High-skilled Labor Research Articles

Assessing the economy-wide impacts of digital transformation in the Korean economy: A social accounting matrix multiplier approach

This study assesses the economic impact of digital transformation (DT) in Korea by utilizing a Social Accounting Matrix to explore interconnections across various datasets. Through a social accounting matrix multiplier analysis, this study empirically identifies the production inducement, income inducement, and income distribution effects driven by DT-related infrastructure investments. We analyze how DT influences production, income, and income distribution through infrastructure investments, focusing on its effects on the labor market and household income. The findings indicate that DT accelerates economic growth, favoring digital capital-intensive industries and high-skilled labor, potentially increasing employment polarization and exacerbating income inequality. This study highlights the need for dual approach as to enhance national competitiveness and inclusiveness in an economy driven by DT technologies. This approach involves two key components that work synergistically to maximize the benefits of DT while addressing its potential challenges, suggesting that industrial and innovation policies should be reconstructed to align with the demands of a DT-driven economy.

Market Access, Population Mobility and Labor Force Structure: Evidence From China’s High-Speed Railway

Population mobility is becoming a common phenomenon worldwide which can contribute to the development of individuals and societies. This paper offers that the launch of HSR would facilitate population mobility and spatial disparity. We also find that the HSR connection mainly affects migration of people under 14 and 15 to 64 years old, with no effect on the old over 65. This implies that labor-led family migration has become the main form of movement under the effect of HSR. Furthermore, HSR connection has promoted the concentration of high-skilled labor and the outflow of low-skilled labor in large cities. This study offers useful insights for policy makers that concerns transport infrastructure and the issue of household and workforce structure it raises. JEL: O30, O18, D60, L31.

Examining the Social Consequences of Automation and Artificial Intelligence in Industrial Management

This article examines the social consequences of automation and artificial intelligence (AI) in industrial management, drawing on the findings of Damioli, Van Rooy, and Vertsy’s (2021) study on the impact of AI on labor productivity. The integration of AI technologies in industrial settings has led to significant improvements in efficiency and productivity. However, these advancements come with substantial social implications that affect both the workforce and broader societal structures. Internally, automation and AI have shifted the demand from low-skilled to high-skilled labor, necessitating ongoing education and skill development for employees. Additionally, the reliance on AI for managerial decisions raises ethical concerns regarding algorithmic biases and the potential erosion of human judgment.Externally, the broader societal impact includes economic disparities, as regions with a high concentration of low-skilled jobs experience greater unemployment and underemployment. The displacement of workers due to automation presents challenges such as increased socio-economic divides and disruptions to community stability. Ethical considerations, including data privacy and security, are crucial to ensuring fair and responsible AI deployment. This article underscores the need for strategic collaboration between industry leaders, policymakers, and educational institutions to address these social consequences. By fostering an inclusive approach to technological advancement, it is possible to balance the productivity benefits of AI with the imperative to mitigate its adverse social impacts.

Impact of high-speed rail on wage premiums for migrant workers in China

The extensive high-speed rail (HSR) network in China has significantly enhanced regional population mobility, yet its impact on the well-being of migrant workers remains underexplored. Utilizing data from the China Migrant Dynamic Survey (CMDS) from 2014 to 2018, this study investigates the influence of HSR on wage premiums for migrant workers and identifies the factors driving these premiums using the non-parametric nearest neighbor matching method. The findings reveal that HSR confers a significant wage premium of 13.1% for migrant workers in cities with HSR infrastructure compared to cities without it. This wage premium effect is particularly pronounced in less developed western China and in cities with larger populations and a higher proportion of tertiary sector economy. The study finds a 10.54% increase in wage premiums for intra-province migrant workers due to HSR, while the effect remains negligible for inter-province migrant workers. Moreover, the wage premium effect is stronger for higher-skilled labor and for migrant workers aged 16 to 45. It is particularly significant for professionals, administrative staff, and workers in the service industry. The HSR wage premium effect is multifaceted, exhibiting both level and growth effects, with the latter intensifying over time after HSR introduction. Overall, the findings suggest that HSR deployment positively influences migrant worker wages by enhancing travel convenience, improving market access, fostering knowledge spillovers, and facilitating industry transfers. However, these impacts vary by location, skill level, and industry.

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.

Uncovering regional inequalities in digitalization: A multifaceted measurement for Germany

The ongoing global digital transformation has significant implications for economies and societies, with potential benefits and challenges. This study addresses the critical need for a comprehensive measurement of regional digitalization in Germany to better understand its impact on various aspects of life, including education, employment, and working conditions. Using confirmatory factor analysis (CFA), it introduces a multifaceted regional digitalization measure at the administrative district level (NUTS-3) that incorporates digital infrastructure, culture, technology capacity, high-tech human capital, and digitalization-related innovativeness. Results for 2013 and 2017 are compared. The study reveals that digitalization varies significantly across regions, but hardly over time. Urban regions tend to have higher digitalization levels, which are positively associated with economic productivity and high-skilled labor demand. Our developed measurement of regional digitalization is publicly available.

Scaling innovation: unveiling urban factors shaping creativity

It is crucial to explain the city scale effect from the perspective of innovation, which directly affects the formulation of city governance policies and, ultimately, city well-being. This paper employs urban panel data from China spanning the years 2008–2018 to empirically investigate the causal link between city size, measured by population, and the number of innovation outputs. The empirical results show that city size has a significant positive impact on the quantity of innovation output. Mechanism testing shows that knowledge spillover effects and high-skilled labor selection effects have credible explanatory power for city size effects. The research conclusions of this article have certain practical significance for urban governance and related policy formulation.

Do talent housing policies foster regional innovation? An analysis based on labor force heterogeneity

Human capital plays a crucial role in regional innovation and economic progress, which has increasingly been highlighted in research. However, rising housing costs in cities can push talent away, causing brain drain and hindering local innovation. Within this context, China has seen widespread adoption of talent housing policies (THPs) aimed at providing housing assistance for the high-skilled labor force, although the ramifications of these initiatives remain largely underexplored. Based on labor force heterogeneity, this study investigates the effects of THPs targeted at high-skilled labor on the mobility of both high- and low-skilled workers and their consequent impact on regional innovation. Our findings reveal that while THPs bolster the local pool of highly skilled human capital and attract more low-skilled workforce, enhancing regional innovation through labor–skill complementarities, they concurrently elevate housing costs for low-skilled laborers, reducing their long-term settlement intensions and potentially hindering a city's innovation trajectory.

Industrial automation and product quality: the role of robotic production transformation

ABSTRACT We investigate the impact of industrial automation, specifically robots, on product quality in international trade. By analysing detailed data from Chinese listed firms, we examine how the adoption of robots is associated with higher product quality compared to that of nonrobot users. This effect is more pronounced among heterogeneous products and in ordinary trade patterns. We identify several mechanisms through which robots contribute to product quality improvements, including resource reallocation, adaptability to complex production processes, and enhancement of input quality. Moreover, we demonstrate that replacing low-skilled labour with complex robots and employing high-skilled labour to collaborate with robots improved product quality, confirming that robotic displacement and reinstatement effects generate better product quality upgrading. Overall, our empirical evidence supports the positive influence of robot adoption on product quality and provides insights into the mechanisms underlying robotic production transformation.

Impact of digital economy development on labor force employment

Since the first year of the commercial application of big data, the digital economy has become an important driving force for global economic development and has had a huge impact on the labor employment market. This paper aims to explore how the development of the digital economy reshapes the employment pattern of the labor market. By analyzing the core features of the digital economy, which is highly informative, networked, and intelligent, it reveals the dual effects of the digital economy on the total and structural employment of the workforce and puts forward policy recommendations to address the employment challenges posed by the digital economy. The study finds that, on the one hand, the application of automation technology and artificial intelligence has reduced the demand for traditional low-skilled labor, triggering employment reductions in some industries; on the other hand, emerging digital technologies have created new career opportunities, especially in the fields of data analysis, software development, and digital marketing, which have brought more employment opportunities for high-skilled labor. In addition, the digital economy has given rise to numerous flexible forms of employment.

Does Industrial Intelligence Promote Sustainable Employment?

One of the key driving factors for achieving the goal of sustainable economic development is to ensure decent employment opportunities. This article explores the relationship between industrial intelligence and sustainable development in China from the perspective of employment. Based on interprovincial panel data from 2006 to 2019, using the fixed-effect regression model and mediating-effect regression model, this study empirically tests the impact of industrial intelligence on sustainable employment in China. The following conclusions are drawn: (1) Industrial intelligence has a significant positive impact on the overall scale of employment. Industrial intelligence has promoted the optimization and upgrading of employment skill structure and industrial structure. Industrial intelligence will reduce the employment proportion of low-skilled labor and increase the employment proportion of medium-skilled labor and high-skilled labor. Industrial intelligence significantly reduces the employment share of the manufacturing sector and increases the employment share of the service sector. (2) Industrial intelligence reduces employment levels through capital deepening effects. Industrial intelligence has significantly improved regional labor productivity and significantly improved employment levels through productivity effects. (3) The results of regional heterogeneity show that industrial intelligence has promoted the improvement of employment level and the upgrading of employment structure in the eastern region but has not had a significant positive impact on other regions.

R&D innovation, industrial evolution and the labor skill structure in China manufacturing

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education.

The impact of capital goods trade liberalization on regional labor market in China

The impact of trade liberalization on regional employment dynamics remains a topic of ongoing debate and scrutiny. This study aims to provide a nuanced understanding by focusing specifically on the effects of imported capital goods trade. Our study analysis reveals that capital goods trade liberalization predominantly leads to a substitution effect between capital and labor, resulting in a reduction in overall regional employment levels. However, we also find complementary effects between capital and high-skilled labor, alongside substitution effects on low-skilled labor, which collectively influence the regional labor market's skill structure. Furthermore, our study identifies the heterogeneous spillover effects of capital goods trade liberalization on regional labor markets, varying across different regions and city scales. We argue that investing in region skill development resources can help align the workforce with global factors, fostering positive interaction between regions.

The Impact of Artificial Intelligence on Current Social Employment and Structure: Empirical Evidence from Provincial Industrial Robots

Artificial intelligence, as the core driving force of the new round of technological revolution and industrial change, has far-reaching impact on current social employment and structure. This study, based on the data of provincial industrial robots, explores the impact of artificial intelligence on employment. Through literature review, it is found that artificial intelligence has impact on the labor market, which may lead to changes in employment scale, employment structure, and income distribution. The study uses panel data model to analyze the impact of artificial intelligence development on employment scale and structure in 31 provinces (autonomous regions, municipalities directly under the Central Government) of China from 2010 to 2019. The results show that the development of artificial intelligence has a significant promoting effect on the employment scale in China, and also affects the employment structure towards “high-end” direction, but there is also a spillover effect towards “polarization”. In addition, the study finds that artificial intelligence has different effects on the employment of labor with different skills, promoting the employment of high-skilled labor in the manufacturing industry and inhibiting the employment of low-skilled labor, leading to a trend of high-end employment structure in the manufacturing industry. Therefore, the study proposes some prospects and suggestions for the impact of artificial intelligence on the social employment structure, in order to promote the transformation and upgrading of the industry, and achieve efficient intelligent production and optimal adjustment of employment structure. Through in-depth analysis of this study, important reference basis is provided for the current social issues facing the impact of artificial intelligence.

China's biased technological progress, labor market allocation and the change of trade division structure

China is facing the dual background of the biased technology change and the contraction of global division system. It is important to understand the evolution of the direction of technological progress and the resulting change in the shares of value added at home and abroad. This paper reveals the typical facts of the coexistence of China's technological progress in favor of high-skilled labor and the increase in the share of value added of the domestic division. We prove that skill-biased technological progress has a positive effect on improving the revenue of the domestic industrial division. The distortion of labor market allocation has an inhibitory effect on the local share of division brought about by biased technological progress. The change of technological gap has a nonlinear regulating effect. The narrowing of the technological gap between China and advanced countries is an important driving force to promote the reconfiguration of the industrial division system in domestic and foreign markets. Under the background of the contraction of the global division system, the conclusions have some enlightening significance to form resilient industrial chains in the domestic division system.

How automated machines influence employment in manufacturing enterprises?

This paper theoretically analyzes and empirically examines the impact and mechanisms of automated machines on employment in manufacturing enterprises, drawing on task-based model and using micro data from listed Chinese manufacturing enterprises between 2012 and 2019. Our findings reveal that: (1) Automated machines in manufacturing enterprises leads to a substitution effect on the total labor force, with a substitution effect on low-skilled labor and a creation effect on high-skilled labor in terms of employment structure. (2) Further analysis indicates that automated machines primarily have a positive effect on R&D and technical staff, a non-significant effect on sales staff, and a negative impact on production, administrative, and financial staff. (3) The primary influencing mechanisms of automated machines on employment in manufacturing firms are productivity effects and output scale effects, based on the mediation effect model. (4) Considering the industry linkage effect, we employ the input-output method and the Input-Output Table and find that automated machines for upstream (downstream) manufacturing enterprises will result in a substitution effect on employment for downstream (upstream) enterprises. The novelties and research contributions are as follows: (1) we conduct a structural decomposition of total employment, and further decompose employment positions into production, R&D, sales, finance, and administration. (2) We try to investigate the industry linkage effect about the impact of automated machines on the employment of upstream and downstream enterprises. (3) We use data from listed manufacturing companies, and the data of existing research are about provincial and industry-level data.

Artificial intelligence and the skill premium: A numerical analysis of theoretical models

As a new engine in guiding China's high-quality economic development, it is important to study whether the development of artificial intelligence (AI) will increase the skill premium and affect labor income inequality. Based on Acemoglu and Restrepo's (2018a) task-based model, this study constructs a multi-sector dynamic general equilibrium (DGE) model to analyze the impact and mechanism of AI on the skill premium and performs a numerical simulation using China's industrial panel data from 2010 to 2019. The results show that AI widens the skill premium by substituting low-skilled labor with industrial robots and performing high-skilled labor tasks. The mechanism analysis reveals that AI also affects the skill premium by influencing factor flow and structural transformation. Based on these findings, this study provides policy suggestions for governments to mitigate the impact of AI on the labor market.

The effect of digital government on corporate total factor productivity.

This study examines the influence of digital government initiatives on corporate total factor productivity (TFP). Employing a difference-in-differences (DID) methodology and analyzing data from publicly listed companies spanning the period 2010 to 2020, we investigate the impact of digital governance on corporate TFP. Our findings reveal a noteworthy positive effect, with an average TFP increase of 5%. Further exploration through heterogeneity analysis indicates that this impact is particularly pronounced in regions with robust network infrastructure, increased marketization, and decreased economic uncertainty, particularly among privately-owned enterprises. Moreover, we identify key mechanisms through which digital governance fosters this enhancement in TFP, including the facilitation of technological innovation, efficient allocation of high-skilled labor, and improved investment efficiency. Our research underscores the significant role of digital government initiatives in bolstering corporate TFP and contributes to a deeper understanding of the mechanisms underlying this relationship.

Is there life after product quality failures? Evidence from employment decisions

Prior literature has documented that product quality failures have negative impacts on the interest of stakeholders, such as consumers and shareholders. We extend this literature by investigating its potential effects on the interest of employees. Using a novel dataset that tracks the recall incidents of Chinese firms in an industry where product quality is crucial (i.e., the drug industry), our empirical results show that the effects on corporate employment decisions are twofold. On the one hand, we show that firms’ employment growth decreases after product recalls. The effect is more pronounced in non-state-owned firms, firms with higher labor costs, firms in regions with weaker labor protection, and during periods when local politicians lack promotion incentives. On the other hand, product recalls mainly induce firms to reduce the employment growth of low-grade employees, consistent with recalling firms relying on high-skilled labor to improve drug quality. Collectively, our study suggests that product quality failures could be a broader issue of corporate social responsibility (CSR) than being documented, and carries implications for policymakers concerning employment as well as product safety.

Labor skill and accounting conservatism

We examine the role of labor market frictions in shaping firms’ accounting policies. We find that firms’ reliance on high-skill labor is negatively related to accounting conservatism, presumably because higher labor adjustment costs make conservative accounting policies more costly. Cross-sectional tests further support the notion that labor adjustment costs are the main driver for the negative relation between labor skill and accounting conservatism. Specifically, the negative relation is more pronounced when a firm i) operates in more competitive industries, ii) has a higher voluntary employee turnover rate, and iii) is more financially constrained. Finally, we show that the positive implication of accounting conservatism for future operating performance and cost of debt is weaker for firms requiring high labor skill, providing plausible explanations for a negative relation between labor skill and accounting conservatism. Collectively, we provide novel evidence on the relation between labor force heterogeneity and accounting conservatism.