis that the cost inefficiency effects are not influenced by the level of the explanatory variables in equation 3 is expressed by Η 0 : δ = 0, where δ denotes the vector δ, with constant term δ 0 omitted, given that it is included in the expression Z it δ. This specification is preferred over that used by Huang and Liu (1994), because it includes a constant term in Z it, and also over the specification of Reifschneider and Stevenson (1991), which presumes µ i is the sum of mi and a truncated normal random variable, both of which must be non-negative. Production input variable was chosen as a proxy for firm size in this study. This approach had been adopted by application of the stochastic frontier function on agriculture in which farm size was represented by a function of the input land (Coelli and Battese, 1996; and Nsanzugwano et al. 1996). In the choice of this model for firm size was the cost of material. There was an implicit wide claim that the material input in gross output was constant over time, which could be interpreted as that the elasticity of substitution between material input and output is zero, in Malaysian manufacturing data (Maisom, 1991). However, Berndt and Wood (1975) questioned this assumption when they used the American manufacturing data. The time was used as a proxy for firm age because an additional year of experience of a firm was expected to have a greater influence on new firms than older ones (Lundvall and Battese, 1998). Also labour productivity, caal productivity and the output were added, Bushara (001). Data Sources and Variables: Briefly, the information and statistics of oil and fat industry were not easily available. However, the department of statistics and the national productivity corporation (NPC) were the main sources of information and data. The time frame of this study was

5 Cost efficiency of Malaysian oil and fat industry: An empirical evidence and data used was a complete panel of annual observations on four decision maing units (DMU) for 1 years each, comprising 48 observations at 5-digit level of the international standard of industrial classification (SIC) for Malaysian four sectors of oil and fat industry observed during the period of study. These data were derived from census of the manufacturing industries, conducted each year by the Department of Statistics (DOS). Those sectors were coconut oil, palm oil, palm ernel oil and other oil and fat. The output was defined as the total firm gross output and had been deflated by using the Malaysian producer price index for locally produced commodities i.e., animal and vegetable oils and fats here after producer price index. Labour expenditure was defined as the total remuneration (wages, salaries, pension and employee provident fund EPF) paid by employers. Caal expenditure was defined as the flow of caal services, which included the depreciation (i.e. building & land, machines, equipment, and furniture & fixtures) plus interest paid, (Griliches 1979). Caal cost had been deflated by producer price index (Elsadig, 1997). Energy has been computed as the aggregate of electricity, fuel and lubricants and had been deflated by the prices of locally produced commodities i.e., mineral fuels, lubricants and related materials index which include petroleum, gas, and electricity. The aggregate price of these commodities could be taen as a proxy for" energy producer price index (DOS, 1997). Material expenditure which constituted the other variable costs and labour cost had been deflated by producer price index although some authors used consumer price index (Maisom, 1991; DOS, 1998 and World Ban, 1999). The total cost was the sum of the four deflated input expenditures (NPC, 1997 and DOS, 1994). The deflator should be sector specific and input specific however, the only available indices for this sector in the locally produced commodities were (PPI) and energy (PPI). Hence it was seen appropriate to deflate output, labour, caal and material by the (PPI). The price of labour was the total remuneration of labour divided by the total labour force of the firm. The price of caal was the caal expenditure divided by the caal stoc (Kwon and Martin, 198; Vashist, 1985; and Coelli et al. 1997). The price of material was the producer price index here after material price. The price of energy was the energy producer price index. All prices had been converted to the price indices using 1994=100 as the base year. Theoretically, deflating the time series data would yield efficient and unbiased estimators. A further advantage of using deflated time series for each firm was that extreme observations would have less effect on the estimation, and would reduce the bias due to those outlying observations. Deflating output, labour material and caal with producer price index and energy with energy producer price index eliminated biases due to inflation and cyclical price movements. Results and Discussion Fat Industry: The maximum lielihood estimates of the parameters of the trans-log stochastic frontier model, defined by equations 1 and 3, were obtained using the computer program Frontier Version 4.1c. This computer program used a three-step procedure to obtain the maximum lielihood estimates. The first step involved obtaining ordinary least squares (OLS) estimates of equation 1. These estimates are unbiased estimates of the βi coefficients, with exception of β 0, which will be biased because of the non-zero expectation of µ it. The second step involved evaluating the log-lielihood function for a number of values of γ between zero and one. During this procedure, the β i parameters (excepting β 0 ) were set to OLS values and the β 0 and σ parameters were adjusted

6 140 Mohamed O. A. Bushara and Mohd. Ghazali Mohayidin according to the corrected ordinary least square formulae for the half normal model. The estimates corresponding to the largest log-lielihood value in this second step were used as starting values in the Davidon-Fletcher-Powell iterative maximization procedure in the third and final part of the estimation procedure. The maximum lielihood estimates of the Cobb-Douglas stochastic frontier function, which is a subset of the trans-log stochastic frontier model, defined by equations 1 and 3, are presented in the first column of Table 1. Asymptotic standard errors are presented in the second column. In the third column are the t-ratios, the ratio of the estimated coefficients to their corresponding standard errors, providing the significance of the coefficients. Looing first at the estimates of the coefficients associating the input prices in the Cobb-Douglas cost function equation, 5 of these βi coefficients had t-ratios larger than 1.96 in absolute value, suggesting that most of them were significantly different from zero at the 5% level. This might suggest that the model was strongly fit. The consideration of t-ratios, however, could be misleading on two counts. First, the sizes of these tests will not be equal to 5% when more than one test is conducted in sequence. The second, and probably the most important point, when one uses the translog cost model the multicollinearity, resulting from inclusion of squared and interaction terms, might be contributing to some of the higher standard errors and some wrong directions of the coefficients observed. If this was the case, the consideration of the wrong direction and these a few t-tests may lead to the omission of some important coefficients, resulting in the misspecification of the model. These null hypotheses were tested using the generalized lielihood ratio statistic test λ, which was defined by : λ=-two [L (Η 0) L (Η A )], (5) where Η 0 and Η A are the null and alternative hypotheses involved. If the null hypothesis, Η 0 is true, then λ is asymptotically distributed as Chi square (or mixed Chi-square) random variable. If the null hypothesis involves γ = zero, then λ has mixed Chi square distribution because γ = zero is a value on the boundary of the parameter space forγ. The first test considered was a test of the null hypothesis of Hics-neutral technical change. Technical change will be Hics-neutral when the coefficients of the interactions between the logs of the input prices, output and the time trend are all zero that is test of β 15 = β 5 = β 35 = β 45 = 0.The maximum lielihood estimates of the parameters of the model defined by equations 1 and 3 are listed in the second column of Table 1. The log-lielihood function values of this restricted model and those of the unrestricted model are presented in Table, where the results of a number of hypothesis tests are presented. As can be seen from the results in Table, the value of the lielihood ratio statistic was calculated to be (-119.9), which was less than 9.49 of the 5% critical value from χ 4 distribution. Hence the null hypothesis of Hics-neutral technical was accepted, implying that the technical change did not favour the use of one particular input over another in Malaysian oil and fat industry. The next hypothesis test considered ased whether there had been any technical change over the sample period. This test involved a test of the restrictions of all the coefficients associated with the time trend equal to zero, that is, a test of β 15 = β 5 = β 35 = β 45 = β 5 = β 55 =0. The results for this test, listed in Table, show a lielihood ratio statistic of ( ) which is less than the χ 6 critical value of 1.59, resulting in accepting of the null hypothesis of no technical change. It worth mentioning here that all hypothesis tests listed in Table has been conducted with unrestricted translog as the model under the null hypothesis. A test of the null hypothesis that the Cobb-Douglas function was appropriate would be of considerable interest. The acceptance of this null hypothesis would greatly

7 Cost efficiency of Malaysian oil and fat industry: An empirical evidence 141 help the estimation and analysis of cost function in this analysis. This hypothesis involved a test of the restrictions that β 11 = β = β 33 = β 44 = β 1 = β 13 = β 14 = β 15 = β 3 = β 4 = β 5 = β 34 = β 35 = β 45 = β 55 = 0. The value of the log-lielihood of the Cobb- Douglas model is listed in Table. The value of the log-lielihood function had increased substantially to This provided a lielihood ratio test statistic of ( ) which was far less than the χ 15 critical value of by a reasonable amount. Thus the Cobb-Douglas model was confidently accepted in place of the translog. It therefore appeared that extra effort involved in estimating and analyzing the trans-log form to reach to this conclusion was justified. Hence the Cobb-Douglas stochastic cost function was used in analyzing cost efficiency of Malaysian oil and fat industry. Furthermore, it was fortunate that the trans-log functional form was tested and rejected promptly, not because of the wrong direction and size associated with the individual βi coefficient. It was also not important to concentrate on the signs rather than functional form and probability distribution which were more important for efficiency studies, because the concentration would be on the residual rather than the βi. However, this was not a problem in this model since the size and direction of the coefficients were appropriate. Table 1. Maximum-lielihood estimates for parameters of the stochastic frontier cost function for Malaysian oil and fat industry, Variable Parameters Estimates Standard Error t- ratio Frontier Model Variables Intercept β * Output =ly β * Labour price = lw β Table 1.( continued) Energy price =lf β * Material price = lm β * Year = t β * Inefficiency Model Variable Intercept δ * Table.1.( continued) LN(Output(in value))ly δ * LN(Caal productivity )lpp δ LN(Labour Productivity )llp δ LN(Energy Productivity )lfpp δ LN(Material(in value) )lm δ * Year δ * (Year) δ * Variance Parameters Variables Sigma Squared σ s = (σ µ * σ ν) Gamma ϒ= * σ µ/(σ µ σ ν) Log-lielihood Ηa Mean Cost Efficiency MCE *significant at 5% level of significance.

8 14 Mohamed O. A. Bushara and Mohd. Ghazali Mohayidin Hypotheses on the Cost Inefficiency Effects: Attention was given to the estimates of the coefficients associated with the cost inefficiency section of the model specification. The study noted that from the t-ratios in column 5 of Table 1 that all of the t-ratios associated with γ and δi are significant, with the exception of three. Thus the indications were that these terms were significant additions to the model. However, even though multicollinearity was unliely to have as large an influence upon these coefficients, as was the case with the βi coefficients, a few lielihood ratio tests were conducted to confirm the observations. First, a test of the null hypothesis that there was no cost inefficiency in Malaysian oil and fat industry was considered. If this was the case, the cost inefficiency error term µ it, would be removed from equation 1, and the resulting model could be appropriately estimated using OLS. The omission of µ it, was equivalent to imposing the restrictions that γ = δ 0 = δ 1 = δ = δ 3 =δ 4 = δ 5 = δ 6 = δ 7 =0. The relevant information for this hypothesis test can be found in Table. The lielihood ratio statistic was calculated to be , which was substantially greater than the χ 9 critical value of Thus the null hypothesis was rejected and the study concluded that a model which accounts for cost inefficiency effects was justified in this instance. One question of particular interest to this analysis was whether the seven firm specific factors considered in the model had significant influence upon the degree of cost inefficiency experienced by Malaysian oil and fat industry. Thus a test of the hypothesis that δ 1 = δ = δ 3 =δ 4 = δ 5 = δ 6 = δ 7 =0 was conducted, and the results of this test are listed in the final row of Table. The lielihood ratio statistic was calculated to be 16.38, which was greater than the χ 7 critical value of Table. Hypothesis tests. Restriction β 15 = β 5 = β 35 = β 45 = β 5 = β 55 =0 Model Description Loglielihood Lielihoodratio statistic (λ) χ Critical values (5%) Decision None Trans-log Trans-log (Hics-neutral Accept Η 0 β 15 = β 5 = β 35 = β 45 = 0 technical change Trans-log (no technical Accept Η 0 change) β 11 = β = β 33 = β 44 = β 1 = β 13 = β 14 = β 15 =β 3 =β 4 =β 5 =β 34 = β 35 = β 45 =β 55 =0 Cobb Douglas Accept Η 0 γ =δ 0 = δ 1 = δ = δ 3 =δ 4 =δ 5 = δ 6 = δ 7 =0 δ 1 =δ =δ 3 =δ 4 = δ 5 = δ 6 = δ 7 =0 Trans-log (OLS) Reject Η 0 Trans-log (no firm specific Reject Η 0 factors)

9 Cost efficiency of Malaysian oil and fat industry: An empirical evidence 143 Results of Cost Efficiency: When the conclusions of the above five hypothesis tests were considered together, the preferred model appeared to be defined by a subset of equations 1 and 3, with Hics-neutral technical change imposed. The estimates of this model are listed in the second column of Table1. The concern here was to direct the attention to the parameters associated with the cost inefficiency error µ it, namely γ and δi. The maximum lielihood estimates of these parameters, listed towards the bottom of the third column of Table 1, show that all t-ratios were greater than 1.96 in absolute value, with the exception of δ, δ 3 and δ 4. These significant t-ratios were not surprising given the conclusions of the lielihood listed earlier. The interpretation of the estimate of γ = σ µ/(σ µ σ ν) of was not clear in this model specification as it was in half-normal stochastic frontier (i.e. the model where all δi are zero), (Coelli etal1997), where it could be shown to be a simple function of the variance ratio of the inefficiency error term to the sum of the variances of the two error terms γ = σ µ/(σ µ σ ν). For the model specification used in this study, it might be loosely interpreted as an indication of the amount of unexplained variation in the cost inefficiency effects, relative to the sum of this value and the variance of v it. The signs of δi needed to be considered carefully. The negative sign of the estimated coefficient of the output indicated that an increase in output production would result in the decrease of cost inefficiency effects and hence an increase in economic efficiency. This conformed to the expectation that a firm, which was permitted to utilise more of its capacity, was liely to appear to be more economically efficient using the measures defined in this research. The estimated coefficient associated with caal productivity was negative but not significant as output, which means that output per ringgit cost of caal decreases cost inefficiency. The estimated coefficient associated with labour productivity was negative but not significant, which indicated that the increase of labour productivity (i.e. output per one labour) would decrease cost inefficiency. The estimated coefficient associated with the productivity of energy was positive but not significant which indicated that the increment of the productivity of energy (i.e. output per ringgit of energy used) would increase cost inefficiency. The estimated coefficient associated with the material cost which was used here as proxy indicator of firm size (when size was defined as the value of intermediate inputs) was positive and highly significant. It was expected that cost inefficiency would decrease with unit size rather than increase, with the labour saving, and so on. The estimated coefficient associated with the year was positive and significant and the coefficient of the squared year was negative and significant which implied that cost inefficiency effects increase first and decline latter. The maximum lielihood estimates, expressed in equation 3 are ^ µ = ly lpp llp lfpp lm t t These results indicated that the cost inefficiency effects for Malaysian pooled data of oil and fat industry tended to: decrease as the output increased, decrease as the caal productivity increased, decrease as labour productivity increased, increase as energy productivity increased, increase as intermediate input use increased, increase at first; then decreased.

10 144 Mohamed O. A. Bushara and Mohd. Ghazali Mohayidin The estimate for ϒ-parameter of with estimated standard error of implied that the variance of cost inefficiency effects was a significant component of the total of the variances of the error terms. The cost efficiencies of each firm in Malaysian oil and fat industry in each year could be predicted from the estimated model. Given the model defined by equations (1 and 3), the cost efficiency of cost production of i-th firm in t-th year was defined by Exp (µit)... () Since the estimated error associated with each observation m could not be easily decomposed into estimates of µ it and ν it, the approach suggested by Jondrow et al. (198) was followed for half and normal models (Appendices C-1 and C-). The conditional expectation of Exp (µ it ) was obtained given the value of e it = µ it ν it. An operational predictor of the cost efficiency of the i-th firm in the t-th year was obtained by replacing the unnown parameters in this expression with the maximum lielihood estimates. Since µ it was a non-negative random variable, these cost efficiencies prediction would be one more or equal one, with a value of one indicating full economic efficient firm. The cost efficiency predictions for preferred model were calculated by Frontier program, and have been listed in Table 3. They have also been plotted in Fig.1. This cost inefficiency ranged from 0.197% to 1.886% for coconut oil sector while the overall mean for this sector was %. The cost inefficiency ranged from.45% to 35.88% for palm oil sector while the overall mean for this sector was %. The cost inefficiency ranged from 0.55% to.59% for palm ernel oil sector while the overall mean for this sector was %. The cost inefficiency ranged from 0.78% to 44.09% for other oil and fat sector while the overall mean for this sector was %. The overall cost inefficiency mean of Malaysian oil and fat industry ranged from.416% to %. The mean cost inefficiency in this industry was calculated to be 1.914%. This suggested that on average, firms produce their output with % of their input cost. This same output could be theoretically produced with 1.914% less of its input cost. The mean cost inefficiencies of Malaysian oil and fat industry in the 1 different years ( ) are graphed in Fig.1. The cost inefficiencies tend to increase from 1985 to After this in 1989, the mean cost inefficiencies decreased slowly and slightly up to Among these four sectors, palm oil sector relatively seems to be the most economically inefficient sector. Table 3. Mean cost efficiency of Malaysian oil and fat industry by sector (Cost Efficiency Effect Model. Year/Firm Coconut oil Palm oil Palm ernel oil Other oils and Fats Mean cost efficiency Table.3. (continued) Mean cost efficiency

11 Cost efficiency of Malaysian oil and fat industry: An empirical evidence 145 Cost Efficiency Year Coconut oil Palm ernel oil Mean cost efficiency Palm oil Other oils and Fats Fig.1. Cost efficiency of Malaysian oil and fat industry( ). Conclusion The primary contribution of this research study was a substantial addition to the stoc nowledge regarding relative cost inefficiencies of Malaysian oil and fat industry. Since there had been no previous econometric analysis of oil and fat industry and also no past analysis of relative cost efficiency, other than those using simple partial measures such as ratios, this analysis had broen new ground in this industry. The main conclusions were that the Cobb-Douglas cost function, a homothetic cost structure, was found to be preferred over the translog functional form in Malaysian oil and fat industry. The industry appears to be characterized by Hics-neutral technical progress. It was often termed as unbiased technical change in that the movement in the function would not be biased in favour of certain inputs or outputs against others. The mean cost inefficiency in this industry was calculated to be 1.9%. This suggested that, on average, firms produced their output with 11.9 of their input cost. This same output could be theoretically produced with 1.9% less of its input costs. The mean cost inefficiencies of Malaysian oil and fat industry in the 1 different years ( ) tended to increase from 1985 to 1988 then decreased slowly and slightly up to Among these four sectors, palm oil relatively seemed to be the most economically inefficient while coconut oil sector is the best of the peers. The cost inefficiency effect results indicated that the cost inefficiency effects for Malaysian balanced pooled data of oil and fat industry tended to: i) decrease as the output increases; ii) decrease as the caal productivity increases; iii) decrease as labour productivity increases; iv) increase as energy productivity increases; v) increase as intermediate input use increases; and vi) increase at first then decrease. All these factors had significant effects on cost inefficiency of the average firm except for caal, labour and energy productivities. The mean cost inefficiency appeared to decline over the sample period which is liely to be partially the result of an increase in excess capacity in the industry during this time.

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