Ismail, Rahmah


The emergence of Malay entrepreneurs is in tandem with government policy to create Bumiputera Commerce and Industrial Community (BCIC), as stated in the New Economic Policy (NEP), 1971-1990. The Malays form major composition of Bumiputera entrepreneurs. Despite of many privileges received by the Malay entrepreneurs, it is always a claim that their businesses are less efficient. The Malay firms are said to encounter many problems in doing business such as lack of fund, lack of skilled workers, using obsolete technology and limited marketing channel. This paper attempts to investigate this claim through measuring technical efficiency for 264 Malay manufacturing firms surveyed in 2001/2002 in Peninsular Malaysia. They are involved in several types of manufacturing industries. In measuring technical efficiency, Data Envelopment Analysis (DEA) will be adopted. Further, this paper looks at the determinant of efficiency using Tobit model. The results from this study show that the majority of Malay firms are operating inefficiently. More efficient firms are found in the metal and fabricated metal products. The important factors that determine positively level of efficiency are percentage of R&D expenditure, percentage of training expenditure and level of technology. Nevertheless, not all types of industry portray these variables as efficiency determinants. The heavy and export-oriented industries like metal and fabricated metal products are seemed to benefit more from these variables and they are also more efficient than the other types of industry.

Keywords: Technical efficiency; Malay entrepreneurs; Manufacturing firms; Data envelopment analysis; Tobit model.


The importance of manufacturing sector in contributing Malaysian economic growdi is not deniable. This sector acts as growdi catalyst especially beginning of the 1980s as exhibited by its continual increasing in its percentage output and employment from the total national. Viewing from its capability to generate high output, hence higher income of the involving parties, the government has relied on this sector in achieving more equal income distribution amongst the ethnic groups in Malaysia. Racial tension that occurred on May 13, 1969 had rocked the Malaysian stability and as a response to this event the government aimed to increase the Malays participation in trade and business through promoting the Bumiputera Commerce and Business Community (BCIC).

In tandem with this objective the Bumiputera entrepreneurs, where the Malays form the majority have been assisted in terms of financial incentives and infrastructures under the New Economic Policy (NEP), which is now replaced by the National Development Policy (NDP). There has been more than 30 years since the implementation of NEP, but we have yet to see satisfactory achievement of the majority of the Malay entrepreneurs. The majority of them are still facing old problems and unable to succeed as what have been achieved by the Chinese. Even diough the number of Malay enterprises is dramatically increased, their involvement is still limited to certain activities that associated with low productivity. Most of the Malay enterprises are small in size and dealing with light industry like food and beverage and using low initial capital and technology. As a result, they are less competitive and tiieir ability to penetrate the export market is very limited.

Apart from the above, the Malay firms frequently face shortage of capital, lack of knowledge and skills and too dependent on the government contract (Salih and Hussein, 1992). A Study by Mat Zin and Ismail (1996) found that the majority of small-scale industries (SSIs), where most of them were Malays had less ability to penetrate the export market because of low quality products, lack of business context and too specific product design. Apart from this, SSIs also adapt low technology and the majority of workers are with low educational level (Abdullah, 1999; Ismail and Fung, 2002).

One of the pertinent issues facing the Malay entrepreneurs is inability to produce efficiently due to lack of skills and low technology. With limited resources in hand, the likely strategy is to optimize its utilization, reducing cost of production and able to be at the higher competitive edge when the firms are operating efficiently. However, to achieve these objectives, firms need to have a good combination of inputs, where the majority will be laid to human capital. In this regards, knowledge is particularly important in searching new technology, innovation and design. The possession of knowledge by firms can be associated with human capital attainment among their workers. Therefore, to improve knowledge, firms must invest in human capital either in terms of education or training. The importance of human capital emerges from its power to generate higher productivity, hence higher earnings (Becker, 1962; Mincer, 1974). Subsequently this will increase firms’ efficiency and level of competitiveness.

This paper attempts to analyse level of Malay firms’ efficiency using Data Envelopment Analysis (DEA) and identifies factors that influence efficiency using Tobit model. The analysis is based on data of 264 Malay entrepreneurs registered with the Malaysian Malays Chamber of Commerce (MMCC) in Peninsular Malaysia surveyed in 2001/2002 covering nine types of manufacturing enterprises. The paper is organized into five sections. The following sections include theoretical framework, empirical literature review, and source of data, estimation procedure and analysis of the results, and conclusion and policy implications.

In this study technical efficiency of the Malay entrepreneurs is analyzed using a non-parametric programming approach. Non- parametric frontiers were originally proposed by Farrel (1957) who computed the frontier using the programming metiiod. Chames, et al. (1978) using DEA further explored this metiiod.


Studies of technical efficiency (TE) in die long run usually aimed to look at its contribution to total factor productivity (TFPG). This kind of studies uses time series or panel data in computing TE. TFPG is decomposed into two components, i.e. technical efficiency and technical progress (Nishimizu and Page, 1982). A study by Wu (2000) in all APEC countries using the stochastic frontier approach found tiiat technical progress was a dominant contribution of TFPG, while the technical efficiency even though positive but very small. In Singapore, there were few studies on technical efficiency using stochastic frontier approach (Tay, 1992; Mahadevan, 2000).

Measuring TE for the individual enterprises is more meaningful because of micro data and further analysis on factors that influenced TE can be investigated. Firms’ data is also considered as more efficient than the aggregated time series data since the former wUl have the advantage of overcoming some of the measurement problems and aggregation bias associated with aggregate industry data. Many stuthes of TE are conducted at firms’ level (see for example Wu, 2003; Yao and Zhang, 2001; Danlin, et al, 2001; Wu, et al. (2003). Many empirical stuthes on farms’ efficiency have been undertaken using non-parametric approach (Byrnes, et al, 1987; Weersink, et al, 1990; Kalaitzandonakes, et al, 1992; Chavas and Aliber, 1993 and Featherstone, et al, 1997).

Byrnes, et al. (1987) found that the major source of technical inefficiency in the Illinois grain farms was scale inefficiency. Weersink, et al (1990) found tiiat the source of efficiency in the Ontario diary farms was pure technical allocation and non-optimal scale of production. Whereas in other stuthes efficiency was related to farm size, financial structure and degree of specialization (see for example Kalaitzandonakes, et al, 1992; Chavas and Aliber, 1993 and Featiierstone, et al, 1997). In China enterprises efficiency was affected by incentive, location, wage system, vintage capital, FDI and R&D investment (Wu, 2003; Yao and Zhang, 2001). A study by Wu (2003) found that efficiency best performers were transport machinery and sugar processing, whereas the worst performers were consumer electronic and telecommunication and equipment repair. Wu et al (2003) showed tiiat 45% of firms in the sugar beets industry were efficient.

Danlin, et al (2001) stuthed technical efficiency in cotton enterprises in the Soviet Union. They found tiiat more man half of the enterprises have estimated rate of TE in excess of 94% and 90% of the firms were at least 84% technically efficient. They concluded tiiat the normative metiiods employed by the Soviets to provide a reasonably effective mechanism for monitoring and controlling overt enterprises technical efficiency.

In Malaysia, Mohd Noor and Ismail (2004) stuthed technical efficiency and its determinants for 138 rnanufacturing firms. They found that only 6.3% firms were fully efficient at CRS estimates, and 92.6% firms were not efficient with less than 0.5 efficiency score. The estimation at VRS increases the percentage of firms that efficient and reduces tremendously its percentage with less than 0.5 efficiency score. Further this study found diat level of mechanization and firm size significantly positively determine the level of technical efficiency


Data for this analysis were gathered from the field study conducted in the 2001/2002 on 264 Malay manufacturing firms. They are located in all states in Semenanjung Malaysia and operating at all size, small, medium and large. The sample consists of Malay enterprises diat registered with Malaysia Malays Chamber of Commerce (MMCC). The sample consists of nine types of manufacturing enterprises, namely, food and beverage, textiles, furniture and fixture, paper products, chemical products, basic metal, fabricated metal, wood-based and other manufacturing. However, due limited sample in the last two industries this study will employ only seven industries.

Table 1 presents profile of the entrepreneurs and firms in the study. The majority of respondents are males that comprises of 92.2%. The majority of them (67.8%) are aged between 31-50 years old. In terms of educational achievement, 96.4% are at least SPM holders. The percentage of entrepreneurs with high level of education is quite large comprising of 40.5%. There are very few of them that have working experience in current business of less than 5 years or more than 7 years. A large portion of respondents are already in their business for about 5-19 years. Regarding firms’ profile, the majority of mem (more than 90.0%) are parent companies and hold individual ownership status. Most of them are small with number of full-time workers of less than 50. The majority of the firms under study receive quite high yearly earnings of RM1-5 million.


In this section, discussion will focus on the results of the estimation of technical efficiency (equation 3) and the Tobit model (equation 4). Technical efficiency estimation using nonparametric DEA program version 2.1 by Coelli (1996) employed two types of method, i.e. input oriented and output oriented. These two methods will produce results at constant returns to scale (CRS) and variation returns to scale (VRS). The Tobit model, on the other hand, will determine the relationship between firm’s technical efficiency and its criteria. This estimation of Tobit model is carried out using PROC QLIM version 9 in SAS program.

Technical Efficiency

According to production function theory, frontier production is a maximum value for output that can be achieved through each combination of input. This means, frontier production is a technological aspect for each firm taking into account the ‘best practice ‘ in production. In practice, firms usually produce output under the frontier production stage and not at the point of the curve. For the input given, the level of inefficiency of the firm at certain output can be estimated relatively at the maximal values of output. When the value of technical efficiency equal to one, it presents the ‘bestpractice’ used in firm’s production and firm is considered as fully efficient. On the other hand, if the values are between 0 and 1, it exhibits the firm’s technical inefficiency.

In this study, firm’s performance will be determined through their ability in producing output with the minimal use of input. The DEA score of the technical efficiency stated that the optimal input should be used in order to produce certain level of output. As an example, if firm A has a DEA score of technical efficiency at 75 per cent, it reflects that the input used by the firm in producing output should be decreased at 25 per cent in order to achieve the 100 per cent level of technical efficiency.

In this study technical efficiency estimator is measured through input orientated approach, which will produce efficiency at CRS and VRS. In deriving technical efficiency three variables are involved, i.e. total output of each firm and two input variables, i.e. value of physical capital and number of labour of each firm. Score of technical efficiency, scale efficiency and position of each firm are estimated. The results of estimation are presented in Table 2. Based on the CRS results, the furniture and fixture industry indicates the highest number of firms (10.5%) that fully efficient followed by the chemical products (11.8%), the metal products (11.8%), and fabricated metal product industry (11.5%), respectively. Other industries like textiles, food and beverage; and paper products have a small percentage of the firms that fully efficient.

The majority of firms in Malay manufacturing industries have an efficiency score of less than 0.5. The results show that five industries have more than 80.0% firms with less than 0.5 level of technical efficiency. The percentage is lower for the chemical product (76.5%) and metal product industry (64.7%). The results reflect that the majority of the firms are operating inefficiently and should increase their outputs using the same level inputs; hence producing at the frontier stage of production.

The estimation of technical efficiency at VRS produces higher number of firms that are fully efficient. Analysis by types of industry shows that the furniture and fixture industry has the highest number of firms with the efficiency score equal to one (9 firms), followed by the metal product industry (8 firms), and the paper industry (6 firms). On the other hand, number of firms for every industry with efficiency scores less than 0.5 decreases. The VRS technical efficiency is used to measure the relative output decrease due to deviation of constant returns to scale. The score of the technical efficiency at CRS or VRS determines the trend of either increasing or decreasing returns to scale. If the value of the technical efficiency at VRS is bigger than the value at CRS, thus it presents diat firms increase their returns to scale. Based on that principle, results from this study show that all inefficient firms are operating at the increasing returns to scale.

Some previous studies using DEA approach showed similar findings, i.e. inefficient firms were operating at increasing returns to scale (Wu, et al, 2003; Byrnes, et al, 1987).

Table 3 shows descriptive statistics of technical efficiency by industry. In general, the mean value of technical efficiency is between 0.232 and 0.438. This implies that on average, Malay firms will be able to produce the same level of output if they reduce their inputs approximately from 56.2% to 76.8% and the metal product industry achieves the highest technical efficiency followed by chemical products and furniture and fixture. The lowest technical efficiency is shown in food industry, where from it indicate that it should reduce its inputs approximately from 71.3% to 86.3% to produce the same volume of output.

Determinants of Technical Efficiency

Technical efficiency estimates for each firm is further analyzed using Tobit model to identify important determinants of efficiency. For this estimation, the chemical and textile industries are dropped due to too small sample size of less than 30. Based on the Malaysian Standard Industry Classification (MSIC) at 3 digits level, we find that the metal and fabricated metal products industries fall under the same category, thus these two industries are combined to get a bigger sample size. After dropping two industries the analysis will cover four industries. The selection of these industries covers various industry characteristics. For example the food and beverages industry represents the domestic- oriented industry, the furniture and fixture industry represents export- oriented industry, the metal and fabricated metal products represents the heavy industry and finally the paper products represents the light industry.

In this analysis, the values of technical efficiency at CRS are used as dependent variable. While the independent variables are percentage of training expenditure (TREXP), percentage of research and development (R&D) expenditure (RDEXP), level of education among entrepreneurs (EDU), percentage export (X), level of mechanization (MU), computer utilization (CU) and size of firms (FS). Dummy variables are used to group some explanatory variables which are not in numeric form. They are firm size, level of education, level of mechanization and computer utilization. The size of firms is based on number of employees, which are categorized as a small (5-49 employees), and large (50-199 employees). Even though definition of firm size will be more appropriate using value of sales definition, many respondents do not answer this question. Therefore, an alternative measure for firm size is number of employment. The Small and Medium Industries Development Corporation (SMIDEC) (1999) suggested that the definition of firm size can be based on either number of employment or total sales. Education level is categorized into two groups, which is high level that comprises a degree, diploma and certificate holders, and low level that comprises of high and secondary school holders. The computer usage and level of mechanization are also divided into two categories, i.e high level and low level utilization.

The detail definition of variables for Tobit model is presented in Table 4 below.

The results of the estimation are presented in Table 5. The results show that for food and beverage industry, there is only one variable that has a positively significant effect on level of efficiency, namely, percentage of training expenditure. Even though the entrepreneur’s level of education is significant, but it is negative which indicates that entrepreneur’s with higher level of education contribute less to firm’s efficiency as compared to tiiose with lower level of education. This may be associated with the important of working experience in determining efficiency. Our sample shows that there is a negative relationship between experience and education, meaning to say that those who are more educated have less experience as compared to less educated entrepreneurs. The furniture and fixture industry as export-oriented industry seemed to gain more from the percentage export variable as shown by the highly significant relationship of this variable with efficiency. For this industry the level of mechanization is also positively significant.

There are four variables that significantly explain level of efficiency for paper products industry, namely, the percentage of training expenditure, percentage of export, level of mechanization and firm size. Even though the percentage export variable is significant, its relationship with efficiency level is negative that may due to high export cost or cost of producing exported goods. However, firm size is positively significant in determining efficiency of this industry, which shows that the bigger firms are more efficient than the smaller firms. Further, this study finds that most variables in the metal and fabricated metal products industry are positive and statistically significant in determining efficiency. Variables like percentage expenditure on training and R&D, percentage export, level of mechanization, computer usage and firm size are highly significant in determining firm’s technical efficiency of this industry. The larger firm is more efficient than the smaller firms. Even though, the coefficient of percentage training expenditure, level of mechanization and computer usage are significant, they are all negative which indicate that an increase in these variables will decrease level of firm’s efficiency of the metal and fabricated metal products industry. These findings may due to inappropriateness of training programs and high cost of adapting new technology.


In general the majority of the firms in the Malays manufacturing enterprises are operating inefficiently. There are three types of industry that have more than 10% firms who are operating efficiently, i.e. chemical products, metal products and fabricated metal products. However, percentage of firms that operating at less than 0.5 efficiency levels is quite large in all industries which accounted more than 60%. It is found that light industry like food textile and furniture have higher proportion of inefficient firms with less than 50% efficiency level.

The results from this study show that the determinants of level of efficiency are different by industries. Among the important determinant factors are percentage of training expenditure and entrepreneurs’ education level. The percentage of training expenditure is a significant determinant of efficiency for the heavy industries like metal and fabricated metal products as well as for the light industry like paper and food products. However, this variable explains negatively the level of efficiency for metal and fabricated metal products. This may be due to inappropriateness of training program that mostly conducted by training institutions and not in-house training. In-house training is perceived to be more effective since it can be tailored to firm’s needs.

Even though the entrepreneur’ level of education is significantly influence technical efficiency in food and beverage industry, its relationships is negative. In other industries it is not significant. This can be associated with the entrepreneur’ experience, where our sample shows that there is a negative relationship between experience and education, meaning to say that those who are more educated have less experience as compared to less educated entrepreneurs. In this regard, experience may play more important role than education especially when particular business exposures are needed as portrayed in food and beverage industry.

Level of mechanization and computer usage is found to be significant determinants of technical efficiency for the furniture and fixture and paper products metal and fabricated metal products. But tiiey are negatively significant for the metal and fabricated metal products industry. This may due to high cost of adapting new technology, which is higher in the heavy industry. The percentage of export is also found to be statistically significant for the furniture and fixture, paper products and metal and fabricated metal products. For the paper products die percentage of export is negatively related to efficiency, meaning that the higher the percentage exports the lower will be the efficiency. This may due to high export cost or cost of producing better quality output for the export market.

The percentage of R&D expenditure is a positively significant determinant of efficiency for larger set up firms like in the metal and fabricated metal products industries. This industry seems to spend greater proportion of their expenditure for R&D activities. In other industries, the percentage of R&D expenditure is not significant. This may due to weaknesses in conducting R&D activities that do not assist the firms to churn new products or other related efficiency measures like technical and marketing aspects. R&D expenditure will be very meaningful if its activities can be realized into product, technology and marketing development. Firm size is found to be positively related to firm’s efficiency for two industries, namely the paper products; and metal and fabricated metal products industries. The larger firm’s may gain from economics of scale and lower their cost of production.

Technical efficiency is crucial for firms to compete especially in the era of globalization and liberalization. The study shows that the majority of Malay firms are still operating inefficiently and some of the firms’ characteristics do not contribute significantly to their efficiency level. The study suggests that Malay firms should reduce their inputs in order to produce the same level of output or vis-à-vis they must increase their output with the same level of inputs in order to be efficient.

Variable like training expenditure contributes positively to firms’ technical efficiency especially for the light industry like food and beverage and paper products, where the involvement of the Malays is great. Therefore, the Malay firms should give greater emphasis on workers training through allocating a larger expenditure to this activity. We suggest that firms should conduct in-house training more frequently rather than rely on other source of training. In order R&D expenditure to be more beneficial firms should channeled their R&D allocation to activities that really in needs like on marketing and product development. It is found that the expenditure allocation for R&D and training activities is still small in most Malay firms and this may result an ineffective outcome from these two activities. Level of technology that measured by level of mechanization must be increase especially for furniture and fixture and paper products viewing from its positive contribution to efficiency level.

In order to increase level of technology, the Malay firms must operate at bigger scale. Beside easier adoption of advance technology by bigger size firms, the advantage of operating at larger scale can also be viewed from economies of scale. When firms operate at large scale, they will gain economic of scale that can reduce average cost of production; hence they will have comparative advantage in pricing by lowering price per unit output. Since the majority of inefficient firms are operating at increasing returns to scale, there is a room for them to grow bigger to achieve optimum level of output.


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Rahman Ismail * and Noorasiah Sulaiman

School of Economic Studies.Faculty of Economics and Business

University Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia

* Corresponding author: Rahmah Ismail School of Economic Studies, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. Email:

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