Industrial Robots and the Employment Quality of Migrant Workers in the Manufacturing Industry

Bo, Chen; Tan, Danielle

doi:10.3390/su15107998

Cited by 2 publications

(1 citation statement)

References 77 publications

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“…Employment quality refers to the degree of all the resources brought by workers’ work in the employment process in exchange for remuneration, mainly including income, employment environment, career prospects, social security, job satisfaction, and other aspects ( 30 ). Improving the employment quality is a response to the people’s growing aspirations for a better life and is also a specific requirement of China’s “14th Five-Year Plan.” Digital financial inclusion can fully release its universality and improve the employment quality of migrant workers.…”

Section: Theoretical Analysis and Research Hypothesesmentioning

confidence: 99%

Can digital financial inclusion help reduce migrant workers’ overwork? Evidence from China

Zhang,

Li,

Zhuang

et al. 2024

Front. Public Health

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IntroductionMigrant workers in China are migrants from the rural to the urban areas who usually work in the cities and return to the countryside after a certain period. Due to China’s strict household registration system, they differ significantly from urban residents’ access to public services. However, at the same time, China’s workers are facing a severe phenomenon of overwork, and the group of migrant workers is even more hard-hit by overwork, which will cause various adverse effects on workers and society and should attract the attention of all sectors of society.MethodsThis paper focuses on the impact of digital financial inclusion on the overwork of migrant workers. This study considered cross-sectional data containing 98,047 samples based on the 2017 China Migrants Dynamic Survey 2017 (CMDS) and China Municipal Statistical Yearbook after robustness tests and heterogeneity analysis using probit models.Results(1) digital financial inclusion can effectively alleviate overwork among migrant workers; (2) the impact of digital finance on overwork is more significant for the new generation, digitized industries, and self-employed migrant workers; it is also more significant for the South, East, and small and medium-sized cities than for the North, the Midwest, and large cities; (3) job quality and income are crucial factors in how digital financial inclusion affects overwork among migrant workers. Digital financial inclusion can improve the quality of employment for migrant workers and alleviate overwork. However, the income substitution effect partially reduces the inhibitory impact of digital financial inclusion on overwork.ConclusionContinuously promote the development of digital inclusive finance, improve laws and regulations, and protect the labor rights and interests of migrant workers. At the same time, vocational training and skills upgrading for rural migrant workers should be strengthened to improve the quality of their employment so that they can leave the secondary labor market and enter the primary labor market.

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Section: Theoretical Analysis and Research Hypothesesmentioning

confidence: 99%

Can digital financial inclusion help reduce migrant workers’ overwork? Evidence from China

Zhang,

Li,

Zhuang

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

Novel concept for the healthy population influencing factors

Shen,

Wang,

et al. 2024

Front. Public Health

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In the rapid urbanization process in China, due to reasons such as employment, education, and family reunification, the number of mobile population without registered residence in the local area has increased significantly. By 2020, the group had a population of 276 million, accounting for over 20% of the total population, making significant contributions to urban economic development and resource optimization. However, the health status of migrant populations is affected by unique issues such as occupational risks and socio-economic disparities, which play an important role in personal welfare, social stability, and sustainable economic growth. The deterioration of the health of the floating population will lead to a decrease in productivity, an increase in medical expenses, and an increase in pressure on the public health system. In order to analyze and predict the main elements affecting the well-being of transient population, this study uses advanced machine learning algorithms such as principal component analysis, backpropagation (BP) neural networks, community analysis, random forest models, etc. Principal component analysis will identify and extract the most important variables that affect the health status of mobile populations. The BP neural network models the nonlinear interaction between health determinants and health outcomes. Community analysis divides the floating population into different health records and promotes targeted intervention measures. The random forest model improves the accuracy and universality of predictions. The insights generated by these models will help develop health policies and intervention policies to improve the health status of mobile populations, narrow disparities, and promote social and economic stability. Integrating data-driven methods and emphasizing a shift towards correct, effective, and impactful public health management provides a robust framework for understanding and addressing the complex health issues faced by mobile populations.

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Industrial Robots and the Employment Quality of Migrant Workers in the Manufacturing Industry

Cited by 2 publications

References 77 publications

Can digital financial inclusion help reduce migrant workers’ overwork? Evidence from China

Can digital financial inclusion help reduce migrant workers’ overwork? Evidence from China

Novel concept for the healthy population influencing factors

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