Level Of Human Capital Research Articles

Beautifying urban environment: Smart city construction and sustainable pollution control in China

Faced with the environmental pollution challenges brought by urbanization, smart city construction (SCC) offers a practical and viable pathway for environmental protection. This study treats China's SCC policy as a quasi-natural experiment, utilizing balanced panel data from 285 cities across the country spanning from 2003 to 2019. Through the multi-period Difference-in-Differences (DID) model, the influence of smart city initiatives on urban pollution emissions is systematically examined. Results demonstrate that SCC effectively control urban pollution emissions, leading to a 1.46% decrease in pollution levels in pilot cities. These findings are robust to various tests, including parallel trends analysis, placebo tests, Propensity Score Matching (PSM)-DID, alternative pollution calculation methods, pollution intensity adjustments, control for other policy interferences, and sample adjustments, alongside endogeneity tests using instrumental variables. SCC achieves pollution control through mechanisms that enhance urban technological innovation, facilitate industrial restructuring, and talent agglomeration and financial agglomeration. Heterogeneity analysis reveals a more pronounced impact in the eastern area, large cities, higher administrative levels, cities with superior digital infrastructure, and those with higher human capital levels and pollution severity. This research explores a practical approach towards achieving sustainable pollution control by optimizing technology, industrial structure, and human resources.

The effects of internal migration on regional convergence: Evidence from Serbia

The paper investigates the effects of internal migration flows on regional convergence in Serbia, a country with large regional disparities, during the period 2000–2018. For this purpose, we implement both the net and the gross (in- and out-) migration approaches. We undertake separate analyses for different categories of the migrant population to explore the impact of migrations involving different levels of human capital. The results show that internal migration flows between NUTS3 units in the observed period mainly fostered divergence, thus contradicting the neoclassical theory. This effect is especially pronounced when considering migrations of the active population and prime-age specialists.

Higher Education Investment, Human Capital Level, and High-Quality Economic Development: Evidence from Chinese Provincial Data

While extensive research has examined economic growth in China, the complex relationship between knowledge-based inputs and comprehensive development remains poorly understood, particularly at the provincial level. Previous studies have largely focused on traditional economic indicators, overlooking how education investment and human capital collectively shape multidimensional development outcomes across China's diverse regions. To address this gap, we construct a novel development index incorporating innovation, sustainability, and social progress metrics, analyzing panel data from 31 Chinese provinces (2000-2020) through two-way fixed effects models with instrumental variables. Our findings reveal that higher education investment significantly enhances comprehensive development, with this effect amplified in regions with greater human capital accumulation. Notably, regional heterogeneity analysis demonstrates stronger investment effectiveness in eastern provinces compared to central regions, while western provinces show no significant relationship. These results underscore the importance of regionally differentiated approaches to educational investment and human capital development, offering valuable insights for policymakers seeking to promote balanced growth across China's economically diverse landscape.

Heterogeneous job ladders

We investigate different wage growth rates over the life cycle for poor and rich workers, and how they relate to the frequency and quality of job-to-job transitions. Using the universe of labor market histories for Austrian workers born in 1960–62 to, we show that workers who are the bottom of the earnings distribution have higher employer-to-employer transition rates than richer workers throughout their life. Nevertheless, they work for worse- and worse-paying firms as they age and are more likely to undergo unemployment spells at all ages. We propose a structural framework with learning by doing and heterogeneity along five dimensions: initial level of human capital, learning ability, and job separation propensity on the worker side, and productivity level and quality of offered learning opportunities on the employer side. Our model replicates the wage gap and the difference in the frequency of labor market transitions we document in the data, and allows us to investigate several dimensions of heterogeneity in the quality of labor market transitions. We find that poor workers’ lackluster wage growth stems from a combination of deteriorating human capital, employment in low-productivity jobs, and scarce on-the-job learning opportunities. We then evaluate a policy which matches low-wage workers to high-learning employers. We find that ameliorating the learning opportunities early in a worker’s career has a non-negligible impact on lifetime earnings. The gains from matching with a better employer greatly increase with job stability, as lower separation rates limit human capital depreciation and improve the odds of matching with high-productivity employers in the future.

Exploring the relationship between digital infrastructure and carbon emission efficiency: New insights from the resource curse and green technology innovation in China

Resource-rich regions can fall prey to the resource curse, leading to a decline in carbon emissions efficiency (CEE). But amid this challenge, building digital infrastructure offers an opportunity to find solutions that will pave the way for sustainable development. An indicator evaluation system was constructed to measure the level of digital infrastructure (DI) using panel data for 275 Chinese cities from 2010 to 2020. The impact of DI on CEE is validated using several econometric models. As an innovation, the time lag effect and spatial spillover effects are considered in the analysis. The study found that DI significantly improves CEE, and this effect is more effective in central and resource-based cities, but only significant in southern cities, cities with high levels of marketization, and cities with high levels of human capital. Reducing the resource curse and increasing green technological innovation are important transmission pathways, which are considered to be the key role of DI. Further, the third period of lagged DI has the most significant effect, and there is a positive spatial spillover effect of DI on the CEE of neighboring cities, which is most prominent around 600 km. Policymakers are advised to improve the level of construction of DI, focus on digital management, and utilize the role of DI at the optimal time and distance.

Influence of Livelihood Capitals on Landscape Service Cognition and Behavioral Intentions in Rural Heritage Sites

Farmers’ livelihoods are critical for global sustainable development and the conservation and transmission of rural heritage. However, neglecting farmers’ livelihoods increases the risks to living heritage conservation. Therefore, it is essential to explore the impact of livelihood capital on farmers’ landscape services cognition and their behavioral intentions. Based on the Sustainable Livelihoods Approach, this study examines the rural areas of Mulanbei irrigation district, which was recognized as a World Heritage Irrigation Structure in 2014, and uses a structural equation model to investigate the relationships between farmers’ livelihood capital, landscape services cognition and behavioral intentions. The study’s key findings include the following: (1) The levels of human capital (0.541), social capital (0.671), and cultural capital (0.645) are relatively high, while the levels of natural, physical, and financial capital are comparatively low. (2) There are significant differences in landscape service cognition and behavioral intentions among farmers of different livelihood strategies, with diversified livelihood farmers demonstrating the highest levels of both cognition and intentions, while subsidy-dependent farmers show the lowest levels. (3) Natural, cultural and financial capital play a crucial role in influencing farmers’ landscape services cognition and their behavioral intentions. Landscape service cognition mediates the relationship between livelihood capital and behavioral intentions. (4) To increase farmers’ willingness to protect and promote rural heritage, efforts should focus on enhancing natural, physical, and financial capital while fostering cultural capital to promote advocacy.

Digital development and rural financial inclusion: Evidence from China

Digital technology has profoundly influenced the development of the financial sector and is of great importance to financial inclusion. This paper employs sub-provincial panel data from 2005 to 2019 to analyse the impact mechanisms of digital economy development on rural finance, exploring the impact of digitalization and overall digital literacy on rural finance. Our findings verify that digitalization has a positive contribution to rural finance, while digital literacy has a significant inhibitory effect on rural finance. This still holds after a series of robustness tests. Subsequent mechanism analysis reveals that digital technology benefits rural finance by optimizing industrial structure and promoting technological progress, while digital literacy accelerates urbanization, leading to the loss of rural labor and thus inhibiting rural finance. Heterogeneity analysis shows that the role of the digital economy in rural inclusive finance is more pronounced in the eastern and central regions. Additionally, higher levels of human capital, economic development, entrepreneurship, and digitalization will further enhance the impact of the digital economy on rural inclusive finance. This study offers a new pathway for the realization of financial inclusion and a theoretical reference for financial development in rural areas. Our empirical analysis also provides new perspectives and insights for the formulation of policies and measures to coordinate the development of various elements of digital finance and cope with the impact of the digital economy on rural finance.

Data factor agglomeration and urban green finance: A quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone

Using panel data from 246 cities in China from 2008 to 2021, we investigate the impact of data factor agglomeration on the development of urban green finance through a quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone. Findings reveal that the effect of data agglomeration, characterized by the construction of big data comprehensive pilot zone, has significantly improved urban green finance development in the pilot zones. Further research shows that data factor agglomeration can expand the development scale of urban green finance by promoting industrial structure upgrading and improve the development efficiency of urban green finance by driving innovation in digital technology, both of which can promote urban green finance development. Furthermore, the impact of data factor agglomeration on urban green finance development is influenced by geographic location and urban administrative level, with greater significance for cities in the eastern region and those with high administrative levels. Meanwhile, the human capital level and environmental regulation strength positively moderate the efficacy of the data factors' agglomeration. Our study explores the effective and realistic approaches to promote the development of urban green finance from the perspective of data factor agglomeration, providing a reference for countries to accelerate the construction of good data factor ecosystems and promote green finance reform in depth.

Flattening of government hierarchies and growth of farmers’ income

Continuously raising farmers’ income is important for poverty reduction. In a multilevel government system, institutional arrangements can influence the role of grassroots governments in economic and social development. Based on the rural statistical data of 743 townships in Province A in central China from 2001 to 2012, we use the difference-in-differences (DID) approach to investigate the effect of the reform of flattening governmental hierarchy at the township level on increasing farmers’ income. We find that the reform of grassroots flattening hierarchies significantly promotes the growth of farmers’ income. Specifically, in the face of economic-assessment pressure from the higher-level government and the incentives of fiscal excess revenue in the reform, the township government will make full use of the greater administrative power and resources endowed by the reform to vigorously attract investment to increase the number of enterprises and provide farmers with more non-agricultural employment opportunities, thus raising their wage income. Moreover, this reform’s income-increasing effect is more obvious in townships that are closer to the county seat and have more convenient transportation, a higher level of human capital, and a stronger ability to exercise administrative power.

New energy policy and new quality productive forces: A quasi-natural experiment based on demonstration cities

The impact of new energy policy on the new quality productive forces (NQPF) in developing countries still lacks empirical evidence. The new energy demonstration city policy (NEDCP), serving as a comprehensive energy strategy in China, holds significant importance in driving the transformation of energy structures and fostering the NQPF. Based on the panel data collected from 276 Chinese cities, this study employs the difference-in-differences approach to examine the impact of the NEDCP on the NQPF. The findings indicate a substantial enhancement in the NQPF as a result of the NEDCP. While the NEDCP has a short-term effect on the NQPF, the long-term effect is insignificant. When considering the removal of low-carbon city variables, the influence of NEDCP was significantly improved. The NEDCP indirectly improves the NQPF through urban innovation capacity, government fiscal support, and industrial structure upgrading. The human capital level positively moderates the NEDCP's effect on the NQPF. In contrast to cities reliant on natural resources, the NEDCP exhibits a significantly greater influence on augmenting the NQPF in cities that are not dependent on such resources. Cities with higher urbanization levels experience a more substantial positive impact on the NQPF from the NEDCP compared to cities with lower urbanization levels. Our findings have important theoretical significance and can provide practical guidance to help developing countries improve NQPF through new energy policies.

INFORMATION SYSTEM FOR ASSESSING THE LEVEL OF HUMAN CAPITAL MANAGEMENT

The article offers conceptual foundations for formalizing the process of assessing a level of human capital (HC) management at the enterprise using mathematical and computer modeling based on neural network technologies. The methodological approach for assessing the level of human capital management has been improved. This allows the use of neural network tools to identify accurately and reasonably the level of HC management with the help of self-learning multilayer perceptron. The weight coefficients of such a network were calculated. An appropriate artificial neural network – a multilayer perceptron – was built using the mathematical software MatLab and it was successfully diagnosed. The improved mathematical model for assessing the level of HC management at the enterprise makes it possible to display transparently a set of input parameters on a set of output solutions, to decompose such a process, and to simplify the procedure of its formalization. The designed neural network allows us to determine quickly and accurately the level of HC management at the enterprise. The conceptual approach proposed by the authors has several significant advantages over existing alternative methods: accuracy of assessment; taking into account a wide range of various evaluation parameters of impact; high speed of making decisions and self-learning ability. The proposed approach was successfully implemented to assess the level of HC management at 24 domestic enterprises. The information system "HC" developed by the authors allows to calculate the estimated parameters of the evaluation process; to determine the level of HC management based on the mathematical apparatus of the multilayer perceptron. Such estimates correlate with the estimates obtained by the experts of these enterprises which indicates the adequacy of the approach proposed by the authors. Therefore, the proposed information system for assessing the level of management of the HC allows accurate implementation of such a process with minimal time and money costs.

Audit firm informatization and audit quality during COVID-19

PurposeCOVID-19 has forced audit firms to change the way they operate. One change has been to rely more on information technology (IT) and IT human capital to overcome COVID-19-related challenges. We refer to audit firms’ use of these two resources as audit firm informatization (AFI). It is important to understand whether AFI helps audit firms address challenges created by the pandemic. Thus, this study examines the impact of AFI on audit quality during the COVID-19 pandemic in China with a focus on IT human capital.Design/methodology/approachWe use a mixed-methods approach. First, we perform multivariate regression analyses on archival data. Specifically, we investigate the relationship between IT human capital and audit quality and the two mechanisms (i.e. improved efficiency and reduced audit risk) underlying the relationship. We also investigate how this relationship is moderated by features of clients, audit firms and individual auditors. Then we use interviews to corroborate the results of our regression analyses.FindingsOur analyses of archival data show that IT human capital positively affects audit quality through improved efficiency and reduced audit risk and that this positive impact is more pronounced for clients in non-manufacturing industries, those with a more opaque information environment, audit firms with greater industry coverage and individual auditors with less experience. Our interview data indicate that audit firms with more advanced AFI and a higher level of IT human capital in particular are less disrupted by the pandemic and are better able to use IT to address challenges associated with COVID-19. Furthermore, the results confirm that improved efficiency and reduced audit risk are the mechanisms through which AFI enhances audit quality. Finally, we identify issues associated with the use of IT.Originality/valueThis study is the first to investigate how IT human capital (and by extension AFI) influences audit quality in the context of the COVID-19 pandemic. Our findings should be of interest to practitioners and setters of auditing standards.

Green Development of Chinese Agriculture from the Perspective of Bidirectional Correlation

China’s agricultural green development exhibits spatial imbalances. To promote the coordinated green development of agriculture across various regions, this paper explores the evolution of spatial association networks and driving factors of agricultural green development from a bidirectional correlation perspective, using social network analysis and spatial econometric methods. This research indicates that China’s agricultural green efficiency has steadily improved, with a complex multi-threaded network structure. Although the spatial interaction and spillover effects of the overall network structure have increased, they remain relatively weak. The individual network structure shows significant regional imbalances. The spatial association network of agricultural green efficiency forms four major blocks, with sparse connections within the blocks but close connections between blocks, demonstrating strong spillover effects. Regarding the driving factors, the proportion of the primary industry, labor level, and R&D investment have significant spatial impacts, while the spatial impacts of human capital level, degree of openness, economic development level, and new quality productivity level are not significant. Therefore, we believe that it is necessary to establish the concept of coordinated green development in agriculture, fully leverage regional associations and spillover effects, and formulate differentiated policies to improve agricultural green efficiency.

Smarter and Cleaner? The Carbon Reduction Effect of Smart Cities: A Perspective on Green Technology Progress

In the context of the global climate change problem intensifying due to a dramatic increase in carbon emissions, smart cities, as a topical application of digitalization and intelligence, have become a new urban governance mode for countries, which helps to achieve sustainable development. This research studies the relationship between smart city construction (SCC) and carbon dioxide emissions based on the differences-in-differences model (DID) and propensity score matching (PSM) to promote China to achieve dual carbon goals and high-quality development. The findings are as follows: (a) SCC could promote carbon emission reduction by reducing urban carbon dioxide emissions by an average of 11.4%, which also has significant long-term dynamic effects. Specifically, SCC has more obvious emission reduction effects on activities, such as industrial production and waste treatment. (b) Mechanism verification shows that green technology progress is a significant booster for the carbon reduction effect in SCC. The pilot project can increase output of green patents, which helps transfer production mode and consumption patterns in an environmentally friendly manner. SCC could increase the total factor productivity (TFP) through the rational allocation and efficient use of resources, and thus reducing carbon emissions. (c) Research on city heterogeneity shows that a high level of human capital, material, and financial resources can provide support for smart cities to better achieve the carbon reduction effect. Among them, material resources have the best carbon reduction effect in the process of SCC, which could reduce carbon dioxide emissions by about 6.6–17.7%. This study is useful for policymakers to continuously and dynamically adjust urban development strategies in the future, to achieve a balance between socioeconomic prosperity and environmental sustainability.

The impact of industrial robots on low-carbon green performance: Evidence from the belt and road initiative countries

Global warming is intensifying, with Belt and Road Initiative (BRI) countries accounting for over two-thirds of global carbon dioxide emissions. As a pivotal technology in advanced manufacturing, industrial robots significantly impact the overall carbon footprint through their role in production processes. This study examines how the use of industrial robots influences the Low-Carbon Green Performance (LCGP) across BRI nations, utilizing a dataset spanning from 2004 to 2020. The findings reveal that the integration of industrial robots notably boosts the LCGP within these countries, a conclusion supported by extensive robustness evaluations. The novelty of this study lies in uncovering the underlying mechanism by which industrial robots improve LCGP through the promotion of technological innovation. Specifically, we find that industrial robots have a pronounced effect on technological progress (TC), an impact that is further amplified by increases in labor productivity and human capital levels. This discovery provides policy implications for BRI governments, suggesting that actively promoting the development of industrial robots can accelerate energy transformation and upgrading, thereby reducing carbon emissions. Our research fills a significant void concerning the environmental impact of industrial robots and offers new perspectives and strategies for BRI countries to achieve low-carbon development goals. These findings contribute significant theoretical and practical value to global environmental protection and sustainable development.

Scientific and Technological Innovation Effects on High-Quality Agricultural Development: Spatial Boundaries and Mechanisms

This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major grain-producing regions spanning from 2004 to 2021, we revealed significant spatiotemporal variations in the impact of STI on HQA in both local and adjacent cities. Our findings remained robust after rigorous testing. The study identified the spillover range of STI to be 420 km, displaying a distinctive inverted U-shaped trend around 170 km. Mechanism analysis indicates that both agricultural industry upgrades and human capital levels within 420 km amplify the influence of STI on local HQA, with only the latter demonstrating spillover effects. Within 170 km, both factors effectively regulate HQA in adjacent cities, while beyond this distance, only human capital regulatory impact continues to exhibit spillover effects. These insights offer theoretical guidance for designing effective agricultural scientific and technology promotion policies aimed at elevating the quality of HQA.

Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China?

Carbon reduction in livestock is a necessary path for the green transformation of the livestock industry. Has the digital economy as an emerging productive force in recent years driven carbon reduction in the livestock sector? This paper employs dynamic panel regression models, mediation effect models, and spatial econometric models to investigate the impact mechanisms of the digital economy on carbon emission reduction in livestock husbandry. The results indicate the following: (1) The digital economy has a positive impact on carbon reduction in livestock farming by fostering technical innovation, enhancing the level of human capital, and transforming the mode of production. (2) From 2013 to 2021, the digital economy did not show a significant carbon reduction effect until 2018. It has had a substantial impact on carbon reduction in livestock husbandry in the northeastern and western regions, while its influence in the eastern and central regions has not been significant. The digital economy positively affects carbon emission reduction in non-dairy cows and pigs, negatively affects poultry, and shows no significant effect on dairy cows and sheep. (3) In terms of spatial effects, the digital economy is not only driving carbon emission reductions from livestock farming in the local region but is also significantly driving carbon emission reductions from livestock farming in the surrounding provinces. The findings of this article provide some insights into future policy formulation for low-carbon development in the livestock sector.

FDI and economic vulnerability: The role of local human capital

This study explores how the impact of Foreign Direct Investment (FDI) on economic vulnerability is influenced by the level of local human capital. Using global data from 2000 to 2018, our analysis finds that higher levels of local human capital are associated with lower economic vulnerability when FDI inflows increase. This indicates that a more educated and skilled workforce enhances a country’s ability to effectively utilize FDI, thereby reducing susceptibility to external economic shocks. However, the impact of human capital varies over time. The effect is not significant from 2000 to 2009, but becomes pronounced from 2010 to 2018. This suggests that the role of human capital in reducing economic vulnerability in the context of FDI has become more important in recent years.

The application of industrial robots and changes in capital-labour income disparity in China

The technological revolution, particularly the rise of industrial robots and artificial intelligence, has significantly impacted global economic growth and income disparity. This study examines the impact of industrial robot applications on the capital-labour income disparity in China, a country with the highest number of industrial robot applications and a commitment to reducing income inequality. The findings indicate that industrial robots intensify the income disparity between capital and labour, particularly in economically underdeveloped regions characterized by less sophisticated industrial structures, low levels of human capital, underdeveloped physical capital, and inadequate market economies. The promotion of industrial structure sophistication, enhancement of human capital levels, deepening of physical capital, and advancement of market development can mitigate the adverse impact of industrial robot applications on the income disparity between capital and labour. The study underscores the necessity of institutional reforms and targeted policies across these dimensions to address the negative impacts of new technologies.

THE IMPACT OF HUMAN CAPITAL DEVELOPMENT ON ENVIRONMENTAL PERFORMANCE

Modern society is faced with many environmental problems that require the development of new, most effective solutions, which can be implemented through the development of the level of human capital, since the main result of its development is the use of accumulated knowledge and competencies in order to obtain rent and solve the most important socio-­economic problems. However, despite the importance and relevance of solving environmental problems, today methods for assessing the impact of human capital on environmental activities are beyond the achievements of modern economists. The purpose of this study is to propose a method for quantitative assessment of the influence of human capital on the development of environmental activities. The main hypothesis is that the development of human capital is the main driver of the development of environmental activities. The information base for the study was the data from the official statistical collection of the Federal State Statistics Service for the period 2015–2022, which determines the representativeness and reliability of the study conducted by the author. A tool for quantitative assessment of the influence of the dynamics of human capital development on the dynamics of financing environmental activities is proposed — ​the coefficient of the influence of human capital on environmental activities. Based on the correlation and regression analysis carried out, an assessment was made of the influence of the dynamics of human capital development on environmental activities, and the importance of the development of education as the main component of human capital was determined. The proposed regression model allows for a quantitative assessment of the impact of human capital development on environmental activities. This will be useful for assessing the effectiveness of environmental activities.