Can the Digital Economy Promote Sustainable Improvement in the Quality of Employment for Chinese Residents?—Moderated Mediation Effect Test Based on Innovation Environments

Abstract

Employment significantly impacts the national economy and people’s livelihoods and affects millions of households. How to effectively and sustainably improve the quality of employment for the population has thus become a key issue facing China at present. In recent years, under the strategic background of “Digital China” and “Employment Priority”, the digital economy has brought about profound changes in the efficiency, dynamics, and distribution of social production. It affects the quality of employment by creating jobs, reshaping employment patterns, and improving labor quality, but its impact and transmission path are still unclear. This research employs a multi-dimensional evaluation approach to score the digital economy and employment quality at the provincial level in China, utilizing data from the country’s provincial panels between 2012 and 2022, and examines the feasibility and effect pathways of the digital economy in promoting sustainable improvement in the quality of residential employment. The empirical results provide ample evidence of the significant role played by the digital economy in the sustainable improvement of the employment quality of the inhabitants. Regions with more robust innovation environments tend to benefit more from this impact, and there are discernible regional variations in the impact. The upgrading of industrial structure mediates this process, and the influencing mechanism is regulated by the innovation environment—that is, the innovation environment exerts a facilitating influence on the process of industrial restructuring. The findings herein offer novel insights into the intrinsic mechanism of the digital economy in promoting sustainable improvement in the quality of residential employment.

1. Introduction

Stabilizing employment, finance, foreign trade, foreign investment, investment, and expectations are China’s six current macro-level national tasks. Stabilizing employment is the first task, and the importance of social stability and economic development can be imagined. Over the last few years, China’s fast-developing digital economy has triggered revolutionary changes in production methods and employment models, becoming an emerging driving force leading China’s economic development. The China Digital Economy Development Research Report (2023) indicates that the scale of China’s digital economy reached RMB 50.18 trillion in 2022, representing a nominal year-on-year growth of 10.3%, this figure accounts for 41.7% of the country’s GDP, placing China among the world’s leading digital economies. The report additionally indicates that the scale of digital industrialization and industrial digitization reached RMB 9.2 trillion and RMB 41 trillion, respectively, representing 18.3% and 81.7% of the digital economy. The deployment of digital technology in a timely and effective manner during the COVID-19 pandemic has demonstrated its remarkable adaptability and resilience, which is equally indicative of its potential benefits for policy objectives such as maintaining employment stability and safeguarding livelihoods.

The digital economy enhances production efficiency, promotes scale expansion, and gives new connotations to traditional occupations and positions, such as online doctors, drivers, etc. Furthermore, it has also created various digital platforms that maximize the freedom of choice of workers, leading to the rapid development of flexible employment such as remote work, freelancing, and casual work, such as the “Spring Returning Plan” launched by China’s Meituan Company, the “Shared Employee” model attempted by Freshippo Company’ model, etc. This not only increases the number of jobs, job satisfaction, and work efficiency of the population but also provides more possibilities for different groups to realize employment, which contributes to the promotion of sustained improvement in the quality of employment. Frontiers of China’s Digital Economy 2023: Platforms and High-Quality Full Employment predicts that the digital economy in China will support 449 million jobs by 2030. Moreover, it suggests that the creation of high-quality employment opportunities will continue to grow as digital technology matures.

In light of this, this paper comprehensively evaluates China’s provincial digital economy from three perspectives: Digital Infrastructure Development Vehicle, Digital Industry Development, and Digital Economy Development Environment. It also evaluates the quality of residential employment from a multi-dimensional perspective, tries to explore the effective ways to improve the quality of employment sustainably under the current context of China’s economic transformation, and whether the industrial structure mediates the mechanism by which the digital economy affects the quality of employment, and to demonstrate the back-end moderating role of the regional innovation environment in this transmission path. It helps to enrich the current research related to employment quality and provides policy references for policy makers.

The remainder of the paper is as follows. Section 2 is the literature review that describes the current state of research in academia. Section 3 is the theoretical analysis and hypotheses and provides a deep analysis of the influence pathways. Section 4 introduces the model design and data variables, including the measurement of indicators and the selection of models. Section 5 conducts an empirical analysis. Finally, the research conclusions as well as relevant recommendations are presented.

2. Literature Review

Facilitating sustainable growth in the quality of employment for the inhabitants is an important cornerstone of enhancing social cohesion, and research in this area is on the rise. Some studies suggest that life factors should be brought into work quality and that the dynamic balance between work and life should be used as the assessment standard of work and life quality [1]. In 1995, the International Labor Organization (ILO) proposed that international consensus should be reached and labor rights should be defined in the form of conventions that focus on four main aspects: employment discrimination, forced labor, abolition of child labor, and organization of trade unions (ILO, 1995). Subsequently, at the International Labor Asia Regional Conference held in Busan, South Korea in 2002, the ILO put forward the concept of decent work, suggesting that it should be used as the main basis for the quality of employment and to establish an employment quality scoring system from the aspects of job security, welfare level, and skills training.

Some scholars also pointed out that the connotation of labor remuneration should be enhanced throughout the labor process, encompassing labor income, social insurance, and the safeguarding of fundamental rights and interests [2]. For the measurement of China’s employment quality, a set of index systems should be constructed in line with the characteristics of China’s economy, which must include several key aspects such as employment environment, employability, employment status, labor compensation, social security, labor relations and so on, in order to have a comprehensive understanding of the labor market [3].

Academia is currently studying the relationship between the digital economy and employment in three main ways. First, there is the discussion on whether the development of digital technologies will inhibit the sustained growth in the scale of employment; i.e., the substitution effect and the creation effect. Some scholars argue that the substitution effect is more pronounced in the short term. Digital technologies displace a greater proportion of jobs in industry than they create in the short term, resulting in increased income inequality [4]. Research on the U.S. labor market shows an intriguing phenomenon in which the introduction of 1 robot for every 1000 workers would result in a 0.2% decrease in the overall employment-to-population ratio [5]. Not coincidentally, other studies show that both frictional and structural unemployment due to digital technology are inevitable in the short run [6]. The risk of future labor force replacement by smart machines will inevitably occur in developing countries within Asia and Africa, including China and India [7].

In the long term, the negative substitution effect brought about by the digital economy is less pronounced than the job creation effect brought about by technological progress [8], which is the prevailing view among scholars. While the advent of robots has led to the displacement of certain roles for low-skilled workers, it has not resulted in a pronounced negative impact on overall employment figures [9]. Similarly, with the Austrian data sample, it can be observed that the creation of new technological elements contributes to the size of employment for both high-skilled and low-skilled labor [10]. In addition, the newer iterations of information technology have created a plethora of novel forms of employment and economic development models, which have concomitantly created more jobs and opportunities [11]. Some scholars have also pointed out that the structure of employment will be improved by the digital economy in the short term and also posit interdependence between the two [12]. An analysis of micro-individual data on the French manufacturing sector from 1994 to 2015 concluded that digital technologies, including machines and artificial intelligence, have a driving rather than a substituting effect on sustained labor force employment growth [13]. The advent of digital technology will result in the creation of a new cohort of quality jobs, due to its advanced application that facilitates employee mobility [14].

Second, there is a discussion of the impact of structural changes in relevant industries due to advances in digital technology on sustained employment growth. For every additional job in high-value-added technology firms, around four brand new jobs are created in the non-traded services sector, indicating a significant multiplier effect [15,16]. Through an empirical study using a sample of 17 countries from 1993 to 2007, it has been found that industrial robot density can boost labor productivity in industrial firms by roughly 2.6% to 4.1% per year [9]. Furthermore, they also note that while digitalization may have a negative impact on the quantity of industrial positions, it can also increase the quantity of positions in those high-value-added service areas. Not only that, the digital economy can also accelerate the digital transformation of regional industries and raise the level of local human capital, which can increase social labor productivity and broaden workers’ income channels [17]. Simultaneously, it also provides new employment opportunities for those with lower skill levels in the labor market [18,19], leading to a sustained increase in the level of employment in society. The current digital economy already has a non-linear impact on China’s employment structure, with a positive U-shape pattern at the skill and trade level and an inverted U-shape pattern at the industry level, implying that the labor force will increasingly shift to manufacturing and high-tech industries and turn into highly skilled personnel [18].

Third and finally, there are studies on the spatial dimension. By constructing a dynamic spatial Durbin model, some scholars empirically examine the regionally differentiated influence of digital technology at the provincial level in China [19]. Their findings indicate that the progression of digital technology makes a substantial contribution to the green economy and plays an indispensable role in restructuring employment. Similarly, Another study empirically proved that digital finance will promote regional industrial transformation by constructing the same model and measuring the industrial structure transformation index of Chinese provinces and cities [20]. The way that Internet technology advances in one area will influence how it advances in related businesses, which will promote employment in adjacent industries as well as in industry [21].

To summarize, scholars’ research related to the digital economy and employment quality is booming, but there are still the following shortcomings. First, the examination of employment quality is slightly insufficient, and most of the existing literature on employment or employment quality uses the number of employed people and regional employment rate as proxy variables, which is too single form. In addition, China’s National Bureau of Statistics has adjusted the caliber of registered unemployment statistics since 2020, which is not comparable to historical data, and will no longer release data on the urban registered unemployment rate from 2022, making the use of the urban registered unemployment rate as a proxy variable for employment or employment quality no longer desirable. Therefore, this paper measures the quality of employment at the provincial level in China from the dimensions of employment level, structure, and environment, and explores the path of influence of the digital economy in promoting its sustainable enhancement.

3. Theoretical Analysis and Hypothesis

3.1. Employment-Enhancing Quality Effects of the Digital Economy

The advent of the digital economy has led to the convergence and iteration of a multitude of digital technologies, which have facilitated the industrialization and digitization of all sectors. This progress has effectively improved the employment environment, optimized the employment structure, and increased labor remuneration, thereby enabling the overall quality of employment to continue to grow.

Firstly, the digital economy can optimize regional employment conditions and promote the continuous growth of the total employment scale of society. The growth of the digital economy is driving the demand for digital infrastructure construction, and in order to meet this demand, the government implements digital infrastructure projects through supportive policies that channel resources and factors of production to the relevant industries, thereby realizing sustained growth in the scale of employment. Moreover, the advent of novel organizational models, including the sharing economy, platform economy, gig economy, and live commerce, has been propelled by the iteration of digital technologies that have been facilitating the diversification of the industrial landscape. The advent of these innovations has led to the emergence of a multitude of novel business forms and associated industrial chains, thereby enhancing the economy’s capacity to absorb employment and increasing social employment rates [22,23]. Not only that, newer iterations of digital technology help to reduce gender-based discrimination and increase the employment level of women [24], and increase the productivity of workers, which reduces business costs, thereby encouraging business expansion and creating more employment opportunities [25]. The implementation of intelligent production processes has the potential to generate a significant number of knowledge-intensive tasks, which will generate a wide range of new forms of employment [26]. The birth of new high-value-added industries will impact the entire employment pattern. Moreover, they will form a huge talent gap, to some extent, that will reduce the dependence on low-skilled workers and expand the number of high-knowledge talent requirements to contribute to the enhancement of the general quality of employment within society.

Secondly, the employability of the labor force itself and the average salary of workers in each region may receive indirect effects from the digital economy. Due to the biased nature of technological change, its development inevitably leads to changes in the skill structure of the labor force [27]. Therefore, as digital technology advances, the demand gap for digital talents will become a significant driving force for the continued optimization of the regional job market itself, and the emergence of a variety of high-paying, high-skilled positions will encourage workers to upgrade their digital literacy. Additionally, the ongoing enhancement and expansion of digital platforms and new media platforms lower the barriers for workers to learn new skills and improve their digital literacy. Using large-scale data and leveraging sophisticated platform technologies, comprehensive dissemination and visualization of labor market information can be achieved, thus facilitating the effective matching of labor resources and demand, for both job seekers and employers, this similarly reduces their process costs, contributes to the accumulation and management of human capital in society, thereby promotes the sustainable improvement of overall employment quality [18,28,29].

Finally, the digital economy can also indirectly contribute to the sustainable improvement of legislation on labor security, promoting innovation and progress. It is often observed that traditional labor-employment relationships exhibit deficiencies in the rights of workers and the social security available to them, leading to weakened labor relations and insufficient protection. Derivative developments in digital technology can drive digital change in government and increase the transparency of market information, thereby enriching research on employment protection in a digital context. This, in turn, can foster sustainable reform of the employment security system and improve social security. Furthermore, under the impetus of the digital economy, various types of self-media will spring up, leading to a diversification of information on digital media platforms, as well as an increase in legal services such as online legal advice, case analysis, and labor protection advice. These resources will help raise workers’ awareness of self-protection, reduce labor disputes, promote innovation and progress in labor protection management, and make labor protection more rational and scientific. We therefore reasonably propose to hypothesize as follows.

H1. 

The digital economy can contribute to a sustained improvement in the quality of residential employment, creating a cumulative multiplier effect that “makes the cake bigger”.

3.2. Mediating Effects of Industrial Structure

Given the strong correlation between industrial structure and employment, any subtle impact on industrial structure from the digital economy will also have a multifaceted effect on the quality of employment. First, traditional industries will bear the brunt of the impact of digital technologies and will gradually achieve digital transformation and industrialization [30]. The industrialization of digital technology has also led to the emergence of technologies such as artificial intelligence, the Internet of Things, and smart platforms. The widespread use of these technologies is driving sustained industrial transformation and structural change [31]. This not only enables traditional industries to become more intellectually intensive and form new industries such as smart villages and intelligent sectors but also helps service enterprises to use big data technology to improve transaction matching and production efficiency. It will also facilitate the sustained integration of online and offline services in the fields of healthcare, education, culture, etc., achieving the transformation of service modes and enriching service content.

Second, the digital economy will contribute to the sustained expansion of employment in the tertiary sector. There will be a sustained migration of factors of production to the service sector due to differences in relative rates of return resulting from differences in productivity across industries [32]. Meanwhile, the depth of digital technologies’ integration in the tertiary industry is much higher than in other industries, and this trend is sustained and deepening. According to the data, by 2022 the penetration rate of the digital economy in China’s tertiary industry is forecast to be as high as 44.7%, or much higher than 10.5% in the primary industry and 24.0% in the secondary industry. The gradual introduction of digital technology does not have as strong an impact on the level of employment in agriculture as it does in the service sector, but it can achieve labor transfer between different industries, which will have a sustained and far-reaching impact on the employment rate in different industries [33].

The quality of employment may also be sustainably facilitated by the upgrading and transformation of the industrial structure. First, the industrial structure will gradually concentrate on the tertiary sector, owing to the continuous advancement of digital technology. In comparison with other sectors, the tertiary sector has a higher labor absorption capacity and synergy capacity. As the tertiary sector expands and the industrial structure advances, the labor force will slowly migrate to this sector, optimizing the social employment structure and improving the overall employment level [18,34].

Second, as the process of industrial digital transformation continues to accelerate, the share of high-value-added industries in the tertiary sector, such as finance, high-tech, culture, and education, will continue to grow. This not only absorbs more labor but also allows workers in these industries to earn higher wages. The following hypothesis is therefore put forward.

H2. 

The digital economy contributes to the sustainable improvement of the quality of residential employment, which can be achieved by increasing the level of the industrial structure of the region.

3.3. Moderating Effects of Innovation Environment

The innovation environment refers to the various factors and conditions that promote and support innovation, including scientific and technological development, talent cultivation, and policy support. A high-quality regional innovation environment can activate local potential, add vitality to regional innovation, and contribute to a sustained increase in innovative activity in the market, which can boost the regional digital economy [35,36]. Product and technological innovations in the market not only create new jobs and boost employment levels but also significantly impact the sustainability of job creation [37,38]. For instance, the extent of technological innovation in a territory has a dramatic effect on the employment quality in the hospitality sector, as it drives labor flows to the hospitality industry [39]. For the government, the local innovation ecosystem also has a fundamental and irreplaceable role in enhancing the local extent of the digital economy [40].

The continuous optimization of the innovation environment attracts more external investment and talent, enriching the region’s social resources and human capital. This initiative contributes to increasing the sustainability of innovation and entrepreneurship in the digital economy, fosters high-tech enterprises and start-ups, and injects new dynamism and opportunities into the labor market. Innovation-driven industries often demand high-quality talent, creating more opportunities for skilled workers and pushing the labor market toward greater efficiency and intelligence. This process optimizes the social employment structure, raises the overall employment level, improves urbanization and information infrastructure, and promotes industrial structure upgrading [41,42,43,44].

To sum up, the cluster effect brought about by the sustained improvement of relevant industrial clusters and various digital economic infrastructure environments and the strengthening of the regional innovation environment brought about by the continuous enhancement of regional human capital will expedite and stabilize the upgrading of the industrial structure, thus enhancing its positive influence on the quality of employment. The following hypotheses are therefore put forward.

H3. 

The more favorable the regional innovation environment, the stronger the effect of the digital economy in improving the quality of employment.

H4. 

The mediating back-end effects of industrial upgrading are positively moderated by the innovation environment.

In summary, we elaborate on the relationships among the digital economy, industrial structure, and employment quality from a theoretical level and explore the regulatory role that the regional innovation environment can play between them. Figure 1 depicts this pathway in detail.

4. Variables, Data, and Models

4.1. Description of Variables

4.1.1. Employment Quality

There has been a gradual rise in the quantity and quality of academic research on the topic of employment quality over time. However, the definition of its connotation has not formed an authoritative and unified standard, and scholars have different understandings of the concept and measurement of employment quality and thus different choices of dimensions and indicators. Some studies have argued that the measurement of employment quality should include educational attainment, social security, and equal opportunity factors [45]; other scholars have argued that the quality of employment should also include consideration of employment opportunities, adequate income, and other factors [2]. Summarizing the existing literature, we constructed a provincial employment quality evaluation index system from six aspects: employment level, employment structure, employment environment, employability, labor remuneration, and labor protection, focusing on the sustainable improvement of residents’ employment quality. Specifically, first, we include consideration of the number of urban and rural employment and the structure of male and female employment ratio [46], etc., as a measure of employment level and employment structure. Second, we include measures of transportation access, urbanization rate [47], and the percentage of higher education students in schools to measure the regional employment environment. Third, we include considerations of the labor force’s years of education [46] and the percentage of the labor force in vocational training to measure the labor force’s employability. Fourth, we include considerations such as the average wage of employed persons in urban units [48], and the urban-rural income gap Theil index as measures of labor compensation. Fifth, we include considerations such as the union participation rate and the severity of labor disputes as a measure of labor protection [49]. Overall, we score regional employment quality from a more comprehensive and scientific perspective to explore the path to sustainable improvement. The specific parts are presented in

Table 1. Construction of China’s Provincial Employment Quality Score Indicator System.

Target Level	Dimension	Indicator Selection	Indicator Type	Weights
Employment quality	Employment level	Urban employment rate (%)	Positive (+)	0.0379
		Rural employment rate (%)	Positive (+)	0.0418
		Overall employment rate (%)	Positive (+)	0.0261
	Employment structure	Ratio of urban and rural employed population	Positive (+)	0.1178
		Percentage of labor force in the tertiary sector (%)	Positive (+)	0.0276
		Ratio of female to male employed persons in urban non-private sector employment	Positive (+)	0.0141
	Employment environment	Level of real GDP per capita (10,000 yuan)	Positive (+)	0.0586
		Mean number of students enrolled in tertiary educational institutions per 100,000 population (persons)	Positive (+)	0.0343
		Urbanization rate (%)	Positive (+)	0.0162
		Transportation accessibility (billion yuan/10,000 people)	Positive (+)	0.1031
	Employability	Average time of labor force education (years)	Positive (+)	0.0102
		Percentage of workforce receiving training (%)	Positive (+)	0.0566
		Percentage of people with vocational skills (%)	Positive (+)	0.0673
		Percentage of employed persons with a college degree or above (%)	Positive (+)	0.0532
	Labor compensation	Average salary of employed persons in urban units (10,000)	Positive (+)	0.0659
		Proportion of total residents’ wage income in primary distribution (%)	Negative (−)	0.0075
		Departmental wage gap	Negative (−)	0.0011
		Rural-urban income gap	Negative (−)	0.0144
	Labor protection	Union participation rate (%)	Positive (+)	0.0210
		Labor dispute severity	Negative (−)	0.0021
		Expenditures on social security and employment as a share of GDP (%)	Positive (+)	0.0689
		Workers’ injury insurance coverage (%)	Positive (+)	0.0678
		Unemployment insurance participation rate (%)	Positive (+)	0.0864

. By standardizing the data in the table below and assigning weights using the entropy method, a score reflecting the quality of employment at the provincial level in China was obtained.

4.1.2. Digital Economy

For the measurement of the digital economy, some scholars point out that the digital economy should include ICT infrastructure and information digitization [50]. Other scholars believe that it should include digital industrialization and industrial digitization, in which digital industrialization mainly refers to the added value of the information industry. Industrial digitization includes the contribution of information technology to other industries [51]. It has also been argued that the digital economy consists of three components: e-commerce, e-commerce infrastructure, and e-commerce processes [52,53]. In summary, we draw on existing literature to assess the development level of China’s provincial digital economy from three dimensions of digital infrastructure development, digital industry development, and digital economy development environment with a total of 22 sub-indicators and use the entropy method to assign weights. The composition of the indicators is presented in

Table 2. Construction of China’s Provincial Digital Economy Score Indicator System.

Target Level	Dimension	Indicator Selection	Indicator Type	Weights
Digital Infrastructure Development Vehicle	Traditional digital economy infrastructure	Number of Internet access ports (10,000)	Positive (+)	0.0317
		Number of subscribers with broadband access to the Internet (10,000)	Positive (+)	0.0343
		Domain names per 1000 inhabitants (number)	Positive (+)	0.0861
		Actual number of cable broadcasting and television subscribers as a proportion of total number of households (%)	Positive (+)	0.0243
	New digital economy infrastructure	Cell phone subscribers at the end of the year (10,000)	Positive (+)	0.0262
		Fiber optic cable density (kilometers per ten 10,000 people)	Positive (+)	0.0243
		Capacity of mobile telephone exchanges per unit of urban area (10,000 homes/km2)	Positive (+)	0.0223
		Mobile penetration (units/100 inhabitants)	Positive (+)	0.0131
Digital industry development	Digital industrialization	Proportion of software business revenue as a share of GDP (%)	Positive (+)	0.0745
		Revenue from information technology services as a share of gross domestic product (%)	Positive (+)	0.0850
		Total telecom business as a share of gross domestic product (%)	Positive (+)	0.0423
		Proportion of enterprises with e-commerce trading activities (%)	Positive (+)	0.0096
	Industrial Digitization	Business e-commerce as a share of gross domestic product (%)	Positive (+)	0.0306
		Number of computers in use in business per 100 persons (units)	Positive (+)	0.0193
		Websites per 100 enterprises (number)	Positive (+)	0.0051
		Index of Digital Inclusive Finance	Positive (+)	0.0127
Digital economy development environment	Talent environment	Persons employed in information-related services (10,000)	Positive (+)	0.0607
		R&D personnel in industrial enterprises above a certain size in full-time equivalents (10,000-years)	Positive (+)	0.0722
	Innovation environment	R&D expenditure by industrial corporation above a specified size (billion yuan)	Positive (+)	0.0665
		R&D activities carried out by industrial enterprises above a specified size (in 1000 s)	Positive (+)	0.0782
		Total technology contract revenue (billions of yuan)	Positive (+)	0.1023
		Number of authorized patents granted (1000 s)	Positive (+)	0.0786

.

4.1.3. Mediating Variable

Industrial Structure Upgrading (ISU). The more pronounced the trend in a region’s service-oriented employment structure is, the more pronounced is the positive influence of the digital economy on employment. Referring to Clark’s Law to measure this trend, the proxy employed in this study is the ratio of the value added by the tertiary industry to the value added by the secondary industry.

4.1.4. Regulatory Variable

Innovation Environment (IE). The level of innovation in a region influences the generation and application of local new technologies. The data used in this paper are derived from the regional innovation environment level index, published in the Evaluation Report of China’s Regional Innovation Capability.

4.1.5. Control Variable

The mechanism by which the digital economy affects regional employment quality is affected by a number of environmental factors, inter alia, education, finance, regional disparities, and so forth. To minimize estimation errors due to omitted variables, this paper includes the following control variables.

(1)

Fixed asset investment level (FI). It is defined as the share of regional GDP spent on fixed capital formation.

(2)

Real estate price (Rep). The level of housing prices exerts a significant influence on the quality of life of local workers. This study employs the logarithm of the average sales price of commercial real estate published by each province as a means of measuring this influence.

(3)

Urban workers’ pension insurance coverage (OIC). This study takes the rate of the amount of urban workers who purchase pension insurance to the total quantities of urban workers in each province as a measurement standard.

(4)

Level of local education expenditure (Fe). The degree of local emphasis on education has a remarkable influence on the level of local human capital. To quantify this impact, we utilize the ratio of education expenditure of local governments in each province and city to the total local fiscal expenditure.

(5)

Foreign direct investment (FDI) levels. The indicator is calculated by multiplying the foreign direct investment inflows to each region by the exchange rate as a percentage of the region’s nominal GDP for the year in question.

4.2. Data Sources

This paper collates and analyzes the macroeconomic data of 31 provinces, municipalities, and autonomous regions in China between 2012 and 2022 (Excluding Hong Kong, Macao, and Taiwan). Among them, the raw data of the employment quality indicators are based on 2012, and GDP per capita is deflated by the GDP deflator. The sectoral wage gap is calculated by taking the absolute value of the ratio of the average wages paid to those employed in state-owned enterprises (SOEs) and other units minus one as a proxy variable. To ascertain the extent of the revenue imbalance between residents of urban and rural areas, we combine the resident population and per capita disposable income and finally employ the Thiel index to calculate the result. In consideration of data accessibility, we use the proportion of economically active individuals between the ages of 15 and 64 in each region as a proxy variable for each employment rate within the employment level dimension. The above original data come from China Statistical Yearbook, China Labor Statistics Yearbook, China Population and Employment Statistics Yearbook, and Provincial Statistical Yearbooks (Autonomous Communities, Local Authorities directly subordinate to the Central State). Detailed descriptive statistics of the variables are shown in

Table 3. Descriptive statistical analysis.

Category	Variable	Mean	p50	Std. Dev.	N
Indep	Digital Economy	13.87	10.78	9.688	341
Dep	Employment Quality	25.28	22.63	9.236
	Combination Weighting Method	31.41	29.92	5.965
Mediating	Industrial Structure	1.392	1.225	0.74
Moderating	Innovation Environment	27.23	24.71	9.576
Controls	Fixed Asset Investment	84.43	87.11	29.31
	Real Estate Price	8.905	8.799	0.49
	Pension Insurance Coverage	52.43	47.34	26.56
	Education Expenditure	16.20	16.44	2.76
	Foreign Direct Investment	1.70	1.51	1.70

below.

4.3. Model Setting

4.3.1. Empowerment Methodology

Based on the index system designed above, this paper adopts the entropy value method to score the digital economy and employment quality at the provincial level in China. The entropy value method, as an objective empowerment method, is calculated based on the discrete degree of the data itself, which reduces the influence of subjective judgment on the evaluation results, and the specific calculation steps are as follows:

The first step is to standardize the indicator data.

Standardization of positive indicators:

$${X\prime }_{ij}=\frac{X_{ij}-min\{X_{1j},\cdots ,X_{nj}\}}{max\{X_{1j},\cdots ,X_{nj}\}-min\{X_{1j},\cdots ,X_{nj}\}}$$

(1)

Standardization of negative indicators:

$${X\prime }_{ij}=\frac{max\{X_{1j},\cdots ,X_{nj}\}-X_{ij}}{max\{X_{1j},\cdots ,X_{nj}\}-min\{X_{1j},\cdots ,X_{nj}\}}$$

(2)

where $X_{ij}$ is the “j” indicator data of the “i” province, with the value of “i” ranging from 1–31, and “j” is each sub-indicator in the above indicator system.

The second step is to calculate the percentage of data values for the “i” province under the “j” indicator.

$$P_{ij}=\frac{{X\prime }_{ij}}{\sum _{i=1}^{31}{X\prime }_{ij}}$$

(3)

The third step is to calculate the entropy of the “j” indicators.

$$e_j=-\frac{1}{ln\left(n\right)}\sum _{i=1}^{31}{(P}_{ij}·{ln(P}_{ij}))$$

(4)

The fourth step is to calculate the weights assigned to each indicator.

$$w_j=\frac{1-e_j}{\sum _{j=1}^m(1-e_j)}$$

(5)

The final step calculates the composite score based on the weights.

$${Score}_i=\sum _{j=1}^m{w}_j·{X\prime }_{ij}$$

(6)

4.3.2. Empirical Model Setting

So as to validate Hypothesis 1, it is necessary to consider the inter-provincial individual differences that exist in the specific circumstances of the provinces in China, as well as the possible temporal effects. Accordingly, this paper presents a bidirectional fixed-effect model of panel data for regression, which is designed to address the aforementioned issues.

$${Emp}_{it}={\alpha }_0+{\alpha }_1{De}_{it}+{\alpha }_2{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(7)

Emp_it represents the regional employment quality score we measure, where i and t stand for the controlled province’s individuals and time, respectively. The explanatory variable De_it stands for the regional digital economy development as measured, while X_it represents the aforementioned control variables, The individual, time, and random disturbance terms, λ_i, η_t, and μ_it, respectively, are included in the model.

So as to validate Hypothesis 2 and test the mediating intent of industrial structure upgrading in the quality effect of digital economy promotion on employment, we draw on some scholars’ research and build a model combined with Equation (7) as follows [54]:

$${ISU}_{it}={\beta }_0+{\beta }_1{De}_{it}+{\beta }_2{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(8)

$${Emp}_{it}={\gamma }_0+{\gamma }_1{De}_{it}+{\gamma }_2{ISU}_{it}+{\gamma }_3{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(9)

For the purpose of validating Hypotheses 3 and 4 and to examine the positive moderating role of the innovation environment on the regression of the main effect of the digital economy on employment quality, as well as the back-end moderating role of industrial structure upgrading, we extend model (7). This extension introduces the innovation environment, the interaction term between the innovation environment and the digital economy, and the interaction term between the innovation environment and the industrial structure upgrading, as illustrated in Equations (10) and (11). The other variables correspond to those in Equation (7), and the extended model is given below:

$${Emp}_{it}={\delta }_0+{\delta }_1{De}_{it}+{\delta }_2{IE}_{it}+{\delta }_3{De}_{it}\times {IE}_{it}{+\delta }_4{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(10)

$${Emp}_{it}={\theta }_0+{\theta }_1{De}_{it}+{\theta }_2{ISU}_{it}+{\theta }_3{ISU}_{it}\times {IE}_{it}{+\theta }_4{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(11)

5. Empirical Analysis

5.1. Analysis of Baseline Regression Results

We regress the quality of employment (Emp) on the digital economy (De) and each of its three sub-dimensions (DI, DEI, and DEE) as explanatory variables according to the study’s individual time-fixed model (1) described above. The results are presented in

Table 4. Benchmark regression and sub-dimensional tests.

Variable	(1)	(2)	(3)	(4)
	Emp	Emp	Emp	Emp
De	0.161 ***
	(0.024)
DI		0.026
		(0.031)
DEI			0.132 ***
			(0.017)
DEE				0.075 ***
				(0.016)
FI	0.004	0.011 **	0.009 *	0.006
	(0.005)	(0.006)	(0.005)	(0.005)
Rep	1.802 *	3.035 ***	1.468	2.210 **
	(0.944)	(0.995)	(0.926)	(0.974)
OIC	0.012	−0.007	−0.007	0.012
	(0.013)	(0.014)	(0.013)	(0.014)
Fe	−0.026	0.023	0.027	−0.037
	(0.076)	(0.081)	(0.074)	(0.079)
FDI	0.077	0.005	0.045	0.075
	(0.073)	(0.078)	(0.071)	(0.076)
_cons	3.412	−6.488	5.694	0.937
	(8.433)	(8.957)	(8.248)	(8.724)
Fixed effect	Yes	Yes	Yes	Yes
Observations	341	341	341	341
R2	0.870	0.851	0.876	0.861

Note: ***, ** and * represent 1%, 5% and 10% levels of statistical significance, respectively; standard errors are in parentheses.

below.

The table visually demonstrates the significant promotion effect of DE on Emp at 0.161. The two sub-dimensions DEI and DEE are also significant on employment quality at 0.132 and 0.075, respectively, while the promotion effect of DEI is stronger than that of DEE. This may be because the fact that the digital economy facilitates the profound integration of technology with the real economy, which not only improves labor supply and demand matching efficiency, but also creates new employment opportunities. The stronger effect of the digital industry development dimension versus the environment dimension may be due to the augment in productivity and innovation brought by the course of the digital industry, which not only encourages private investment and entrepreneurship, but also indirectly improves the remuneration of workers to some degree. However, the regression coefficient of the digital basic development carrier dimension on employment quality is positive, but not significant. It’s supposed to be caused by the highly uneven development of China’s regions, with large disparities in the level of digital infrastructure across provinces and municipalities, resulting in its impact on employment mainly in terms of breadth of coverage, rather than quality, leading to the non-significant regression coefficients.

5.2. Robustness Tests

5.2.1. Replacing Explanatory Variables

To enhance the credibility and accuracy of the above benchmark regression results, we refer to other scholars and use the combination assignment approach to replace the explanatory variables in the regression [47]. The combination assignment method integrates the subjective assignment method (equal weight method) and the objective assignment method (entropy weight method and principal component analysis method). By averaging the results measured by the three methods, the new method mitigates data dependency and takes into account the variability, conflict, and information content of the data, resulting in more convincing regression results, as shown in the first column of

Table 5. Robustness test.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	CW	GMM	SD		L.De
De	0.068 ***	0.094 **		0.329 **		0.171 **
	(0.014)	(0.056)		(0.142)		(0.067)
IV			−0.513 ***		0.852 ***
			(0.134)		(0.081)
L.Emp		0.447 ***
		(0.062)
FI	0.002	0.018	0.026	−0.004	0.014 *	0.004
	(0.003)	(0.011)	(0.017)	(0.009)	(0.007)	(0.007)
Rep	1.188 **	4.773 **	7.071	0.579	0.555	1.762
	(0.555)	(2.042)	(5.223)	(1.605)	(1.577)	(1.681)
OIC	−0.000	0.026	−0.086	0.032	−0.048 *	0.024
	(0.008)	(0.032)	(0.106)	(0.036)	(0.024)	(0.033)
Fe	0.032	−0.174	0.375	−0.072	0.039	−0.044
	(0.045)	(0.144)	(0.335)	(0.147)	(0.099)	(0.104)
FDI	0.092 **	0.543 **	−0.259	0.145	−0.159 *	0.076
	(0.043)	(0.246)	(0.379)	(0.119)	(0.089)	(0.095)
Wald F Statistics			50.800		484.356
			(16.380)		(16.380)
LM Statistics			8.481 ***		8.058 ***
_cons	16.613 ***	−27.428 *	1167.651 ***		−0.461
	(4.954)	(16.619)	(308.185)		(14.767)
AR(1)		0.000
AR(2)		0.229
Hansen Statistics		0.402
Fixed effect	Yes	Yes	Yes		Yes
Observations	341	310	341	341	310	310
R2	0.890		0.685	0.849	0.875	0.864

Note: ***, ** and * represent 1%, 5% and 10% levels of statistical significance, respectively; standard errors are in parentheses.

.

5.2.2. SYS-GMM Model

As the effect of the digital economy in promoting sustainable improvement in the quality of employment of residents may have a lag, meaning the quality of employment in the previous period tends to have a certain impact on the next period. In order to ensure the robustness of the model estimation, the lagged term of employment quality needs to be put into the model as an explanatory variable. However, the introduction of the lagged term may result in the individual effects contained in it being correlated with the disturbance term. Moreover, the presence of omitted variables may lead to the problem of endogeneity of the residual term with the explanatory variables. Therefore, in this paper, the dynamic system GMM model is used for estimation and the model is shown in Equation (12) below.

$${Emp}_{it}={\omega }_0+{\omega }_1{{Emp}_{it-1}+{\omega }_{2}De}_{it}+{\omega }_3{X}_{it}+{\lambda }_i+{\eta }_t+{\mu }_{it}$$

(12)

By introducing a dynamic panel model for the analysis, we add lagged employment quality as an explanatory variable for the regression, and the results are shown in column (2) of Table 5. The p-value of AR(1) is less than 0.01, and the p-value of AR(2) with Hansen Statistics is greater than 0.1, which indicates that there is only a first-order serial correlation of residual terms, proving the validity of the previous paper.

5.2.3. Instrumental Variable Method

To tackle the issue of potential endogeneity in the model, we draw on existing literature and use the product of each province’s spherical distance from Hangzhou and a year dummy variable as the instrumental variable [55]. As a coastal city in eastern China, Hangzhou is not only an early developer of the digital economy, but also located in the core economic belt, with more economic exchanges, talent flows, and technological exchanges between regions. It is reasonable to assume that provinces closer to Hangzhou, which benefit from the progress of the digital economy, have more employment chances and higher employment quality, which also fulfils the principle of relevance in IV selection. However, as a geographic constant, it is generally not affected by the digital direction of economic development, which is consistent with exogeneity of the selection of IV. We also regress De lagged by one period as instrumental variables, and the results appear in Table 5 below. As can be seen from the results in columns (3)–(6) in Table 5, the Wald F-values are both greater than the Stock-Yogo 10% critical value of 16.380, and the k-park LM statistic is statistically significant at the 1% level, which proves the rationality for the selection of the instrumental variables.

Whether the variables are replaced, dynamic panel estimation is used or instrumental variables are used, the regression results obtained are generally consistent with the conclusions above, proving the robustness of the above conclusions. Therefore, Hypothesis 1 is supported.

5.3. Mediation Effect Test

In order to validate Hypothesis 2 (i.e., the mediating role of ISU), we conduct a regression based on models (2) and (3) constructed above to explore the role path of De on Emp. The main results appear in the first three columns of

Table 6. Mediation effect results.

Variable	(1)	(2)	(3)	(4)	(5)
	Emp	ISU	Emp	Emp	Emp
De	0.140 ***	0.006 **	0.124 ***	0.091 **	0.079 ***
	(0.051)	(0.006)	(0.041)	(0.038)	(0.040)
ISU			2.565 ***		2.023 ***
			(1.088)		(1.069)
$\mathit{ISU}\times$ IE					0.068 ***
					(0.027)
IE	0.067 **	−0.002	0.072 ***	0.060 **	0.059 **
	(0.026)	(0.003)	(0.025)	(0.026)	(0.025)
$\mathit{De}\times$ IE				0.002 *
				(0.001)
FI	0.005	0.002 ***	0.000	0.005	0.004
	(0.005)	(0.001)	(0.005)	(0.005)	(0.005)
Rep	1.549	0.634 ***	−0.077	1.269	−0.800
	(0.941)	(0.103)	(0.961)	(0.951)	(0.950)
OIC	0.011	0.002	0.006	0.011	−0.007
	(0.013)	(0.001)	(0.013)	(0.013)	(0.013)
Fe	−0.022	−0.033 ***	0.062	−0.047	0.026
	(0.075)	(0.008)	(0.074)	(0.076)	(0.073)
FDI	0.039	−0.006	0.056	0.046	−0.007
	(0.074)	(0.008)	(0.071)	(0.074)	(0.071)
_cons	4.135	−3.991 ***	14.372 *	7.467	22.679 ***
	(8.360)	(0.916)	(8.293)	(8.537)	(8.299)
Fixed effect	Yes	Yes	Yes	Yes	Yes
Observations	341	341	341	341	341
R2	0.873	0.686	0.883	0.874	0.890

Note: ***, ** and * represent 1%, 5% and 10% levels of statistical significance, respectively; standard errors are in parentheses.

below. The main effect is 0.124, and the indirect effect is 2.565. This also justifies that the mediating role of industrial structure is significant. We analyze that this result, arising from the integration and use of advanced technologies has enabled traditional industries to optimize production processes and increase productivity and innovation. In addition, it has spawned new areas such as e-commerce, fintech, digital marketing, cybersecurity, and other previously less prominent areas, a shift that has led to the creation of new business models and industries, the creation of new types of jobs and flexible employment, and allowing individuals to be able to choose jobs that suit their skills and preferences. Not only that, but the widespread use of digital platforms can provide employment opportunities for individuals in remote or underserved areas, contributing to inclusive growth and reducing regional disparities. Likewise, it facilitates international trade and investment, enabling seamless collaboration and knowledge-sharing across borders, and accelerating technological progress and industrial upgrading. In addition, the digital economy fosters a strong entrepreneurial ecosystem, encourages start-ups and innovation, leads to the development of cutting-edge technologies and services, increases the demand for digital literacy and advanced technological skills, circumvents simple repetitive tasks, and allows employees to focus on more creative and strategic activities. Overall, the continued shift in industrial structure from labor-intensive to technology-intensive, especially the expansion of high-value-added services, has created more high-quality jobs, and workers have been able to more effectively use digital platforms to enhance their own labor literacy and improve their labor efficiency, which has continued to improve the quality of regional employment. Therefore, Hypothesis 2 is supported.

5.4. Moderating Effect Test

In order to validate Hypothesis 3 and Hypothesis 4, we refer to the existing studies and expand the interaction terms IE and De as well as IE and ISU in Equation (3) and perform centralized processing on them at the same time to test Equations (4) and (5) [54]. The results are reflected in Table 6 below. Column (4) lists the consequence of adjusting the main effect by the level of the regional innovation environment, where the interaction coefficient is 0.002, which passes the 10% level test. Thus, Hypothesis 3 is supported. Column (5) of Table 6 represents the consequence of the adjustment of the regional innovation environment to the mediating effect on the back-end. The regression coefficient of the intermediate effect is 2.023, and the coefficient of the interaction term is 0.068, both of which are statistically significant at the 1% level. This proves that the adjustment effect of the innovation environment on the back-end is valid. This may be due to the fact that when a region has a higher level of innovation environment, it tends to have a higher level of R&D investment, which can facilitate the birth of new technologies and processes, introduce more advanced and efficient production methods, and new types of industries, and thus continue to improve the quality of employment for its residents. Not only that, an improved regional innovation environment usually includes better educational institutions and training programs. This also indirectly improves the skill level of the labor force, making them more adaptable to new industrial demands. As the industrial structure upgrades, the demand for skilled labor increases, and skilled labor improves the quality of employment by offering better wages and working conditions. In addition, a favorable regional innovation environment is conducive to entrepreneurship and new business creation, facilitating the flow of knowledge and technology among different stakeholders. For example, Shenzhen, China, has invested 188.049 billion yuan in research and development (R&D) in 2023, accounting for 5.81% of regional GDP, added 1615 new high-tech enterprises, and promoted the “5G+Industrial Internet” and “AI+Smart Manufacturing” models. It has built a modernized industrial system with strong competitiveness, brought tens of thousands of jobs, and improved the quality of employment, thus supporting Hypothesis 4.

5.5. Heterogeneity Test

Considering that the development between regions is not balanced, we divide the research sample into two regions: east and central, based on the regional division of the National Bureau of Statistics of China, with the eastern region being: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, and the rest being the central region. At the same time, using data from the 25th and 75th quantiles of ISU, we divide the research sample into three different levels: low: ISU ≤ 1.022, medium: 1.022< ISU ≤ 1.437, high: 1.437 ≤ ISU. The regression results in

Table 7. Heterogeneity test result.

Variable	(1)	(2)	(3)	(4)	(5)
	East	Central	Low	Medium	High
De	0.097 **	0.015	−0.064	0.078 ***	0.363 ***
	(0.039)	(0.046)	(0.089)	(0.026)	(0.071)
FI	0.012	0.006	−0.024	0.012 **	0.013
	(0.013)	(0.005)	(0.016)	(0.005)	(0.022)
Rep	5.507 ***	−2.505 **	−3.567 *	−2.165 *	3.730
	(1.979)	(1.043)	(1.823)	(1.225)	(2.490)
OIC	0.033 *	0.018	0.019	−0.001	0.005
	(0.019)	(0.021)	(0.070)	(0.019)	(0.024)
Fe	0.017	0.084	−0.291	0.105	−0.007
	(0.174)	(0.077)	(0.193)	(0.082)	(0.201)
FDI	0.235 **	−0.050	−0.241	0.002	0.022
	(0.112)	(0.153)	(0.368)	(0.095)	(0.137)
_cons	−28.196	36.025 ***	56.291 ***	33.747 ***	−10.683
	(19.563)	(8.750)	(14.833)	(10.655)	(23.450)
Fixed effect	Yes	Yes	Yes	Yes	Yes
Observations	121	220	44	209	88
R2	0.897	0.890	0.939	0.920	0.871

Note: ***, ** and * represent 1%, 5% and 10% levels of statistical significance, respectively; standard errors are in parentheses.

report a 0.097 employment promotion quality effect of the digital economy in the east region and a positive but non-significant regression coefficient in the central region. This is due to the fact that the east region has much better infrastructure and education system, which help the labor force to better adapt to the digital economy, while there is a lag in the promotional effect in the central region. Viewed from the three levels of upgrading industrial structure, the regression coefficient of Emp in regions with a low industrial structure upgrading index is insignificantly negative, while the regression coefficients of Emp in the middle and high level areas are 0.078 and 0.363, respectively. We see that the promotion effect of De has a non-linear growth trend. This may be due to the fact that in lower level regions, especially in central and western provinces where traditional industries still dominate, the market size is small, the network effect is insufficient, and the progress brought by the digital economy has less impact, resulting in a lag in improving the quality of employment.

6. Conclusions and Comments

On the basis of data that we use to measure the digital economy and employment quality in China’s provinces and cities from 2012 to 2022, we note the following results. First, the quality of regional employment can be sustainably improved by the digital economy, with the dimension of the digital industry having the greatest effect on promoting employment quality. Second, the industrial structure transmits the employment quality effect of the digital economy, and the enhancement of the regional innovation environment not only positively moderates this employment creation main effect, but also positively strengthens the mediating role of the industrial structure. Third, the degree of integration into the digital economy varies across regions, with the east region having a stronger employment quality promotion effect, and its higher degree of industrial structure transformation amplifying this positive effect.

Digitalization is changing the way people live, work, learn, consume, and do business, with technological advances and the spread of the Internet and smart terminals driving the exponential growth of online transactions. According to a relevant report from the United Nations Conference on Trade and Development, in 2021, enterprises in 43 developed and developing economies generated nearly $25 trillion in e-commerce sales. The strong vitality demonstrated by the digital economy has not only brought new economic growth points to developed countries but also brought the potential for the economic transformation of developing countries.

China’s digital economy is currently in a booming stage, promoting the transformation of more and more manufacturing enterprises using digital manufacturing. At the end of 2023, the World Economic Forum announced the latest batch of “lighthouse factories”, of the 21 newly promoted manufacturing “lighthouse factories”, 11 of them are located in China, including photovoltaic, automotive, new energy, and other high-tech enterprises. “Lighthouse factory” is known as “the world’s most advanced factory”, using digital, networked, and intelligent methods along with advanced artificial intelligence and large model technology to achieve a fully automated and precise production process. It is recognized as a global leader in intelligent manufacturing and digitalization in the manufacturing industry. At present, of the 153 “lighthouse factories” in the world, China has 62, which is the largest number of “lighthouse factories” in the world, and this also provides a strong driving force to continuously improve the quality of employment for residents. This is due to three advantages. First, enterprises have become more flexible and agile, breaking away from traditional business models. Second, they encourage innovation and have developed a constructive competitive culture. Finally, they focus on incubating and expanding new business platforms. Therefore, this paper makes the following three recommendations.

First, it is of vital importance for the government to actively promote the development of the regional digital economy, so that digital technology can be deeply integrated with regional industries, improve the efficiency of matching labor supply and demand, and efforts can be made to enhance the level of development of the digital industry in the region. On the one hand, it is proposed that the potential of digital industrialization be harnessed by targeting cutting-edge technologies, strengthening and expanding the depth of the industry, and modernizing the digital industrial chain. This includes increased investment in new digital infrastructure, such as the Internet of Things, cloud computing, and 5G communications, driving productivity and innovation, and facilitating the advanced and large-scale growth of the digital industry, thereby enhancing its capacity to create high-end employment opportunities. On the other hand, it is suggested to emphasize industrial digital development by transforming production chains, expanding industrial chains, upgrading value chains, and strengthening innovation chains. This involves promoting the digital transformation of traditional offline businesses, encouraging both large and small companies to embrace digital transformation, and fostering the healthy growth of new business models like the platform economy, thus generating more jobs. At the same time, we need to encourage nongovernmental entrepreneurship and investment and raise the rate of return for workers. It is imperative to continue to foster the advancement, expansion, and diversification of the digital industry in order to enhance its capacity to contribute to the employment structure.

Second, to optimize the regional innovation environment and enhance the job-creating role of the digital economy, local governments need to use innovation as a core driving force to foster the cultivation of new quality productive forces such as intelligent manufacturing, industrial Internet platforms, and next-generation information technology, so as to increase China’s international influence. The aim should be to continuously improve the level of intelligence in production equipment and infrastructure across a wide range of industries, promote integrated infrastructure, and help develop high-value industries, thereby facilitating industrial structure upgrading and transformation. In addition, with the optimization of the innovation environment as an important direction, governments should build an innovation industry incubation platform, introduce foreign or nurture local high-quality enterprises, technologies, and talents, gather innovative elements, and strive to establish a comprehensive innovation chain that encompasses “basic research + technological research + achievement transformation + technological financing + talent development”. On the domestic level, look at the international, cultivate and create a digital innovation benchmark city, plan a batch, cultivate a batch, create a batch, and create an international innovation city group. Thereby creating a robust innovation atmosphere and a high-quality environment for innovation across all sectors. Additionally, it is crucial to improve the incentive mechanism for core enterprises, encourage collaboration to form a digital ecosystem based on digital innovation, and explore the formation of a synergistic governance model among enterprises, society, and the government. These efforts are aimed at promoting healthy business development and ultimately enhancing employment opportunities.

Third, local governments need to strengthen digital infrastructure, enhance regional coordination, support the mature use of digital technologies, and improve the quality of employment. An important way to improve efficiency is to establish a regional network for coordinated development, strengthen the central digital economy hubs, and facilitate the interconnection of policies, resources, technologies, and human resources. This will amplify regional spillover effects and leverage the digital economy to create high-quality employment clusters, by utilizing modern digital technologies to optimize employment structures. It is further suggested that broadening the coverage of digital infrastructure coverage in the central region is crucial. In addition, there is a need to strengthen the circulation of digital resources between regions which will enable them to seize strategic opportunities for new high-quality productivity. By leading advancements in digital and intelligent industries, these regions can upgrade their industrial structures and improve employment quality. The east region should maintain its leading position in employment development through the establishment of a perfect urban digital innovation talent training system, maximize its coastal geographical advantages and the benefits of regional human capital, and provide financial and technical support to the central region in order to narrow the regional development gap and to achieve stable development of the quality of employment among regions through the construction of a regional synergy network.