Introduction
Problem Statement
In recent decades, technology has become integral to workplaces in advanced countries, significantly impacting various industries Research from the U.S Bureau of Labor Statistics in 1987 highlights how technological advancements enable manufacturers to reduce costs and enhance competitiveness in both domestic and international markets Numerous studies, including those by Berman, Bound, Griliches (1993), and Doms, Dunne, and Troske (1997), provide empirical evidence that the adoption of advanced technologies in manufacturing alters employment structures and boosts labor productivity These studies indicate that the demand for skilled workers is rising as companies increasingly seek to adapt to new innovations, while low-skilled employees face job losses due to technological advancements Furthermore, researchers assert that technology allows workers to complete more tasks in less time, thereby improving overall productivity.
In Vietnam, the manufacturing sector is vital for economic growth, significantly contributing to GDP, job creation, and foreign trade As globalization intensifies and competition rises, innovation becomes essential for competitiveness, particularly following the establishment of the ASEAN Economic Community in 2015 This necessitates increased investment in advanced technology and productivity improvements Over the past three decades, Vietnam has introduced numerous new technologies, leveraging its low-cost labor force and government support to attract advanced manufacturers Consequently, there is a growing demand for skilled workers due to technological upgrades Employment has notably shifted from agriculture to manufacturing and services, with the manufacturing sector experiencing rapid labor productivity growth This study investigates the impact of technology adoption on the employment structure between production and non-production workers and its effect on labor productivity in Vietnamese manufacturing firms from 2007 to 2013, utilizing panel data for a comprehensive analysis Understanding these dynamics can aid policymakers in formulating strategies to enhance labor quality and productivity, address potential challenges posed by technological advancements, and boost national competitiveness in the global market.
Research objectives
The purpose of this paper is to reach the two following research objectives:
(1) Investigate the effect of technology adoption on employment structure between non-production workers and production workers in Vietnamese manufacturing enterprises
(2) Investigate the effect of technology adoption on labor productivity in Vietnamese manufacturing firms.
Research questions
This study examines how technology adoption affects employment structures and productivity within Vietnamese manufacturing firms, focusing on two primary research questions.
(1) Do technological advanced companies have a larger share of nonproduction workers?
(2) Do technological advanced companies gain higher productivity?
The scope of the study
This study investigates how technology adoption impacts employment structures and productivity within Vietnamese manufacturing enterprises Utilizing firm panel data from the Survey of Small and Medium Scale Manufacturing Enterprises (SMEs) in Vietnam, collected between 2007 and 2013, the research categorizes firms according to the two-digit classifications of the International Standard Industrial Classification of All Economic Activities (ISIC) 4, specifically from categories 10 to 33.
The structure of the study
This research is structured into three main chapters: Chapter 1 serves as an introduction, while Chapter 2 provides a concise review of the theoretical and empirical literature concerning the relationship between technology and employment structures, as well as its effects on labor productivity Finally, Chapter 3 outlines the research methodology employed in the study.
Chapter 4 provides an overview of technology, employment structures, and productivity in Vietnam, while Chapter 5 presents the data analysis, estimation techniques, and regression results Lastly, Chapter 6 concludes with a discussion of limitations and policy implications.
Literature review
Key concepts
Technology encompasses the knowledge and methods for transforming resources into outputs Key measures of technology include total factor productivity (TFP), the NBER TFP growth series, the investment ratio in computers relative to total investment, R&D funding compared to net sales, the number of patents utilized in the industry, and the proportion of scientific and engineering jobs to total employment, as outlined by Bartel and Sicherman (1999) and Dunne and Schmitz.
In the context of technology adoption, various studies have explored the factors influencing its implementation in manufacturing settings Doms, Dunne, and Roberts (1995) highlighted the significance of production equipment types as a technology variable, while Berman et al (1994) identified computer investment as a proxy for technological change rates Hall and Khan (2002) defined technology adoption as the process of deciding to acquire and utilize new innovations, weighing the uncertain benefits against the costs Similarly, Rogers (1983) described technology adoption as a decision-making process that involves five stages: awareness, interest, evaluation, trial, and ultimately, adoption.
1995, the author explored the adoption of technological innovations occurred not only within but also outside of organizations
The employment structure is defined by the distribution of production and nonproduction workers, with nonproduction workers, or white-collar workers, typically occupying roles such as managers, office staff, sales personnel, and professionals In contrast, production workers, often referred to as blue-collar or manual workers, are involved in manufacturing tasks, including fabrication, assembly, maintenance, material handling, warehousing, shipping, and security services Additionally, production workers participate in auxiliary production and related manufacturing services, while it's important to note that apprentices are not classified as production workers.
According to the OECD, productivity measures the relationship between output volume and the inputs utilized, such as capital, land, and raw materials Mukherji (1962) emphasized that productivity reflects how effectively resources are utilized.
Productivity is the effective integration of various factors, including scientific management, technology development, and optimal resource allocation It can be measured in different ways, such as capital productivity, multifactor productivity, and labor productivity Among these, labor productivity—defined as output per unit of labor input—is crucial for economic and statistical analysis Higher employee productivity indicates greater efficiency in productive activities, enabling workers to produce more goods and services within the same work hours, ultimately benefiting the economy by maximizing output for the same input.
The relationship between technology adoption and employment structures
Numerous studies over the past few decades have empirically examined the relationship between technology and employment structures Evidence suggests that the adoption of technology has significantly increased the proportion of nonproduction workers in various workplaces.
In 1821, Ricardo analyzed the impact of machinery adoption on various social classes, revealing that mechanization increased productivity and reduced production costs, which subsequently lowered real prices of goods While landowners and capitalists benefitted from these lower prices, workers faced the threat of job loss as capitalists might cut wages to afford costly machinery, leading to technological unemployment Ricardo emphasized that competition among workers drove wages down, indicating that the introduction of new machinery could negatively affect the well-being of the working class.
Berman, Bound, and Griliches (1994) analyzed data from the U.S Annual Survey of Manufactures and concluded that the defense buildup and trade deficits contributed to a slight shift in demand towards non-production workers They identified that labor-saving technological changes in production are a significant factor influencing this shift Additionally, their study found a positive correlation between industry-level increases in the non-production labor share and investments in computer technology and research and development (R&D).
Additionally, Timothy, Haltiwanger and Troske (1996) using plant-level data for U.S manufacturing from 1970s to 1980s found the relationship between technological changes and the employment structure in U.S productive enterprises
The research focused on the proportion of nonproduction labor and the impact of observable indicators related to plant-level technology By defining nonproduction labor as skilled employees, the study found a positive correlation between capital-skill and R&D-skill complements and the increasing average share of nonproduction labor over time.
Doms, Dunne, and R Troske (1997) explored the effects of technology on workers and wages in U.S manufacturing plants through cross-sectional and time series research Their cross-sectional findings revealed that plants utilizing advanced technologies tended to hire more skilled workers, including managers, professionals, and precision-craft workers This indicated that production workers were generally less skilled than their nonproduction counterparts, with an increase in the nonproduction labor share signifying skill upgrades within the industry However, the time series analysis did not establish a strong correlation between technology adoption and nonproduction labor share, except for plants that implemented new factory automation technologies, which showed a more skilled workforce both before and after adoption Additionally, plants that invested more in computing equipment, a key resource for managerial and clerical roles, saw a significant rise in the share of nonproduction workers.
Between 1909 and 1929, U.S manufacturing data analyzed by Goldin and Katz (1998) demonstrated that the rise in capital intensity and the use of purchased electricity as a source of motive energy significantly contributed to the growth of educated production workers in manufacturing plants.
A study by Liu, Tsou, and Hammett (2000) examined the effects of advanced technology adoption on wage and employment structures within Taiwan's manufacturing firms Utilizing a model similar to that of Dunne and Schmitz (1995), the findings revealed that firms implementing advanced technologies employed a greater proportion of non-production labor, particularly in roles such as engineers, technicians, managers, and supervisors.
The research by Dunne and Troske (2005) highlights the relationship between technology adoption and the skill mix in the U.S labor market, building on earlier findings by Doms, Dunne, and Troske (1997) By examining seven different information technologies, the study revealed that the impact of technology adoption on workforce skill varies significantly across these technologies Notably, plants with a higher proportion of nonproduction employees tended to engage more in engineering and design tasks, and those that adopted more technologies in this area experienced faster growth from 1987 to 1997 However, the study found no evidence linking technology adoption to changes in workforce skills at the plant level.
In summary, a general judgment is that almost previous studies provide evidences to support the hypothesis that firms adopting advanced technologies in production hire relative more fractions of nonproduction workers.
The relationship between technology adoption and labor productivity
From the production function, Mankiw (2010) described the relationship between technology and labor productivity through the following function:
Y/L denotes the output per worker, which is a measure of productivity per worker;
K/L refers to physical capital per worker;
H/L refers to human capital per worker;
N/L represents for natural resources per worker
In this equation, worker productivity is influenced by physical capital, human capital, and natural resources available per worker Additionally, productivity is also affected by technical knowledge, represented by the variable A.
Numerous empirical studies demonstrate a positive correlation between technology and labor productivity For instance, Lakhani (1982) utilized time series and cross-sectional data from U.S coal mines provided by the Energy Information Administration to support this finding.
1977, showed that adoption of the latest technologies increased labor productivity in both underground and surface mines
In 1997, Black and Lynch analyzed the Cobb-Douglas production function using cross-section and panel data from 1987 to 1993, focusing on the impact of workplace practices, information technology, and human capital investments on productivity Their findings revealed that while the use of computers by managerial workers did not significantly affect labor productivity, the use of computers by non-managerial workers had a substantial positive influence on plant productivity Similarly, Doms, Dunne, and R Troske corroborated this perspective, demonstrating through cross-sectional analysis that technologically advanced plants achieved higher productivity levels.
The OECD's 1998 report on Technology, Productivity, and Job Creation highlighted the significant impact of technology on economic performance It demonstrated that the diffusion and adoption of technology not only enhanced the productivity of innovative firms but also contributed to an overall increase in economy-wide productivity.
A study by Mcguckin, Streitwieser, and Doms (1996) revealed a significant correlation between the adoption of advanced technologies and increased productivity, based on data from the 1993 and 1988 Survey of Manufacturing Technology The findings indicated that enterprises utilizing advanced technologies experienced higher productivity levels Furthermore, the analysis suggested that well-performing establishments were more inclined to adopt these technologies compared to their poorly performing counterparts.
Huergo and Jaumandreu (2004) examined the link between total factor productivity growth and innovation in Spanish firms from 1990 to 1998 Utilizing the Solow residual for measuring productivity and semiparametric methods, they found that process innovations significantly boosted productivity growth throughout the period This increase in productivity continued for several years, but once the innovation ceased, the gains in production growth appeared to vanish in subsequent years.
Filippetti and Peyrache (2012) employed the conditional frontier approach to analyze the impact of capital accumulation, exogenous technical change, efficiency, and endogenous technological capabilities on labor productivity growth across 211 European regions in 18 countries from 1995 onward.
In 2007, it was argued that capital accumulation and exogenous technical change are key factors driving the convergence in labor productivity growth However, the contributions of advanced and backward regions differ significantly; for backward regions, capital accumulation is the primary driver of productivity growth Despite this, the convergence process raises concerns due to the absence of endogenous technological capabilities.
Empirical studies suggest that technologically advanced firms tend to exhibit higher labor productivity However, variations in research methodologies, country-specific characteristics, study periods, and proxy variables contribute to differences in findings among these studies.
The relationship between firm characteristics and employment structures
The firm characteristics in this study will involve firm size, firm age and the share of male workers in workforce
Liu, Tsou and Hammett (2000) explored that large firms employed a smaller share of managers and supervisors when they investigated the occupational mix of workers in plants of Taiwan
Manuel Adelino and Song Ma (2014) discovered that startups play a crucial role in job creation by providing new investment opportunities driven by innovation, which is influenced by regional industrial structures and national shifts in manufacturing employment.
In contrast, Liu, Tsou and Hammett (2000) found that the rate of managers, supervisors, clerical and sales workers were higher among older firms
According to Wootton (1997), there are distinct occupational trends between women and men, with women predominantly pursuing clerical and service roles, while men are more likely to focus on craft, operator, and laborer positions.
The relationship between firm characteristics and labor productivity
Research by Leung, Meh, and Terajima (2008) alongside Tran Xuan Huong (2014) indicates a positive correlation between firm size and labor productivity, as well as total factor productivity (TFP), in both manufacturing and non-manufacturing sectors Notably, the findings suggest that this relationship is more pronounced in the manufacturing sector compared to the non-manufacturing sector.
Research by Huergo and Jaumandreu (2004) indicates that newly established firms often exhibit significant productivity growth, eventually aligning with average growth rates In contrast, Celikkol (2003) found that in the U.S food and kindred products industry, older plants demonstrated higher productivity growth rates compared to their younger counterparts, suggesting a complex relationship between a firm's age and its productivity.
A study by Petersen, Snartland, and Milgrom (2000) revealed that women exhibited slightly lower productivity than men in traditionally male-dominated blue-collar jobs This comparison was conducted among male and female workers in the same occupations across firms in Sweden, the U.S., and Norway.
The relationship between capital-labor ratio and labor productivity as well as
as the correlation of capital-labor ratio and employment structures
Research by Doms, Dunne, and R.Troske (1997), along with Liu, Tsou, and Hammett (2000), highlights a positive and significant relationship between the capital-labor ratio, employment structures, and labor productivity Specifically, capital-intensive firms tend to employ a greater proportion of nonproduction workers, resulting in enhanced productivity levels.
Conceptual framework
This study aims to investigate the impact of technology adoption on employment structures and labor productivity, while also considering firm characteristics that may influence these changes Specifically, factory equipment and personal computers will serve as proxies for technology adoption In the context of Vietnamese manufacturing, two types of machinery are commonly used: manually operated machinery (MOM) and power-driven machinery (PDM), both of which will be analyzed as indicators of technology adoption Additionally, key firm characteristics such as firm size (SIZE), firm age (FIAGE), the log of the capital-labor ratio (CLR), and the proportion of male workers (MALE) will be included in the research A conceptual framework illustrating the relationship between technology adoption, employment structures, and labor productivity is also presented based on a thorough literature review.
Manually operated machinery only (MOM) Power driven machinery only (PDM) Both manually operated machinery and power driven machinery (BOTH)
Firm size (SIZE) Firm age (FIAGE) The log of Capital-Labor ratio (CLR) Male ratio (MALE)
Data and Research methodology
Model specification
This study estimates two models: the employment structures model, which analyzes changes in employment structures due to technology adoption, and the labor productivity model, which assesses the impact of technology adoption on labor productivity Both models are based on Solow’s production function and treat technology adoption as an exogenous variable Given the unique characteristics of each industry and occupation, the research will apply these models separately to evaluate how technology adoption influences employment structures and labor productivity across different sectors and labor types.
This study explores the impact of technology adoption on workforce composition, utilizing a model akin to those developed by Doms, Dunne, and Troske (1997) as well as Liu, Tsou, and Hammett (2000) The model is expressed as y it = f(TECH it , X it ) + à it, highlighting the relationship between technology and various workforce factors.
Yitz represents the share of nonproduction workers within a firm, which includes managers, professional staff, office personnel, and sales workers This share is calculated as the ratio of nonproduction workers to the regular labor force of the company Additionally, the proportions of managers, professionals, office staff, and sales workers are defined as their respective rates compared to the total regular labor force.
TECHit refers to the adoption of technology within firms, which has been shown to increase the number of non-production workers across various workforces This relationship has been supported by empirical studies, highlighting the impact of technology on employment dynamics (Ricardo, 1821; Berman, Bound, and Griliches, 1994; Timothy, Haltiwanger, and Troske, 1996).
Xit encompasses key firm characteristics such as size, age, capital-labor ratio, and the proportion of male employees Previous studies, including those by Tsou and Hammett (2000), indicate that larger firms typically employ fewer nonproduction workers Additionally, startups are recognized as significant contributors to job creation, as highlighted by researchers Manuel Adelino and Song.
The capital-labor ratio has a positive correlation with the share of nonproduction employees, highlighting the significant role of workforce composition in manufacturing firms Moreover, women are often found in clerical and sales positions within these industries, reflecting occupational trends in the workforce.
And àit is the error term
A production function is a mathematical model that illustrates the maximum output a firm can achieve with various combinations of inputs It serves as a crucial tool for firms in determining the optimal setup for their production processes.
Y denotes the quantity of output,
K is the quantity of physical capital such as plant and equipment which used in production,
L is the quantity of labor, And A is a level of technology (TECH)
The average product of labor in the workforce (APL): APL = (2)
From equation (1) and (2) the labor productivity function can be rewritten as following:
APL represents the labor productivity on average
Taking into account the control variable as firm characteristics, the model estimating the labor productivity in this study will be as following:
APL it = (Y/L) it = A it F(K/L, 1) it + X’ it + à it
APLit refers to labor productivity, defined as the value-added per worker In the context of small and medium-sized enterprises (SMEs), labor productivity can be assessed through real revenue or real value added per full-time employee This paper specifically measures labor productivity as the value-added per worker, following the framework established by Doms, Dunne, and R Troske (1997).
Ait is level of technology, K/L is the capital-labor ratio (CLR), X’it refers to firm characteristics (firm size, firm age and male ratio), And àit is the error term
As the results from papers of some economists such as Black and Lynch
Adopting new technology in manufacturing processes can significantly enhance worker productivity (Mcguckin, Streitwieser, and Doms, 1996; Huergo and Jaumandreu, 2004) Additionally, employees in capital-intensive firms tend to produce more goods compared to their counterparts in less capital-intensive settings (Doms, Dunne, and Troske, 1997).
In the manufacturing sector, larger entrant firms demonstrate higher labor productivity, as supported by studies from Leung, Meh, and Terajima (2008), Tran Xuan Huong (2014), and Huergo and Jaumandreu (2004) Additionally, research by Petersen, Snartland, and Milgrom (2000) indicates that male workers generally achieve greater productivity levels compared to their female counterparts.
This paper employs two measures of adopted technology including:
(1) A set of dummy variables representing for different types of manufacturing machinery used;
(2) A number of operating personal computer (OPC) used
The technological dummy variables analyzed in this study encompass hand tools, manually operated machinery, power-driven machinery, and a combination of both A key objective of this paper is to examine the shifts in employment structures and productivity within firms that adopt technology compared to those that do not Although the category of "only hand tools, no machinery" is included in the analysis, it will be excluded from the regression model.
The study focuses on service workers in the Survey of Small and Medium Scale Manufacturing Enterprises, specifically cleaners, food preparers, and servers, while excluding them from productivity analysis as they are deemed nonproduction workers Unlike previous research, this paper avoids using the terms skilled and unskilled workers to categorize nonproduction and production workers, respectively, due to the classification system in the SME survey The survey defines employment structures in Vietnamese manufacturing firms, indicating that only a portion of production workers are classified as unskilled, referred to as "Labor," while other roles may still be considered skilled To account for industry-specific factors affecting employment structures and productivity, the study will conduct separate regressions for different industries based on a two-digit classification system.
Table 3.1: Employment structures in SMEs
Source: The Survey of Small and Medium Scale Manufacturing Enterprises (SMEs)
1 Managers (Top management) 2.Professionals (university and college degree)
2.1 Engineer and similar 2.2 Accountant/Economist 2.3 Technicians
5 Service workers (cleaners, food prep/servers)
6 Production workers 6.1 Foreman and supervisor 6.2 Electrician, plumber, etc 6.3 Mach maintenance/repair 6.4 Mach operator/assembler 6.5 Laborer (unskilled) 6.6 Master
Nonproduction The proportion of nonproduction workers including managers, professionals, sales workers, office workers Manager The proportion of manager
Professionals The proportion of professionals Sales and Offices The proportion of sales workers and office workers
VA Value-added per worker
HT Only hand tools, no machinery
MOM Manually operated machinery only
PDM Power driven machinery only
BOTH Both manually and power driven machinery
OPC A number of Operated personal computer
The size of a firm's regular labor force is a crucial factor in its operational dynamics The firm's age, measured in years since its establishment, significantly influences its growth and stability Additionally, the log ratio of the book value of fixed capital stock to the number of regular labor provides insights into the firm's capital efficiency and productivity.
MALE The proportion of employees who are male
Data source
Recent research on the impact of technological adoption on employment and productivity has primarily relied on cross-sectional data, with limited studies utilizing panel data This study leverages panel data from the Survey of Small and Medium Scale Manufacturing Enterprises (SMEs) in Vietnam, conducted from 2007 to 2013 by the Institute of Labor Studies and Social Affairs (ILSSA) in collaboration with the University of Copenhagen and funded by DANIDA The survey covers three major cities—Ha Noi, Ho Chi Minh City, and Hai Phong—and seven provinces, ultimately analyzing data from nearly 1,350 manufacturing firms after excluding those not interviewed consistently over the four years or that collapsed during the period These firms are categorized according to the International Standard Industrial Classification (ISIC) 4, covering industries 10-33, although the distribution of surveyed firms across industries is uneven Notably, only 11 out of 24 industries have more than 30 firms surveyed, with significant representation from sectors such as food products, fabricated metal products, furniture, wood products, and rubber and plastics Additionally, the textile industry, a vital sector in Vietnam, is also included in the analysis.
Figure 3.1: The structure of industries considered
Source: Author’s calculation from the data
Estimate methods
This article discusses the advantages of using panel data over time series and cross-sectional data in regression analysis It outlines various panel data regression models, including the Fixed-Effects Model, Random-Effects Models, and Pooled Ordinary Least Squares model To determine the most suitable models for specific samples, statistical tests such as the Hausman test, F-test, and Breusch and Pagan Lagrangian multiplier test will be employed Once the appropriate models are identified, robust regression techniques will be applied to address issues of heteroskedasticity and autocorrelation Additionally, the variance inflation factor (VIF) will be utilized post-regression to assess multicollinearity among the variables.
The Harris-Tzavalis test, developed by Harris and Tzavalis in 1999, will be employed to assess the stationarity of the data, assuming an infinite number of panels with a fixed number of time periods Detailed results of all tests are provided in the appendix of this paper.
Technological revolution, employment structures and labor productivity
Technology innovation
Between 1975 and 2005, technological innovations played a minimal role in Vietnam's economic growth (Ngoc, 2008) Recently, the country's innovation system has begun to develop, although it remains in its early stages with limited capabilities in science, technology, and innovation (World Bank, 2014) Vietnam has integrated into global value chains across various sectors, including textiles, garments, food, and furniture However, the growth of high-tech exports has been notably sluggish (Anh, Hung, Mai, 2013).
In a comparison of manufacturing exports between Vietnam and other countries in 2000 and 2008, Table 4.1 reveals a notable shift in technology usage While the share of medium and low technology exports increased, high technology exports in Vietnam decreased from 11.1% in 2000 to 10.1% in 2008 This positions Vietnam among the lowest in high-tech export percentages, only surpassing Cambodia, which had 0.1% in 2008 Consequently, Vietnam lags behind many nations in the adoption of advanced technologies in production, primarily due to a tendency among Vietnamese manufacturing firms to rely more on labor-intensive processes.
Table 4.1: Technological content of manufactured exports (%, 2000, 2008)
Philippines 69% 12.4% 11.9% 6.6% 62.1% 15.5% 8.1% 14.4% Singapore 59.4% 20.9% 6.9% 12.7% 44.8% 22% 6.7% 26.6% Taiwan 43.2% 28.2% 24.3% 4.3% 35.8% 32.5% 18.5% 13.2% Thailand 32.4% 27.2% 21.9% 18.5% 22.7% 37.7% 16.1% 23.5% Vietnam 11.1% 10.3% 64.7% 13.8% 10.1% 14.5% 67.1% 8.2%
According to Viet, Hien, Quy, and Qui (2011), despite the Vietnamese government's policies aimed at promoting technological change, significant reforms have not occurred The private and foreign direct investment (FDI) sectors in Vietnam lag behind other countries in the region and globally due to a shortage of skilled workers capable of implementing advanced technologies As a result, only 2% of Vietnamese enterprises utilize high technology, compared to 30% in Thailand, 51% in Malaysia, and 73% in Singapore Additionally, Vietnam's competitiveness, particularly in technological indicators, is low, with rankings of 55 out of 133 for creativity and 99 out of 133 for innovation (World Economic Forum, 2009) Most firms in Vietnam still rely on manually operated and power-driven machinery, with the use of hand tools declining steadily.
7.8% to 5.0% but most of utilized technologies were quite old A number of machinery belonged to a range of 6-20 years old accounted for large percentages (Table 4.2, Appendix 1)
Figure 4.1: The proportion of enterprises that obtained a new technology by location and size
Source: The Survey of Small and Medium Scale Manufacturing Enterprises, 2007- 2013
Between 2007 and 2013, the adoption of new technologies by enterprises declined significantly, with the rate in manufacturing dropping from approximately 15% to 6.4% This decrease can be attributed to a decline in innovation ratios, largely influenced by the financial crisis, which created greater uncertainty in business operations and reduced demand for new technologies Notably, urban firms initially had a higher likelihood of adopting new technologies compared to their rural counterparts until 2013, when adoption rates became nearly equal in both areas Additionally, larger enterprises showed a greater propensity to utilize new technologies in their manufacturing processes.
Employment structures
Prior to the 1986 reform, Vietnam's labor market was heavily regulated, with the government directly overseeing hiring and wage-setting in state-owned enterprises (SOEs), leaving managers with little influence However, in the past thirty years, significant improvements have been made in Vietnam's labor market, particularly in terms of regulations governing recruitment, termination, and wage policies.
Between 1990 and 2011, the manufacturing sector in Vietnam experienced significant employment growth, rising from 2.8% to 7.3%, which facilitated a major shift of labor from agriculture to manufacturing According to CIEM, from 2007 to 2013, there were notable fluctuations in job roles within Vietnamese manufacturing, with an increase in managerial and professional positions, while the percentage of production workers decreased from 66.2% to 59.3% This indicates a transition from production to non-production roles during this period Additionally, larger enterprises and those located in urban areas were more likely to employ professional workers, as these firms had the financial resources to invest in new technologies and benefited from easier access to advanced innovations.
Employee productivity
Between 2000 and 2010, Vietnamese manufacturing firms experienced significant productivity improvements (Tran Xuan Huong, 2014) Despite this progress, Vietnam's labor productivity levels remained comparatively low on the international stage.
From 2011 to 2013, Vietnam's labor productivity rose from 5.08% to 5,440 USD per labor (adjusted to 2005 PPP), but the overall productivity growth from 2007 to 2013 was only 3.9% In comparison to neighboring countries, Vietnam's labor productivity is significantly lower, at just one-eighth that of Singapore and one-third that of Thailand Currently, Vietnam's productivity exceeds only that of Myanmar and Cambodia, and is comparable to Laos (Ngoc and Thu, 2013) This disparity can be attributed to the fact that workers in countries like Singapore and Thailand are concentrated in high-value-added sectors such as services, whereas Vietnamese workers primarily engage in the textile and garment industries, which yield lower added value.
Figure 4.2: Levels of labor productivity per hour worked, 1970-2010
Note: GDP at constant basic prices per hour, using 2005 PPPs, reference year 2010, USD Source: APO (2012), APO Productivity Data book 2012, Keio University Press, Tokyo
In Vietnam, a significant portion of the labor force remains in agriculture and unofficial sectors, which exhibit lower productivity compared to manufacturing and services This limited exposure to modern technologies contributes to a low labor productivity rate The International Labor Organization (ILO) has found that productivity levels in manufacturing and services are substantially higher than in agriculture, suggesting that the adoption of modern technologies and effective worker training could enhance productivity Furthermore, labor productivity tends to increase with the size of manufacturing firms, as larger firms consistently outperform smaller ones, and similar trends are observed when comparing productivity between urban and rural areas.
Table 4.5: Labor productivity by firm size and location
Note: Million VND Source: CIEM
Summary of the chapter
To successfully integrate into the international market, Vietnam must focus on investing in technology, enhancing labor quality, and increasing productivity Statistics reveal that larger firms and those located in urban areas are more likely to adopt new technologies Additionally, CIEM found that the proportion of professionals, office, and sales workers in Vietnamese manufacturing firms rises with firm size and urbanization This raises the question of whether technology adoption is linked to the share of non-production workers in manufacturing Moreover, larger urban firms demonstrate higher productivity compared to smaller rural counterparts Evidence also indicates that technology-adopting firms experience significantly greater labor productivity These topics will be explored further in the next chapter.
Empirical Results
Descriptive analysis
As the results of data analysis, among industries, the manufacture of food
In the analysis of nonproduction workers across various industries, the highest proportion was found in the management sector, particularly in the fabricated metal products industry, followed closely by furniture manufacturing Other industries exhibited similar ratios of nonproduction workers, but overall, the presence of professional, office, and sales workers remained notably low.
Table 5.1: List of considered industries
ISIC two-digit Classification Industry
16 The manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials
22 The manufacture of rubber and plastics products
25 The manufacture of fabricated metal products, except machinery and equipment
Source: United Nations Statistics Division
Figure 5.1: Changing in employment structures form 2007-2013
Source: Author’s calculation from the data
Between 2007 and 2013, employment structures underwent notable changes, with a significant decline in the number of production workers, decreasing from around 8,600 to approximately 6,730 employees In contrast, the share of nonproduction workers experienced a slight increase during this period These findings align with the performance analysis presented in Chapter 4 and detailed in Table 4.3 of Appendix 2.
In terms of labor productivity, the manufacture of wood and cork products, excluding furniture, along with articles made from straw and plaiting materials, recorded the lowest added value at only 38,898.27 VND, which is nearly half that of the rubber and plastics manufacturing sector Despite this, Vietnamese manufacturing firms experienced a significant surge in productivity across various industries, particularly in rubber and plastics and fabricated metal products, excluding machinery and equipment Notably, by 2013, the added values for all industries had more than doubled compared to 2007.
Figure 5.2: Added value per worker
Source: Author’s calculation from the data
Over seven years, the number of firms relying solely on hand tools (HT) or manually operated machinery (MOM) in production significantly declined, representing a minimal share of total manufacturing firms In contrast, many enterprises adopted power-driven machinery (PDM) or a combination of both PDM and MOM While the use of both machinery types saw a slight decrease in 2011, it experienced positive growth in the following two years Additionally, the number of computers in the workforce rose notably from approximately 710 units in 2007 to nearly 820 units by 2013; however, this investment remained relatively low compared to over 1,040 surveyed firms across six industries Specifically, only the textile and rubber/plastics manufacturing sectors averaged 1.25 and nearly 2.25 computers per workforce, respectively, while firms in other industries had less than one computer on average.
Figure 5.3: Numbers of machineries and computer used
Source: Author’s calculation from the data
Between 2007 and 2013, the highest proportion of firms in various industries utilized both manually operated and power-driven machinery (BOTH) for production, while those employing only power-driven machinery (PDM) also represented significant rates Notably, the textile industry continued to rely heavily on hand tools (HT), which constituted 25% of their production methods Overall, Vietnamese manufacturing firms saw positive workforce changes, with a notable increase in the use of power-driven machinery and computers (OPC), leading to enhanced labor productivity and a reduction in the share of production workers Interestingly, despite the larger sizes of the food and textile manufacturing sectors, these industries exhibited a lower proportion of male workers compared to female workers.
The workforce composition reveals that 50% consists of female employees, while the food manufacturing sector exhibits a nearly equal distribution of male and female workers, with a balance rate of around 10% In contrast, other industries show a significant male dominance, with over 60% of their total labor force being male (Table 5.3, Appendix 5).
Table 5.4: The summary of statistics by mean of each industry
Source: Author’s calculation from the data
Table 5.4 presents the average values of OPC, SIZE, CLR, and MALE variables across six industries from 2007 to 2013 During this period, the number of operational personal computers in these industries gradually increased, yet remained relatively low, with manufacturing averaging only 0.65 to 0.75 computers Notably, the rubber and plastics manufacturing sector saw a higher usage, exceeding three computers in 2011, while the furniture manufacturing industry lagged behind with just 0.37 computers Additionally, Vietnamese manufacturing firms experienced a downward trend in size, maintaining an average of around 11 during this timeframe.
Total 0.66 0.72 0.70 0.74 11.37 11.46 11.06 9.52 workers from 2007 to 2011 However, in 2013 this figure dropped to over 9 workers Among considered industries, the manufacture of rubber and plastics (22) and the two following industries including the manufacture of textile (13) as well as the manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials (16) respectively had the largest sizes Meanwhile the manufacture of food only contained about 7 workers in their firms Nevertheless, the firm size of all these industries also decreased within seven years Similarly, the rate of male workers in all industries decreased slightly Compared among industries, the two industries such as the manufacture of fabricated metal products (25) and the manufacture of furniture had the highest proportion of male employees On the other hand, the manufacture of food and textiles industries employed more female workers than others On average, manufacturing firms tended to hire more male workers than their peers In the contrary, over seven years, Vietnamese manufacturing firms invested more capital to production This explained why the capital-labor ratio in various industries rose gradually despite the slight decrease in 2013 except the manufacture of rubbers and plastics which had the log of capital-labor ratio accreted each year
Across all industries, there has been a noticeable decline in firm size and the percentage of male workers in the workforce Meanwhile, there has been a slight increase in the average number of personal computers utilized and the capital-labor ratio, though these increases are minimal.
The Harris-Tzavalis test results indicate that all p-values are below 0.05, confirming the stationarity of the data (Appendix 7) The correlation matrix reveals a strong correlation between both manually operated and power-driven machinery (BOTH) and solely power-driven machinery (PDM) (Appendix 6) Additionally, the Variance Inflation Factor (VIF) test shows high VIF values for BOTH and PDM (Appendix 8) Consequently, this paper will address the multicollinearity between these two variables by omitting the BOTH variable from the regression models.
Empirical results
This section analyzes the results of regression models to explore the impact of technology adoption on employment structures and labor productivity within Vietnamese manufacturing firms To assess these relationships, the analysis incorporates three technology dummy variables along with the number of operational personal computers in the regression models.
This section examines how technology adoption influences the employment structure within enterprises, specifically analyzing the ratio of non-production to production workers Additionally, it evaluates labor productivity, defined as the added value generated per worker across the entire workforce.
(a) Technology adoption and nonproduction worker share
The analysis of technology dummy variables reveals that the adoption of machinery and operated personal computers (OPC) had minimal impact on the proportion of nonproduction workers across most Vietnamese industries from 2007 to 2013, with notable exceptions in the food and furniture manufacturing sectors Specifically, the use of manually operated machinery (MOM) increased the share of nonproduction workers in the food industry, while it decreased that share in the furniture industry.
The findings of this study contrast with those of Doms, Dunne, and R Troske (1997) and Liu, Tsou, and Hammett (2000), indicating that technology adoption does not significantly impact employment structures and labor productivity in Vietnamese manufacturing firms This discrepancy may arise from two key factors: previous research utilized cross-sectional data to assess the effects of technology adoption, whereas this study employs panel data, potentially leading to differing results.
Table 5.5: The coefficient signs between employment structures and other independent variables
Products of wood, cork, straw
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
Research indicates that most studies on advanced technologies in production are primarily conducted in developed countries In contrast, Vietnam's innovation system, as highlighted in the World Bank's 2014 report and studies by Anh, Hung, and Mai (2013), is still in its early stages, characterized by limited capabilities in science, technology, and innovation Vietnamese manufacturing firms largely depend on labor-intensive methods, and investment in innovative production technologies remains insufficient, with many technologies being outdated However, reports from CIEM show a notable increase in the proportion of non-production workers, including managers, professionals, sales, and office staff.
The descriptive analysis revealed a decline in the proportion of production workers, suggesting that changes in firm characteristics, rather than technology adoption, may be influencing employment structures Significant regression results indicate that older firms and a higher capital-labor ratio are positively correlated with an increase in nonproduction employees and labor productivity Additionally, larger firms typically hire a smaller percentage of nonproduction workers, and those with a higher proportion of male employees, particularly in the food manufacturing sector, tend to employ fewer nonproduction workers.
Tables 5.6, 5.7, and 5.8 provide detailed insights into the shifts in the proportions of managers, professional workers, and sales and office employees within the workforce, influenced by technology adoption and various firm characteristics.
Firms that exclusively utilized manually operated machinery (MOM) in food manufacturing experienced an increase in managerial positions Conversely, in the fabricated metal products sector, investment in operated personal computers (OPC) correlated with a decrease in managers Notably, food manufacturing firms with a higher proportion of male workers tended to employ fewer managers, while the opposite was true for fabricated metal products Additionally, both the capital-labor ratio (CLR) and the firm's age (FIAGE) positively influenced the percentage of managers across all industries, whereas larger firm sizes (SIZE) were associated with fewer managerial hires.
Table 5.6: The coefficient signs between the proportion of managers and other independent variables
Products of wood, cork, straw
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
The adoption of technology significantly influences the ratio of professional workers, with the use of manually operated machinery (MOM), power-driven machinery (PDM), and operated personal computers (OPC) positively correlating with the number of professionals in the workforce, except in the rubber and plastics manufacturing sector Additionally, factors such as firm size (SIZE) and the proportion of male workers (MALE) negatively impact the rate of professionals Interestingly, the age of the firm (FIAGE) shows a contrasting effect on non-production workers, particularly in newly established textile manufacturing firms.
(13) employed a larger share of professional workers Moreover, the capital-labor ratio (CLR) did not have any effect on professional workers (Table 5.7)
Table 5.7: The coefficient signs between the proportion of professional workers and other independent variables
Products of wood, cork, straw
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
The adoption of technology had minimal impact on the share of sales and office workers in manufacturing firms, with the textile industry experiencing a decline in these positions when utilizing manually operated machinery Conversely, the food manufacturing sector saw an increase in sales and office employees when investing in personal computers Young firms tended to employ more sales and office staff, and the capital-labor ratio did not significantly influence these roles However, the furniture manufacturing industry was negatively affected by firm size, and a higher proportion of male workers was also linked to a decrease in sales and office occupations.
Table 5.8: The coefficient signs between the proportion of sales and office workers and other independent variables
Sales and office Sample Food Textile
Products of wood, cork, straw
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
(b) Technology adoption and labor productivity
This study reveals an inverse relationship between technology adoption and labor productivity, contrasting with previous research The use of manually operated machinery (MOM) in production can diminish the added value per worker, especially in the wood industry, which relies heavily on manual skills Similarly, operated personal computers (OPC) have been linked to decreased labor productivity in rubber and plastics manufacturing These discrepancies may stem from the relatively low technology levels in Vietnamese manufacturing firms, where many raw materials are imported, and numerous companies primarily process products for foreign entities Additionally, household businesses, which constitute a significant portion of the manufacturing sector, often focus on manual production, indicating that the introduction of machinery may inadvertently lead to reduced productivity.
The positive changes in labor productivity within Vietnamese manufacturing can be attributed to specific firm characteristics, such as firm age, capital-labor ratio, and the proportion of male employees, all of which show a significant positive correlation with labor productivity Conversely, firm size exhibits a negative correlation with productivity, which contrasts with typical findings that larger firms are generally more productive This anomaly can be explained by the prevalence of micro and small firms in Vietnam's manufacturing sector, as highlighted by CIEM (2013), where the majority of firms in the food manufacturing industry were micro (637) or small (97), with only 21 classified as medium-sized.
Table 5.9: The coefficient signs between the labor productivity and other independent variables
Value Added Sample Food Textile
Products of wood, cork, straw
No of Observatio ns (group)
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
5.2.2 For the specified industries (a) Manufacture of food
The regression analysis aligns with Liu, Tsou, and Hammett's (2000) findings that technology adoption in the food manufacturing sector increases the proportion of nonproduction workers, particularly managers The food industry is a key sector in Vietnam, with numerous enterprises experiencing significant growth and enhancing their market presence, as reported by the Ministry of Industry and Trade (2013) This industry has made strides in product quality and reputation, leading to increased exports Many manufacturing firms are now utilizing advanced technology and modern equipment, which not only improves product refinement but also reduces the need for production workers Consequently, firms that utilize manually operated machinery (MOM) tend to hire more nonproduction staff, especially in managerial roles Additionally, the investment in operating personal computers (OPC) has led to an increase in sales and office personnel, as computers facilitate easier access to the internet and customer engagement.
Table 5.10: The coefficient signs among the employment structures, labor productivity and other independent variables in the manufacture of food
N88 Nonproduction Manager Professionals Sales and
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively N is a number of groups of observations
The adoption of technology in the industry does not enhance worker productivity due to a heavy reliance on imported raw materials and insufficient quality control For instance, the dairy sector sources 75% of its raw materials from overseas, while the cooking oil industry depends on imports for 90% of its inputs.
Many small businesses managed by local officials face capital shortages, leading to the use of outdated equipment and resulting in production inefficiencies (Ministry of Industry and Trade, 2013) Firms that have been in the market for an extended period typically hire fewer sales and office staff, focusing more on managerial positions Larger firms with a higher proportion of male employees tend to employ fewer non-production staff, particularly in managerial roles This aligns with Wootton's (1997) findings that men and women exhibit different occupational trends, with women often gravitating towards clerical and service roles while men predominantly occupy craft, operator, and laborer positions Additionally, similar trends are observed in the food manufacturing industry, supporting the conclusions of Doms, Dunne, and R Troske.
(1997) that capital-intensive firms hired more nonproduction workers and gain higher added value per worker
Summary of the chapter
This chapter examines the effects of technology adoption on employment structures and labor productivity across six industries The overall analysis reveals no significant impact on the research objectives; however, specific manufacturing sectors show that technology use has increased the proportion of professionals Conversely, the reliance on manually operated machinery and personal computers has decreased the added value per worker in Vietnamese manufacturing, particularly in the wood and cork sectors, rubber and plastics, and fabricated metal products Despite these findings, the effects remain relatively weak Additionally, factors such as firm age, size, male worker proportion, and capital-labor ratio significantly influence employment structures and productivity.
Conclusion and policy recommendation
This study investigates the impact of technology adoption on employment structures and labor productivity in Vietnamese manufacturing in the period from
Between 2007 and 2013, an analysis of six industries revealed that the share of nonproduction workers in technology-using firms was not significantly higher than in firms that do not utilize such technologies Additionally, advanced firms did not exhibit greater labor productivity compared to their counterparts Interestingly, even the use of manually operated machinery and computers was associated with a decrease in added value per worker, contradicting the findings of Black and Lynch (1997).
Industry estimators indicate a significant relationship between technology adoption, employment structures, and labor productivity The share of professional workers, including engineers, technicians, and accountants, is most affected, followed by managers Additionally, a positive correlation exists between the capital-labor ratio and the proportion of nonproduction workers and overall labor productivity in most firms This suggests that strategic investment in technology can enhance productivity and increase the number of high-quality professional and managerial workers.
The study reveals that firm characteristics significantly influence employment structures and productivity Specifically, older firms (FIAGE) tend to have a positive correlation with enhanced productivity levels Additionally, larger firms (SIZE) typically employ a smaller percentage of nonproduction workers, while smaller Vietnamese manufacturing firms demonstrate higher productivity Furthermore, firms with a higher proportion of male employees (MALE) not only hire fewer nonproduction workers but also show a positive relationship between the male workforce and overall labor productivity.
The distinctive differences in research regarding the impact of technology adoption on employment structures and labor productivity in Vietnamese manufacturing firms can be attributed to several factors Primarily, the technologies utilized by these firms lag behind global advancements Vietnam is recognized as one of the largest processing factories globally, yet many industries rely heavily on exporting raw materials and importing finished products, while others struggle with insufficient local material supplies Additionally, only a limited number of firms, particularly those with strong financial backing or foreign investments, have sufficiently invested in their production processes and workforce training As a result, the overall technology levels and labor quality within Vietnamese manufacturing firms remain low, leading to minimal significant changes in the effects of technology adoption on employment and productivity.
This study offers valuable recommendations for policymakers and managers in Vietnamese manufacturing firms aimed at enhancing labor productivity and fostering positive changes within the workforce.
The limited impact of technology adoption on employment structures and labor productivity in Vietnam can be attributed to the underdevelopment of manufacturing technologies The government must play a crucial role in fostering scientific and technological advancements by providing long-term support for social and economic entities Currently, Vietnam's innovation capability is weak, resulting in a low proportion of high technology in manufacturing To enhance innovation, it is essential to improve the framework conditions, including the business environment, infrastructure, entrepreneurial quality, and product standards Additionally, promoting trade openness and attracting foreign direct investment can facilitate technology transfer Supporting industries are vital for creating added value, boosting the competitiveness of industrial products, and accelerating national industrialization, making the development of these sectors a priority As technology levels in Vietnamese industries improve, there may be a reduced demand for production workers, potentially affecting unemployment rates To mitigate instability in the job market, officials must ensure that displaced workers have opportunities for skill enhancement, necessitating increased investment in vocational education systems.
Besides the efforts of officials, each industry as well as each firm should have their own efforts to enhance the labor productivity and employment structures
Improving awareness of product quality standards and entrepreneurship is essential in the context of global integration Additionally, providing training for workers to keep pace with rapidly evolving advanced technologies is a crucial task.
This study has several limitations, primarily related to the duration of the research period, which is insufficient to capture fluctuating trends among the variables Additionally, the timing of the survey coincides with a global economic crisis, particularly affecting Vietnam, potentially biasing the findings on the impact of technology adoption on employment structures and employee productivity Lastly, there is a significant disparity in the number of surveyed firms across different manufacturing industries, leading to the reliance on only a few industries with the largest sample sizes to represent the entire dataset.
Abukhzam, Lee, “Workforce Attitude on Technology Adoption and Diffusion”, The
Built & Human Environment Review, Volume 3, 2010
Adam Tipper (2012), “Capital-labor substitution elasticities in New Zealand: one for all industries?”, Statistics New Zealand Working Paper No 12-01
Ann P Bartel and Frank R Lichtenberg (1987),“The Comparative Advantage of
Educated Workers in Implementing New Technology”, Review of Economics and Statistics, 59, No 1, pp.1-11
Black, Lynch (1997), “How to compete: The impact of workplace practices and information technology on productivity”, Center for Economic Performance,
Berman, Bound, Griliches (1994), “Changes in the Demand for Skilled Labor within U.S Manufacturing: Evidence from the Annual Survey of Manufacturers”, The Quarterly Journal of Economics, Vol 109, No 2, pp.367-397
Blechinger, D., Kleinknecht, A., Licht, G., and Pfeiffer, F (1998), “The Impact of
Innovation on Employment in Europe: An Analysis using CIS Data”, ZEW Documentation Nr 98-02
Caves, Krepps (1993), “Fat: The Displacement of Nonproduction Workers from
U.S Manufacturing Industries”, Harvard University, Brookings Papers: Microeconomics 2, 1993
Doms, Dunne, Troske (1997), “Workers, Wages, and Technology”, The Quarterly
Dunne, Haltiwanger, Troske (1996), “Technology and jobs: secular changes and cyclical dynamics”, National Bureau of Economic Research, NBER Working
Dunne, Troske (2005), “Technology Adoption and the Skill mix of U.S
Manufacturing plants”, Scottish Journal of Political Economy, Vol 52, No.3,
Elsie Echeverri-Carroll And, Sofia G Ayala (2006), “High Technology
Agglomeration and Gender Inequalities”, McCombs School of Business, University of Texas at Austin, Austin, Texas 78713
Filippetti, Peyrache (2012), “Labour productivity and technology gap in European regions: A non-parametric approach”, School of Economics University of Queensland, Australia, ISSN No 1932-4398
Jerome A Mark, “Technological Change and Employment: Some Results from
BLS Research”, United State Department of Labor, Bureau Labor Statistics,
Jin-Tan Liu, Men Wen Tsou and James K Hammett (2000), “The Impact of
Advanced Technology Adoption on Wage Structures: Evidence from Taiwan Manufacturing Firms”, Economica (2001), 68, 359-378
Goldin and Katz’s (1998), “The Origins of Technology-Skill Complementarity”,
The Quarterly Journal of Economics
Hall and Khan (2002), “Adoption of New Technology”, University of California at
Huergo, Jaumandreu (2004), “Firm’s age, process innovation and productivity growth”, International Journal of industrial Organization
Huong Xuan Tran (2014), “The Evolution of Productivity in Vietnam’s
Manunfacturing Sector”, University of Wollongong
Leung, Meh, Terajima (2008), “Firm Size and Productivity”, Bank of Canada,
Mankiw (2010), “The Principles of Macroeconomics”, 7 th Edition
Manuel Adelino, David T Robinson and Song Ma (2014), “Firm Age, Investment
Opportunities, and Job Creation”, Fuqua School of Business Duke University and NBER
McCaig and Pavcnik (2012), “Moving out of agriculture: structural change in
Vietnam”, Wilfrid Laurier University, Dartmouth College BREAD, CEPR, and NBER
McGuckin, Streitwieser, Doms (1996), “The effect of Technology use on productivity growth”, the Center for Economic Studies (CES)
Meyer, Yen Thi Thu Tran and Hung Vo Nguyen (2005), “Doing business in
Vietnam”, CEES, Working paper No.58 Nguyen Trung Kien (2014), “Economic Reforms, Manufacturing Employment and
Wages in Vietnam”, The Australia National University
Nguyen Trung Kien (2014), “Employment Transformation in the Vietnamese
Economy in Light of the Lewis-Fie-Ranis Growth Model of A Labor- Abundant Economy”, Danang University of Economics, Journal of Economics and Development, Vol 16, No 3, December 2014, pp 49-67
Petersen, Snartland, Milgrom (2005), “Are Female Workers Less Productivity than
Male Workers? Productivity and the Gender Wage Gap”, University of California, Institute for Research in Economics and Business Administration (SNF) and University Bergen, Stanford University and Stockholm University
Ramsterter and Phan Minh Ngoc (2011), Productivity, Ownership, and Producer
Concentration in Transition: Further Evidence from Vietnamese Manufacturing”, ESRI Discussion Paper Series No.278
Rogers, E M (1983), “Diffusion of Innovations”, New York: Free Press, 1983 Rogers, E M (1995), “Diffusion of innovations”, New York: Free Press
Romer (1990), “Endogenous Technological Change”, Journal of Political
Samson, M, Mac Quene, K and van Niekerk, I 2001, “Capital/Skills-Intensity and
Job Creation: An Analysis of Policy Options”, Economic Policy Research Institute
Timothy F Bresnahan, Erik Brynjolfsson, Lorin M Hitt (2000), “Information
Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-level Evidence”, Quarterly Journal of Economics
In the 2010 paper by Tran and Doan, titled "Industrialization, Economic and Employment Structure Changes in Vietnam During Economic Transition," the authors explore the significant transformations in Vietnam's economy and labor market amid its transition The study, published by the College of Economics at Vietnam National University and the Department of Economics at the University of Waikato, highlights the impact of industrialization on economic growth and employment patterns in Vietnam This research, available as MPRA Paper No 26996, was posted on the 26th, providing valuable insights into the ongoing changes in Vietnam's economic landscape.
Tran (2014), “The evolution of productivity in Vietnam’s manufacturing sector”,
United Nations Industrial Development Organization (2013), “Sustaining
Employment Growth: The role of Manufacturing and Structural Change”,
Ramstetter, Ngoc (2011), “Productivity, Ownership, and Producer Concentration in
Transition: Further Evidence from Vietnamese Manufacturing”, Economic and Social Research Institute, ESRI Discussion Paper Series No 278
Viet Le, Harvie (2010), “Firm performance in Vietnam: evidence from manufacturing small and medium enterprises”, University of Wollongong Wootton (1997), “Gender differences in occupational employment”, Bureau of
Labor Statistics, Monthly Labor Review.