Productive Service Agglomeration, Human Capital Level, and Urban Economic Performance

Abstract

This study aims to investigate the impact of productive service industry agglomeration on the economic performance of Chinese cities. The study analyzes data from 285 prefecture-level cities over a 16-year period (2003–2019) and develops a comprehensive urban economic index evaluation system based on three dimensions: social, economic, and resource and environmental benefits. The empirical analysis reveals that high-end productive service industry agglomeration has a significant positive relationship with urban economic performance (effect size of 0.012), whereas low-end productive service industry agglomeration does not. The study also finds that the impact of productive service industry agglomeration on economic performance varies across different regions and cities. Specifically, productive service industry agglomeration has a significant positive impact on the economic performance of cities in the eastern, central, northeastern, and western regions, with the impact being twice as much in the western region compared to the eastern region. Moreover, the effect of productive service industry agglomeration on the economic performance of non-resource-based cities is almost one-third more than that of resource-based cities. The study further reveals that human capital plays a mediating role in the relationship between productive service industry agglomeration and urban economic performance. Based on these findings, the study suggests that Chinese cities, particularly non-resource-based cities and those in the central and western regions, should focus on supporting high-end productive service industry agglomeration, improve the quality of productive service industry agglomeration, strengthen the matching between the accumulation of human capital and the development of productive service industry agglomeration, and tailor their strategies to promote the development of productive service industry agglomeration effectively to improve their economic performance.

1. Introduction

1.1. Background

Since entering the 21st century, China’s information technology and knowledge-based economy have gained rapid development, the industrial structure has undergone profound changes, and the productive service industry has expanded at an unprecedented rate, becoming an important force influencing economic development. From 2000 to 2021, the value-added of service industry increased from 39.79% to 53.31% of GDP (China Statistics Yearbook, 2022), and in 2020, the value-added of productive service industry exceeded 60% of service industry [1]. At the same time, from a worldwide perspective, the proportion of the value added of the service industry to GDP in Western developed countries is generally above 70%, far exceeding China’s 53.31%, and the proportion of the output value of the productive service industry to the output value of the service industry can reach more than 70% [2]. This indicates that productive service industry has become the main driver of service industry growth. As a modern service industry cluster, productive service agglomeration is highly industry-related and human capital-intensive, and thus can attract a large amount of human capital and intellectual capital into the production process of goods and services [3]. This is not only conducive to enterprises’ access to technology, knowledge, and information with a complementary nature, but it can also enhance the quality and quantity of human capital level [4]. The level of human capital is the basis of innovation and the key to stimulating innovation potential and promoting economic growth [5], and by improving the level of human capital, it can promote the transformation of China’s industries from manufacturing to production services, give full play to the advantages of talent resources, promote the upgrading of urban industrial structures, and improve the overall economic efficiency of cities. In accordance with the 14th Five-Year Plan, which emphasizes the importance of labor, knowledge, talent, and innovation, as well as the reform of talent development mechanisms, this study aims to examine how the agglomeration of productive service industries can support the high-quality development of China’s economy. Specifically, the study seeks to uncover the relationship between productive service industry agglomeration, human capital levels, and urban economic performance. The answers to these questions are of great theoretical and practical significance in guiding the transformation of economic development in the new era.

1.2. The Literature Review

Currently, there is no consensus on the impact of productive service industry agglomeration on economic growth. Some studies suggest that the agglomeration of productive service industries can promote economic growth by driving regional innovation and knowledge spillover effects, as well as the economy of scale and factor restructuring effects based on market externalities [6]. These effects can pull regional economic development, promote economic structure upgrading, and reduce regional income disparity [7]. Additionally, productive service industries, as a modern service industry, can leverage technology externalities to further enhance their impact [8]. Second, some scholars point out that the concentration of productive service industries may have a negative impact on the economy. Gong argued that the agglomeration of productive service industries will lead to the decrease in enterprise productivity due to the increase in commuting costs, production factor costs, and housing and living costs, which in turn will negatively affect regional economic development [9]. According to some scholars’ sub-regional studies, productive service industries do not drive regional economic development in the eastern and central regions of China, and in the absence of factors, such as human capital, urban economic development, and government intervention, the agglomeration of productive service industries may even harm the quality of urban economic growth [10]. Moreover, in other studies, the impact of productive service agglomeration on economic growth from a non-linear perspective was examined, and it was found that the relationship between them is dynamic and non-coherent, and it is not a simple linear relationship. For instance, the Williamson hypothesis suggests that productive service industry agglomeration has a positive effect on regional economic growth at the early stages of economic development, but, as the level of regional economic development increases, this effect may weaken or become negative [11]. Domestic studies have also supported the Williamson hypothesis [12,13]. These varying findings demonstrate that there is currently no consensus on the impact of productive service industry agglomeration on economic growth.

There exist two primary perspectives regarding the correlation between human capital and economic growth. One of them implies that human capital can be a contributing factor to the advancement of economic growth. According to this viewpoint, some experts argue that human capital is an advanced production factor that can moderately replace conventional factors, such as labor, physical capital, and natural resources, thus enhancing the efficiency of resource allocation, promoting societal productivity, and facilitating regional economic growth [14,15,16]. Additionally, human capital can indirectly drive economic growth by stimulating technological innovation and upgrading industrial structures [17,18]. Likewise, owing to the disparities among various levels of human capital, a nonlinear association between human capital and economic growth exists. This results in an apparent threshold effect of the human capital level on economic growth, whereby different levels of human capital—primary, intermediate, and advanced—have distinct effects on the economic development of diverse regions in China [19]. Furthermore, there is an inverted U-shaped relationship between the overall concentration level of human capital and economic growth [16]. In addition, the contribution of human capital to regional economic growth can affect the level of regional economic development, as well as the type of industrial structure of the region and foreign direct investment, and some scholars have shown that, in low-income countries, human capital and economic development show a positive relationship. While, in high-income countries, human capital and economic development show opposite conclusions [20]. Moreover, another study found that, in a country’s normal economic development process, excessive concentration of human capital can lead to a reduction in the efficiency of resource allocation, resulting in the waste of human resources and adversely affecting regional economic growth [21,22].

Since the relationship between productive service agglomeration, human capital level, and urban economic performance is not conclusive, this study will further analyze the relationship between productive service agglomeration and human capital level and the mechanism of their effects on urban economic performance. On the one hand, there are few studies on the impact of productive service industry agglomeration on human capital level in the existing literature, and the existing literature is mostly analyzed from the perspective of productive service industry agglomeration on technological innovation, while human capital itself is an important production factor in reality, and productive service industry is a knowledge-intensive industry, which is more dependent on human capital than the manufacturing industry. Nonetheless, the association between the level of human capital and the economic performance of cities remains unclear, and no definitive viewpoint exists on this matter. On the other hand, due to the differences in theories, methods, selection of industries, and economic development index measurements on which the studies are based, the academic community has not yet reached a consensus view on the mechanism and effect of productive service agglomeration on urban economic performance. Previous research has primarily emphasized the influence of productive service agglomeration on specific aspects, such as economic growth and green growth efficiency. Currently, there are limited studies available on the direct correlation between productive service agglomeration and urban economic performance. Moreover, both productive service agglomeration and the level of human capital have an impact on urban economic performance, yet existing studies have yet to investigate all three factors within the same analytical framework.

This research employs panel data from 285 prefecture-level cities in China between 2003 and 2019 to investigate the relationship between productive service agglomeration and urban economic performance. Furthermore, this study explores the relationship between agglomeration in different industries and urban economic performance, based on industry and regional heterogeneity. The study also tests a mediating effect model to determine whether human capital plays a mediating role in the process of productive service agglomeration, affecting urban economic performance. By investigating these relationships, this study enhances our understanding of these issues and assists governments at all levels in formulating reasonable industrial development sequences, optimizing the spatial allocation of productive service resources, and improving urban economic performance. This study offers potential innovations in two key ways.

Firstly, it constructs a comprehensive index evaluation system of urban economic performance that reflects the efficiency of urban economic development more comprehensively and accurately than single indicators, such as total factor productivity, encompassing social, resource, environmental, and economic benefits.

Secondly, it identifies the key factors that impact urban economic performance by analyzing the positive externalities of productive service agglomeration and uses the level of human capital as a mediating variable to examine the mechanism of the agglomeration of productive service industries affecting the economic performance of cities. The study also explores the heterogeneous influence of human capital in the process of productive service industry agglomeration on the economic performance of cities, considering industry level and city scale.

1.3. Contributions of the Study

This paper contributes to three specific aspects: economy and society, theory and knowledge, and technology and information.

• Contributions to Theory and Knowledge:
  • The paper enriches the research related to the relationship between productive service agglomeration, human capital level, and urban economic performance.
  • The paper highlights that the current literature mainly focuses on the analysis of productive service agglomeration and urban economic performance or human capital level and urban economic performance, with limited research on the relationship between productive service agglomeration and human capital level.
  • The paper also recognizes that the literature on the analysis of the relationship between the three is scarce, and the current literature on the relationship between productive service agglomeration and economic performance typically uses a single indicator, such as total factor productivity or GDP per capita, to measure urban economic performance.
• Contributions to the Economy and Society:
  • The paper examines the impact of productive service industry agglomeration on urban economic performance and verifies it through empirical tests.
  • The paper explores the industry heterogeneity and regional heterogeneity of productive service industry agglomeration, affecting urban economic performance and providing a scientific basis for the government to promote regional productive service industry agglomeration.
  • The findings of this paper can help improve urban economic performance and provide a more efficient basis for China’s economic development.
• Contributions to Technology and Information:
  • The paper investigates the path between productive service agglomeration and urban economic performance through the mediating effect model, demonstrating whether human capital level is the path between productive service agglomeration and urban economic performance.
  • The paper recognizes that productive service industries are mostly technology and knowledge-intensive and require a high level of human capital.
  • If the conclusion of this paper holds, it indicates that the agglomeration of productive service industries can promote technological innovation and provide new impetus to economic development by improving human capital level.

Overall, this study provides a comprehensive analysis of the relationship between productive service agglomeration, human capital level, and urban economic performance, deepening our understanding of these issues and contributing to theory and knowledge, the economy and society, and technology and information.

2. Theoretical Framework and Research Hypotheses

2.1. The Impact of Productive Service Industry Agglomeration on the Economic Performance of Cities

As an indispensable part of the modern economic development system, the benefits brought by the agglomeration of productive service industries to the regional economic development are not only limited to the growth of regional GDP, but also bring about the improvement of all-round economic performance of cities in terms of economic, social, and resource environmental benefits. Therefore, this study analyzes the improvement of economic performance of cities by agglomeration of production service industry in three aspects: economic benefit, social benefit, and resource and environmental benefit.

The agglomeration of production service industry improves the economic benefits of cities. First, the agglomeration of productive service industries can optimize the economic structure of cities, thus promoting the transfer of the dominant sector of Chinese industries from the secondary industry to the tertiary sector, which, in turn, optimizes the economic structure of cities and enhances the stability of China’s economic growth [23]. Second, the new economic geography theory suggests that, in order to reduce the “iceberg transportation cost”, the productive service industry is adjacent to the manufacturing layout by strengthening the input–output linkage between industries or upstream and downstream of the industrial chain [24], thus reducing the production cost of enterprises through shared infrastructure and generating economies of scale. The effect of shared infrastructure reduces the production costs of enterprises and generates economies of scale, which in turn improves the total factor productivity of enterprises. Finally, the agglomeration of productive service industries can increase the scale of employment. The agglomeration of productive service industry expands the scale of service industry, generates many new jobs, creates a large amount of labor demand, eases the pressure of social employment to a certain extent, promotes the increase in labor compensation, enhances the consumption ability of residents, and realizes the transformation from “investment-driven” to “consumption-driven” economic growth mode. The economic growth mode changes from “investment-driven” to “consumption-driven” and improves the regional economic development level.

The agglomeration of the productive service industries enhances the social benefits of urban development. On the one hand, the agglomeration of the production service industry generates economies of scale through the “input-output linkage” of industries at the upper and lower ends of the value chain. It helps manufacturing enterprises obtain intermediate services or products with low prices and high-quality and reduce their own production costs. At the same time, it makes the production service industry synergize with the whole economic sector, thus in the process of urbanization, a large amount of rural surplus labor can obtain more employment opportunities in cities [25], which helps to narrow the income gap between urban and rural areas and share the fruits of economic development. On the other hand, productive service industries are mostly knowledge- and technology-intensive industries [26]. The economies of scale and technology spillover effects of agglomeration can increase the average regional labor productivity. Increasing government revenue allows the government to have more resources to invest in the construction of infrastructure, such as roads and communications, as well as to increase expenditures on research and education, which increases social benefits.

The agglomeration of productive service industries enhances the resource and environmental benefits of urban development. On the one hand, the agglomeration of production service industries can strengthen the communication and cooperation between the same industries and different industries. It accelerates the diffusion of environmental protection knowledge and clean technology, enhances the green technology innovation ability, improves the efficiency of resource utilization [27,28], and thus promotes the development of the circular economy. Some scholars believe that the agglomeration of productive service industries will generate economies of scale, reduce their own operating costs, and can provide manufacturing enterprises with adequate and cost-effective intermediate services, especially environmental intermediate services, which can reduce the costs of manufacturing enterprises to reduce environmental pollution and enhance the efficiency of green development of urban economy [29]. On the other hand, specialized agglomeration of productive service industries achieves cost savings through intra-industry sharing mechanism and revenue growth through learning mechanism, which significantly reduces the cost of pollution emission management, while diversified agglomeration of productive service industries acts on urban green total factor productivity through inter-industry technological innovation effect and industrial structure effect [30], which improves urban economic performance.

Hypothesis 1 (H1).

The agglomeration of productive service industries significantly contributes to the improvement of urban economic performance.

2.2. Productive Service Industry Agglomeration, Human Capital Level, and Urban Economic Performance

The agglomeration of productive service industries can improve human capital through knowledge accumulation, knowledge progress, and knowledge spillover, which can moderately replace the demand for traditional factors of production, such as physical capital, natural resources, and labor in the process of regional economic development. The impact of human capital and new technology on productivity improvement is transmitted to downstream enterprises through inter-industry linkages [31], which accelerates the improvement of labor productivity. In addition, as a knowledge- and talent-intensive industry, the production service industry not only plays an important role in the diffusion of new technologies, but also is a creator of new knowledge and technologies, providing a learning and innovation environment for enterprises in the agglomeration [32], which facilitates the sharing of knowledge, technology, and information needed by each other. This further improves the quality of talent training and promotes the level of regional human capital [33].

Hypothesis 2 (H2).

The agglomeration of productive services can increase the level of human capital.

The agglomeration of productive services cannot only directly contribute to the economic performance of a city, but it can also indirectly influence the economic performance of a city by increasing the level of human capital. Specialized agglomeration and diversified agglomeration are two types of externalities that can improve the economic performance of a city by increasing the level of human capital. First, diversified agglomeration of productive service industries allows different types of industries to form horizontal or vertical industrial chains, creating a diversified development environment for the region, realizing diversified and balanced development among industries, and facilitating cross-industry knowledge exchange and inter-industry complementation [34]. Diversified agglomeration expands the width of talent flow, attracts high-quality talents to the region [35], enhances the level of human capital in the region, and affects the human capital required by different industries cooperating and dividing labor among each other, realizing the crossover and complementarity of technology and knowledge among different industries, accelerating the knowledge spillover and technological innovation in the region, and promoting the development of the regional economy. Second, the specialized agglomeration of productive service industries promotes the sharing and diffusion of human capital, knowledge, and technology within the same industry, and it exerts spillover effects through the creation and diffusion of technological knowledge [36]. With the increase in specialization agglomeration, cooperative research and development in the same industry can even out the enterprise risk, and enterprises can use the funds for risk control more for innovation research and development, talent recruitment, and training, thus attracting more professional and technical talents, better utilizing the human capital effect, and achieving substantial improvement of economic quality.

Third, the agglomeration of productive service industry strengthens the competitive incentive effect of human capital and promotes the improvement of local human capital level, which in turn promotes the improvement of urban economic performance. In the context of competitive agglomeration, manufacturing industries within a region engage in competition with one another, and in order to enhance their competitiveness, manufacturing enterprises divest from sectors where they lack a comparative advantage and seek out specialized productive service enterprises in the market to supplement and concentrate on their core advantage industries. As a result, the productive service industry has gained greater opportunities for survival, and a more competitive environment has emerged. In order to ensure that the enterprises can obtain excessive profits, each productive service enterprise starts to compete for talents, advanced technology, and capital. Since most of the production service industries are knowledge-intensive, human capital is the focus of competition among production service industries, which compels enterprises to offer more favorable treatment and conditions to high-end technical talents, accelerating the flow of industry, talents, and other factors in the region. The efficiency of urban economic development is thus affected.

Hypothesis 3 (H3).

The agglomeration of productive service industries can promote the improvement of urban economic performance by enhancing the level of human capital.

3. Material and Methods

3.1. Econometric Model

To test the impact of productive services agglomeration on urban economic performance, this study sets the benchmark model by referring to some scholars [5,37] and combining the above analysis.

$${UEP}_{it}={\alpha }_0+{\alpha }_1{agg}_{it}+{\alpha }_2{control}_{it}+{\mu }_i+{\nu }_t+{\xi }_{it}$$

(1)

where $t$ represents time; ${UEP}_{it}$ is the explanatory variable city economic performance; ${agg}_{it}$ is the core explanatory variable productive service industry agglomeration; ${control}_{it}$ is a series of control variables; area fixed effect is denoted by by ${\mu }_i$; time fixed effect is denoted by ${\nu }_t$; and ε is a random error term, which is assumed to be normally distributed at zero mean value [38,39,40] and constant variance [41,42,43].

In this study, we aim to examine whether technological innovation acts as a mediator in the relationship between productive service industry agglomeration, and urban economic performance. To achieve this, we have constructed a model based on the stepwise regression test conducted by Zhonglin and Baojuan [44], which is outlined below:

$${HUM}_{it}={\beta }_0+{\beta }_1{agg}_{it}+{\beta }_2{control}_{it}+{\mu }_i+{\nu }_t+{\xi }_{it}$$

(2)

$${UEP}_{it}={\gamma }_0+{\gamma }_1{HUM}_{it}+{\gamma }_2{agg}_{it}+{\gamma }_3{control}_{it}+{\mu }_i+{\nu }_t+{\xi }_{it}$$

(3)

In Equations (2) and (3), ${HUM}_{it}$ is the level of human capital (mediating variable). Equation (2) is mainly used to test the effect of productive service industry agglomeration on human capital level. Equation (3) is mainly used to test whether the mediating effect of human capital level exists, i.e., whether productive service industry agglomeration significantly affects urban economic performance after controlling for the indirect effect of human capital level.

According to the intermediation effect test process proposed by Baron and Kenny [45], the test process of intermediation effect needs to satisfy the following steps: first, test the coefficient in Equation (1), and, if it is significant, the intermediation effect is established, and the subsequent test is conducted. Second, the coefficients in Equations (2) and (3) are tested in turn, and if both coefficients are significant, then the indirect effect is significant, and the follow-up test is conducted. If at least one of them is not significant, the original hypothesis is directly tested by the bootstrap method: =0. The significance of the result indicates whether the indirect effect is significant or not. If it is significant, the analysis continues; otherwise, it is terminated. The next step involves testing the coefficients in Equation (3). If they are not significant, it suggests that there is only a mediating effect in the model. On the other hand, if the coefficients are significant and have the same sign, it indicates a partial mediating effect. However, if the signs are not the same, it suggests a masking effect.

3.2. Variable Description

3.2.1. Explanatory Variable: Urban Economic Performance (UEP)

Following Li Bin et al. [1], a comprehensive index system was constructed to measure urban economic performance from three dimensions: economic benefits, resource and environmental benefits, and social benefits, based on the full consideration of data availability and reliability. Regarding the measurement of urban economic performance indicators, firstly, the index data are dimensionless, and then the entropy weight method is used to calculate the weights of each indicator, and finally, the economic performance scores of each city are calculated according to the indicator weights, and the indicator evaluation system is shown in

Table 1. Comprehensive evaluation index system of urban economic performance.

First-Order Index	Second-Order Index	Third-Order Index	Unit
Economic benefit Social benefit Resource environmental benefit	Level of economic development Urban economic structure Green growth efficiency Infrastructure construction Cultivation of innovation ability Government support Ecological and environmental protection ability Resource utilization efficiency Circular economy development	Gross national product per capita Total social investment in fixed assets Proportion of employment in tertiary industry The proportion of tertiary industry output in GDP Intensity of industrial wastewater discharge Industrial sulfur dioxide emission intensity Industrial smoke and dust emission intensity	Yuan ten thousand yuan % % Ton/ten thousand yuan Ton/ten thousand yuan Ton/ten thousand yuan Ton/ten thousand yuan % SQM a car % a % % % % hm2/P t KWH % % %
		Industrial nitrogen oxide emission intensity Internet penetration rate Per capita urban road area Number of telephones per 10,000 people Number of buses per 10,000 people The proportion of students in regular institutions of higher learning in the registered population at the end of the year Number of institutions of higher learning The proportion of teachers in regular institutions of higher learning in the registered population at the end of the year The proportion of scientific research expenditure in local general public budgets The proportion of education expenditure in local general public budget expenditure Green coverage of built-up areas Per capita green space Water consumption per unit of GDP Electricity consumption per unit of GDP Centralized treatment rate of sewage treatment plant Comprehensive utilization rate of industrial solid waste Harmless treatment rate of household garbage

. To facilitate the specific analysis in this paper, the economic benefits (ECO), social benefits (SOCIAL), and resource and environmental benefits (RES) are calculated sequentially by the above-mentioned way.

3.2.2. Core Explanatory Variables: Agglomeration of Productive Service Industry (agg)

Drawing on the views of many scholars [1,8,10] and referring to the industry classification standards of the National Economic Classification and Codes (GB/T4754-2011), this study finally determines that the productive service industry includes transportation, storage and postal services, accommodation and catering, wholesale and retail trade, real estate, leasing and business services, water, and environment and public facilities management, and the nine industries include information transmission, computer services and software, finance, scientific research, technical services, and geological survey. The productive service industry can be categorized into two segments based on their level of R&D and technology investment: the high-end productive service industry (highagg) and the low-end productive service industry (lowagg). The high-end productive service industry comprises information transmission, computer services, software, finance, scientific research, technical services, geological survey, among others. The high-end productive service industry includes information transmission, computer services and software, finance, scientific research, technical services, and geological exploration, while the low-end productive service industry includes transportation, storage and postal services, accommodation and catering, wholesale and retail trade, real estate, leasing and business services, and water, environment, and public facilities management. In this study, we followed the practice of many scholars and adopted the locational entropy to calculate the production service industry agglomeration, which is calculated by the formula.

$$agg=\frac{\raisebox{1ex}{$E_{it}$}\!\left/ \!\raisebox{-1ex}{$E_i$}\right.}{\raisebox{1ex}{$E_s$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}$$

(4)

where $E_{it}$ represents the total employment in the productive service industry in a particular city $i$ during a given year $t$, while $E_S$ represents the total employment in the productive service industry across the entire country. $E_i$ represents the total employment in a specific city $i$, while $E$ represents the total employment nationwide.

3.2.3. Mediating Variable: Human Capital Level (HUM)

According to Morett [46] and Xue et al. [47], having human capital that combines knowledge and skills can enhance the development efficiency of urban economies. In this regard, individuals with college and higher education are considered to be part of the highly skilled labor force. The level of human capital intensity in a region is measured by the percentage of the total regional population consisting of students and teachers enrolled in local general colleges and universities.

3.2.4. Control Variables

LNgov is the degree of government involvement and is determined by taking the logarithm of the ratio between the local general public budget expenditures and the local regional GDP. Market refers to the level of marketization, which is evaluated by dividing the number of private and self-employed urban workers by the number of urban unit workers at the end of the period. Inform measures the degree of informatization by examining the per capita telecommunication business income. Edu is an indicator of the strength of science and education support, which is calculated by dividing the sum of expenditures on science and technology and education by the local general budget expenditures. Finally, reve reflects the government revenue and expenditure status, which is expressed as the ratio of local general budget expenditures to local general budget revenues.

3.3. Data Sources

The data used for this study were obtained from the China City Statistical Yearbook and the China Regional Economic Statistical Yearbook. However, some cities had missing data, which were more severe in certain parts. For instance, due to serious missing data, the Tibet Autonomous Region was excluded from this study. Additionally, some cities experienced changes in their administrative status during the observation period. Therefore, to ensure consistency in the statistical caliber, we excluded the affected cities, such as Tongren, Bijie, Sansha, Haidong, Danzhou, Tulufan, and Hami. After the exclusion, a total of 285 prefecture-level cities were selected as the research sample from 2003 to 2019.

Since the agglomeration of productive service industries mainly occurs in municipal districts, the statistical caliber of each indicator data is based on municipal districts, and any missing indicators in municipal districts were replaced by city-wide data. To fill in the unavailable data, we used linear interpolation and tailed continuous variables at the 1% and 99% levels. The main descriptive statistical results can be found in

Table 2. Summary of descriptive statistics.

Variables	Sample Number	Mean	p50	SD	Minimum Value	Maximum Value
UEP	4845	0.155	0.137	0.077	0.030	0.434
eco	4845	0.161	0.139	0.088	0.0520	0.547
res	4845	0.288	0.282	0.078	0.125	0.614
social	4845	0.150	0.135	0.087	0.010	0.411
agg	4845	0.823	0.804	0.275	0.284	1.674
HUM	4845	0.046	0.034	0.041	0	0.196
LNgov	4845	−1.884	−1.971	0.645	−3.181	0.274
LNLPOP	4845	2.624	2.621	0.055	2.503	2.787
inform	4845	0.265	0.200	0.230	0.021	1.367
market	4845	0.932	0.773	0.623	0.103	3.360
edu	4845	0.179	0.175	0.054	0.067	0.349
reve	4845	0.569	0.561	0.235	0.099	1.201

SD represents the standard deviation.

.

3.4. Flow Chart of the Methodology

The method involves several key steps. The first step is to determine the research topic by reviewing the relevant literature and identifying the necessary indicators and calculation methods. The second step is to extract relevant data from the China Urban Statistical Yearbook and the China Regional Economic Statistical Yearbook and organize it in Excel. The third step is to import the organized data into stata16, fill in any missing data using linear interpolation, and calculate the necessary indicators in Excel. The fourth step is to construct the panel data required for empirical testing. Finally, the fifth step is to test the data using the constructed model to validate the research hypotheses. The flowchart below provides a visual representation of the method (Figure 1).

4. Results and Discussion

Based on the full consideration of data availability and reliability, a comprehensive index system is constructed to measure and measure urban economic performance from three dimensions: economic benefits, resource and environmental benefits, and social benefits. To more clearly describe the dynamic trend of urban economic performance from 2003–2019, this study presents the average urban economic performance of each province as a line graph, as shown in the line graph of urban economic performance (Figure 2).

As can be seen from the urban economic performance line chart, the level of urban economic performance nationwide has fluctuated over the 17-year period from 2003 to 2019, but the overall trend has been steady and progressive. Specifically, the eastern region has the highest level of urban economic performance, and urban economic performance is always higher than the national average, and the development rate decreases after 2013. Analyzing the specific reasons, from the perspective of location, the eastern region is the frontier of reform and opening up, with early and fast economic development, a more complete industrial system, and a more mature factor market, so the efficiency of urban economic development in the eastern region is higher than other regions of the country. As the economy gradually enters the “new normal”, the industrial development is in the painful period of transformation and upgrading, which requires the economic development of each region to be oriented to quality and efficiency and try to avoid the “sloppy” economic development method relied on in the past. Therefore, after 2013, the speed of urban economic development has been reduced, and the level of urban economic performance has been slow. Secondly, although the development level of western and central regions is always lower than the national average, the overall trend is up, and the average urban economic performance level of northeastern regions is the lowest in the country, and the development speed is slow and decreasing. Analyzing the specific reasons, China has implemented the strategy of rising in the central part of the country and the strategy of western development, and the economic development of the central and western regions is good, and they have taken over the industrial transfer from the eastern regions, so the development speed is faster, and the level of urban economic performance is improving, among which, the average urban economic performance of the western regions is slightly higher than that of the central regions, mainly in the western regions of Xinjiang Uygur Autonomous Region, Qinghai Province, Ningxia Hui Autonomous Region, etc. There is a serious lack of data on cities, and most of the available data samples are provincial capitals and local resource-based cities, so the development level of the western region is slightly overestimated. The past development of the northeastern region of China relied heavily on natural resource endowments, and with the depletion of natural resources and a single industrial structure, the economy has been sluggish.

4.1. Overall Analysis of Productive Service Industries

Table 3. The impact of productive service agglomeration on the economic performance of cities.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	UEP	UEP	UEP	ECO	RES	SOCIAL	UEP
agg	0.092 ***	0.078 ***	0.014 ***	0.032 ***	0.004	0.008 *	0.026 ***
	(20.609)	(20.844)	(5.469)	(10.872)	(0.670)	(1.948)	(2.661)
LNgov		−0.021 ***	0.007 ***	0.001	0.005 **	0.004	0.007 ***
		(−12.509)	(3.921)	(0.820)	(2.161)	(1.501)	(3.873)
inform		0.056 ***	0.026 ***	0.006 ***	0.023 ***	0.047 ***	0.026 ***
		(11.781)	(9.274)	(2.606)	(5.510)	(9.745)	(9.195)
edu		−0.086 ***	0.042 **	0.017	−0.020	0.032	0.043 **
		(−5.536)	(2.439)	(1.351)	(−0.969)	(1.436)	(2.460)
reve		0.112 ***	0.018 ***	0.003	0.006	0.024 ***	0.018 ***
		(23.414)	(2.950)	(0.707)	(0.771)	(3.226)	(2.940)
market		0.002	0.002 **	0.005 ***	0.006 ***	0.001	0.002 **
		(1.410)	(2.165)	(5.506)	(3.580)	(0.356)	(2.134)
agg^2							−0.007
							(−1.269)
_cons	0.080 ***	−0.013 ***	0.130 ***	0.125 ***	0.284 ***	0.117 ***	0.125 ***
	(23.762)	(−2.612)	(23.169)	(26.152)	(38.752)	(16.212)	(18.041)
N	4845	4845	4845	4845	4845	4845	4845
Fixed Urban	No	No	Yes	Yes	Yes	Yes	Yes
Fixed time	No	No	Yes	Yes	Yes	Yes	Yes
r2_a	0.107	0.340	0.909	0.914	0.669	0.829	0.909

***, ** and * indicate the significance levels of the parameters at 1%, 5% and 10%, respectively. The given values in the parentheses are the t values.

reports the results of the estimation of the agglomeration of productive service industries on the economic performance of the city. Among them, column (1) shows the regression results considering only the overall production service industry agglomeration on the city’s economic performance, and it can be seen that the estimated coefficient of production service industry agglomeration is significantly positive, indicating that production service industry agglomeration promotes the city’s economic performance; in column (2), all control variables are included, and the results show that, after controlling for numerous factors, production service industry agglomeration still significantly promotes the city’s economic performance. Column (3) examines the relationship after controlling for region and year double fixed effects, in which the coefficient of influence of overall production service industry agglomeration is 0.014, which has a significant positive relationship with city economic performance, with no change in sign and significance level compared with columns (1) and (2). Therefore, the overall agglomeration of production service industry does have a significant positive contribution to the economic performance of the city, and the results are stable. The agglomeration of production service industry improves the economic development efficiency of the same industry and different industries through the economy of scale effect and knowledge and technology spillover effect and promotes the economic performance of the city. Several scholars have expressed similar perspectives, as presented in this paper, with supporting evidence from their respective studies. For instance, Li Bin [1] utilized the spatial Durbin model to showcase the positive impact of both specialized and diversified agglomeration of productive service industries on urban economic performance. Similarly, Gong, Qinlin and Wang, Shuhe [10] contend that, in the western region of China, the agglomeration of productive service industries plays a crucial role in promoting high-quality development of the regional economy. These findings complement and support the results of this study, providing additional justification for the outcomes presented in this paper. In addition, this study adds the quadratic term of the overall agglomeration of productive service industries to model (1) for regression, and the results of the quadratic term are not significant, as shown in column (7), indicating that there is no non-linear relationship between the overall agglomeration of productive service industries and the economic performance of the city (Hypothesis 1 is verified).

Columns (4)–(6) of Table 3 show the regressions after subdividing the city’s economic performance into economic benefits (ECO), resource and environmental benefits (RES), and social benefits (SOCIAL). It can be seen that the overall agglomeration of productive services has the most significant contribution to economic benefits, followed by social benefits, while there is no significant effect on resource and environmental benefits. The possible explanations are as follows: this may be along the lines of the rapid development of the agglomeration of productive service industries. On the one hand, the positive externality brought by the scale agglomeration reduces the unit cost of enterprises, strengthens the correlation between various industries, promotes the formation of service networks, effectively improves the operational efficiency in the production process of productive service industries, and promotes the improvement of economic benefits. On the other hand, it significantly promotes the regional innovation capacity through the spillover effect of talents and technology. The spillover effect of talents and technologies significantly promotes the improvement of regional innovation capacity and the improvement of social benefits. In addition, since China is a large industrial manufacturing country, its economic development has long relied on heavy industries with high energy consumption and high pollution. Although the agglomeration of production service industries has promoted the improvement and diffusion of environmental protection technologies, it cannot fundamentally change the rough economic development mode of China in a short time. Therefore, it is reasonable to say that the promotional effect of the agglomeration of productive service industry on resource and environmental efficiency is not obvious.

Among the control variables, the estimated coefficient of the degree of government intervention (LNgov) is positive at the 1% significance level, indicating that the degree of government intervention has a positive effect on the economic performance of cities, probably due to the fact that China has accelerated the transformation of government functions, decentralized the government, and strived to build a first-class business environment in recent years, which has significantly promoted the regional economic development. The estimated coefficient of market is 0.002, which is positive at the 1% level. This may be explained by the fact that regions with a high level of market are more likely to attract capital, talent, and other factors to the region, and regions with a high level of market have more mature and perfect trading rules, and the market can make autonomous adjustments under the price mechanism to achieve a reasonable allocation of resources. Thus, the Pareto optimum of the economy is achieved. The influence coefficient of informatization (inform) on the economic performance of cities is 0.026, which indicates that the improvement of informatization helps different industries to overcome the limitation of spatial distance and reduce the cost of communication, thus promoting the improvement of economic performance of cities. The strength of scientific and educational support (edu) significantly contributes to the improvement of cities’ economic performance at the 1% statistical level, which may be explained by the fact that, on the one hand, with the increasing government investment in scientific and educational undertakings, a large number of high-quality talents have been cultivated, providing a talent pool for socio-economic development. On the other hand, the increase in scientific research investment helps to enhance cities’ scientific and technological innovation capabilities, promote industrial structure upgrading, and facilitate economic development. The correlation coefficient between government revenue and expenditure status (REVE) and city economic performance is 0.013, which is significant at 1% statistical level, indicating a significant positive relationship between government revenue and expenditure status and improving city economic performance.

4.2. Robustness Test

To determine the reliability and robustness of the regression results, we tested the baseline regression results by endogeneity test, replacing the core explanatory variables and reducing the sample size.

Endogeneity problem: The endogeneity test is conducted using the 2sls method. Considering that along with the continuous improvement of cities’ economic performance, some cities will be the first to become economically developed big cities and become the core of regional development, and these cities, benefiting from the formation of the core position, will have a siphon effect on the surrounding cities, and the high-end productive service industries of the surrounding cities start to gather in the big cities, which accelerates the agglomeration of productive service industries, resulting in a possible reverse causality between the agglomeration of productive service industries and cities’ economic prospects. This accelerates the agglomeration of productive service industries, which may lead to a reverse causality between productive service industry agglomeration and urban economic performance, and this affects the reliability of the estimation results.

This study draws on the approach of some scholars [7,37] to mitigate this endogeneity by using a two-stage panel fixed-effects model with the lagged term of productive service agglomeration (L.agg) as an instrumental variable. The results of model (1) in

Table 4. Results of robustness test (the impact of productive service agglomeration on the economic performance of cities).

	(1)	(2)	(3)	(4)	(5)
Variables	first	2sls
	agg	UEP	UEP	UEP	UEP
L.agg	0.618 ***
	(23.61)
agg		0.018 ***			0.016 ***
		(4.365)			(6.236)
LNgov	−0.000	0.007 ***	0.007 ***	0.007 ***	0.002
	(−0.14)	(4.004)	(3.924)	(3.922)	(1.546)
inform	0.037 ***	0.025 ***	0.027 ***	0.026 ***	0.023 ***
	(3.56)	(8.909)	(9.421)	(9.325)	(8.250)
edu	−0.120 ***	0.050 ***	0.040 **	0.044 **	0.029
	(−2.61)	(2.773)	(2.290)	(2.534)	(1.589)
reve	0.008	0.018 ***	0.018 ***	0.018 ***	0.020 ***
	(0.54)	(2.858)	(2.936)	(3.029)	(3.038)
market	0.019 ***	0.002 *	0.003 ***	0.002 *	0.002 **
	(3.45)	(1.853)	(2.697)	(1.769)	(2.328)
agg1			0.004 ***
			(3.244)
agg3				0.012 ***
				(5.558)
_cons	0.373 ***	0.081 ***	0.140 ***	0.132 ***	0.106 ***
	(11.37)	(10.941)	(26.568)	(23.943)	(18.191)
N	4560	4560	4845	4845	4250
Fixed Urban	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes
r2_a	0.909	0.909	0.908	0.909	0.864
LM statistic	475
Wald Fstatistic	2803.612

***, ** and * indicate the significance levels of the parameters at 1%, 5%, and 10%, respectively. The given values in the parentheses are the t values.

show that the lagged term of productive service agglomeration (L.agg) can be used as an instrumental variable. When examining the correlation between the instrumental variables and the core explanatory variables, the correlation test yields a first stage F-statistic of 558.024 (over 10) and a p-value of 0.000 for the F-statistics. If the significance of the endogenous explanatory variables in the structural equation is subjected to a “nominal significance level” of 5%, the Wald test, with a minimum characteristic statistic of 2803.61, is much larger than the critical value of 8.96, corresponding to the “true significance level” of 15%, which strongly rejects the original hypothesis of weak instrumental variables. Therefore, according to Equation (1), L.agg was regressed as an instrumental variable. As can be seen from column (2) of Table 4, if it is found that, after the endogeneity of the core explanatory variable of productive service agglomeration is reduced, there is still a significant positive effect on the explanatory variable of urban economic performance, and the results are basically consistent with the baseline regression results, and the coefficients are significantly improved, then this indicates that the estimation results of the two-way fixed effects model are robust and valid.

Substitution of core explanatory variables: Following the study of Wang et al. [48], the degree of agglomeration of productive service industries in the city is measured by the proportion of total employment in productive service industries to total employment in tertiary industries, calculated as follows:

$${agg}_1=\frac{E_{it}}{Q_i}$$

(5)

where $E_{it}$ denotes the number of people employed in the city’s total productive service industry in year $t$. The inclusive definition of the productive service industry in this context encompasses the nine industries that were previously enumerated in the paper, and $Q_i$ denotes the number of people employed in the tertiary industry in the city. At this point, only the core explanatory variables are transformed into the degree of productive service industry agglomeration measured by Equation (5), and the results of the benchmark regression are shown in column (3) of Table 4. It can be seen that, after replacing the core explanatory variables, there is still a significant positive correlation between productive service industry agglomeration and city economic performance, which is consistent with the benchmark regression, indicating that the benchmark results have good robustness.

In addition, many scholars believe that productive service industries mainly include transportation, storage and postal services, leasing and business services, finance, scientific research, technical services and geological exploration, and information transmission, computer services, and software, which are different from this paper. As a result, the study has included the five aforementioned industries in Equation (4) to compute the agglomeration index for the productive service industry, referred to as agg3. The regression results for agg3 are presented in column (4) and are consistent with the coefficients obtained for the overall agglomeration of productive service industries on urban economic performance. This further strengthens the conclusion that the initial regression findings are robust.

Reduced sample size: Since municipalities are directly under the central government, provincial capitals and planned cities themselves have higher political status, and these cities have a more robust institutional environment, greater potential for market cooperation, higher level of openness to the outside world, and better urban infrastructure development than other prefecture-level cities. Due to the special characteristics of these research samples, which may cause bias in the results of this paper, this paper decides to exclude the samples of municipalities directly under the central government, provincial capitals and planned cities, and conducts a two-way fixed effects test with fixed cities and years. The regression results are shown in column (4) of Table 4, and the sign and significance do not change significantly compared with the baseline regression, indicating that the research findings are robust.

4.3. Heterogeneity Analysis

The study above reveals that the agglomeration of productive service industries has a considerable impact on enhancing the economic performance of cities. Nonetheless, it remains unclear whether this effect is consistent across different industries and regions. This study aims to explore the possible effects of heterogeneity from two perspectives: industry and region. Specifically, the study will investigate whether there are differences in the promotion effect on the economic performance of cities among various industries and regions.

4.3.1. Heterogeneity Analysis for Industry

The agglomeration of productive service industries in different industries may have heterogeneous effects on urban economic performance, and this study draws on some scholars [49] to classify productive service industries into high-end productive service industries and low-end productive service industries based on factors, such as R&D investment and technology investment, and the specific results are shown in

Table 5. Results of industry heterogeneity analysis (the impact of productive service agglomeration on the economic performance of cities).

Variables	(1)	(2)
	UEP	UEP
highagg	0.012 *
	(1.924)
LNgov	0.007 ***	0.007 ***
	(3.914)	(3.913)
inform	0.027 ***	0.027 ***
	(9.520)	(9.481)
edu	0.040 **	0.040 **
	(2.295)	(2.274)
reve	0.018 ***	0.018 ***
	(3.046)	(2.964)
market	0.003 ***	0.002 **
	(2.641)	(2.569)
lowagg		0.000
		(0.022)
_cons	0.139 ***	0.142 ***
	(25.726)	(26.289)
N	4845	4845
Fixed Urban	Yes	Yes
Fixed time	Yes	Yes
r2_a	0.908	0.908

***, ** and * indicate the significance levels of the parameters at 1%, 5% and 10%, respectively. The given values in the parentheses are the t values.

. The coefficient of the effect of high-end productive service industry on urban economic performance is 0.012, which is significantly positive at 1% statistical level, indicating that high-end productive service industry agglomeration has a significant positive contribution to urban economic performance, while the effect of low-end productive service industry agglomeration on urban economic performance is not significant. Some scholars have come up with views similar to this paper through their studies, and Zhang Haoran [50] used a panel threshold model to explore the relationship between productive service industry agglomeration and urban economic efficiency, and it was concluded that high-end productive service industry agglomeration can significantly contribute to the improvement of urban economic efficiency, while low-end productive service industry agglomeration does not have a significant impact on urban economic performance, showing an inverted U-shaped change with the change in economic volume. Wen [10] argued that high-end productive service industry agglomeration can significantly contribute to the improvement of the quality of China’s economic growth, while the enhancement effect of low-end productive service industry agglomeration is less obvious, which is basically consistent with the findings of this study. The possible reason is that the high-end productive service industry is characterized by high knowledge and technology intensity, which contributes more to the spillover of knowledge and technology than the low-end productive service industry agglomeration, and the spillover effect will be transmitted among different industries and the same industries to promote technological change in the related industries, while the low-end productive service industry is generally dominated by labor-intensive industries and lacks innovation ability and technology level, as well as its contribution to the city’s economic performance. The promotional effect of low-end production service industries is not obvious.

4.3.2. Regional Heterogeneity Analysis

Due to the differences in factor endowment, geographic location, and economic development of each region in China, the impact of productive service industry agglomeration on urban economic performance is regionally heterogeneous. This study adopts the regional division proposed by Wang and Jiang [8] and Yu et al. [50], and based on its own characteristics, divides China into four regions—Eastern, Central, Western, and Northeastern—for further analysis. The productive service industry is then grouped into regressions for each of the four regions, namely, Eastern, Central, Northeastern, and Western China.

Columns (1)–(4) of

Table 6. Results of regional heterogeneity distribution (the impact of productive service agglomeration on the economic performance of cities).

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	East	Central	Western	Northeast	Resource-based	Non-resource-based
	UEP	UEP	UEP	UEP	UEP	UEP
agg	0.011 *	0.015 ***	0.019 ***	0.018 **	0.010 ***	0.016 ***
	(1.924)	(3.693)	(3.867)	(2.354)	(2.845)	(4.403)
LNgov	0.011 ***	0.002	0.004 **	0.002	−0.002	0.012 ***
	(2.697)	(0.674)	(1.977)	(0.642)	(−0.800)	(4.568)
inform	0.024 ***	0.030 ***	0.014 ***	0.024 **	0.026 ***	0.026 ***
	(4.947)	(5.563)	(3.287)	(1.980)	(5.862)	(7.107)
edu	0.012	0.074	0.040 **	0.008	0.010	0.060 **
	(0.461)	(1.328)	(2.283)	(0.289)	(0.648)	(2.090)
reve	0.022 ***	0.029	0.008	0.002	0.020 ***	0.018 **
	(2.855)	(1.370)	(1.167)	(0.231)	(3.648)	(2.008)
market	0.000	0.003	0.001	0.003	0.001	0.002 *
	(0.192)	(1.429)	(0.935)	(1.228)	(0.660)	(1.799)
_cons	0.169 ***	0.105 ***	0.112 ***	0.122 ***	0.099 ***	0.152 ***
	(16.916)	(5.110)	(17.699)	(17.536)	(21.945)	(16.482)
N	1479	1360	1428	578	1955	2890
Fixed Urban	Yes	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes	Yes
r2_a	0.918	0.892	0.902	0.927	0.852	0.917

***, ** and * indicate the significance levels of the parameters at 1%, 5% and 10%, respectively. The given values in the parentheses are the t values.

report the estimation results for the eastern, central, western, and northeastern samples, respectively, and show that the concentration of productive service industries significantly contributes to the improvement of urban economic performance in the eastern, central, northeastern, and western regions of China. The contribution is increasing from east to central, to northeast, to west. One possible explanation for this finding is that the Eastern region of China began its economic reform and opening up earlier, resulting in a more established industrial system, a more mature factor market, and a higher degree of concentration in productive service industries. Some small and medium-sized cities have taken over the industrial transfer from big cities, so the degree of agglomeration of production service industry in the eastern region has been reduced. In the central and western regions, especially in the northeast, the economic development has been slow in recent years, and the manufacturing industry has been more concentrated. As a modern service industry, the production service industry mainly provides intermediate products and corresponding intermediate services for the manufacturing industry. Therefore, it naturally has to contact its corresponding industry as much as possible. The promotion of urban economic development is more obvious.

In addition, some scholars have reached the opposite conclusion from this paper through their research, and Wen [10] argued that due to the overall low level of development of the industrial structure in the central and western regions, the role that the agglomeration of productive service industries can play in the region is limited, and at the same time, the agglomeration of a large number of unrelated industries leads to the waste of resources, which is instead detrimental to the improvement of the quality of economic growth in the central and western regions. The result of this study is contradicted with Wen’s analysis of productive service industry agglomeration due to differences in the approach to measuring agglomeration indicators. To be more precise, Wen’s analysis was centered on Jacobs’ externality, whereas this study utilizes an alternative approach.

4.3.3. Heterogeneity Analysis of the Degree of Resource Endowment

China is a country rich in natural resources, and, along with the economic development, a number of resource-based cities have gradually formed, and their number accounts for about 40% of the total number of cities in China. The biggest difference between resource-based cities and non-resource-based cities is that resource-based cities take natural resource extraction and processing as the leading industry, and their development is extremely dependent on the degree of local natural resource endowment. On the one hand, the low technological content contained in industries with natural resource extraction and processing as the leading business leads to insufficient demand for highly skilled talents in cities with mainly resource-consuming industries, resulting in brain drain, which is not conducive to knowledge spillover and technological innovation in the region and limits the further development of the regional economy. On the other hand, the economic development approach that focuses on high input and energy consumption could result in enterprises having to channel a majority of their resources towards daily production activities, which could leave limited opportunities for investing in talent training, research and development, and innovation. As a result, the question arises whether there are any differences in the impact of productive service industry clustering on the economic performance of non-resource-based cities and resource-based cities. To address this, the study refers to the National Sustainable Development Plan for Resource-based Cities (2013–2020), issued by the State Council, and it classifies the sample cities accordingly. Ultimately, the full sample of cities is divided into 115 resource-based cities and 170 non-resource-based cities for analysis. Columns (5) and (6) in Table 4 reported the base regression results for the resource-based city sample and the non-resource-based city sample, respectively. The results show that the coefficients of the impact of the overall agglomeration of productive service industries on the economic performance of cities are 0.010 and 0.016, respectively, both of which are statistically significant at the 1% level when distinguishing between resource-based cities and non-resource-based cities. It indicates that the agglomeration of productive service industries in non-resource-based cities is more effective in promoting regional economic development compared to resource-based cities.

4.4. Mechanism of Action Test

The theoretical analysis above argues that the agglomeration of productive service industries promotes the improvement of urban economic performance by enhancing the level of human capital. Therefore, to reveal whether the level of human capital plays a mediating role in the process of overall agglomeration of productive service industries affecting urban economic performance, this section will empirically test the above possible mechanisms using a mediating effect model, which is subdivided as follows:

First, the regression of model (1) is conducted to verify the impact of overall agglomeration of production service industry on urban economic performance. Second, the regression of model (2) is conducted to verify the impact of overall agglomeration of production service industry on human capital level. Finally, human capital level is included in model (3) to verify the mediating role of human capital level in the process of overall agglomeration of production service industry to promote urban economic performance, as well as to see whether it exists or not.

According to the mediation effect test procedure proposed by Baron and Kenny (1986), the regression results shown in

Table 7. Results of the mechanism (the impact of productive service agglomeration on the economic performance of cities).

	(1)	(2)	(3)
Variables	UEP	HUM	UEP
agg	0.0139 ***	0.0072 ***	0.0094 ***
	(5.469)	(4.272)	(4.119)
LNgov	0.0070 ***	−0.0009	0.0076 ***
	(3.921)	(−0.922)	(4.300)
inform	0.0263 ***	0.0175 ***	0.0153 ***
	(9.274)	(8.971)	(6.539)
edu	0.0425 **	−0.0195 **	0.0546 ***
	(2.439)	(−2.437)	(3.247)
reve	0.0178 ***	0.0051 **	0.0146 **
	(2.950)	(2.219)	(2.508)
market	0.0021 **	−0.0011 *	0.0027 ***
	(2.165)	(−1.857)	(3.176)
HUM			0.6247 ***
			(21.962)
_cons	0.1304 ***	0.0350 ***	0.1085 ***
	(23.169)	(14.287)	(19.550)
N	4845	4845	4845
Fixed Urban	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes
r2_a	0.9089	0.8652	0.9239

***, ** and * indicate the significance levels of the parameters at 1%, 5% and 10%, respectively. The given values in the parentheses are the t values.

are analyzed step by step. In the first step, model (1) is regressed, and the regression results are shown by column (1) of Table 7. The results show that the coefficient of influence of the overall agglomeration of productive service industries is 0.0139, which is significant at the 1% statistical level. Thus, it is clear that the overall agglomeration of productive service industries has a significant positive contribution to the city’s economic performance, and the first condition of the mediating effect is satisfied. In the second step, model (2) is regressed, and the results are presented in column (2) of Table 7. The results show that the overall agglomeration of productive service industries has a significant positive contribution to human capital level, and the correlation coefficient is 0.0072, which shows that the hypothesis 2 of this paper is proved, and the second condition for the mediating effect is satisfied. The regression results of model (3) are shown in column (3) of Table 7. The results show that the coefficient of influence of the overall agglomeration of productive service industries decreases to 0.0094 after adding the mediating variable human capital level, which is statistically significant at the 1% level, and the human capital level has a significant contribution to the economic performance of the city with an influence coefficient of 0.6247, and the third condition for the mediating effect to be established is satisfied. Therefore, according to the test method of intermediary effect, it is known that the level of human capital plays a partly intermediary role in the process of overall agglomeration of productive service industries to promote the economic performance of cities. In addition, according to the formula of the mediating effect, it is concluded that the mediating effect of human capital level accounts for 32.3% of the total effect.

5. Conclusions and Policy Implications

5.1. Conclusions

This study utilizes panel data from 285 prefecture-level and above cities in China between 2003 and 2019. A comprehensive evaluation system for urban economic index is constructed, considering three dimensions of social, economic, and resource-environmental benefits. Through an empirical analysis using a two-way fixed-effect model that controls for city and year, and with the mediator of human capital level, the impact of productive service industry agglomeration on urban economic performance is examined. The study also tests whether human capital level can act as a mediator between the agglomeration of productive service industries and the economic performance of cities. The main findings of the study are as follows.

First, there is a significant positive relationship between productive service industry agglomeration and urban economic performance, and the findings remain robust after overcoming the endogeneity problem, in which productive service industry agglomeration promotes economic benefits most significantly, followed by social benefits, while it has no significant effect on resource and environmental benefits. Moreover, the degree of government intervention, the level of marketization, the degree of informatization, the level of scientific and educational support, and the government revenue and expenditure are all conducive to the improvement of urban economic performance.

Second, by industry, the agglomeration of high-end productive service industries significantly contributes to the improvement of urban economic performance, while there is no significant relationship between the agglomeration of low-end productive service industries and urban economic performance, indicating that the agglomeration of high-end productive service industries is more effective in improving urban economic performance; by region, the agglomeration of productive service industries significantly contributes to the improvement of urban economic performance in the eastern, central, northeastern, and western regions, and the improvement effect is in descending order. According to the analysis of the heterogeneity of resource endowments, the agglomeration of production service industries has a more significant effect on the economic performance of non-resource-based cities than resource-based cities.

Third, the results of the mechanism of action test indicate that the level of human capital plays a partly mediating role in the overall agglomeration of productive service industries to promote the economic performance of cities, yielding a mediating effect of human capital level accounting for 32.3% of the total effect. In other words, the agglomeration of productive service industries can promote the improvement of urban economic performance by increasing the level of human capital.

5.2. Policy Implications

The study proposes following policy implications.

• To promote urban economic performance by utilizing the productive service industry as an intrinsic driving force, reasonable agglomeration of this industry should be guided. The government can strengthen its agglomeration by following market laws and encouraging the development of productive service industries that match local comparative advantages. Additionally, the government can break barriers to inter-regional factor flow, provide a conducive institutional environment, and standardize the market organization system for productive service industries based on their knowledge and innovation characteristics.
• It suggests that agglomeration of the production service industry should consider location conditions and each city should formulate policies according to its own resource endowment conditions and market demand structure. In the eastern region, information-based and networked systems should be established, while high-end production service industries should be developed. In the central and western regions, reducing constraints on market access and administrative approval should be a focus, and innovative enterprises should receive policy support to improve technological innovation capacity and form local industrial characteristics.
• It is necessary to improve the level of human capital to promote technological innovation and the transfer of new knowledge, as well as to fully release the positive external effect of the agglomeration of productive service industry to improve the economic performance of the city. In addition, enterprises should cooperate with local governments to establish higher education institutions and scientific research institutions to attract talents with the advantages of housing subsidies and economic subsidies, combine the cultivation of talents and labor force with industrial development, provide matching human resources for the effective development of the agglomeration effect of productive service industry, promote enterprise innovation with the knowledge spillover effect between industries, and thus improve the economic performance of the city.

5.3. The Limitations and Recommendation for Future Studies

The study has certain limitations, and suggestions for future research are as follows.

Firstly, since there are not many studies on the relationship between productive service industry agglomeration, human capital level and urban economic performance, the literature that can be referred to in this study is relatively small, and the theoretical depth needs to be improved.

Secondly, in the selection of indicators for measuring urban economic performance, domestic and foreign scholars have not yet reached a consensus, and this study adopts the entropy value method to calculate comprehensive indicators as an alternative indicator of urban economic performance, which still needs to be supplemented or reduced by subsequent scholars according to the needs of research and reality.

Thirdly, the administrative areas of prefecture-level cities changed a lot in 2019 compared with the years after it, making it difficult to obtain data for some prefecture-level cities. To maintain a more reasonable sample size of prefecture-level cities, this study makes data selection only up to 2019, which has a certain time lag with the publication time of this study, making this paper inadequate. On this basis, subsequent studies can be conducted on the basis of processing data or reducing the sample of cities by extending the years to enrich the relevant research.

Fourthly, to focus on the productive service industry in certain areas, this study used data from specific municipalities instead of the entire cities, but the data were limited. To fill in missing data, linear interpolation or substitution was used, affecting accuracy. Using one-period lagged data as instrumental variables may also impact estimation accuracy. Future research should address these shortcomings and improve upon this study.