Introduction
Problem Statement
In recent decades, technology has become integral to workplaces in many advanced countries, significantly impacting various industries Research from the U.S Bureau of Labor Statistics in 1987 highlights that the rapid advancement and adoption of technology enable manufacturers to lower costs and enhance competitiveness in both domestic and international markets Numerous studies have demonstrated that the integration of advanced technologies in manufacturing not only alters employment structures but also boosts labor productivity For instance, research by Berman, Bound, Griliches (1993), and Doms, Dunne, and Troske (1997) indicates that the demand for skilled workers increases as companies adapt to new innovations, often leading to job losses for low-skilled employees Furthermore, the effective use of technology allows workers to complete more tasks in less time, further driving productivity gains.
The manufacturing sector in Vietnam is a key driver of economic growth, significantly contributing to GDP, job creation, and foreign trade With globalization and international competition intensifying, innovation has become essential for competitiveness, particularly following the establishment of the ASEAN Economic Community in 2015, which necessitates investment in advanced technology and productivity improvements Since the economic reforms in 1986, Vietnam has seen the introduction of new technologies, attracting advanced manufacturers due to its low-cost labor and government support for foreign investments This shift has created a demand for skilled workers, as employment has transitioned from agriculture to manufacturing and services This study aims to analyze the impact of technology adoption on the employment structure between production and non-production workers, as well as its effect on labor productivity in Vietnamese manufacturing firms from 2007 to 2013 While previous research has primarily utilized cross-sectional analysis, this study employs panel data to explore the relationship between technology adoption and employment structures, an area that has received limited attention in developing countries like Vietnam Understanding these dynamics will assist policymakers in formulating strategies to enhance labor quality and productivity, mitigate potential negative impacts of technological progress, 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 the adoption of technology influences employment structures and productivity within Vietnamese manufacturing firms It aims to answer two primary research questions regarding the effects of technological advancements on these key areas.
(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 collected from the Survey of Small and Medium Scale Manufacturing Enterprises (SMEs) in Vietnam between 2007 and 2013, the research categorizes firms based on 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 regarding 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 data analysis, estimation techniques, and regression results Finally, Chapter 6 concludes the study, discussing limitations and offering policy implications.
Literature review
Key concepts
Technology encompasses the knowledge and methods used to transform resources into outputs Key measures of technology include total factor productivity (TFP), the NBER TFP growth series, the investment ratio in computers compared to total investment, the ratio of R&D funding to net sales, the number of patents utilized within the industry, and the proportion of scientific and engineering employment relative to total employment.
In the context of technology adoption in manufacturing, various studies have explored different variables influencing this process Doms, Dunne, and Roberts (1995) highlighted the role of production equipment as a technology variable, while Berman et al (1994) identified computer investment as a proxy for the rate of technological change Hall and Khan (2002) defined technology adoption as the decision-making process to acquire and utilize new inventions, weighing the uncertain benefits against the costs Additionally, Rogers (1983) proposed that technology adoption involves a series of 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 divided into two main categories: production workers and nonproduction workers (Liu, Tsou, and Hammett, 2000) Nonproduction workers, often referred to as white-collar workers, include professionals such as managers, office staff, and sales personnel In contrast, production workers, commonly known as blue-collar or manual workers, are involved in manufacturing tasks, including fabrication, assembly, maintenance, material handling, warehousing, and shipping, as well as security services Additionally, production workers participate in auxiliary production and other manufacturing-related services, while apprentices are not counted among production workers (OECD, Labor Statistics).
According to the OECD, productivity measures the relationship between the volume of output produced and the inputs utilized, such as capital, land, and raw materials Mukherji (1962) further emphasized that productivity reflects the effective use of resources.
Productivity is the effective integration of various factors, including scientific management, technological advancements, and optimal resource allocation Different measures of productivity, such as capital productivity, multifactor productivity, and labor productivity, exist depending on the chosen output and input variables Notably, 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 generate more goods and services within the same work hours, ultimately benefiting the economy by maximizing output from the available labor.
The relationship between technology adoption and employment structures
Numerous studies over the past few decades have empirically explored the connection between technology and employment structures Evidence strongly suggests that the adoption of technology has led to a rise in the proportion of nonproduction workers across various workplaces.
In his 1821 analysis, Ricardo explored the impact of machinery adoption on various social classes, highlighting that mechanization increased productivity and lowered production costs, which in turn reduced the real prices of goods While landowners and capitalists benefitted from these lower prices, workers faced the threat of job loss due to potential reductions in the wage fund to accommodate costly machinery, resulting in technological unemployment Ricardo emphasized that competition among workers led to decreased wages, suggesting 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 identified the defense buildup and trade deficits as factors contributing to a shift in demand towards non-production workers They emphasized that technological advancements aimed at labor-saving in production are the primary drivers behind this trend Additionally, their study found a positive correlation between industry-level changes in the non-production labor share and investments in computers 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 impact of nonproduction labor and observable indicators of plant-level technology By defining nonproduction labor as skilled employees, the study found a positive correlation between capital-skill and R&D-skill complementarity and the increasing average share of nonproduction labor over time.
Doms, Dunne, and R Troske (1997) explored the impact of technology on workers and wages in U.S manufacturing plants through cross-sectional and time series research Their findings showed that plants utilizing advanced technologies tended to hire more skilled workers, particularly managers, professionals, and precision-craft workers, indicating a positive correlation between technological advancement and skill upgrading However, the time series analysis did not establish a consistent link between technology adoption and the nonproduction labor share, except for plants that implemented new factory automation technologies, which saw a more skilled workforce before and after adoption Additionally, plants that invested more in computing equipment, a key resource for managerial and clerical roles, experienced a significant increase in the share of nonproduction workers.
Between 1909 and 1929, U.S manufacturing data indicated that capital intensity and the use of purchased electricity as a source of motive energy significantly contributed to the rise in the number of educated production workers in factories, as highlighted by Goldin and Katz (1998).
A study by Liu, Tsou, and Hammett (2000) examined the effects of advanced technology adoption on wage and employment structures in Taiwan's manufacturing sector Utilizing a model similar to that of Dunne and Schmitz (1995), the findings indicated that firms implementing advanced technologies tend to employ a greater proportion of nonproduction labor, particularly in roles such as engineers, technicians, managers, and supervisors.
Dunne and Troske (2005) explored the relationship between technology adoption and labor market skill mix in U.S productive plants, building on the nonproduction concept from Doms, Dunne, and Troske (1997) Their study, which analyzed the adoption of seven different information technologies, revealed that the impact of technology on workforce skill varied by technology type Notably, plants with a higher proportion of nonproduction employees tended to utilize engineering and design tasks more extensively Additionally, those plants that adopted a greater number of engineering and design technologies experienced faster growth between 1987 and 1997 However, the research did not find conclusive evidence linking technology adoption to changes in workforce skill 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
The productivity of workers is influenced simultaneously by physical capital, human capital, and natural resources available per worker Additionally, the level of technical knowledge, represented by the variable A, plays a crucial role in determining overall productivity.
Numerous empirical studies demonstrate a positive correlation between technology and labor productivity For instance, Lakhani (1982) utilized both time series and cross-sectional data from U.S coal mines, as reported 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 to assess the impact of workplace practices, information technology, and human capital investments on productivity Their findings revealed that while the proportion of managerial workers using computers did not significantly influence labor productivity, the presence of non-managerial workers utilizing computers had a substantial positive effect on plant productivity Supporting this perspective, Doms, Dunne, and R Troske conducted cross-sectional analyses that indicated technologically advanced plants achieved higher productivity levels.
The OECD's 1998 report on Technology, Productivity, and Job Creation highlights the significant impact of technology on economic performance It reveals that the diffusion and adoption of technology not only enhance the productivity of innovative firms but also contribute to overall productivity growth across the economy.
A study by Mcguckin, Streitwieser, and Doms (1996) examined the link between advanced technology adoption and productivity, utilizing data from the 1993 and 1988 Survey of Manufacturing Technology The research revealed that companies employing advanced technologies experienced greater productivity gains Furthermore, the analysis indicated that higher-performing enterprises were more inclined to adopt these technologies compared to their lower-performing counterparts, highlighting a significant correlation between technological advancement and productivity success.
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 and semiparametric methods to measure productivity growth, their findings revealed that process innovations significantly contributed to sustained productivity increases over several years However, they also noted that once innovation ceased, the gains in production growth tended to diminish in subsequent years.
Filippetti and Peyrache (2012) employed the conditional frontier approach to analyze the factors influencing labor productivity growth across 211 European regions in 18 countries from 1995 onwards Their study focused on the relative impacts of capital accumulation, exogenous technical change, efficiency, and endogenous technological capabilities on productivity growth.
In 2007, it was argued that capital accumulation and exogenous technical change contribute to the convergence of labor productivity growth, though advanced and backward regions exhibit differing relative impacts For backward regions, capital accumulation serves as the primary driver of productivity growth However, the convergence process raises concerns due to the absence of endogenous technological capabilities.
Empirical studies suggest that technologically advanced firms tend to achieve higher labor productivity However, variations in research methodologies, country-specific characteristics, study periods, and proxy variables contribute to differing findings across 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 driving innovation and generating new investment opportunities, 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 differences in occupational choices between women and men, with women more likely to pursue careers in clerical and service roles, while men predominantly 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) demonstrates 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 indicate that this relationship is more pronounced within the manufacturing sector compared to the non-manufacturing sector.
Research on the relationship between firm age and productivity reveals mixed findings Huergo and Jaumandreu (2004) noted that new firms often experience significant productivity growth, eventually converging to average growth rates Conversely, Celikkol (2003) focused on the U.S food and kindred products industry, indicating that older plants tend to exhibit higher productivity growth rates compared to their younger counterparts.
A study by Petersen, Snartland,
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) indicates 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, which contributes to 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 influence these changes Specifically, the research will utilize factory equipment and operated personal computers as proxies for technology adoption In Vietnamese manufacturing, two primary types of machinery are employed: manually operated machinery (MOM) and power-driven machinery (PDM), which will also serve as indicators for technology adoption Additionally, the study will examine firm characteristics such as firm size (SIZE), firm age (FIAGE), the log of capital-labor ratio (CLR), and the proportion of male workers (MALE) Based on a thorough literature review, a conceptual framework illustrating the relationship between technology adoption, employment structures, and labor productivity will be presented.
Manually operated machinery only (MOM)
Power driven machinery only (PDM)
Both manually operated machinery and power driven machinery (BOTH)
The log of Capital-Labor ratio (CLR)
Data and Research methodology
Model specification
This study aims to estimate two models: the employment structures model, which analyzes changes in employment structures due to technology adoption in production, and the labor productivity model, which assesses the impact of technology on labor productivity Both models are based on Solow’s production function and treat technology adoption as an exogenous variable Recognizing that different industries and occupations respond uniquely to technology, the research will apply these models separately across various industries and labor types to evaluate the effects of technology adoption on employment structures and labor productivity.
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 structured as y it = f(TECH it , X it ) + à it, highlighting the relationship between technological factors and workforce dynamics.
Yitz represents the proportion of nonproduction workers within a firm, encompassing managers, professionals, office staff, and sales personnel This share is calculated by comparing the number of nonproduction workers to the regular labor force of the company Additionally, the individual proportions of managers, professionals, office workers, and sales staff are determined by their respective ratios to the regular labor force.
TECHit refers to the adoption of technology within firms, which has been shown to empirically increase the number of non-production workers across various workforces (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 percentage of male employees Previous studies, including research 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 Manuel Adelino and Song.
The capital-labor ratio has been positively correlated with the share of nonproduction employees, indicating a significant relationship (Tsou and Hammett, 2000) Furthermore, in manufacturing firms, women are often employed in roles such as clerical and sales positions, highlighting the gender distribution within these occupations (Petersen, Snartland, and Milgrom, 2000).
And àit is the error term
A production function mathematically illustrates the optimal output a firm can achieve for various combinations of inputs, guiding its production process decisions.
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, which is defined as the value-added per worker In the context of small and medium-sized enterprises (SMEs), this productivity metric can be assessed through either real revenue generated per full-time employee or real value added per full-time employee This paper specifically measures labor productivity as the value-added per worker, as outlined 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 the manufacturing process can significantly enhance worker productivity, as highlighted by Mcguckin, Streitwieser, and Doms (1996) and Huergo and Jaumandreu (2004) Additionally, research by Doms, Dunne, and R Troske (1997) indicates that employees in capital-intensive firms tend to produce a greater volume of products compared to those in less capital-intensive environments.
In the manufacturing sector, larger firms and new entrants tend to exhibit higher labor productivity, as noted by 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
This paper analyzes the impact of different types of technological adoption on employment structures and productivity within firms The study categorizes technology into four dummy variables: hand tools only (HT), manually operated machinery (MOM), power-driven machinery (PDM), and a combination of both (BOTH) Although the variable representing firms that use only hand tools and no machinery is included in the analysis, it will be excluded from the regression to focus on the effects of machinery adoption.
This study focuses on service workers, specifically cleaners, food preparers, and servers, as classified in the Survey of Small and Medium Scale Manufacturing Enterprises (SMEs) It posits that factory machinery and personal computers do not significantly impact their roles and productivity, thus excluding them from analysis despite being categorized as nonproduction workers Unlike previous research, this paper refrains from labeling nonproduction workers as skilled and production workers as unskilled, adhering instead to the classifications outlined in the SMEs survey The survey indicates that only a portion of production workers are unskilled, referred to as "Labor," while other roles may still be considered skilled To address industry-specific factors affecting employment structures and productivity within Vietnamese manufacturing firms, this study will conduct separate regressions based on a two-digit industry classification.
Table 3.1: Employment structures in SMEs
Source: The Survey of Small and Medium Scale Manufacturing Enterprises (SMEs)
2.Professionals (university and college degree)
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)
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
SIZE A regular labor force of firm
FIAGE The age of firm since establishment (year)
CLR The log ratio of the book value of fixed capital stock to number of regular labor
MALE The proportion of employees who are male
Data source
Recent research has begun to explore the impact of technological adoption on employment structures and productivity within firms using panel data, a shift from the previously dominant cross-sectional studies This study utilizes panel data from the Survey of Small and Medium Scale Manufacturing Enterprises (SMEs) in Vietnam, covering the years 2007 to 2013 Conducted by the Institute of Labor Studies, Social Affairs (ILSSA) in collaboration with the University of Copenhagen and funded by DANIDA, the survey encompasses three major cities—Ha Noi, Ho Chi Minh City, and Hai Phong—as well as seven provinces After filtering out firms that were not consistently surveyed or that collapsed during the period, the analysis focuses on nearly 1,350 manufacturing firms classified according to the International Standard Industrial Classification (ISIC) 4 Notably, the sample distribution across industries is uneven, with only 11 out of 24 industries featuring more than 30 surveyed firms, including significant representations from food products, fabricated metal products, and furniture manufacturing Additionally, the textiles industry, a crucial 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 use of panel data for regression analysis, highlighting its advantages over time series and cross-sectional data 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 model for specific samples, the Hausman test, F-test, and Breusch and Pagan Lagrangian multiplier test will be employed Following model selection, 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 identify multicollinearity among the variables.
In this study, we assume an infinite number of panels with a fixed number of time periods, leading us to employ the Harris-Tzavalis test, developed by Harris and Tzavalis in 1999, to assess the stationarity of the data Detailed results of all tests can be found in the appendix of this paper.
Technological revolution, employment structures and labor productivity
Technology innovation
From 1975 to 2005, Vietnam's economic growth was largely unaffected by technological innovations (Ngoc, 2008) However, the country's innovation system has recently 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 Despite this progress, the growth of high-tech exports has been notably sluggish (Anh, Hung, Mai, 2013).
Table 4.1 illustrates the technological structure of Vietnam's manufacturing exports in comparison to other countries for the years 2000 and 2008 While the share of medium and low technology exports increased, high technology exports experienced a slight decline from 11.1% in 2000 to 10.1% in 2008 During this period, Vietnam ranked among the three countries with the lowest percentages of high-tech exports, surpassing only Cambodia, which had 0.1% in 2008 This indicates that Vietnam lags behind many nations in the adoption of advanced technologies in production, primarily due to the labor-intensive nature of its manufacturing firms.
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%
Despite the Vietnamese government's efforts to promote technological change, significant reforms have not materialized, as noted by Viet, Hien, Quy, and Qui (2011) The private and foreign direct investment (FDI) sectors in Vietnam lag behind other regional countries in technological advancement due to a shortage of skilled workers capable of implementing new technologies Consequently, only 2% of Vietnamese enterprises utilize high technology, in stark contrast to 30% in Thailand, 51% in Malaysia, and 73% in Singapore Vietnam's competitiveness remains low, particularly in technological metrics, with rankings of 55 out of 133 for creativity and innovation, according to the World Economic Forum (2009) Additionally, most firms continue to rely on manually operated and power-driven machinery, with a gradual decline in the use of hand tools.
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
From 2007 to 2013, there was a notable decline in the adoption of new technologies among enterprises, with the rate dropping from approximately 15% in 2007 to 6.4% in 2013 This decrease can be attributed to a decline in innovation ratios, often influenced by the financial crisis, which created uncertainty in business and limited demand for new technologies While urban firms initially had a higher likelihood of adopting new technologies compared to their rural counterparts from 2007 to 2011, this disparity diminished by 2013, resulting in nearly equal adoption rates in both areas Additionally, larger enterprises showed a greater tendency to utilize new technologies in their manufacturing processes.
Employment structures
Before the 1986 reform, Vietnam's labor market was heavily regulated, with government control over hiring and wage-setting in state-owned enterprises (SOEs) However, in the past thirty years, significant positive changes have occurred, particularly in recruitment, termination practices, and wage policies, leading to a more flexible and dynamic labor market.
Between 1990 and 2011, the manufacturing sector in Vietnam experienced significant employment growth, rising from 2.8% to 7.3%, which facilitated a substantial shift of labor from agriculture to manufacturing According to the CIEM report, from 2007 to 2013, there was a notable fluctuation in occupations within Vietnamese manufacturing, with managerial, professional, and office roles increasing, 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 hire professional workers, as they had the financial resources to invest in new technologies and benefited from easier access to advanced technology in major cities.
Employee productivity
Between 2000 and 2010, Vietnamese manufacturing firms experienced notable improvements in productivity (Tran Xuan Huong, 2014) Despite these advancements, labor productivity in Vietnam 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% When compared to neighboring countries, Vietnam's labor productivity is just one-eighth that of Singapore and one-third that of Thailand, surpassing only Myanmar and Cambodia, and being similar to Laos (Ngoc and Thu, 2013) This disparity is largely due to the fact that workers in countries like Singapore and Thailand are employed in high-value-added sectors such as services, while Vietnamese workers predominantly operate in the lower-value textile and garment industries.
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 workforce remains in agriculture and informal sectors, which exhibit lower productivity compared to manufacturing and services This limited exposure to modern machinery and technology contributes to the low labor productivity rates observed According to recent International Labor Organization (ILO) surveys, manufacturing and services sectors demonstrate substantially higher productivity levels than agriculture The ILO suggests that integrating modern technologies and implementing effective training strategies for workers are essential for enhancing labor productivity Additionally, productivity levels in manufacturing firms tend to increase with the size of the firm, as evidenced by data showing larger firms outperforming smaller ones, and urban locations exhibiting higher productivity compared to rural areas.
Table 4.5: Labor productivity by firm size and location
Summary of the chapter
To successfully integrate into the international market, Vietnam must focus on investing in technology, enhancing the quality of its workforce, and increasing productivity Statistics indicate that larger firms and those located in urban areas are more likely to adopt new technologies Additionally, the Center for Industry and Trade Research (CIEM) found that the proportion of professionals, office, and sales workers in Vietnamese manufacturing firms rises in tandem with firm size and urbanization This raises the question of whether technology adoption is linked to the percentage of non-production workers in manufacturing Moreover, larger enterprises in urban settings demonstrate higher productivity levels compared to smaller, rural firms Evidence also suggests that companies that embrace technology experience significant improvements in labor productivity These topics will be further explored in the next chapter.
Empirical Results
Descriptive analysis
As the results of data analysis, among industries, the manufacture of food
The industry with the highest proportion of nonproduction workers, particularly managers, was identified as the manufacture of fabricated metal products, excluding machinery and equipment, followed closely by the manufacture of furniture Other industries exhibited similar ratios of nonproduction workers, but overall, the percentages of professional, office, and sales workers remained relatively low across all sectors.
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, the employment structures experienced notable shifts, with a significant decline in production workers, whose numbers fell from around 8,600 to approximately 6,730 In contrast, the share of nonproduction workers saw a slight increase during this period These findings align with the performance analysis presented in Chapter 4, as detailed in Table 4.3 and Appendix 2.
In terms of labor productivity, the wood and cork manufacturing sector, excluding furniture, along with the production of straw and plaiting materials, reported the lowest added value at just 38,898.27 VND, which is nearly half that of the rubber and plastics products sector Despite this, Vietnamese manufacturing firms experienced a remarkable increase in productivity, particularly in the rubber and plastics products and fabricated metal products industries Notably, by 2013, the added values across all industries had more than doubled compared to 2007.
Figure 5.2: Added value per worker
Source: Author’s calculation from the data
Over a seven-year period, the use of hand tools (HT) and manually operated machinery (MOM) in manufacturing firms significantly declined, while the adoption of power-driven machinery (PDM) and a combination of both PDM and MOM (BOTH) increased Despite a slight decrease in firms utilizing both types of machinery in 2011, there was a positive trend in the following two years Additionally, the number of computers in the workforce rose from approximately 710 units in 2007 to nearly 820 units by 2013; however, this investment was relatively low compared to over 1,040 surveyed firms across six industries Specifically, only the textile and rubber and plastics manufacturing industries had average computer counts of 1.25 and nearly 2.25, respectively, while other industries reported less than one computer per firm.
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, while those employing only power-driven machinery also represented a significant share Notably, the textile manufacturing sector maintained a considerable reliance on hand tools, accounting for 25% of production Overall, Vietnamese manufacturing firms witnessed positive workforce changes, with a marked increase in the use of power-driven machinery and computers, leading to enhanced labor productivity over seven years and a reduction in the proportion of production workers Interestingly, despite the larger size of the food and textile manufacturing industries, they exhibited a lower percentage of male workers compared to female workers.
The workforce composition reveals that women make up 50% of the total labor force, while the food manufacturing sector exhibits a nearly equal distribution of male and female employees, with a ratio of 10% In contrast, other industries report over 60% male workers, highlighting a significant gender disparity in the overall workforce.
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 means of the OPC, SIZE, CLR, and MALE variables across six industries from 2007 to 2013 During this period, the number of operational personal computers in industries gradually increased, yet remained relatively low, averaging only 0.65 to 0.75 computers per manufacturing unit Notably, the rubber and plastics manufacturing sector stood out, with computer usage exceeding three units in 2011, while the furniture manufacturing industry reported an average of just 0.37 units Additionally, Vietnamese manufacturing firms experienced a decline in average firm size, which hovered 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 notable decline in both firm size and the percentage of male workers in the workforce In contrast, the average number of personal computers in use and the capital-labor ratio have seen slight increases, albeit at minimal rates.
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 the manually operated and power-driven machineries (BOTH) and the exclusively 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 outcomes of regression models to explore the impact of technology adoption on the employment structures and labor productivity of manufacturing firms in Vietnam To investigate 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 of enterprises, specifically focusing on the ratio of non-production workers to production workers within the workforce Additionally, it analyzes labor productivity, measured as the added value generated per worker across the entire sample.
(a) Technology adoption and nonproduction worker share
The analysis of technology dummy variable coefficients reveals that the adoption of machinery and operated personal computers (OPC) did not significantly affect the proportion of nonproduction workers across most Vietnamese industries from 2007 to 2013, with the exceptions being the food manufacturing and furniture manufacturing sectors Specifically, the use of manually operated machinery (MOM) increased the share of nonproduction workers in the food manufacturing industry, while it decreased the share in the furniture manufacturing industry.
The findings of this study diverge from 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 stem from two primary factors: firstly, previous studies utilized cross-sectional data to analyze the effects of technology adoption, whereas this research employs panel data, potentially leading to differing outcomes.
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
Recent studies indicate that most research has been conducted in developed countries utilizing advanced technologies in production In contrast, the World Bank's 2014 report and research by Anh, Hung, and Mai (2013) highlight that Vietnam's innovation system is still in its infancy, characterized by weak capabilities in science, technology, and innovation Vietnamese manufacturing firms predominantly rely on labor-intensive methods, and investment in innovative production technologies remains inadequate, leading to the use of outdated technologies However, as noted in Chapter 4 of CIEM's reports, there has been a notable increase in the share 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 drive this trend Regression results indicate a significant correlation between firm age and the capital-labor ratio with employment structures, highlighting that older firms and those with a higher capital-labor ratio tend to have more nonproduction employees and greater labor productivity Additionally, larger firms typically hire a smaller percentage of nonproduction workers, while firms with a higher male workforce, particularly in the food manufacturing sector, also employ fewer nonproduction workers.
Tables 5.6, 5.7, and 5.8 provide detailed insights into the shifts in the distribution of managers, professional workers, and sales and office employees, highlighting the effects of technology adoption and firm characteristics on workforce composition.
Firms that exclusively utilized manually operated machinery (MOM) in food manufacturing experienced an increase in managerial positions In contrast, in the fabricated metal products sector, investment in operated personal computers (OPC) correlated with a decline in the number of managers Additionally, in food manufacturing, companies with a higher percentage of male workers tended to employ fewer managers, while the opposite was true for fabricated metal products The capital-labor ratio (CLR) and the age of the firm (FIAGE) positively influenced the percentage of managers across all industries However, larger firms (SIZE) tended to hire fewer managers.
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 ration of professional workers is the occupation which is affected most clearly by technology adoption The using of only manually operated machinery
The use of machinery, including motor-operated machinery (MOM), power-driven machinery (PDM), and operated personal computers (OPC), has a positive correlation with the number of professionals in the workforce, except in the rubber and plastics manufacturing sector However, factors such as firm size and the proportion of male workers negatively impact the rate of professionals employed.
However, contrary to some above conclusions about the impact of firm’s age
(FIAGE) to the nonproduction workers, entrant firms of the manufacture of textile
(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 has had minimal impact on the share of sales and office workers in manufacturing firms, with the textile industry experiencing a decline when using manually operated machinery Conversely, the food manufacturing sector saw an increase in sales and office employees after investing in personal computers Young firms tend to employ more sales and office staff, and the capital-labor ratio did not significantly affect these roles However, the furniture manufacturing industry faced a negative impact on the share of sales and office workers due to firm size, and a higher rate of male workers was also negatively correlated with these 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 that the adoption of technology negatively impacts labor productivity, contrasting with findings from other research Specifically, the use of manually operated machinery (MOM) in the wood industry diminishes the added value per worker, while operated personal computers (OPC) also contribute to reduced productivity in rubber and plastics manufacturing These discrepancies can be attributed to the relatively low technology levels in Vietnamese manufacturing firms, which often rely on imported raw materials and primarily process products for foreign companies Additionally, the prevalence of household businesses in Vietnam, which focus on manual production, suggests that machinery implementation may sometimes lead to decreased productivity.
The positive changes in labor productivity within Vietnamese manufacturing can be attributed to specific firm characteristics, such as firm age (AGE), capital-labor ratio (CLR), and the proportion of male employees (MALE), all of which show a significant positive correlation with labor productivity (VA) Conversely, firm size (SIZE) exhibits a negative correlation with labor productivity This finding contrasts with previous studies that typically indicate larger firms are more productive However, data from CIEM (2013) reveals that the majority of Vietnamese manufacturing firms are micro and small, with a notable distribution in the food manufacturing sector: 637 micro firms, 97 small firms, and only 21 medium firms This prevalence of smaller firms helps explain the observed negative correlation between firm size and labor productivity in the Vietnamese context.
Table 5.9: The coefficient signs between the labor productivity and other independent variables
Products of wood, cork, straw
Note: (*), (**) and (***) represent statistical significance level at lower 1%, 5% and 10%, respectively
The employment structure in Vietnam's food manufacturing industry reflects a trend identified by Liu, Tsou, and Hammett (2000), where technology adoption leads to an increased proportion of nonproduction workers, particularly managers The food manufacturing sector, a key component of Vietnam's economy, has seen significant growth, with advancements in product quality enhancing its market presence and export potential, as noted by the Ministry of Industry and Trade (2013) Many firms within this industry have embraced modern technology and industrial-scale equipment, which not only improves product refinement but also reduces the demand for production labor Consequently, companies utilizing manually operated machinery (MOM) tend to hire more nonproduction staff, especially in managerial roles Furthermore, the implementation of operating personal computers (OPC) has resulted in a higher number of sales and office personnel, as computers facilitate easier internet access 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 the added value per worker, primarily due to a heavy reliance on imported raw materials and inadequate product quality control For instance, the dairy sector imports 75% of its raw materials, while the cooking oil industry relies on imports for 90% of its raw materials.
Many small businesses managed by local officials struggle with outdated equipment due to limited capital, resulting in production inefficiencies (Ministry of Industry and Trade, 2013) Additionally, long-established firms typically hire fewer sales and office staff, focusing more on managerial roles Larger firms and those with a higher percentage of male employees tend to employ fewer non-production staff, particularly managers This aligns with Wootton's (1997) findings that occupational choices differ by gender, with women often in clerical and service roles, while men are more likely to work in crafts, operations, and labor Furthermore, in the food manufacturing sector, we concur with the research 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 presents the findings on the influence of technology adoption on employment structures and labor productivity across six industries The overall analysis reveals no significant effect of technology adoption on these objectives; however, a closer examination of individual manufacturing sectors indicates that technology use has increased the share of professional roles Conversely, the reliance on manually operated machinery and personal computers has led to a decline in added value per worker, particularly in the wood manufacturing industry and related sectors Despite these trends, the effects remain relatively weak Additionally, firm characteristics such as age, size, the proportion of male workers, and the capital-labor ratio significantly impact employment structures and labor 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 firms utilizing technology is not significantly higher than in those that do not Additionally, advanced firms do not necessarily achieve greater labor productivity compared to their counterparts Interestingly, the use of manually operated machinery and computers can result in a decrease in added value per worker, contradicting the findings of Black and Lynch (1997).
Recent analyses indicate a significant relationship between technology adoption, employment structures, and labor productivity across various industries The share of professional workers, including engineers, technicians, and accountants, experienced the most considerable impact, followed closely by managers Additionally, a positive correlation exists between the capital-labor ratio and the proportion of nonproduction workers, as well as overall labor productivity in most firms This suggests that strategic investment in technology can enhance productivity and increase the proportion of skilled professionals and managers within organizations.
Research indicates that certain firm characteristics significantly impact employment structures and productivity Specifically, the age of a firm (FIAGE) is positively correlated with both employment structures and productivity levels Additionally, larger firms (SIZE) tend to 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 percentage of male workers and overall labor productivity.
The distinct differences in research regarding the impact of technology adoption on employment structures and labor productivity in Vietnamese manufacturing firms stem from various factors A primary reason is the outdated technologies utilized by these firms, which do not align with global advancements Vietnam is recognized as one of the largest processing hubs worldwide, yet many industries primarily export raw materials and rely on imported finished products, facing challenges due to insufficient local material supplies Additionally, only a limited number of firms, particularly those with strong financial backing or foreign investments, invest adequately in their production processes and workforce training Consequently, the overall technology levels and labor quality within Vietnamese manufacturing remain low, resulting in minimal significant changes in the effects of technology adoption on employment and productivity.
The findings of this study offer valuable recommendations for policymakers and managers in Vietnamese manufacturing firms, aimed at fostering positive changes in the workforce and enhancing labor productivity.
The limited impact of technology adoption on employment structures and labor productivity in Vietnam is primarily due to the underdevelopment of manufacturing technologies The government plays a crucial role in fostering scientific and technological advancements by providing long-term support for social and economic entities Currently, Vietnam's innovation capabilities are weak, resulting in a low integration of high technology in manufacturing To enhance innovation, improvements in the business environment, infrastructure, entrepreneurial quality, and product standards are essential Additionally, promoting trade openness and attracting foreign direct investment can facilitate technology transfer Supporting industries are vital for adding value, boosting competitiveness, and advancing national industrialization, making the development of these sectors a critical focus As technology levels in Vietnamese industries rise, there may be a reduction in demand for production workers, potentially increasing unemployment rates To mitigate fluctuations in the job market, it is important for officials to ensure that displaced workers have opportunities for skill enhancement, emphasizing the need for greater 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
To succeed in a globally integrated market, it is essential to raise awareness of product quality standards and foster a culture of entrepreneurship Additionally, providing training for workers to keep pace with rapidly evolving advanced technologies is a crucial priority.
This study faces several limitations, primarily due to the short duration of the research, which may not adequately capture fluctuating trends among the variables Additionally, the timing of the survey coincides with a global economic crisis, particularly affecting Vietnam's economy, potentially biasing the results regarding the impact of technology adoption on employment structures and employee productivity Furthermore, 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 highest number of surveyed firms to represent the entire sample.
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