The problem statement
Since the initiation of economic renovation in 1986, Vietnam has effectively transitioned from a centrally-planned to a market economy, achieving significant social and economic progress Between 2000 and 2010, the country experienced stable economic growth, averaging an annual rate of 7.2% By 2010, Vietnam's real GDP had increased 3.4 times compared to 2000, with state budget collections rising fivefold and GDP per capita reaching US$1,168 This remarkable growth has elevated Vietnam from the ranks of the poorest nations to that of middle-income countries Furthermore, Vietnam has made substantial strides in poverty alleviation, nearing universal primary education, enhancing maternal health, reducing child mortality, and advancing gender equality and women's empowerment.
In contribution to economic and social development, Vietnamese enterprises play a crucial role Business activities of enterprises have made significant progress In
From 1995 to 2007, the contribution of enterprises to GDP rose significantly from 45.3% to over 60%, reflecting a structural transformation in the economy This shift was driven by the growth of enterprises across various sectors and regions, resulting in a decreased reliance on agriculture and an increased emphasis on industry and services.
Manufacturing enterprises play a crucial role in addressing social issues by generating new job opportunities, enhancing employee income, and contributing significantly to the state budget Specifically, these enterprises have created 2.203 million jobs, which represents 47.3% of the total employment across all sectors (GSO, 2007).
The development of the economy, particularly in the manufacturing sector, faces significant challenges due to incomplete infrastructure and a lack of essential resources like electricity and water These shortcomings hinder productivity and diminish the overall efficiency and competitiveness of the economy, preventing it from reaching its full potential.
The performance of enterprises in Vietnam varies significantly due to factors such as resources, ownership types, business scale, and location Despite a more transparent and flexible business environment, individual enterprises may not experience consistent growth Overall, Vietnamese businesses exhibit unique characteristics that influence their outcomes.
Since the implementation of the Enterprise Law in 2000, there has been a remarkable surge in the establishment of new private companies, with over 72,600 new enterprises founded within three years, resulting in the creation of approximately 1.6 to 2 million jobs (ClEM, 2004) This growth is particularly striking when contrasted with the mere 26,000 private enterprises that were in operation by the end of 1998.
Enterprises in major cities like Hanoi and Ho Chi Minh City benefit from favorable conditions, including strategic geographical locations, advanced telecommunications and transportation, and a skilled labor force adept at implementing new technologies As a result, these cities account for approximately 47% of the country's total enterprises and generate 45% of national revenue (GSO, 2007) However, these businesses face significant challenges, such as inadequate infrastructure and a shortage of skilled labor Additionally, intense competition among local and domestic firms, coupled with ineffective policies, may hinder their growth and operational efficiency.
Vietnam's economy operates under a multi-sector market model that integrates market mechanisms with state regulations This framework encompasses state-owned, private, and foreign-invested enterprises, with state enterprises holding a pivotal role in driving economic growth.
The government leverages state enterprises to stabilize the microeconomic environment and regulate market prices for essential commodities like electricity, coal, transport, rice, and rubber As a result, these enterprises benefit from significant government support, prioritization, and subsidies Consequently, there are ongoing concerns regarding the efficiency of state enterprises compared to other sectors of the economy.
To enhance understanding of firm performance in Vietnam, it is crucial to clarify several key issues, including the production efficiency of businesses located in former Hanoi and Ho Chi Minh City compared to other regions Additionally, examining the efficiency levels of state-owned, foreign, and other sector firms is essential, along with identifying the factors that influence the technical efficiency of these enterprises.
This thesis aims to address critical issues within the manufacturing sector, which is chosen for research due to its significant role in the economy Manufacturing enterprises represent over 20% of all industries, contributing more than 30% of total revenue, employing approximately 50% of the workforce, and accounting for 22% of export value (GSO, 2006).
This thesis utilizes a stochastic frontier production model alongside a technical efficiency model to evaluate the technical efficiency of manufacturing firms It aims to identify the key determinants influencing the technical efficiency of these firms.
Figure 1.1: The share of manufacturing enterprises in all industries of Vietnam tn Q) ãc - =
Objectives of the research S
Basically, this thesis aims at four objectives as follows:
(1) To measure the level of technical efficiency of manufacturing firms in the period 2000 to 2004
This study aims to analyze the technical efficiency disparities between manufacturing firms situated in former Hanoi and Ho Chi Minh City compared to those in other provinces Additionally, it will evaluate the differences in efficiency among state-owned enterprises, foreign firms, and other types of businesses.
(3) To identify factors influencing the technical efficiency of manufacturing firms
(4) To suggest appropriate policies for improving technical efficiency of manufacturing firms
*Note: Former Hanoi: Because the data applied in the thesis from 2000 to 2004 Since August
1, 2008 Hanoi has merged with Hatay province and parts of neighboring of Vinhphuc and
Research questions
With the research objectives, the thesis is therefore going to answer the following questions:
(1) What is the level oftechnical efficiency of manufacturing firms?
(2) What are differences in technical efficiency of manufacturing enterprises located in former Hanoi*, Hochiminh city and other provinces; state-owned, foreign and other firms?
(3) What are factors affecting the technical efficiency of manufacturing firms?
Research methodology
The descriptive statistics, quantitative analysis are used to solve with the research questions
The Cobb-Douglas production function, utilized within the stochastic frontier production model, serves to estimate and assess the technical efficiency of manufacturing companies This analysis enables a comparative evaluation of technical efficiency among manufacturing firms situated in former Hanoi and Ho Chi Minh City versus those in other regions, as well as between state-owned and foreign enterprises in relation to other manufacturing groups.
The thesis investigates the factors affecting the technical efficiency of enterprises in its second stage Utilizing panel data analysis, it incorporates both time series and cross-sectional data through methods such as Pooled Ordinary Least Squares (OLS), Random Effects Model (REM), and Fixed Effects Model (FEM) to ensure robust results.
The data set applied for this thesis comes from the Vietnam Enterprise Survey conducted by the General Statistic Office in the period 2000- 2004.
Thesis structure
This thesis is organized into five chapters, with the subsequent four chapters detailing key components of the research Chapter 2 reviews literature on production functions, technical efficiency, stochastic frontier production functions, and relevant empirical studies Chapter 3 outlines the research methodology and data utilized in the study Chapter 4 presents the research findings, while Chapter 5 concludes with insights, recommendations, and limitations of the research.
LITERATURE REVIEW
Introduction
This chapter systematically reviews the literature related to the research problems, guiding the thesis in a coherent direction It is organized into four sections: the first introduces fundamental concepts and theories, including production functions, the Cobb-Douglas production function, technical efficiency, its measurement, and the stochastic frontier production function The second section presents empirical studies on technical efficiency, laying the groundwork for the analytical model of the thesis Finally, the chapter concludes with a summary of the theoretical and empirical reviews, along with proposed applied models for this research.
Basic Concepts and Theoretical Review
A production function defines the technical relationship between the quantities of productive factors utilized and the resulting output, based on the most efficient production methods available.
A general production function can be written as:
Q is the quantity of output
X~> Xz, , Xn are the quantity of factor inputs such as capital, labor, raw materials, etc
The production function illustrates the maximum output achievable through a specific combination of inputs, considering the current technology In the production process, inputs are both adjustable and interchangeable, allowing for flexibility in achieving desired results.
The production function typically employs monetary values to represent the relationship between inputs and outputs, despite its fundamentally physical nature This process involves various types of inputs that cannot be easily quantified in physical units and generates multiple outputs measured in different physical units To address the complexities of multiple outputs, one effective method is to aggregate different products by applying price weights to them (Mishra, 2007).
There are many kinds of production function that can be used in empirical studies as follows:
- Linear production function is a function that assumes a perfect linear relationship between inputs and total output
- Leontief production function is a function that assumes the inputs are used in fixed proportions
- Cobb-Douglas production function is a function that assumes some degree of substitutability between inputs
- Other production functions such as quadratic, transcendental-logarithm (translog), and etc
2.2.2 The Cobb-Douglas production function
The Cobb-Douglas functional form is the most commonly used model in econometrics due to its mathematical simplicity, making it a favorite among applied researchers.
The Cobb-Douglas production function with two inputs of labor and capital is as follows:
Y is total production (the monetary value of all goods produced in a year)
A is total factor productivity or the technology state
(2.2) a and ~ are the output elasticities of labor and capital, respectively These values are constant, determined by available technology
Output elasticity measures the responsiveness of output to a change in levels of either labor or capital used in production lfa+~=l Ifa+~ 1: increasing returns to scale
There are various methods for estimating the parameters of a Cobb-Douglas production function, with the most common approach utilizing a linear equation By applying logarithms to both sides of the equation, the function is transformed into a log-linear form, represented as log Yi = log A + a log Li + ~ log Ki.
Y, A, Land K are as defined earlier
The residual from estimation of function 2.3 is a random error term or a disturbance term named Ui The disturbance U is different for each firm and assumed to have normal distribution
According to Farrell (1957), total economic efficiency includes two components that are technical efficiency and allocative efficiency
Technical efficiency refers to a firm's capacity to either maximize output from a specific set of inputs or minimize inputs for a predetermined output level, as defined by Koopmans (1951) In contrast, allocative efficiency highlights the firm's capability to utilize inputs in the most effective proportions based on market prices and the production technology employed.
Figure 2.1 illustrates the relationship between technical efficiency and scale efficiency in the production process The ABC line represents the production frontier, where points along this line indicate maximum pure technical efficiency In contrast, the through-origin line denotes scale efficiency, with points on this line reflecting constant returns to scale Scale efficiency assesses whether a firm operates at its optimal scale, as a firm may achieve technical efficiency through either pure technical efficiency or scale efficiency.
Points A, B, C, D, and E illustrate a specific combination of input and output levels, with observations A, B, and C positioned on the frontier line, indicating pure technical efficiency In contrast, observations D and E fall below this frontier The tangential line at point B signifies the constant returns to scale of technology, highlighting B's role as a benchmark for relative technical efficiency At this point, the firm achieves both pure technical efficiency and scale efficiency, thanks to its location on the frontier and the presence of constant returns to scale.
Observations A and C are on the frontier so they are purely technical efficiency However, these points are not efficient in scale
Observation D demonstrates inefficiency in both scale and technique, indicating that the firm could optimize its resources By reallocating the same level of input, the firm has the potential to enhance its output significantly Transitioning from point D to the frontier between points B and C would enable the firm to achieve better performance and efficiency.
Observation E is technically inefficient as it falls below the efficiency frontier; however, it demonstrates scale efficiency by producing at an input level of x2, matching the scale efficiency of point B.
Figure 2.1 Illustration of technical efficiency
Numerous researchers have analyzed the technical efficiency of the firm, utilizing various methods Among these, the stochastic frontier production function (SFPF), a parametric approach, and data envelopment analysis (DEA), a non-parametric approach, are the most commonly employed techniques.
The stochastic frontier production function (SFPF) approach estimates a production function based on a specified technology, applicable to all firms within the same sector In SFPF, the residuals are divided into two components: the first being a nonnegative distribution representing technical inefficiency This technical inefficiency reflects the gap between a firm's actual production level and the optimal frontier output (Minh and Dong).
2005) The other component is assumed to have a symmetric distribution which refers to as random components
Data Envelopment Analysis (DEA), introduced by Farrell in 1957, utilizes a deterministic non-parametric frontier created through mathematical programming techniques, leveraging observed input-output data from sample firms to assess efficiency.
Data Envelopment Analysis (DEA) operates on minimal assumptions regarding production technology and does not require a predefined functional specification Utilizing these assumptions, DEA empirically constructs a production frontier through mathematical programming techniques, drawing from the observed input-output data of selected firms.
Data Envelopment Analysis (DEA) offers several advantages for estimating efficiency, as it does not necessitate the specification of production technology or the statistical distribution of inefficiency residuals Moreover, DEA effectively handles multiple outputs and eliminates the need for assumptions regarding the functional form of production (Minh and Long).
Empirical Studies
Technical efficiency has been extensively studied in Vietnam and other countries, with research typically concentrating on specific industries or comparing the technical efficiency of various sectors within an economy and among firms in different locations Additionally, analyzing the determinants of technical efficiency remains a compelling area of investigation This section will review several prior studies on the subject.
Oleg et al (2006) uses the panel data set with total of 35,000 firms in the period
Between 1992 and 2004, a study analyzed 256 industries using data from the German Cost Structure Census to assess technical efficiency Researchers investigated the correlation between production inputs—such as materials, labor compensation, energy consumption, capital, and external services—and the outputs, which included the value of gross production adjusted for subsidies and excise taxes The goal was to understand how these factors influence technical efficiency within various industries.
Analysis indicates that industry-specific effects are the primary determinant of variations in technical efficiency, followed by firm size and location as the second and third most significant factors Notably, smaller firms tend to operate more efficiently than their larger counterparts In contrast, factors such as R&D intensity, outsourcing activities, and legal structure have a comparatively lesser impact on technical efficiency, with R&D intensity showing a negative effect.
Research shows that the impact of R&D spending on technical efficiency is influenced by a time lag, resulting in improvements over time The study indicates that technical efficiency remains constant over time; however, it does not clarify whether annual fluctuations lead to an increase or decrease in average efficiency.
This research offers valuable insights for future studies by leveraging the advantages of a panel data approach and employing a transcendental logarithmic (translog) production frontier function However, its applicability may be limited in other contexts where essential input variables are absent Additionally, the study identifies key determinants of technical efficiency, considering both internal factors, such as firm size, outsourcing activities, and ownership structure, as well as external factors like industry affiliation, location, temporal effects, and market share.
Donghyun et al (2009) investigate the productivity growth of the Swedish economy by analyzing panel data from 5,893 manufacturing and service firms between 1992 and 2000, totaling 38,000 observations They employ a production function to estimate technical change and productivity growth, where the output (Yit) represents the firm's value-added, and the inputs (Xit) consist of various factors Additionally, the perpetual inventory method is used as a proxy for the capital stock.
From frontier production function, the authors estimate the error term, Uitã Then it is specified as a two-way error component model as follows:
In this analysis, IJ.i represents firm-specific effects, A.t denotes time-specific effects, and Vit accounts for statistical noise To prevent over-parameterization, the firm-specific effects I-ii are substituted with industry-specific effects, labeled as lldã.
Recent findings indicate a positive correlation between returns to scale and firm size, with smaller firms utilizing labor more efficiently than capital, while larger firms exhibit the opposite trend Small firms tend to operate near their optimal production scale, whereas small-medium, medium, and large firms could enhance their efficiency by reducing their scale Additionally, the estimates of technological change may be biased due to variations in input proportions, highlighting the impact of production technology on input distribution.
Elina (2006) assesses the technical efficiency and factors contributing to inefficiency within the Finnish information and communication technology (ICT) manufacturing sector The study utilizes unbalanced panel data from 1990 to 2003, focusing on firms with a minimum of 20 employees in the ICT equipment manufacturing industry.
Key determinants of inefficiency include R&D investments, the firm-specific Lerner index (operating profit to gross output ratio), leverage ratio, ownership status (domestic vs foreign), exporter status, size, and age Findings reveal that the average firm operates at approximately 56% of the technical efficiency level of frontier firms Additionally, technical efficiency significantly varies among firms, with time-varying efficiency averaging just over 40% of the reference rate of the most efficient firms.
This research highlights the use of a stochastic frontier model employing four distinct approaches to assess both time-invariant and time-varying efficiency levels Notably, the Battese-Coelli maximum likelihood model proves to be more suitable than the ordinary least squares model, while the translog production function emerges as the most effective option for this analysis.
Alvarez and Gonzalez (1999) propose a method that integrates panel and cross-sectional data to assess technical efficiency, utilizing a balanced panel of 82 dairy farms alongside cross-sectional input quality data Their findings indicate a predicted technical efficiency value of 72% Subsequently, they apply the cross-sectional input quality information to calculate a corrected technical efficiency index, which remains unchanged at 72%.
Technical efficiency is significantly influenced by the quality of inputs, particularly in terms of land and livestock, highlighting a relationship not previously identified Initial analyses show a positive correlation between technical efficiency and farm size, which shifts to a negative correlation after adjustments Unobservable factors play a crucial role in explaining variations in technical efficiency, as demonstrated by the Corrected Ordinary Least Squares method, although this approach relies on the availability of pertinent information.
In order to avoid the multi-collinearity, Marco (2010) uses a stochastic frontier production function in the form of Cobb-Douglas including a time trend to capture the Hick-neutral technical change:
Where: t is a time trend which captures the Hicks-neutral technical change; Y is output; K and L are capital and labour, respectively
The researcher analyzes an unbalanced panel of 14 EU member countries from 1970 to 2005 using the Kumbhakar and Lovel (2000) model to decompose Total Factor Productivity (TFP) growth into four components: technical change, scale, technical efficiency change, and allocative inefficiency The study reveals insights into technology change and average TFP growth across these countries during the specified period However, a key limitation is the inadequacy of the dataset for estimating technical efficiency comprehensively across the entire sample For future research, a larger dataset is essential, which poses challenges for studies involving countries outside the EU.
In researching the technical efficiency and its determinant of manufacturing firms in China, Wu (2002) uses data set of many firms in 30 regions in 1995, with total of
The study analyzes 5,160 observations using a two-stage approach to assess technical efficiency In the first stage, a standard frontier production function is utilized to estimate technical efficiency rates specific to various regions and sectors The second stage employs Tobin models to explore how region- and sector-specific factors influence these technical efficiency rates.
Analytical framework for the research
The stochastic production frontier function is a widely recognized and effective method for analyzing production technical efficiency, building on the output-oriented technical efficiency concept introduced by Farrell in 1957 and later popularized by Aigner et al (1977) and Meeusen and Broeck (1977) This two-stage analytical approach has been extensively applied in various studies, as summarized in Table 2.1 and reviewed in this chapter.
In the initial phase, econometric models can be utilized for production functions, including forms like Cobb-Douglas and transcendental-logarithm Technical efficiency is assessed through the estimated frontier model, with support from software such as Frontier version 4.1 or Stata The Cobb-Douglas function is predominantly chosen for its simplicity and clarity, making it a preferred choice in various production functions This thesis specifically adopts the Cobb-Douglas production function for the manufacturing sector.
In the second stage, we estimate technical efficiency using the production function, followed by an analysis of selected factors to assess their impact on the firms' technical efficiency.
In summarizing the technical efficiency of manufacturing firms and its determinants, according to the theoretical and empirical evidence, the analytical framework is presented in the figure 2.2 below
Labour (L): Number of employees Capital (K): Owner's equity Output: Net turnover (Y) l
TE= Real Output/Potential Output l
- Years of operation (YearN ear)
- Location in former Hanoi (Loc 1)
- Location in Hochiminh City (Loc2) l
(1) To measure the level of technical efficiency of manufacturing firms in the period 2000 to 2004
This study aims to analyze the differences in technical efficiency among manufacturing firms situated in former Hanoi and Ho Chi Minh City compared to those in other provinces Additionally, it will evaluate the efficiency variations between state-owned enterprises, foreign firms, and other types of businesses.
(3) To identifY factors influencing the technical efficiency of manufacturing firms
(4) To suggest appropriate policies for improving technical efficiency of manufacturing firms.
RESEARCH METHODOLOGY AND DATA COLLECTION
Introduction
This chapter is organized into four key sections: the first section introduces the models utilized in the thesis, while the second section addresses the treatment of proxy variables, data collection methods, and data analysis techniques The third section focuses on hypothesis testing, and the final section provides a detailed explanation of the data collection process.
Research methodology
The thesis employs a two-stage methodology to analyze technical efficiency Initially, it utilizes a stochastic frontier model to quantify technical efficiency, defined as the ratio of observed output to optimal output In the subsequent stage, the study regresses technical efficiency against various factors and attributes that influence performance efficiency.
(3.1) Where ln(Yi1) is the logarithm of the output for the i firm in the timet;
Total factor productivity (A) represents a common mean value for the intercept, while lnKit denotes the logarithm of capital input for firm i, and lnLit signifies the logarithm of labor input for firm i.
Xi 1: are some individual characteristics of the firm such as location, ownership a, p and 8 are unknown parameters to be estimated;
Vit is a random variation in output due to factors outside the control ofthe firm It's assumed to be independently and identically distributed as normal random variables with zero mean;
Uit is a non-negative random variable accounting for technical inefficiency in the production of the firm And, Uit is measured by two following methods:
-The time-invariant model (ti): Uit is assumed to have a truncated-normal random distribution and has constant value over time within panel Uit=Ui
The time-varying decay model (TVD), also known as the Battese-Coelli model (1995), posits that the inefficiency term (Ui) is represented as a truncated-normal random variable that can change over time This model suggests that the inefficiency is influenced by a specific time-dependent function, allowing for a more dynamic analysis of inefficiency in various contexts.
T corresponds to the last time period in each panel;
The decay parameter, denoted as 11, plays a crucial role in assessing inefficiency over time When 11 is greater than zero, it indicates a decrease in inefficiency as time progresses Conversely, if 11 is less than zero, inefficiency tends to increase over time In cases where 11 equals zero, inefficiency remains constant throughout the observed period.
Ui is assumed to be independent and identically distributed non-negative random variables that are obtained by the truncation at zero of the N(!J.,cr 2 ) distribution
The maximum likelihood estimation is used to calculate technical efficiency as follows and (3.3)
In the context of efficiency analysis, cr²v represents the variance of noise, while cr²u indicates the variance of inefficiency effects When the value of a²u equals zero, it signifies that Ui is also zero, indicating that firms are operating at full efficiency The variable y denotes the total variation of actual output relative to the frontier level, ranging from 0 to 1 A value of y equal to 0 suggests that deviations from the frontier are due to technical inefficiency, whereas a value of y equal to 1 implies that these deviations are attributed to random error.
The next part will present the measurement ofY, K and L variables
The output value (Y) represents the total net turnover at the end of the year, measured in millions of Vietnamese dong (VND) This figure reflects the total earnings of an enterprise from product sales after deducting taxes and reductions such as discounts, price reductions, and returned goods It is important to note that net turnover excludes income from financial activities and special transactions, including asset sales or compensation from contract violations.
The capital input (K) is defined as the total equity of an enterprise at the end of the year, measured in millions of VND This capital represents the ownership stake of the enterprise's proprietor, members of joint-venture companies, shareholders in joint-stock companies, and funds allocated from subsidiary companies to their parent company.
The labour input (L) refers to the total number of employees or the overall compensation provided to them In this model, it represents the aggregate of individuals that a business employs and compensates with wages.
This thesis employs Stata software to analyze a stochastic frontiers model using panel data from 2000 to 2004 The study aims to compare the technical efficiency of manufacturing firms by separately estimating data for firms located in former Hanoi, Ho Chi Minh City, and other provinces, as well as categorizing results by state-owned, foreign-owned, and other sectors.
Table 3.1 Summary ofvariables in the frontier production function:
Variable of Description Unit variable type y Numeric Output value, measured by net
Million VND turnover (million dong)
K Numeric Capital input measured by Owner's
Million VND equity at the end of the year
Labour input measured by the
L Numeric number of employees at the end of employees the year
StaEnt Dummy State-owned firms
ForEnt Dummy Foreign-owned firms
Locl Dummy Firms located in former Hanoi Loc2 Dummy Firms located in Hochiminh city
The technical efficiency for the ith company in the time t Is calculated as the conditional expectation of e-uit with respect to Cit (Cit= Vit - Uit):
Then, the technical efficiency of a firm is modeled to analyze its determinants In this thesis, the selected determinants are as follows
K21 represents the total capital-labour ratio, serving as a key indicator of technical intensification in manufacturing companies This ratio helps determine whether a firm is more labour-intensive or capital-intensive A higher total capital-labour ratio indicates that workers are equipped with more machinery, which is expected to lead to increased output and improved technical efficiency for the firm.
The age of an enterprise is determined by subtracting its establishment year from the survey year, indicating its operational duration Generally, firms with more years in operation benefit from greater experience, improved management, and a more skilled workforce, which typically leads to enhanced technical efficiency.
- Age2: It is the square of year of operation The relationship between year of operation and technical efficiency is expected as not a normal linearity
- Size (Size of firm): There are six categories: firm has less than 10 employees= 1; 10-200 employees= 2; 201-300 employees= 3; 301-500 employees= 4; more than
The liquidity ratio, or Liq, is calculated by subtracting inventories from a company's current assets and then dividing by current liabilities This ratio indicates a firm's capacity to fulfill its short-term financial obligations within a year A positive correlation is anticipated between the liquidity ratio and technical efficiency, highlighting the importance of effective asset management in maintaining financial health.
The StaEnt variable serves as a binary indicator within the Vietnam Enterprise Survey, assigning a value of 1 to firms classified as state enterprises and 0 to those that are not This classification encompasses central state enterprises, local state enterprises, and collective enterprises.
ForEnt (Foreign enterprise) is a binary variable used to categorize firms, assigning a value of 1 if the firm is a foreign enterprise and 0 if it is not According to the Vietnam Enterprise Survey, foreign enterprises encompass those with 100% foreign capital, joint ventures with foreign partners, and other collaborations involving foreign entities.
-Loci (Location 1): It is a dummy variable which has two values as 1 ifthe firm is located in former Hanoi; and 0 if the firm is not located in former Hanoi
- Loc2 (Location 2): It is a dummy variable which has two values as 1 ifthe firm is located in Hochiminh City; and 0 if the firm is not located in Hochiminh City
Where: TE is technical efficiency of the firm i in the time t; and explanatory variables are explained in the table 3.2 below:
Table 3.2 Summary of variables in the technical efficiency model:
Variable of Description Unit type variable
Capital-labor ratio represented by total
Million K21 Numeric capital of the firm divided by total
VND/employee number of employees
Age Numeric The years of operation Year
Age2 Numeric The square of years of operation
Six categories: firm has less than 10 employees= 1; 10-200 employees = 2;
Size Numeric 201-300 employees= 3;301-500 employees = 4; more than 500 employees = 5
Liquidity ratio is the ratio of current Liq Numeric assets of a company minus inventories to current liabilities StaEnt Dummy State enterprise
Loc1 Dummy Firms located in former Hanoi
Loc2 Dummy Firms located in Hochiminh City
Testing Hypothesis
- The first hypothesis is to identify the technical inefficiency effects in the model that can be formulated as: Ho: y=O and H 1: y>O
If H 0 is accepted, there are no technical inefficiency effects in the model In case the hypothesis is rejected, it's concluded that there are technical inefficiency effects in the model
The second hypothesis aims to determine if the manufacturing sector experiences constant returns to scale, represented by Ho: a+~=1 and H1: a+~≠1 If Ho is accepted, it would confirm that the manufacturing industry operates under constant returns to scale.
- The third hypothesis is to identify whether technical inefficiency effect vary over time H0: 11=0 and H 1: Tj:;i:O
IfH0 is accepted, it confirms the technical inefficiency effect is time-invariant And the alternative hypothesis is accepted proving that the technical inefficiency effects vary in the period
The thesis utilizes panel data, necessitating various tests to determine the most suitable model among pooled Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM).
The fourth hypothesis investigates the dependence of technical efficiency on several selected factors, including the capital-labor ratio, years of operation, the square of years of operation, firm size, liquidity ratio, ownership status (state-owned versus foreign-owned), and the geographic location of firms in major cities like Hanoi or Ho Chi Minh City The null hypothesis (Ho) posits that all these factors have no effect, represented as v1 = v2 = v3 = v4 = v5 = v6 = v7 = v8 = v9 = 0, while the alternative hypothesis (H1) suggests that at least one of these factors significantly differs from zero.
Data Collection
The data used in this thesis is the panel data from the Vietnam Enterprise Survey (VES) conducted by the General Statistic Office (GSO) in the period 2000-2004
Manufacturing enterprises are categorized into various sub-sectors based on their distinct production processes, material inputs, equipment, and employee skills These sub-sectors include the manufacture of food products and beverages, tobacco, textiles and apparel, leather goods, wood products, paper, chemicals, rubber, machinery, electronic products, motor vehicles and transport, furniture, and recycling.
According to statistical regulations, each firm is assigned a unique code in the original data set The manufacturing sector data reveals a growth from 10,255 enterprises in 2000 to 20,532 enterprises by 2004 For analysis, only firms meeting specific criteria are selected from this manufacturing data set.
Between 2000 and 2004, the firm collected comprehensive data on output, labor, owner's equity, total capital, and current assets Notably, all metrics, including output, labor, owner's equity, total capital, and current assets, demonstrated positive values during this period.
The firm operates under a unique code, but in cases where two companies share the same code, additional criteria such as province code and year of establishment are utilized for differentiation, leading to the removal of one of the firms.
The dataset, compiled using Excel, encompasses panel data from 2000 to 2004 Manufacturing firms with incomplete information on output value, capital, labor, owner's equity, total capital, or current assets during this period have been excluded from the dataset.
Finally, the data set includes total 3,079 manufacturing companies that have adequate data in the period 2000- 2004.
ANALYSIS RESULTS
Sample profile
Table 4.1 presents the descriptive statistics of production factors, categorizing them by manufacturing firms situated in former Hanoi, Ho Chi Minh City, and other provinces, while also distinguishing between state-owned and foreign-owned enterprises.
Table 4.1: Descriptive statistics of output, capital and labour of manufacturing firms in the period 2000-2004:
All Former Hochiminh Other State Foreign Other Manu Hanoi' city's provinces' owned owned sector'
Firms firms firms firms firms firms firms
Std.~~~~ - -iã-~ã;ã:~~;- -;~:~;;, ~-~~,;~;-T 101,465 132,022 ! 191,266 ! !?&~2 Min -ããã-ãããã-ãããã-ããã-1 0 ãããã-ããã-ããã -~ -~ -~ -ãããiã-~ -~ ããã -~]- ããã -i~T -ã-ããã-ãããã ~~;ãã -~ -ããã 11
Mean 317 222 ããã ããã-ãã-ããã iããã=-:=-= +ããã ã-ããã: ::: : +ããã -=-:: :: +ããã-ããã= -= : + -ã-ããã-=-= -=:-+ããã:::: ::: :: II
From table 4.1, we have some statistical descriptions as follows:
By locality: Manufacturing firms in Hochiminh city have highest mean of capital,
Hochiminh City firms benefit from significant capital and a workforce of 417 employees, resulting in impressive revenues of 84,749 million VND In contrast, former manufacturing firms in Hanoi exhibit lower averages in capital, labor, and output, which is surprising given the advantages typically associated with a major city.
Foreign-owned firms exhibit a significantly higher average owner capital of 81,711 million VND, which is 2.6 times greater than the 37,031 million VND average of State-owned firms Additionally, the average workforce in foreign-owned firms stands at 521 employees, closely paralleling the 464 employees found in State firms.
In the analyzed sector, comprising 1,645 firms, the average workforce is 168 employees, and the average capital is 6.030 million VND, indicating that many manufacturing companies operate on a small scale This suggests a prevalence of small and medium enterprises within the sector The distribution of employees among these firms is illustrated in Graph 4.1.
Graph 4.1: The structure of 1,645 manufacturing firms from other sectors
Technical efficiency
The maximum likelihood estimates of the parameters from the time invariance inefficiency (ti model) and the time-varying inefficiency (tvd model) stochastic frontier production function are presented in table 4.2:
Table 4.2: Estimates ofti model and tvd model:
Time invariance inefficiency Time-varying inefficiency
Variables Par (ti model) (tvd model)
Coef S.E t- p- value value value value lnK a 0.467 0.009 51.07 0.000 0.412 0.009 43.71 0.000 lnL p 0.600 0.010 57.27 0.000 0.606 0.010 58.79 0.000
Firms in fonner Hanoi 81 0.154 0.054 2.83 0.005 0.175 0.0552 3.18 0.001 Firms in HCM City 82 0.143 0.040 3.55 0.000 0.182 0.0411 4.43 0.000 State-owned firms 83 -0.076 0.043 -1.77 0.076 -0.026 0.0436 -0.61 0.543 Foreign-owned finns 84 0.194 0.050 3.89 0.000 0.353 0.0512 6.90 0.000 constant LnA 6.565 1.424 4.61 0.000 6.541 0.2309 28.33 0.000 mu J.! 4.087 1.423 2.87 0.004 3.548 0.2200 16.13 0.000 eta l1 0.020 0.0015 14.02 0.000
Sigma_v2 cr2 v 0.344 0.0044 0.326 0.0042 e (residual error) 0.0284 0.0282 var (u)/var (a) 0.845 0.851
Source: Author's calculation i Table 4.3: The statistical tests of some hypothesis:
Null hypothesis Chi2 p-value Decision
Lr chi 2 (1) H 0 : ti model nested in tvd model 513.89 0.000 Reject Ho
Before concluding the estimation of the stochastic frontier model, it is crucial to determine the presence of technical inefficiency effects The results in Table 4.3 indicate that the null hypothesis (y=0) is rejected in both the ti and tvd models This aligns with the average technical efficiency findings, which are 0.710 for the ti model and 0.715 for the tvd model Consequently, this suggests that manufacturing firms are operating at approximately 71 percent of their potential output on average.
In the two models most of parameter estimates have statistical significant p-value
In the ti model, the output elasticity for capital and labor are 0.467 and 0.600, respectively, while the tvd model shows values of 0.412 for capital and 0.607 for labor These findings indicate that labor accounts for a significantly larger share of output compared to capital.
The combined output elasticity of the two models is slightly above one, measuring 1.066 in the INTI model and 1.018 in the TVD model, indicating that the manufacturing sector experiences increasing returns to scale This finding aligns with the results presented in Table 4.3.
- - - - test rejects null hypothesis that the production function exhibits constant returns to scale
The positive and statistically significant coefficients for loc 1 and loc 2 in both models indicate that manufacturing firms situated in major cities like Hanoi and Ho Chi Minh City experience higher output levels compared to those in other provinces.
The analysis reveals that the coefficient for the foreign variable is both positive and statistically significant, with values of 0.194 in the inti model and 0.353 in the tvd model Conversely, the coefficient for the State variable is negative, recorded at -0.076 in the inti model with a significance level of 10 percent, while it lacks statistical significance in the tvd model.
The rejection of the hypothesis that the ti model is nested within the tvd model highlights the superior relevance of the tvd model in explaining technical inefficiency Consequently, the tvd model is chosen as the preferred framework for analyzing the technical efficiency of the manufacturing sector.
In the TVD model, the positive estimate for the parameter 11 (11 = 0.020) indicates a decline in technical inefficiency effects over time, suggesting a positive technological catch-up rate in the manufacturing sector This finding is further supported by the results in Table 4.3, which reject the null hypothesis that the eta value (11) equals zero, confirming that efficiency varies over time.
The variance ratios for the ti and tvd models show minimal differences, with var(u) accounting for 84.50% and 85.10% of the estimated variance of the residual error term, respectively This indicates that approximately 85% of the variation in manufacturing production output is attributed to differences in technical inefficiency.
Comparison of technical efficiency
Table 4.4 presents the technical efficiency of manufacturing firms across various locations, including former Hanoi, Ho Chi Minh City, and other provinces It also compares the efficiency among state-owned, foreign-owned, and manufacturing firms from other sectors.
Table 4.4: Summary of technical efficiency between ti model and tvd model:
Returns efficiency of Capital - K of Capital - L to Scale 11
3 Manufacturing firms in Hochiminh City (833 firms)
4 Manufacturing firms in other provinces (1,868firms)
5 State-owned manufacturing firms (81 0 firms)
6 Foreign-owned manufacturing firms (624 firms)
7 Other sector's manufacturing firms (1,645 firms)
Note: IRS: increasing returns to scale; DRS: decreasing returns to scale
The analysis of technical efficiency across various manufacturing firms in Vietnam reveals consistent results, with the time-varying parameter (tvd) model indicating higher efficiency than the time-invariant (ti) model This suggests a positive technological catch-up rate within the sector, which varies and improves over time Notably, firms in Ho Chi Minh City exhibit the highest catch-up rate at 0.0323, while those in former Hanoi show the lowest at 0.0002 Additionally, foreign-owned firms lead in efficiency with a catch-up rate of 0.046, contrasting with other sectors that have a lower rate of 0.017.
Most manufacturing sectors, excluding those in Ho Chi Minh City, experience increasing returns to scale Additionally, all firm groups demonstrate a higher output elasticity with respect to labor compared to capital.
The analysis of technical efficiency across 3,079 firms reveals that output elasticity with respect to capital is 0.467, while output elasticity with respect to labor is 0.600 This indicates that a 1% increase in capital input, with labor held constant, leads to a 0.467% increase in output Conversely, maintaining capital input steady, a 1% rise in labor input results in a 0.6% increase in output.
* Technical efficiency by location of firms:
By locality, firms in former Hanoi attain the highest level of technical efficiency, 0.789; manufacturing firms located in other provinces have the lowest level, 0.688;
Foreign-owned firms achieve the highest technical efficiency rating of 0.831, while state-owned manufacturing firms follow closely with a rating of 0.80, placing them in the second position.
Manufacturing firms of other sectors which are small scale of labour and capital have the lowest technical efficiency, 0.683.
Technical efficiency model
From tvd model, the technical efficiency level is estimated first and then it is used to execute some testing process and analyze factors impacting on technical efficiency
4.4.1 Testing for the most appropriate model
When analyzing panel data, three common methods are utilized: Pooled Ordinary Least Squares (POLS), Random Effects Model (REM), and Fixed Effects Model (FEM) To determine the most suitable model for the analysis, it is essential to conduct tests that evaluate the performance of each method.
First, testing for random effects (POLS vs REM) by Breusch-Pagan Lagrange multiplier rejects the null hypothesis that variances across firms are zero So, POLS is un-appropriate (appendix 21 )
The Hausman test, which compares the Random Effects Model (REM) and the Fixed Effects Model (FEM), evaluates whether the difference in coefficients between these two models is systematic, indicating that unique errors are correlated with the coefficients The results of the test reject the null hypothesis, leading to the conclusion that the Fixed Effects Model is the most suitable choice for this analysis (see Appendix 21).
A test for heteroskedasticity is performed to check whether group 1s heteroskedasticity in FEM And, it rejects the null hypothesis which is homoskedasticity and concludes the presence ofheteroskedasticity (appendix 22)
I utilized the fixed effects model to analyze the determinants of technical efficiency using data from 3,079 manufacturing firms spanning 2000 to 2004 To address heteroskedasticity, I employed the "robust" option in Stata The findings regarding the factors affecting technical efficiency are presented in Table 4.4.
Te Coefficients Std Err t p-value
Note: *; * * and * * * denote significance at 10%, 5% and 1% levels, respectively
The model's p-value of zero indicates a rejection of the null hypothesis, suggesting that not all coefficients are equal to zero Additionally, the R-squared (within) value of 0.3343 demonstrates that the chosen factors account for approximately 33.43% of the total variation in technical efficiency.
From the result in table 4.5, the determinants of technical efficiency are expressed in more details as follows:
The coefficients for the K21 and Liq variables lack statistical significance at the 10% level, while the other coefficients demonstrate statistical significance This may indicate that the period from 2000 to 2004 was insufficient to adequately assess the impact of these variables on technical efficiency.
The analysis of the efficiency model reveals that the parameters Age and Age² are statistically significant at the 1% level, indicating that age influences performance in an inverted U-shaped relationship This relationship can be expressed by the equation Te = f(Age, Age²) = 0.08305*Age - 0.00083*Age².
To determine the maximum level of technical efficiency, we take the first derivative of technical efficiency concerning age, set it to zero, and solve for the critical point This analysis reveals that the peak technical efficiency occurs at the age of 50.
The result can be explained that after the firm's foundation, technical efficiency increases along with years of operation It attains the maximum level at the age of
As a firm is established, it experiences increasing efficiency due to the introduction of new machinery and the skill development of employees Over time, the company reaches its optimal production level; however, its operational effectiveness may gradually decline due to fluctuations in labor, management, and other factors.
The Size variable significantly impacts technical efficiency at a 1% level, with a value of 0.42275, indicating that larger companies operate more efficiently than smaller ones in terms of employee quantity This suggests that firms with a higher scale of labor economy achieve greater operational efficiency compared to their smaller counterparts.
Larger firms typically benefit from more substantial and stable trading orders, along with superior management compared to smaller companies However, they often encounter challenges such as administrative management issues, difficulties in fostering business innovation, and conflicts of interest among management teams.
Chapter Summary
Manufacturing firms in Vietnam have increasing returns to scale; the output elasticity of labour is higher that of capital
From 2000 to 2004, manufacturing firms experienced varying levels of technical efficiency, with an average efficiency rate of 71.50 percent Additionally, the manufacturing sector demonstrated a positive technological catch-up rate of 0.020, indicating progress in adopting new technologies.
Significant disparities in technical efficiency exist among various groups of manufacturing firms Firms situated in former Hanoi demonstrate a higher level of technical efficiency, while foreign-owned firms achieve the highest scores in this regard In contrast, state-owned firms exhibit a level of technical efficiency that is nearly on par with that of foreign-owned firms.
The size of a firm significantly enhances its technical efficiency, while the years of operation also play a crucial role Manufacturing firms tend to achieve peak operational efficiency around an average age of 50 years, after which production efficiency begins to decline Additionally, the study indicates no significant relationship between the capital-labor ratio and liquidity ratio with technical efficiency.