UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS STOCHASTIC FRONTIER MODELS REVIEW WITH APPLICATIONS TO VIETNAMESE SMALL AND MEDIUM ENTERPRISES IN METAL MANUFACTURING INDUSTRY A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN QUANG Academic Supervisor: Dr. TRUONG DANG THUY HO CHI MINH CITY, NOVEMBER 2013 Page | 1 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT Metal manufacturing industry has an important role in the economy due to the high demand of metal products, especially steel and iron in daily life, production and, mostly construction. To help maintain and develop the benefit from this industry, it is necessary to have an analysis into the technical efficiency level of small and medium enterprises (SMEs) which takes about 97% of the number of Vietnamese enterprises. This study aims to estimate the technical efficiency level of Vietnamese SMEs using an unbalanced panel dataset in three years: 2005, 2007 and 2009 with stochastic frontier model.
Besides, because of divergent literatures of panel-data stochastic frontier model, this paper also makes a review of popular ones in order to choose the suitable model for the case of Vietnamese metal manufacturing industry. The result shows different technical efficiency levels while using different models due to the divergence among identifications of technical efficiency concept. Page | 2 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com TABLE OF CONTENT Page LIST OF TABLES. 4 LIST OF FIGURES.
4 LIST OF CHARTS. 7 CHAPTER II: LITERATURE REVIEW. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The cross-sectional Stochastic Frontier Model.
Stochastic frontier model with panel data. Time-invariant models. Time varying models. 19 CHAPTER III: METHODOLOGY.
Overview of Vietnamese metal manufacturing industry. Estimating technical inefficiency. 34 CHAPTER IV: RESULT AND DISCUSSION .1 Cobb-Douglas functional form. Translog functional form.1 Models without distribution assumption .2 The distribution of technical inefficiency .3 Technical inefficiency and firm-specific effects.
54 Page | 3 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com LIST OF TABLES: Table 3-1 Output and Input deflators. 31 Table 3-2 Descriptive statistic of key variables. 35 Table 3 – 3 Real outputs and material costs value of different-sized firms. 35 Table 4-1 Time invariant models with Cobb – Douglas function.
37 Table 4-2 Time varying models with Cobb – Douglas function. 41 Table 4-4 Time invariant models with Translog function. 43 Table 4-5 Time varying models with Translog function. 44 Table 4-6 Value of μ in models with truncated distribution.
46 LIST OF FIGURES Figure 2-1 Input-oriented efficiency. 9 Figure 2-2 Output-oriented efficiency. 9 Figure 2-3 various types of technical inefficiency distribution. 14 LIST OF CHARTS Chart 3-1 Firm size and ownership type.
36 Chart 3-2 Firm location. 36 Page | 4 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com CHAPTER I: INTRODUCTION 1. Introduction The rising demand of metal products (especially iron and steel) in daily life, production and, mostly, construction sector makes the role of metal manufacturing industry important. According to World Steel Association, at the end of 2011, Vietnamese steel market was the seventh largest in Asia with the growth rate in tandem with economic expansion.
There are still huge potentials from this industry due to the growing income and an expanding trend of construction. As reported by Viet Nam chamber of Commerce and Industry (VCCI), at the end of 2011, 97% of the number of enterprises in Viet Nam are small and medium sized which employ more than a half of the domestic labor force and contribute more than 40% of GDP. This dynamic group of firms have become have become an important resource for economic growth in Viet Nam. However, this industry is now facing challenges due to outdated technology and the heavy dependence on import materials.
From the reasons above, an analysis into the technical inefficiency level of Vietnamese small and medium enterprises (SMEs) in metal manufacturing industry is necessary to maintain and develop the benefit from this industry. Technical efficiency is the effectiveness with which the firm uses a given set of inputs to produce outputs. The set of highest amounts of output that can be produced from given amounts of inputs is the production frontier. Technical efficiency reflects how close a firm can reach this border: firms producing on this frontier are technically efficient, while those far below from the frontier are technically inefficient.
A technical efficiency analysis is often conducted by constructing a production-possibility boundary (the frontier) and then estimating the distance (the inefficiency level) of firms from that boundary. There are two approaches to measure technical efficiency: deterministic and stochastic. The deterministic approach, called Data Envelopment Analysis (DEA), was first introduced in Charnes, Cooper, and Rhodes (1978) which use linear programming with the data of inputs and outputs to construct the frontier. The advantage of this method is that it does not require the specification of the production function.
However, for being deterministic, this method assumes that there is no statistical noise in data. The stochastic approach, called Stochastic Frontier Analysis (SFA), was mentioned first in Aigner, Lovell, and Schmidt (1977) and Meeusen and Broeck (1977). This method, contrary to DEA, requires a specific functional form for the Page | 5 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com production function and allows data to have noises. SFA is used more often in practice because for many cases, the noiseless assumption are unrealistic.
Since its first appearance in Aigner et al. Being able to deal with various production processes, this method has become a popular tool to analyze the performance of production units such as firms, regions and countries. Those applications can be found in Battese and Corra (1977), Page Jr (1984), Bravo- Ureta and Rieger (1991), Battese (1992), Dong and Putterman (1997), Anderson, Fish, Xia, and Michello (1999) and Cullinane, Wang, Song, and Ji (2006). Despite the fact that a rich literature of this matter has been developed over a long time, researchers at times find it difficult to choose the most appropriate model to estimate the technical efficiency level or determining its sources.
The earliest versions of these models were built to deal with cross sectional data (Aigner et al. These models need assumptions about technical inefficiency distribution and its uncorrelatedness with other parts of the model. Pitt and Lee (1981) and Schmidt and Sickles (1984) criticized that technical inefficiency cannot be estimated consistently with cross-sectional data and suggested models that deal with panel data. The literature of panel data models first come with the assumption of time-invariant technical inefficiency (Battese & Coelli, 1988; Pitt & Lee, 1981; Schmidt & Sickles, 1984).
Researchers, after that, claimed that it is too strict to assume technical inefficiency to be fixed through time and suggested models that allow its time-variation such as Cornwell et al. Those models solved the problems by imposing some time patterns. Nevertheless, the assumption of an unchanged time behavior was also criticized too strict. Then the model with technical inefficiency effects was created by Battese and Coelli (1995) which allows technical inefficiency to vary with time and other determinants.
Greene (2005) introduces “true” fixed and random models which warrant the unrestraint time changing of inefficiency and separate it from other firm specific factors. This thesis aims to estimate the technical efficiency level of Vietnamese metal manufacturing firms with panel-data stochastic frontier models. Besides, this study also reviews those panel data models of technical inefficiency analysis and gives some implication about model choice in this field. This Page | 6 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com study uses an unbalanced panel dataset of firms in metal manufacturing industry in the year 2005, 2007 and 2009 which is withdrawn from Vietnamese SMEs survey.
The result shows different technical efficiency levels among those stochastic frontier models. Research objectives - To give a review of panel-data stochastic frontier models; - To apply those models to investigate the technical efficiency of SME firms in metal manufacturing industry in Viet Nam. Page | 7 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com CHAPTER II: LITERATURE REVIEW 1. Efficiency measurement The main economic function of a business can be expressed as a process which turns its inputs into outputs with a specific producing ability.
The ratio outputs/inputs indicates the productivity of a specific firm (Coelli, Rao, O'Donnell, & Battese, 2005). Change in productivity reflects how well a production unit operates, in other words, how efficient it is. From economic perspective, growth in productivity or efficiency can be considered as the most popular proxy for firm performance. The terms productivity and efficiency need to be discriminated in the context of firm production.
On the one hand, productivity implies all factors that decide how well outputs can be obtained from given amounts of inputs. It can be considered as “Total factor productivity - TFP”. On the other hand, efficiency relates to the production frontier. This frontier shows the maximum output that can be produced with a level of input.
A firm is called efficient technically when it produces on this frontier. Firm production cannot go beyond this frontier for this is the limitation of its performing ability. When the firm performs below this frontier, it is considered inefficient. The farther the distance is, the more inefficient the firm is.
Changes in productivity can be due to the changes in efficiency (the firm becomes more or less efficient technically), a change in the amount and proportion of its inputs (changing its scale efficiency), a change in technical progress (change in technology level over time) or a combination of all the above factors (Coelli et al. Efficiency measurement can be approached from two sides, inputs and outputs. Input-oriented measures relate to cost reduction (minimum amount of inputs to produce a given amount of output). Output-oriented measure, on the other hand, makes use of the maximum level of output produced from a given amount of inputs.
Figure 2-1 and 2-2 illustrate these two approaches. Figure 2-1 demonstrates a firm with two inputs X1 and X2, YY’ is an isoquant which shows every minimum set of inputs that could be used to produce a given output. If a firm operates on this isoquant (the frontier), it will be technically efficient in an input-oriented way for the reason that the inputs amount of this firm is minimized. The iso-cost line CC’ (which can be constructed when the input-price ratio is known) determines the optimal proportion of inputs in order to archive lowest cost.
Technical efficiency (TE) can be calculated by the percentage rate of OR/OP, allocative efficiency (AE) equals the percentage rate of OS/OR. The multiplication of AE and TE Page | 8 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com expresses the overall efficiency of the firm, called economic efficiency (EE) (i. Figure 2-2, illustrate the case where the firm uses one input and produces one output, The f(X) curve determines the maximum output can be obtained by using each level of input X (the frontier). The firm will be technical efficient operating on this frontier.
In this situation, TE equals BD/DE. Figure 2 – 1: Input-oriented efficiency Figure 2 – 2: Output-oriented efficiency Measurements and analyses of TE were conducted by a huge number of studies with two main approaches – Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The next section briefly discusses these two methods. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) a.
Data Envelopment Analysis (DEA) DEA is a non-parametric method in estimating firm efficiency which was first introduced in Charnes, Cooper, and Rhodes (1978) with constant return to scale. Later on, it was extended to allow for decreasing and variable return to scale in Banker, Charnes, and Cooper (1984). Specific instruction can be found in Banker et al. (1984), Charnes et al.