Level Of Human Capital Research Articles

Hindrance or facilitator? The economic consequences of national demonstration eco-industrial parks in China.

Many countries attach great importance to the green, low-carbon, and circular development of industrial parks. China is one of them and has entered an exploration journey of national demonstration eco-industrial parks (NDEIPs). However, the impact of the transformation of industrial parks into NDEIPs on local economic development still remains a mystery. To address this issue, we develop an empirical study using a combination of the multi-period difference-in-differences method and the propensity score matching method based on the panel data for 266 cities in China from 2001 to 2021. The results show that industrial parks becoming NDEIPs promotes cities' economic development. This conclusion still holds after a series of robustness tests, such as the reverse causality test and the placebo test. Moreover, the park heterogeneity tests show that the economic consequences vary according to differences in levels, industry types, life cycle phases, and the degree of foreign firm agglomeration. The city heterogeneity tests show that the economic consequences differ based on administrative levels, innovation capabilities, technology industrialization, and environmental friendliness. The spatial heterogeneity tests show that the economic consequences differ according to geographical location and whether situated in the Yangtze River Economic Belt. The policy upgrading heterogeneity tests show that the economic consequences differ during the process of policy upgrading and transformation. In addition, the mechanism tests reveal that green innovation, human capital level, and firm attractiveness mediate the relationship between industrial parks becoming NDEIPs and cities' economic development. This study provides a new perspective for understanding the economic effects of the transformation of industrial parks into NDEIPs, and provides a reference for the government on how to maximize these economic effects.

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

Human capital of grassroots leaders and vulnerability to poverty: Evidence from rural China

This study examines the impact of the educational level of grassroots leaders on the vulnerability to poverty among villagers in China, using panel data from the National Fixed-Point Survey (NFS) from 2003 to 2015. Our findings indicate that a higher proportion of village cadres with a high school education or above significantly reduces the vulnerability to poverty among rural households in China. We identify two main mechanisms underlying this effect: (1) highly educated village cadres possess greater capabilities in assisting eligible poor households to understand and access government assistance programs, thereby increasing household transfer incomes; and (2) highly educated village cadres can alleviate household vulnerability to poverty by promoting local infrastructure development in the village. These results offer an alternative perspective on targeted poverty alleviation, suggesting that efforts should be directed toward enhancing the human capital level of local grassroots leaders or appointing highly educated leaders in underdeveloped villages.

Industrial structure optimization and green growth in China based on a population heterogeneity perspective

AbstractPopulation and industrial structure, as foundational characteristics of economic and social systems, exhibit significant spatial heterogeneity and dynamic evolutionary trends in their impact on sustainable economic and social development. However, existing research often employs subjective spatial categorization of samples and overlooks the dynamic transitions of influencing patterns, potentially leading to biases in empirical results. To address this, the current study, based on the calculation of green total factor productivity (GTFP) for 30 provinces in China from 2000 to 2018, incorporates a finite mixture model. This model examines the objective heterogeneity and dynamic transition patterns of industrial structure's impact on GTFP, both from the perspectives of industrial structure advancement (ISA) and rationalization (ISR), and reveals the mechanisms of heterogeneity and dynamic changes from a population standpoint. The findings indicate that there are three patterns in the impact of industrial structure on GTFP, with nearly half of the provinces undergoing pattern transitions during the observation period. The key factors for these transitions are identified as the improvement in human capital levels and urbanization rates. In provinces like Beijing, Guangdong, and Shanghai, ISA and ISR significantly promote GTFP, with their effects further enhanced by increased urbanization and human capital levels. Conversely, in regions such as Shanxi and Hebei, ISA does not favor GTFP improvement, and while ISR can enhance GTFP, this positive effect diminishes with increasing urbanization and labor force numbers. This research not only enriches the literature on the positive interaction patterns between industrial and population structures but also provides a comprehensive analytical framework for governments to adopt differentiated policy measures for sustainable economic development.

Analysis of network patterns and its influencing factors in Chengdu-Chongqing urban agglomeration based on multi-flow

With the strengthening of the cross-regional flows of the economy, information, innovation, and population, this paper constructs a network model of multi-flow integration and analyzes the spatial pattern and influencing factors of urban networks in Chengdu-Chongqing Urban Agglomeration using social network analysis and spatial analysis technology. The main conclusions are as follows. (1) The density and efficiency are in the transition stage from the primary level to the medium level in the comprehensive network. (2) The overall pattern keeps a polyhedral pyramid structure with Chengdu ↔ Chongqing as the core axis, and the grade of each axis has been significantly raised. (3) Four groups are formed using the social network method and show a geographic proximity effect. In addition, the connections within each group are relatively close, but the connections between the groups are significantly different. (4) Location conditions, economic development level, enterprise development level, scientific research investment, scientific and technological development level, and government support have a greater impact on the formation of the comprehensive network of Chengdu-Chongqing urban agglomeration. Information application level and transportation accessibility show a small impact and human capital level has not yet produced a significant impact.

Bicycle use in the university community: Empirical analysis using MobiCampus-UdL data (Lyon, France)

Promoting sustainable mobility systems by encouraging the use of the bicycle as a transport mode is now a public policy objective. This political will is also pursued in France where the modal share of cycling is relatively low. However, young people and those with a high level of human capital, such as members of the university community, are observed to be more advanced in their adoption of cycling. An understanding of how cycling is used by university students and staff would therefore help to inform public decision-making and support more efficient targeted policies to develop this mode of transport. Using original data from the MobiCampus-UdL project, the aim of this article is to analyze the determinants of bicycle use by the university community at the University of Lyon, France. Two multivariate logistic regression models are estimated on the subsamples of students and staff: one explaining the probability of using the bicycle as an exclusive mode of transport to get to the campus and the other explaining the probability of using the bicycle in combination with other modes. Our results suggest that while socio-demographic characteristics have little influence within our two relatively homogeneous subsamples, access to mobility resources and the spatial characteristics of the campus and place of residence are crucial. We also find that access to bicycles is an important determinant of the utilization of cycling. Given that the adoption of cycling is still very low, our findings justify policies to increase the availability of bicycles and subsidize their purchase. More specifically, our results suggest that access to a shared bike station on campus encourages the exclusive use of bicycles by students and staff but has no effect when used in combination with other modes. On the other hand, good accessibility to public transport, whether from home or from campus, does not reduce the use of bicycles by either sub-population, either exclusively or in combination. Furthermore, while living far from the city center is an obstacle to the exclusive use of the bicycles, especially for staff, it does not in any way prevent their use in combination with other modes, such as the train. These results open up new avenues for anticipating the development of intermodality between public transport and cycling.

University Admission: Is Achievement a Sufficient Criterion?

Abstract We analyse university admissions using a statistical discrimination model where students differ by ability and social group. In this university system, candidates are evaluated on the basis of their expected human capital, which includes both their innate abilities and the knowledge acquired during their schooling. Consequently, students determine their study effort based on the behaviour of universities. Interestingly, we find that students from a less advantaged group need a lower grade to gain admission to the best universities. If a university cannot discriminate between social groups, all students with the same grade will attend universities of the same quality, but with different levels of human capital.

The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy

China’s economy is stepping into a new stage of high-quality development. The shift not only marks the optimization and upgrading of the economic structure, but also reflects the in-depth implementation of the concept of sustainable development. In this context, the development of AI technology is playing an important role in balancing economic growth and ecological protection with its unique advantages. This paper empirically studied the impact of AI development on urban energy efficiency by constructing panel data for 282 prefecture-level cities from 2006 to 2019 and then using the super-efficiency SBM model based on non-expected outputs to evaluate the urban energy efficiency indicators of prefecture-level cities. It was discovered that the development of AI had a key influence on increasing urban energy efficiency and the optimization of the energy structure by speeding up green technology innovation and digital economy development, which in turn improved urban energy efficiency. In terms of heterogeneity analysis, AI development had a greater impact on urban energy efficiency in the eastern region, which has higher levels of human capital, financial independence, and government intervention. This study combined the smart city pilot policy with a multi-period DID model, based on the treatment of endogeneity issues, in order to perform a parallel trend test and investigate further the effects of policy implementation on the advancement of AI in the context of improving urban energy efficiency. Accordingly, to achieve green and sustainable urban development, the relevant government departments should increase funding for AI research and development, pay attention to the introduction and cultivation of professionals, establish a platform for international exchanges and cooperation between AI and energy management, and continue to advocate for the pilot development of smart cities to increase urban energy efficiency.

The internal mechanism and impact of digital economy on China's smog pollution: based on the perspective of industrial structure coordination

ABSTRACT This study adopts an industrial structure coordination perspective to elucidate the theoretical mechanism through which digital economy (DE) development influences smog pollution (SP), and employs panel data encompassing 214 cities at the prefecture level and above in China over the period 2010–2019 to empirically examine this relationship. The findings reveal the following: (1) Industrial structure rationalisation (ISR) and advancement (ISA) positively contribute to the nexus between DE and governance in SP. Nevertheless, ISR stands amenable for refinement. (2) Despite regional variations in economic development planning and competitive investment behaviours, DE consistently hampers the SP of adjacent cities, contingent on diverse geographical weights. (3) Augmentation in science and technology expenditure and human capital levels enhances the environmental governance effect of the DE.

Does birthplace affect intergenerational social mobility of migrants in urban escalators? Evidence from China

AbstractThere is a growing body of literature on the effects of escalator regions on intergenerational social mobility (ISM) of migrants. Nevertheless, the role of migrant origins in migration outcomes remain neglected. To address this gap, this study attempts to investigate whether migrants' birthplaces continue to have an impact after they move to escalator regions and the mechanism of this impact. Using data from the 2010, 2013, and 2015 Chinese General Social Survey, our findings suggest that the effect of escalator regions on migrants' ISM varies according to the size of their birthplace. Furthermore, the birthplace effect on migrants' ISM when relocating to escalator regions is partially mediated by the birthplace effect on education. Finally, it was found that this birthplace effect varies according to dynamic human capital; migrants with high levels of dynamic human capital can overcome the birthplace effect after relocating to escalator regions.

Analysis of the Effect of Digital Financial Inclusion in Promoting Inclusive Growth: Mechanism and Statistical Verification

Abstract As the main goal of economic development, inclusive growth (IG) is an important strategic measure to achieve common prosperity. Whether digital inclusive finance can make use of the advantages of scientific and technological innovation to promote IG is of great significance to promote the fairness, effectiveness, and inclusiveness of global development. Based on the panel data of 30 provinces in China from 2011 to 2021 (excluding Tibet, Hong Kong, Macao and Taiwan), this article first measures the IG index of China from three dimensions: sustainable economic growth, income distribution, and social equity. Subsequently, the article uses a series of mathematical statistical models to verify the transmission path and mechanism of the influence of digital inclusive finance on IG. The findings are as follows: (1) The level of IG in China shows a decreasing trend from east to middle to west, while the average annual growth rate of IG in the eastern region is obviously lower than that in the central and western regions; (2) digital inclusive finance has a significant promotion effect on IG, and digital inclusive finance in the central and western regions has a more obvious promotion effect on IG; (3) digital inclusive finance can achieve IG by increasing innovation activity and improving the level of human capital. Finally, based on the research conclusions, the article puts forward relevant policy suggestions, which provide reference value for formulating high-quality national development strategies and promoting high-quality economic development.

The Influencing Factors of Exports of Sichuan under the Belt and Road Initiative: An Empirical Study Based on the Extended Trade Gravity Model

This study investigates the influencing factors of exports in Sichuan Province in the context of the Belt and Road Initiative. Based on the panel data of Sichuan's exports to fifty-three countries along the Belt and Road from 2011 to 2022, this study tests eleven potential influencing factors using an expanded trade gravity model. And the results show that the economic development level of both Sichuan and the Belt and Road member countries have significant positive correlation with the export volume; while the distance, foreign merchant participation in Sichuan, exchange rate of RMB and human capital level of Sichuan are significantly negative. Moreover, the trade potential coefficient of each Belt and Road member country is calculated to decide the trade type. Lastly, the c onclusions and strategical suggestions are put up to further explore the trade potential with Belt and Road countries and expand Sichuans export volume.

Gender features of human capital development in the Republic of Kazakhstan

The basis of this study was the analysis of the human capital reproductive processes in the Republic of Kazakhstan, considering the gender component. The object was the human capital of Kazakhstan’s regions, and the subject was the gender aspect of human capital development. Mincer’s supplemented human capital assessment model was the primary model for calculating human capital. Factors influencing the effective use of human capital in the regions, considering the gender component, were identified, and their indices were calculated. The analysis showed a persistent gender gap in life expectancy, education level, income, and labour market. Based on the results, leader and outsider regions in terms of the accumulated human capital level were identified. The analysis indicates the responsible attitude of the government of the Republic of Kazakhstan towards developing gender equality. Over the past ten years, Kazakhstan has advanced significantly in its efforts to empower women in the socio-political sphere. The Republic of Kazakhstan has assumed obligations under several international agreements, adopted many laws, and developed government measures critical for increasing women’s participation in government activities.

The Impact of COVID‐19 on Education in Latin America: Long‐Run Implications for Poverty and Inequality

AbstractThe shock of the COVID‐19 pandemic affected the human capital formation of children and youths. As a consequence of this disruption, the pandemic is likely to imply permanent lower levels of human capital. This paper provides new evidence on the impact of COVID‐19 and school closures on education in Latin America by exploiting harmonized microdata from a large set of national household surveys carried out in 2020, during the pandemic. In addition, the paper uses microsimulations to assess the potential effect of changes in human capital due to the COVID‐19 crisis on future income distributions. The findings show that the pandemic is likely to have significant long‐run consequences in terms of incomes and poverty if strong compensatory measures are not taken soon.

Analysing the effect of foreign aid on industrialization: Evidence from Africa

The importance of industrialization as an engine of economic growth and structural transformation has been largely documented. Despite the benefits of industrialization, Africa has witnessed a de-industrialization in recent decades. This means that some important determinants of industrialization remain unidentified in Africa. This study fills this gap by analysing the effect of foreign aid on industrialization in 42 African countries over the period 1995–2021. Using the two-step system Generalized Method of Moments, the following results are established. First, on average, foreign aid hurts industrialization in Africa. Second, the effect of foreign aid depends on the nature of the aid since education and energy aid improve industrialization while health and humanitarian aid negatively affect it. Third, we find a U-shaped relationship between foreign aid and industrialization. Interestingly, democracy and human capital can mitigate the negative effect of foreign aid on industrialization. These results have strong economic policy implications for African countries, showing that they should put in more effort to strengthen their level of democracy and human capital in order to enjoy the positive effect of foreign aid on industrialization. Finally, this research contributes to the development of aid distribution plans that support African nations' industrialization.

Building strategic human capital resources: The effects of strategic entrepreneurship on employee recruitment and retention

ABSTRACT Although strategic entrepreneurship (SE) and strategic human capital are both crucial for competitive advantage, there is limited knowledge of how SE affects human capital levels within a firm. This study examines the relationship between SE and both employee recruitment and employee retention by drawing on an integrated framework of strategic human capital and incorporating a uniquely created direct construct for measuring SE’s complete domain. Results from survey data of 576 UK-based small- and medium-sized firms indicate that SE is positively related to both employee retention and recruitment. We document the moderating effects of corporate reputation and competitive intensity on those relations.

Return Migration and Entrepreneurship: Does Human Capital Matter?

Return migration is a topic of considerable interest in scholarly literature, with many studies suggesting that returning migrants can impact the economic development of their home country through capital accumulation and entrepreneurship. This study seeks to examine the entrepreneurial intentions of return migrants and the influence of formal education and training on their entrepreneurial tendencies. Utilizing data from the National Workforce Survey (Sakernas) in 2020 and employing logistic regression analysis, the study revealed that formal education tends to steer returning migrants towards the formal sector rather than entrepreneurship upon their return. Conversely, while training was found to potentially increase the likelihood of entrepreneurship, the effect was not statistically significant. Additionally, individual characteristics such as age, gender, and marital status were found to significantly influence entrepreneurial intentions, with older individuals, females, and married individuals exhibiting a higher propensity for entrepreneurship among return migrants. These findings contribute to the existing literature by highlighting that, on average, return migrants in Indonesia tend to have relatively low levels of education, and higher levels of human capital may diminish their interest in entrepreneurship.

Renewable energy and CO2 emissions: Does human capital matter?

Numerous investigations have extensively examined the effects of renewable energy on CO2 emissions. However, a majority of previous studies have overlooked the potential nonlinear effects that renewable energy may have on CO2 emissions. These nonlinear effects could be influenced by various factors, including human capital. Regrettably, the existing literature has not adequately explored the role of human capital in this context. Therefore, the objective of this study was to investigate the impacts of different levels of human capital on the relationship between renewable energy and CO2 emissions. To achieve this objective, the study employed a dynamic threshold panel data model encompassing 67 countries, both developed and developing, over the period from 1999 to 2019. The empirical findings revealed a negative impact of renewable energy on CO2 emissions. The study found that there is a threshold effect associated with the human capital index. For developed countries, the threshold value was 3.407, while for developing countries, it was 2.949. When the human capital index exceeded these thresholds, the impact of renewable energy on reducing CO2 emissions was significantly greater. In developed countries, the impact was 4.75 times more effective, and in developing countries, it was 5 times more effective when the human capital index surpassed the threshold. The results also show human capital had a direct negative effect on CO2 emissions. Additionally, the study found that lagged CO2 emissions, fossil fuel energy consumption, population density, GDP, and industrial activities all exerted positive effects on CO2 emissions.

Patent Protection Policy and Firms’ Green Technology Innovation: Mediating Roles of Open Innovation and Human Capital

Green innovations such as renewable energy technologies and cleaner process modifications are important technical routes and critical directions for reducing carbon emissions from industrial production processes. This study examines the impact of intellectual property protection on green technology innovation, constructing a progressive difference-in-differences model using 849 listed manufacturing firms panel data from 2007 to 2019 and taking the Chinese Intellectual Property Rights model cities as a quasi-natural experiment. Our study finds that the pilot policy significantly enhances corporate green innovation. When considering heterogeneity, the policy treatment effect is more remarkable for large firms, state-owned enterprises, and industries where technology can be easily imitated. Moreover, the mediating effect shows that the policy promotes green innovation by encouraging firms’ research and development cooperation and increasing human capital levels. This study proposes that policymakers should reinforce intellectual property protection, encourage companies to be better and bigger, and emphasize the intermediary function of open innovation and human capital in green technology innovation.

Robots in action

AbstractThis empirical study delves into the intricate factors that shape firms' choices regarding the adoption of robots within the Spanish context. Using a dataset encompassing a diverse set of industries, we employ an empirical analysis to uncover the determinants of robot adoption and investigate the associated outcomes on market variables. Our findings reveal several key factors that significantly influence a firm's likelihood of adopting robots. We find that firm profitability, exporter status, the control variables including share of R&D, and capital intensity exhibit strong positive relationships with robot adoption. Conversely, the impact of the level of human capital on adoption decisions is less pronounced. Furthermore, our study explores the impact of robot adoption on firm performance. We observe that firms embracing robotisation experience notable improvements in the output, exporting activities, and reduction in labour cost share. This study incorporates a gradient boosting‐based machine‐learning model, specifically XGBoost, along with instrumental variable regression models, to conduct rigorous robustness analyses and validate the obtained results. These findings contribute to the understanding of the dynamics and implications of robot adoption in the manufacturing sector, explaining the factors that drive firms' decisions and the subsequent market effects.