The Impact of Public Employment Services Efficiency on the Urban Rural Income Gap and Its Spatial Spillover Effect

Abstract

Urban rural integration is an indispensable prerequisite for achieving the objective of regional sustainable development, and exploring the impact of public employment services (PES) efficiency on the urban rural income gap(URIG) and its spatial spillover effect is of great significance for promoting integration in the new era. This study uses provincial panel data from 2011 to 2022 to explore in depth the impact of PES efficiency on the URIG and its spatial spillover effects by measuring and analyzing PES efficiency and its regional differences and dynamic evolution. It is found that the improvement of PES efficiency can significantly reduce the URIG, and the impact of PES efficiency on the URIG has spatial spillover effects. Therefore, in the future, the system of PES should be improved, the bias of PES should be reversed and the model of PES should be innovated in order to reduce the URIG.

1. Introduction

Income inequality has long been an important issue that continues to receive attention and is crucial to maintaining social stability and promoting sustainable development. The tenth goal of the United Nations Sustainable Development Goals (SDGs) is to “reduce inequality within and among countries”, which includes reducing income inequality. Income inequality is prevalent globally, with a particularly significant impact on developing countries. As the largest developing country, the main cause of income inequality in China is the URIG [1]. according to the National Bureau of Statistics, the ratio of disposable income per capita for urban and rural residents will be 2.39 in 2023, which is lower than the 2.45 seen in 2022, indicating that China’s income distribution has improved, and the absolute gap between urban and rural incomes continues to shrink. However, China’s URIG is still in the risky range of 2.0~2.5, which is located at the middle low level globally, and the problem of uneven income distribution between urban and rural areas is more serious. Urban rural integration represents an advanced phase in the evolution of urban-rural relationships, which implies coordinated development between urban and rural areas in terms of economy, society, culture, ecology, space, and policy (system) [2]. Wide URIG is contrary to the connotation of integration and impedes the realization of the goal of integration. To achieve this important goal, we need to pay special attention to the development of rural areas and solve the problem of uneven development by narrowing the URIG, so that the fruits of reform and development can benefit all the people in a more extensive and equitable way.

The main reason for the formation of the URIG in China lies in the influence of non-market factors, especially the unequal distribution of essential public services between urban and rural regions [3]. As an important content of basic public services, PES, primarily in the form of active labor market programs, provides the workforce with improved employment income and increased opportunities for future employment through the provision of a range of employment service efforts [4,5]. Employment and income-generating ability are the core supporting forces for maintaining the survival and development of workers, and workers may be restricted in their employment and income-generating ability due to subjective and objective factors and then easily fall into the marginal predicament of survival and development. In the process of promoting integration, vulnerable labor groups with insufficient employment and income-generating capacity, especially rural labor, need to be given priority attention [6]. They have a direct impact on whether the URIG can be narrowed in a real sense, optimizing the pattern of social income distribution, which therefore requires external intervention strategies to provide assistance. PES, as an important tool for the government to regulate and intervene in the labor market, will tilt more resources for PES to rural areas to enhance the employment and income-generating capacity of the rural residents, thus providing more possibilities for solving the problem of the URIG. With the booming development of the social economy, the construction of the PES system in China has made remarkable progress in recent years. According to the Ministry of Human Resources and Social Security, China’s financial expenditure on social security and employment has been on an upward trend for many consecutive years, increasing from CNY 10,606.92 billion in 2011 to CNY 38,828.24 billion in 2023. There are currently more than 4400 PES institutions nationwide, serving an average of 110 million people annually. However, it is unrealistic to narrow the URIG by merely relying on a large-scale increase in financial investment in PES and infrastructure construction, and it is more important to improve service efficiency based on limited PES resources than to simply increase inputs [7]. Then, in the process of promoting integration, what is the development status of PES efficiency in China? What kind of regional distribution differences and dynamic evolution characteristics does it present? How does the efficiency of PES affect the URIG? It has become an important topic that needs to be studied urgently.

Therefore, this study applies the DEA Malmquist index model to measure China’s PES efficiency from 2012 to 2022, uses the Dagum Gini coefficient and kernel density estimation to further explore its regional differences and dynamic evolution and then examines the impact of PES efficiency on the URIG and its spatial spillover through the spatial Durbin model effect. This study effectively complements and improves the existing indicator system for the efficiency of PES and injects new content into the research related to PES and the URIG with a view to providing a theoretical basis and empirical support for developing countries to formulate targeted PES development strategies and implement differentiated development policies.

2. Literature Review and Research Hypothesis

2.1. Literature Review

2.1.1. PES Efficiency

Currently, there exists a notable scarcity of academic research on PES efficiency, which focuses more on public service efficiency, covering areas such as basic education, health care, social security and public facilities. Although the system of PES has been commonly established in countries around the world, a unified measurement standard for PES efficiency has not yet been established.

According to Millard and Mortensen (1997), the matching degree model they developed revealed a direct link between PES efficiency and the effectiveness of the matching process in the labor market [8]. Colley (2014) identified a link between changes in public employment policy and changes in the age profile of the workforce in an Australian state and found that the current public employment policy is no longer focused on youth workforce employment, which is driving the public workforce towards an older and more multi-aged workforce [9]. Rehwald et al. (2017) examined the outsourcing of employment services by a group of highly educated job seekers through a randomized field experiment implemented in Denmark, revealing that the unemployed who receive public or private employment services do not exhibit significant differences in employment rates [10]. Baños et al. (2019) applied a stochastic matching frontier approach to analyze the efficiency of PES labor offices in Spain and found that the relative technical efficiency of these labor offices is at an acceptable level [11]. The study by Cichowicz et al. (2021) measured the efficiency of PES institutions in Mazovia Province, Poland, through the two-stage Data Envelopment Analysis (DEA) method, and revealed a significant correlation with the share of unemployed people of different age groups in the total unemployed population [12].

2.1.2. PES Efficiency and the URIG

The formation of the URIG is the result of a combination of factors, including economic development [13,14], urbanization [15,16], foreign investment [17], and transportation infrastructure [18], etc., among which the role of public services [19,20] has been a key area of academic research for a long time, but studies exploring the relationship between the efficiency of PES and the URIG are are relatively few.

In examining the role of PES efficiency on the URIG, two mainstream views have gradually emerged in the academic community. One view is that the increase in PES efficiency will widen the URIG. The theory of urban bias put forward by Lipton (1977) argues that the urban bias policy adopted by developing countries in the development of industrialization and urbanization is the main reason for the widening of the gap and the aggravation of rural poverty [21]. There is not only a serious urban bias in the provision of basic public goods in China, but also an economic bias, both of which are crucial factors that contribute to the formation and subsequent widening of the URIG [22]. This bias is also reflected in the field of PES, where the resources of PES are substantially tilted towards urban areas, bringing about a short-term increase in PES efficiency. Moreover, there exist notable disparities between urban and rural residents in basic rights, such as more employment information, vocational training and employment assistance, etc., and this difference will expand the advantage of urban residents in the initial distribution and redistribution, which will lead to the formation and expansion of the URIG [23]. Another viewpoint is that the improvement of PES efficiency will reduce the URIG. Aaberge and Langørgen (2006) argued that public services can alleviate the problem of uneven income distribution in the long run [24], which is also applicable to the field of PES. Tiebout’s (1956) “voting with feet” theory states that residents will choose where to live based on the public services provided in different areas [25]. This selection process will enable policymakers to rationally optimize the allocation of rural resources and enhance the efficiency of PES in rural areas in order to increase the income of rural residents and reduce the gap. Improving the efficiency of PES, which contributes to increasing the employability skills of the low-skilled unemployed, especially the relatively disadvantaged rural population to reach higher income levels, is important in addressing social and regional imbalances [26]. The improvement of PES efficiency will enhance the development capacity and demographic quality of rural residents so as to enable them to participate effectively in the labor market, thus gradually realizing the convergence of urban and rural residents’ incomes.

Existing studies have the following room for exploration: First, there are insufficient quantitative studies on PES efficiency at the national level, and most of them have focused on specific regions or specific types of PES institutions, without fully considering factors such as the status of financial input and the level of human resource security, which results in a lack of comprehensiveness and wholeness in the measurement results. Second, PES is a key part of the public service system, yet there are few studies that have explored the relationship between its efficiency and the URIG, so studies on the relationship between public services and the URIG are valuable references for this study.

2.2. Mechanism Analysis and Research Hypothesis

2.2.1. The Impact of PES Efficiency on the URIG

The system of PES is a comprehensive service system that mainly covers services in the areas of vocational training, vocational skills appraisal and vocational introduction. Specifically, vocational training services refer to education and training activities to improve the vocational skills and employability of workers. Through vocational training services, rural residents can improve their vocational skills and update their professional knowledge, adapt more quickly to changing labor market demands [27], and better achieve career development and wage income growth, thus bridging the income gap with urban residents. Vocational skills appraisal service refers to the activities of specialized institutions to evaluate and certify the professional knowledge and skill level of workers in accordance with the standards and procedures stipulated by the state. Through vocational skills appraisal services, rural residents are able to accumulate human capital and form a huge employment advantage in the labor market, positively affecting employment opportunities and wage income, which alleviates the problem of income inequality between urban and rural areas. Vocational introduction service is an activity that provides intermediary services for laborers’ job search efforts and employers’ recruitment. Through vocational introduction service, it helps to establish an effective link between workers and employers, expand the channels of employment information search, and increase the probability of matching between rural labourers and high-quality vacancies [28], so as to increase the level of income and reduce the URIG.

The efficiency of PES institutions involved in these services is usually directly related to the status of fiscal inputs, the level of human resource security, and the density of PES institutions. Public expenditure is an important tool for local governments to regulate macroeconomic and income distribution patterns [29]. By correcting the urban bias in fiscal expenditure on PES, the efficiency of PES in rural areas can be improved, creating more equal employment conditions for rural residents, and contributing to narrowing the income gap between urban and rural areas and promoting social equity. The higher the level of the human resource security of PES institutions is, the greater the degree to which it helps to realize the rational allocation and efficient use of human resources, ensuring that PES institutions operate more efficiently, providing more specialized PES, which helps to provide more high-quality employment opportunities for rural residents and facilitate the gradual reduction of the URIG. The increase in the density of PES institutions means that the coverage and accessibility of PES will be improved so that more rural residents can have convenient access to PES, which can have immediate effects on their employment and income, and further reduce the URIG. Based on this, this research puts forward the following research hypothesis:

Hypothesis 1 (H1).

Improving PES efficiency can significantly reduce the URIG.

2.2.2. Spatial Spillover Effects of PES Efficiency on the URIG

As a result of the cross-regional mobility of production factors, spatial segmentation is gradually fading and inter-regional linkages are becoming increasingly strong [30], thus, the PES efficiency of different regions may be spatially correlated with the URIG. On the one hand, the URIG itself has spatial externalities. The differences in the economic foundation and development stage of each region, such as the degree of perfection of industrial layout and the speed of urbanization advancement, lead to the uneven development of the URIG in space and have mutual influence in space [31]. On the other hand, PES, as a bridge and link for integration, will form spatial spillover impacts on neighboring regions through labor mobility and resource redistribution and then regulate the urban-rural income imbalance. When the efficiency of PES in a region is high, the radiation effect on the neighboring regions will be significantly enhanced, showing the development trend of “one glory, all glory”, and positively affecting the efficiency of PES in the neighboring regions. The improvement of PES efficiency will optimize the employment environment of the region and neighboring regions, promote the exchange and sharing of employment resources, and attract workers from other regions to find employment opportunities. This large inflow of labor not only injects new vitality into the economic development of the two regions but also increases job opportunities and broadens the income sources for rural residents [32]. At the same time, the resources of PES are constantly tilted to rural areas, through the provision of specialized employment information, skills training, entrepreneurial guidance and other services, the employability and entrepreneurial willingness of rural residents have been significantly improved, which not only promotes the effective transfer of rural surplus labor [33], but also promotes the diversified development of the rural economy and improves the income level of rural residents, which in turn makes the URIG between different regions gradually narrow. This not only promotes the effective transfer of rural surplus labor, but also promotes the diversified development of the rural economy and increases the income level of rural residents, which in turn makes the URIG between different regions gradually narrow. On this basis, the following research hypothesis is proposed in this study:

Hypothesis 2 (H2).

The effect of PES efficiency on the URIG has spatial spillovers.

3. Research Design

3.1. Data Sources

In this research, 31 provinces, autonomous regions and municipalities directly under the central government of China are selected as the research object, and the relevant data are obtained from China Statistical Yearbook, China Labor Statistical Yearbook, China Population and Employment Statistical Yearbook and China Fiscal Statistical Yearbook for the period of 2012–2023, in which some of the missing values are filled in by linear interpolation. Since the compilation of statistical yearbooks involves the collection, collation and analysis of a large amount of data, their publication usually lags behind the year recorded by one year, so the actual time span corresponding to the sample data is 2011–2022.

3.2. Variable Descriptions

The independent variable in this study is PES efficiency (PESE). PES efficiency measurement is a multi-input, multi-output project. Considering that the data envelopment analysis (DEA) model is widely used in public services in many fields such as education, health care, culture and sports, and has the advantages of not needing to preset weights, avoiding conflicts in the weights of the indicators and distinguishing between the efficiencies of different scales, this study chooses the input-oriented DEA-BCC model is used to analyze the static efficiency of PES. The DEA model can only reflect the efficiency of decision-making units in a specific period, so this study introduces the Malmquist index on the basis of the DEA model for dynamic efficiency analysis. In view of the current situation of China’s PES, therefore, when constructing PES efficiency indicator system, in addition to drawing on the previous measurement of vocational training and vocational introduction [34,35], this study also follows the principle of selecting indicators of comprehensiveness, objectivity, comparability, and operability, and adds the indicators related to financial input of PES and related indicators of vocational skills appraisal services to reflect the efficiency of PES in China more comprehensively. The system of PES efficiency indicators is shown in

Table 1. PES efficiency indicator system.

Dimension	Level 1 Indicators	Level 2 Indicators
Input	Financial input status	PES expenditure accounted for the proportion of total financial expenditure
		Per capita PES financial expenditure
		Vocational training institutions accounted for the proportion of income from financial subsidies
		Per capita financial subsidies for vocational training
	Level of human resources security	The proportion of full-time teachers for vocational training
		The ratio of teachers and students to the number of vocational training attendances in the employment training institutions
		Vocational accreditation agencies accounted for the proportion of appraisal personnel in the number of appraisal and assessment
Output	The density of vocational training service	The number of vocational training centers per 10,000 job seekers
		The number of private vocational training institutions per 10,000 job seekers
	The density of vocational skills appraisal institutions	The number of vocational skills appraisal institutions per 10,000 job seekers
	The density of vocational introduction institutions	The number of vocational introduction institutions per 10,000 job seekers

, and the input dimension is mainly selected from the financial input status and the level of human resources security, the financial input status is the basis and source of China’s PES, and the level of human resources security is also an important guarantee for the smooth operation of China’s PES, so both are the key indicators for measuring its input status. Specific indicators include (1) PES expenditure accounted for the proportion of total financial expenditure; (2) per capita PES financial expenditure (million yuan); (3) vocational training institutions accounted for the proportion of income from financial subsidies; (4) per capita financial subsidies for vocational training (million yuan); (5) the proportion of full-time teachers for vocational training; (6) the ratio of teachers and students to the number of vocational training attendances in the employment training institutions; (7) vocational accreditation agencies accounted for the proportion of appraisal personnel in the number of appraisal and assessment. Since the density of PES institutions serves as a significant factor influencing the accessibility and effectiveness of China’s PES, the output dimension mainly reflects the output level of China’s PES in terms of the density of vocational training service institutions, the density of vocational skills appraisal institutions and the density of vocational introduction institutions. Specific indicators include (1) the number of vocational training centers per 10,000 job seekers; (2) the number of private vocational training institutions per 10,000 job seekers; (3) the number of vocational skills appraisal institutions per 10,000 job seekers; (4) the number of vocational introduction institutions per 10,000 job seekers.

The dependent variable of this study is the URIG. The methods of measuring the URIG are more diverse, with absolute gap, relative gap, Gini coefficient, Tel index, etc., being common indicators for measuring the URIG in a country or region [36]. This study draws on the studies of Yang et al. (2022), selecting the ratio of disposable income per capita of urban and rural residents to measure the URIG [37].

Considering the existing studies [38,39,40,41] and the availability of data, this research introduces urbanization (Urbanization), the level of economic development (Economy), industrial structure (Structure), fixed asset investment (Asset), trade openness (Trade), transportation accessibility (Transportation), foreign direct investment (Foreign), the level of scientific and technological development (Science) and fiscal expenditure (Finance) as control variables. Looking at it specifically (1) Urbanization is measured using the proportion of urban population to the year-end population. The urbanization rate is the key factor affecting the URIG. (2) The level of economic development is measured using the logarithm of per capita GDP. As the country’s economic strength continues to leap, the pattern of income distribution between urban and rural residents will be gradually improved. (3) Industrial structure is measured by the proportion of the added value of the tertiary industry to GDP. The industrial structure affects labor productivity through the scale effect, which in turn has an impact on the income of urban and rural residents. (4) Fixed asset investment is measured using the growth rate of fixed asset investment. Increasing fixed asset investment may increase employment opportunities and attract rural labor transfer, thus affecting the URIG. (5) Trade openness is measured by the total import and export of goods as a share of GDP. The impact of trade openness on the URIG is complex and may vary by region, economic structure and policy environment. (6) Transportation accessibility is measured using mail operations per capita (pieces). Strengthening transportation infrastructure in rural areas is important for promoting interaction between urban and rural areas and driving rural development. (7) Foreign direct investment is measured using the proportion of total foreign investment to GDP. Foreign direct investment affects the URIG in a variety of ways, including increased employment opportunities, technology transfer, industrial upgrading, and regional economic development. (8) The level of scientific and technological development is measured by the proportion of R&D expenditures to GDP. Scientific and technological progress is the driving force of economic growth, which will exacerbate or alleviate the URIG.(9) Fiscal expenditure is measured by the proportion of general public budget expenditure to GDP. The government’s urban tendency of fiscal expenditure will cause the income imbalance. The descriptive statistical results of the above variables are shown in

Table 2. Descriptive statistics results for variables.

Variable	Variable Description	Mean	Std
PESE	PES efficiency measured by DEA Malmquist index model	1.020	0.563
URIG	The ratio of disposable income per capita of urban and rural residents	2.569	0.407
Urbanization	The proportion of urban population to the year-end population	0.598	0.127
Economy	The logarithm of per capita GDP	6.211	3.564
Structure	The proportion of added value of the tertiary industry to GDP	0.505	0.233
Asset	The growth rate of fixed asset investment	0.093	0.117
Trade	The total import and export of goods as a share of GDP	0.279	0.364
Transportation	Mail operations per capita	5.826	1.114
Foreign	The proportion of total foreign investment to GDP	0.856	4.196
Science	The proportion of R&D expenditures to GDP	0.011	0.012
Finance	The proportion of general public budget expenditure to GDP	0.276	0.208

.

3.3. Model Construction

3.3.1. DEA Malmquist Index Model

In order to ensure the objectivity and scientificity of PES efficiency evaluation, this research sets 2011 as the base year and applies the DEA Malmquist index model to measure China’s PES efficiency. Compared with efficiency calculation methods such as DEA and Super SBM, the DEA Malmquist index model demonstrates its unique time-series analysis capability, which is able to accurately depict the evolutionary trend of the efficiency of the PES over time advancement and provide a detailed decomposition of the efficiency, which helps to understand in depth the intrinsic reasons for the changes in the efficiency. The measurement model constructed is as follows:

$$M(X^{t+1},Y^{t+1},X^t,Y^t)={\left[{\displaystyle \frac{D^t(X^{t+1},Y^{t+1})}{D^t(X^t,Y^t)}}\times {\displaystyle \frac{D^{t+1}(X^{t+1},Y^{t+1})}{D^{t+1}(X^t,Y^t)}}\right]}^{1/2}$$

(1)

Among them, t and t + 1 represent the time periods, X represents inputs, Y represents outputs, and D represents the distance function. If M(x_t+1,y_t+1,x_t,y_t) is greater than 1, it indicates an increase in PES efficiency from period t to period t + 1; conversely, it indicates a decrease in PES efficiency.

3.3.2. Dagum Gini Coefficient

In order to identify the sources of regional disparities in PES efficiency in more detail, this research divides the 31 provinces, autonomous regions and municipalities directly under the central government into four groups: eastern, central, western and northeastern regions, and analyzes them using the Dagum Gini coefficient and its decomposition method. The formula for calculating the overall Gini coefficient is as follows:

$$G={\displaystyle \frac{{\displaystyle \sum _{j=1}^k{\displaystyle \sum _{h=1}^k{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}}}}}{2\overline{y}{n}^2}}$$

(2)

Among them, n represents the number of provinces, k represents the number of regions, $\overline{y}$ represents the mean value of the PES efficiency, y_ji(y_hr) represents PES efficiency of any i(r) sample in region j(h), and n_j(n_h) is the number of provinces in region j(h).

When the Gini coefficient is decomposed, it is necessary to rank the regions according to the mean value of PES efficiency in different regions, and then decompose the overall Gini coefficient into three parts: intra-regional differences contribution (G_w), inter-regional differences contribution (G_nb) and hyper-variable density contribution (G_t), and the relationship of the three satisfies: G = G_w + G_nb + G_t. The formula is as follows:

$${\overline{Y}}_h\le \cdots {\overline{Y}}_j\le \cdots \le {\overline{Y}}_k$$

(3)

$$G_{\mathrm{w}}={\displaystyle \sum _{j=1}^k{G}_{jj}{p}_j{s}_j}$$

(4)

$$G_{jj}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_j}\left|y_{ji-}{y}_{jr}\right|}}}{2{\overline{Y}}_j{n_j}^2}}$$

(5)

$$G_{nb}={\displaystyle \sum _{\mathrm{j}=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)D_{jh}}}$$

(6)

$$G_{jh}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji-}{y}_{hr}\right|}}}{n_j{n}_h\left(\overline{Y_j}+\overline{Y_h}\right)}}$$

(7)

$$G_t={\displaystyle \sum _{\mathrm{j}=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)\left(1-D_{jh}\right)}}$$

(8)

Among them, G_jj and G_jh represent the intra-regional Gini coefficient and the inter-regional Gini coefficient, respectively, p_j = n_j/n, and s_j = n_j $\overline{Y_j}/n$ $\overline{Y}$(1, 2, …, k). D_jh represents the degree of influence of relative contribution between regions j(h), d_jh represents the difference in PES efficiency between regions, and p_jh represents the first-order moment of transvariation. The formula is as follows:

$$D_{jh}={\displaystyle \frac{d_{jh}-h_{jh}}{d_{jh}+h_{jh}}}$$

(9)

$$d_{jh}={\displaystyle {\int }_0^{\infty }d{F}_j}\left(y\right){\displaystyle {\int }_0^y\left(y-x\right)d{F}_h}\left(x\right)$$

(10)

$$p_{jh}={\displaystyle {\int }_0^{\infty }d{F}_h}\left(y\right){\displaystyle {\int }_0^y\left(y-x\right)d{F}_j}\left(y\right)$$

(11)

3.3.3. Kernel Density Estimation

In order to more intuitively reflect the characteristics of the distribution location, distribution pattern and polarization trend of PES efficiency, this research adopts kernel density estimation to further analyze its dynamic evolution law in space. Kernel density estimation, as a kind of non-parametric tool, is able to measure the spatial imbalance of China’s PES efficiency by plotting the continuous density curve. Assuming that f(x) is the density function of random variable x, its specific formula is as follows:

$$f\left(x\right)={\displaystyle \frac{1}{Nh}}{\displaystyle \sum _{i=1}^{N}K\left(\frac{x_i-x}{h}\right)}$$

(12)

Among them, N represents the number of observations, h represents the bandwidth, K(x) represents the Gaussian kernel density function, x_i represents the observation point of PES efficiency, and x represents the mean value of PES efficiency observation point. The formula is as follows:

$$K\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}\exp \left(-{\displaystyle \frac{x^2}{2}}\right)$$

(13)

3.3.4. Moran’s I Index

In order to determine whether it is necessary to use a spatial econometric model, it is first necessary to test the spatial autocorrelation of the URIG. Therefore, this research utilizes the global Moran’s I index to measure the degree of spatial autocorrelation of the URIG. The formula is as follows:

$$\mathrm{Moran}’\mathrm{s} \mathrm{I}={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}\left(Y_i-\overline{Y}\right)}}}{S^2{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}}}}}$$

(14)

Among them, n represents the number of provinces, i and j represent provinces i and j, W represents the spatial geographic distance weight matrix, S² represents the sample variance, and $\overline{Y}$ represents the mean value of the URIG. The value of Moran’s I is in the range of [−1, 1], and the closer the value of Moran’s I is to 1 means that the stronger the positive spatial autocorrelation between the data, and the closer it is to −1 means that the stronger the negative spatial autocorrelation between the data. If the Moran’s I value is equal to 0, it means that the data are spatially randomly distributed.

3.3.5. Spatial Durbin Model

In order to explore the spatial spillover effect of the PES efficiency on the URIG, this research chooses the spatial Durbin model for the spatial econometric test, and at the same time applies the LM test, the LR test, the Wald test and the Hausman test to assess the rationality of the model. Combined with the theoretical mechanism explained in the previous section, the spatial Durbin model constructed is as follows:

$$Y_{it}=\alpha {\displaystyle \sum _{i=1}^{n}W{Y}_{it}}+\beta X_{it}+\kappa C_{it}+\rho {\displaystyle \sum _{i=1}^{n}W{X}_{it}}+\mu {\displaystyle \sum _{i=1}^{n}W{C}_{it}}+{\gamma }_i+{\sigma }_t+{\epsilon }_{it}$$

(15)

Among them, n represents the number of provinces, i and t represent provinces and years, W represents the spatial geographic distance weight matrix, Y represents the URIG, X represents PES efficiency, C represents the control variables, and α, β, κ, ρ and μ represent the regression coefficients of each variable; ƴ_i represents the fixed effect of province i, б_t represents the year t’s fixed effects, and Ɛ_it represents the random disturbance term.

4. Analysis of Empirical Results

4.1. Measurement of PES Efficiency in China

4.1.1. Static Efficiency Analysis

In this study, the Stata18 software was used as the main tool for empirical testing, in which the regional differences and dynamic evolution characteristics of the efficiency of PES were described and analyzed with the help of Matlab 2024a software. This research applies the input-oriented DEA-BCC model with variable returns to scale to measure the efficiency of PES in China from 2011 to 2022, and the results are shown in Figure 1.

The first is the overall technical efficiency dimension. The average value of China’s PES overall efficiency from 2011 to 2022 ranges from 0.642 to 0.802, and the overall efficiency needs to be improved. Although in recent years China has been committed to continuously improving the system of PES and providing workers with vocational training, vocational introduction and vocational appraisal, etc., there are still problems such as unbalanced development among regions, imperfect construction of the employment service system, ineffective matching in the labour market, and insufficient support for the employment of key groups, etc., which affect the overall efficiency of PES. From 2011 to 2022, the average value of China’s PES overall efficiency roughly shows the stage of “rising-declining-rising”, with the highest overall efficiency in 2015 and the lowest in 2020. The year 2015 may have been a year with more significant effects of China’s PES policies, and the positive effects of the policies have promoted the development of PES in China. For example, the 2015 Blue Book of Public Services was released, and the satisfaction of public service indicators increased in 2015 compared with 2014, with social security and employment being one of the areas with higher satisfaction. In 2020, The outbreak of COVID-19 led to the control of personnel mobility, and some enterprises delayed the resumption of work and production or held off on the release of job postings, while laborers held a wait-and-see attitude toward the epidemic and postponed their job searches, then these factors together led to a reduction in the overall efficiency of PES.

The second is the pure technical efficiency dimension. The pure technical efficiency of PES in China is the main reason affecting the overall technical efficiency. The mean value of the pure technical efficiency of PES in China from 2011 to 2022 is all less than 1, which usually means that the actual output of PES has not reached the theoretical maximum output during this period, resulting in uneven distribution of resources or inefficient use of resources. The possible reason is that PES hardware construction in the central and western regions of China is lagging behind, the level of specialization, standardization and informatization needs to be improved, and the management service capacity needs to be upgraded, which directly affects the pure technical efficiency of PES.

The third is the scale efficiency dimension. China’s PES scale efficiency is an important driver of overall technical efficiency. From 2011 to 2022, the average value of China’s PES scale efficiency is above 0.950, which is close to the optimal level of 1, and the overall difference is not large. This is all thanks to the fact that in recent years, the state has gradually improved the system of PES by increasing the financial investment in PES, encouraging and guiding social forces to participate in employment services, strengthening the construction of a unified and standardized human resources market system, and promoting the construction of PES informationization, which contributes to the enhancement and balanced development of China’s PES scale efficiency.

4.1.2. Dynamic Efficiency Analysis

This research takes 2011 as the base period and applies the Malmquist index method to measure and decompose the total factor productivity of China’s PES from 2012 to 2022, and the specific outcomes are presented in

Table 3. Average Malmquist productivity index and decomposition in China.

Year	TFPCH	TFPCH Growth Rate(%)	TECH	TECCH	SECH
2012	1.184	18.40	1.019	1.009	1.140
2013	1.111	11.08	0.987	0.997	1.117
2014	0.919	−8.08	0.989	0.992	0.934
2015	1.379	37.94	1.033	1.000	1.333
2016	0.710	−29.02	0.997	0.955	0.742
2017	0.943	−5.66	1.019	0.985	0.907
2018	1.156	15.60	0.988	0.996	1.166
2019	1.361	36.13	1.015	1.002	1.319
2020	1.100	10.04	0.958	0.994	1.107
2021	0.804	−19.61	1.051	0.952	0.799
2022	1.370	37.04	0.999	1.019	1.310
Mean	1.094	9.44	1.005	0.991	1.079

. The total factor productivity (TFPCH) index is 1.094, and the average annual growth rate is 9.44%, which shows an overall positive growth trend, which explains that the efficiency of China’s PES improved annually in the time period. In this study, the TFPCH is decomposed into technical efficiency (TECH), technical progress efficiency (TECCH) and scale efficiency (SECH). First, the TECH index is 1.005, showing a positive growth trend, which indicates that the technical efficiency in this period played an obvious role in promoting the improvement of PES efficiency in China. Secondly, the TECCH index is 0.991, showing a negative growth trend, which indicates that the potential of the technical progress efficiency in this period to improve the efficiency of China’s PES was not deeply explored. Finally, the SECH index is 1.079, showing a positive growth trend, which indicates that the scale efficiency in this period had a significant positive impact on the efficiency of China’s PES.

Overall, China’s PES efficiency steadily improved during the period 2012–2022, and the growth of technical efficiency made up for the lack of technical progress efficiency improvement, in which the growth of scale efficiency contributes more to technical efficiency, so only the synchronized and coordinated growth of technical efficiency and technical progress efficiency is a radical approach to enhance PES efficiency holistically.

4.2. Regional Disparities and Dynamic Evolution of PES Efficiency in China

4.2.1. Regional Disparities of PES Efficiency in China

This research applies the Dagum Gini coefficient and its decomposition method to measure the overall Gini coefficient, intra-regional Gini coefficient, inter-regional Gini coefficient and contribution rate of China’s PES efficiency, and the detailed outcomes are presented in

Table 4. Results of Dagum Gini coefficient decomposition of PES efficiency in China.

Type		2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Total		0.311	0.211	0.160	0.206	0.201	0.190	0.320	0.272	0.320	0.190	0.169
Intra-regional differences	Eastern	0.212	0.207	0.191	0.233	0.135	0.083	0.225	0.259	0.298	0.228	0.128
	Central	0.165	0.095	0.112	0.133	0.169	0.194	0.182	0.046	0.092	0.141	0.027
	Western	0.388	0.216	0.162	0.172	0.230	0.266	0.366	0.309	0.402	0.140	0.195
	Northeastern	0.195	0.219	0.020	0.255	0.155	0.075	0.224	0.098	0.159	0.176	0.225
Inter-regional differences	East central	0.206	0.182	0.160	0.199	0.162	0.155	0.213	0.239	0.232	0.204	0.095
	East west	0.346	0.216	0.190	0.218	0.225	0.198	0.361	0.293	0.370	0.195	0.193
	East northeast	0.215	0.263	0.131	0.291	0.177	0.090	0.292	0.330	0.266	0.232	0.238
	Central western	0.359	0.200	0.160	0.162	0.220	0.249	0.359	0.257	0.324	0.158	0.149
	Center northeast	0.216	0.205	0.087	0.226	0.173	0.155	0.286	0.157	0.151	0.214	0.193
	West northeast	0.333	0.283	0.113	0.246	0.207	0.207	0.323	0.332	0.347	0.212	0.228
Contribution rate (%)	Intra-regional	31.64	29.48	29.79	28.77	29.07	30.48	30.10	30.08	31.86	27.26	27.41
	Inter-regional	36.08	28.08	32.13	27.08	28.03	17.96	42.35	33.84	16.51	25.71	36.05
	Hyper-variable density	32.28	42.44	38.08	44.14	42.90	51.55	27.55	36.09	51.64	47.02	36.54

. In terms of the overall Gini coefficient, the overall Gini coefficient of China’s PES efficiency during the period of 2012–2022 ranged from 0.160 to 0.320, showing a fluctuating downward trend, indicating that the regional differences in China’s PES efficiency have narrowed with the passage of time and are gradually developing in the direction of integration and equalization.

In terms of intra-regional differences, the largest intra-regional differences in China’s PES efficiency during the period of 2012–2022 were in the western region, and the smallest was in the central region. For the western region, the average value of the Gini coefficient for PES efficiency is 0.259, which is greater than the national average of the overall Gini coefficient of 0.236. This may be due to the uneven level of economic development in the western region, for example, Chongqing performs better in terms of comprehensive regional development index, while other provinces lag behind, leading to a large gap in financial investment and infrastructure development for PES in different provinces. In addition, although the central government policy is somewhat skewed towards the western region, the resource allocation and policy implementation effects may vary among different provinces, which in turn leads to large intra-regional differences in PES efficiency. As far as the central region is concerned, the average Gini coefficient of PES efficiency is 0.123, which is much lower than the national average of the overall Gini coefficient of 0.236. This may be due to the fact that, compared with other regions, the central region presents relatively balanced characteristics in terms of economic development and industrial layout, which helps to minimize the regional differences in the investment of financial funds and infrastructure construction for PES. Owing to the smaller number of provinces, the central region can more effectively realize intra-regional synergy and resource sharing in the process of promoting PES, thus promoting the balanced development of PES efficiency.

In terms of inter-regional differences, the largest inter-regional differences in PES efficiency in China over the period of 2012–2022 are in the west northeast region and the smallest in the east central region. For the inter-regional differences in the west northeast region, the average value of the Gini coefficient of PES efficiency is 0.257, which is much greater than the average value of the overall Gini coefficient in the country, 0.236. The probable reason is that the western region has received policy incentives and support at the national level, increasing investment in the system of PES, and making good basic guarantees in terms of human, financial, land and material resources, which has provided important support for enhancing the efficiency of PES. Moreover, the western region has benefited from national policy support in recent years, such as the Strategy for Large-scale Development of the Western Region, and has experienced a faster rate of economic growth, which has provided a solid economic foundation for the improvement of the efficiency of PES. The northeast region, as an old industrial base, lags behind the western region in terms of policy support and economic development, resulting in the largest difference in PES efficiency between the two regions. As far as the difference between the east central regions is concerned, the average value of the Gini coefficient of PES efficiency is 0.186, which is much lower than the average value of the overall Gini coefficient of the whole country, which is 0.236. The possible reason for this is that, with the development of the Yangtze River Economic Belt and other major strategies as leaders in promoting the coordinated development between the east and the central regions, the industries of the eastern region have accelerated their diffusion to the central region, which has caused the eastern region and the central region begin to show a trend of economic integration, which helps the central region improve its PES efficiency and narrow the gap with the eastern region.

In terms of contribution rates, the hyper-variable density contribution rate is the highest with a mean value of 40.93%, followed by the intra-regional contribution rate with a mean value of 29.63%, while the inter-regional contribution rate is the lowest with a mean value of 29.44%. Despite the inter-regional differences, the main sources of regional differences in PES efficiency during the period 2012–2022 are the hyper-variable density contribution rate and the intra-regional differences, which indicates that there is a significant cross-over of PES efficiency in the eastern, central, western and northeastern regions of China, and also emphasizes the need to focus on the intra-regional balance during the development of PES.

4.2.2. Dynamic Evolution of PES Efficiency in China

In this research, a three-dimensional map of PES efficiency kernel density estimation is drawn, which can visually describe the distribution characteristics and dynamic evolution of China’s PES efficiency from 2012 to 2022. As shown in Figure 2, in terms of the distribution location, the center of the kernel density curve shows a tendency to shift to the right in stages, which reveals that the development of China’s PES efficiency is not a continuous straight-line upward process, and there are stage-by-stage fluctuations. In terms of distribution pattern, the kernel density curve shows a phased increase in the height of the main peak and a small widening of the curve’s coverage width, which means that the absolute gap in China’s PES efficiency is gradually widening and the degree of agglomeration is phased enhancing. From the viewpoint of distribution extensibility, the kernel density curve shows an obvious right trailing phenomenon and widens with the passage of time, which indicates that China’s PES efficiency is geographically differentiated, and this difference has shown a gradual widening trend in recent years. In terms of the number of peaks, the density curve generally maintains a single-peak pattern, accompanied by obvious side-peak phenomena and a large gap in peak height, indicating that there is an obvious spatial polarization trend and gradient effect in China’s PES efficiency.

In general, the efficiency of PES in China has improved in stages and may face different challenges and opportunities at different stages. Due to the differences in the level of economic development, industrial structure, educational resources and policy support across regions, the regional differences in PES efficiency have widened over time. Some of these provinces play a leading role in the field of PES, with a significant lead in efficiency. The polarization phenomenon and gradient effect of PES efficiency in China reflect the uneven development among provinces. Therefore, when promoting the development of PES in China, special attention needs to be paid to the continuous improvement of efficiency and balanced development.

4.3. Analysis of Spatial Spillover Effects

4.3.1. Spatial Autocorrelation Analysis

Table 5. Moran’s I index test results of the URIG.

Year	I	E(I)	Sd(I)	Z	p-Value
2012	0.194	−0.033	0.035	6.536	0.000
2013	0.191	−0.033	0.035	6.457	0.000
2014	0.155	−0.033	0.035	5.465	0.000
2015	0.155	−0.033	0.035	5.430	0.000
2016	0.152	−0.033	0.035	5.359	0.000
2017	0.150	−0.033	0.035	5.295	0.000
2018	0.145	−0.033	0.035	5.180	0.000
2019	0.144	−0.033	0.034	5.169	0.000
2020	0.144	−0.033	0.034	5.163	0.000
2021	0.142	−0.033	0.034	5.115	0.000
2022	0.129	−0.033	0.034	4.741	0.000

reports the results of Moran’s I index test for the URIG in China from 2012 to 2022. The results show that the Moran’s I index value of the URIG is always positive and passes the significance test at the 1% level, which indicates the presence of a notable spatial positive correlation for the URIG in China. In view of the important role of spatial autocorrelation, this research applies a spatial econometric model to examine the role of PES efficiency on the URIG, which helps to reveal the intrinsic connection between the two in a deeper way.

4.3.2. Spatial Econometric Model Tests

Table 6. Results of spatial measurement model tests.

Test Methods	Statistical Value
LM-Lag	40.267 ***
LM-Error	11.719 ***
Robust LM-Lag	36.605 ***
Robust LM-Error	8.057 **
Hausman	46.890 ***
LR-ind	16.170 ***
LR-time	820.790 ***
Wald-Sar	52.660 ***
Wald-Sem	95.560 ***
LR-Sar	78.010 ***
LR-Sem	88.320 ***

Note: Values in parentheses are robust standard errors, *** and ** represent 1% and 5%, respectively.

reports the outcomes of the spatial econometric model test. Firstly, the LM test and Robust-LM test are usually used to determine the spatial relationship that exists between the variables, and the test results show that the LM-Lag, LM-Error, and Robust LM-Lag are all significant at the 1% level, and Robust LM-Error is significant at the 5% level, which suggests that choosing the spatial Durbin model is more appropriate. Second, the Hausman test identifies whether the spatial Durbin model utilizes fixed effects or random effects, and the test results show that the fixed effects model is more appropriate. Then, the fixed effects are divided into individual fixed effects, time fixed effects and double fixed effects, and the LR test results show that the double fixed effects are more suitable for the model constructed in this study. Finally, the LR test and Wald test are used to determine whether the spatial Durbin model will be degraded to a spatial lag model or a spatial error model, and the test results imply that the spatial Durbin model will not be degraded. Therefore, this research chooses the spatial Durbin model with time and individual double fixed effects in the empirical research in order to achieve the best research effect.

4.3.3. Spatial Regression and Effects Decomposition

In this research, the spatial geographic distance matrix is used as the spatial weight matrix to estimate the spatial Durbin model.

Table 7. SDM regression results and decomposition of spatial spillover effects.

Variable	SDM		Direct Effect	Indirect Effect	Total Effect
	X	W × X
PESE	−0.000 ***	−0.030 ***	−0.001 **	−0.036 **	−0.037 **
	(0.000)	(0.010)	(0.001)	(0.015)	(0.016)
Urbanization	−1.497 ***	−16.045 ***	−1.635 ***	−18.738 ***	−20.373 ***
	(0.401)	(2.633)	(0.421)	(4.575)	(4.718)
Economy	0.016	−0.186	0.019	−0.237	−0.218
	(0.040)	(0.239)	(0.038)	(0.285)	(0.288)
Structure	0.367 ***	−0.590	0.362 ***	−0.609	−0.246
	(0.105)	(0.718)	(0.106)	(0.864)	(0.901)
Asset	−0.215 ***	−1.171 ***	−0.224 ***	−1.403 ***	−1.627 ***
	(0.051)	(0.344)	(0.051)	(0.462)	(0.481)
Trade	−0.325 ***	0.765	−0.313 ***	0.852	0.539
	(0.064)	(0.481)	(0.067)	(0.657)	(0.694)
Transportation	−0.046 ***	0.014	−0.046 ***	0.017	−0.029
	(0.015)	(0.058)	(0.015)	(0.064)	(0.055)
Foreign	−0.003 **	−0.060 ***	−0.004 ***	−0.072 ***	−0.075 ***
	(0.001)	(0.012)	(0.001)	(0.021)	(0.022)
Science	−0.363	11.333	−0.291	12.608	12.317
	(1.684)	(12.213)	(1.683)	(14.712)	(15.325)
Finance	0.118	−1.691 **	0.106	−2.018 **	−1.912 *
	(0.117)	(0.712)	(0.115)	(1.013)	(1.020)
rho	0.120 ***
	(0.106)
sigma2_e	0.005 ***
	(0.000)
R2	0.463
N	341	341	341	341	341

Note: Values in parentheses are robust standard errors, and ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

reports the regression results, the model’s goodness-of-fit R² effect is good, which shows that the spatial Durbin model demonstrates strong explanatory power. The spatial autocorrelation regression coefficient rho is significantly positive, indicating that the efficiency of PES in this region exhibits a positive spatial spillover effect on the URIG in adjacent regions. The regression coefficients and spatially lagged term coefficients of the independent variable are markedly negative at the 1% significance level, indicating that the improvement of PES efficiency can significantly narrow the URIG. This effect transcends the local area, extending its benefits to neighboring regions through the spatial spillover effect, which verifies the research H1 and H2. The possible reason is that improving PES efficiency can match the demand and supply of the labor market more effectively, providing more employment opportunities and higher quality of employment for urban and rural residents, which raises the income levels of rural inhabitants and narrows the URIG. Additionally, the improvement of PES efficiency can help the rural residents obtain better education and training opportunities, and enhance their personal development ability, and the accumulation of this kind of human capital can help to alleviate the intergenerational transmission of the income gap. At the same time, the improvement of PES efficiency has a positive spatial spillover effect, and the improvement of PES efficiency in one region can lead to the improvement of PES efficiency in neighboring regions, forming a positive regional synergistic development pattern. This kind of regional synergistic development helps to form a positive economic development cycle, reduces the spatial friction of factor flows, improves the mobility and employment probability of the rural labor force, reduces inter-regional URIG, and gradually realizes the goal of integration.

In accordance with the spatial regression model proposed with the partial differential methods by LeSage and Pace (2008), this research decomposes the total effect into direct and indirect effects [42], which enables a more in-depth understanding of the spillover effect of PES efficiency on the URIG in the region and neighboring regions. In order to remove possible biases due to regional and temporal factors, the regression model controls for both two-way fixed. The outcomes of the decomposition analysis for the spatial spillover effect are presented in Table 7, the direct effect coefficient of PES efficiency is −0.001, and the indirect effect coefficient is −0.036, which both pass the 5% significance test, indicating that every 1% increase in local PES efficiency will contribute 0.001 units to the decrease in the URIG within the region and also contribute 0.036 units to the reduction of the URIG in the adjoining area through the inter-provincial spillover effect, which again supports H1 and H2. It is found that the indirect effect coefficient of PES efficiency on the URIG is notably greater than the direct effect coefficient, suggesting that its spatial spillover effect markedly outweighs the local effect. The possible reason is that due to geographical proximity and economic activity links, neighboring regions will imitate or learn from the successful experience of regions with higher PES efficiency and take various measures to provide workers with PES, such as job introduction, employment guidance and vocational training, etc., and strive to improve their own PES efficiency. This “demonstration imitation” mechanism can help realize sustainable regional integrated development, guide inter-regional factor flows, industrial transfers, technological diffusion and institutional imitation, accelerate the formation of a number of employment agglomerations and economic growth poles with integrated services, coherent policies and unimpeded channels, and promote balanced development of urban and rural employment among regions, leading to the gradual reduction of the URIG between regions. Therefore, the spatial spillover effect of PES is stronger than the local effect.

4.4. Robustness Tests

To ensure the stability and reliability of the research results, it is imperative to further test the robustness of the model constructed in this study. This study adopts the methods of replacing the dependent variables, changing the spatial weight matrix and adjusting the sample size to carry out the robustness test. First, replacing the dependent variable. Since the Theil index not only takes population structure and income distribution into account but also is more sensitive to income polarization, this research selects the Theil index as a replacement variable for the ratio of per capita disposable income of urban and rural residents to measure the URIG [43] and examines whether there is any change in the sign and significance of the coefficients of the dependent variables. Second, changing the spatial weight matrix. Different weighting matrices may have different impacts on the research results, so this research exchanges the spatial geographic distance matrix for the spatial economic distance matrix [44], in order to re-examine the spatial spillover effect of PES efficiency on the URIG. Third, adjusting the sample size. The generalization ability of the model is improved by selecting 2016–2022 panel data for regression to test the model’s performance in the subsample. This method helps to verify whether the model results are sensitive to a specific sample and enhances the credibility and persuasiveness of the research results. The outcomes of the three robustness test sets are presented in

Table 8. Robustness test results.

Variable	Replacing the Dependent Variable		Changing the Spatial Weight Matrix		Adjusting the Sample Size
	Direct Effect	Indirect Effect	Direct Effect	Indirect Effect	Direct Effect	Indirect Effect
PESE	−0.000 **	−0.003 **	−0.001 **	−0.004 **	−0.001 **	−0.026 ***
	(0.000)	(0.001)	(0.001)	(0.002)	(0.000)	(0.009)
Urbanization	−0.263 ***	−1.285 ***	−1.887 ***	−1.297	−1.630 ***	−9.335 ***
	(0.034)	(0.331)	(0.517)	(1.314)	(0.336)	(3.019)
Economy	0.001	−0.008	−0.036	−0.042	0.020	0.404 **
	(0.003)	(0.022)	(0.041)	(0.098)	(0.021)	(0.180)
Structure	0.023 ***	−0.063	0.424 ***	0.380	0.033	−0.189
	(0.009)	(0.069)	(0.117)	(0.306)	(0.053)	(0.374)
Asset	−0.014 ***	−0.071 **	−0.146 ***	0.181	−0.097 ***	−0.507 **
	(0.004)	(0.034)	(0.054)	(0.158)	(0.026)	(0.235)
Trade	−0.023 ***	0.023	−0.346 ***	0.134	−0.007	0.438
	(0.005)	(0.048)	(0.067)	(0.206)	(0.035)	(0.350)
Transportation	−0.005 ***	0.006	−0.023 *	−0.016	−0.029 *	−0.209 *
	(0.001)	(0.005)	(0.012)	(0.027)	(0.015)	(0.122)
Foreign	−0.000 ***	−0.005 ***	−0.001	−0.005	−0.002 ***	−0.029 ***
	(0.000)	(0.002)	(0.001)	(0.007)	(0.001)	(0.010)
Science	0.025	1.632	−3.432 *	−9.059 *	0.585	9.598
	(0.140)	(1.192)	(1.899)	(4.727)	(0.902)	(7.120)
Finance	0.006	−0.072	0.286 **	−0.357	−0.023	−0.719 *
	(0.009)	(0.072)	(0.120)	(0.338)	(0.054)	(0.430)
N	341	341	341	341	217	217

Note: Values in parentheses are robust standard errors, and ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

, by controlling for year-fixed effects and province-fixed effects, the regression results of model re-estimation under different conditions are basically similar to the findings of the preceding paragraph, which proves that the research conclusions are still robust and reliable.

5. Conclusions and Policy Recommendations

5.1. Research Conclusions

Reducing the URIG is an important element in promoting integration and realizing sustainable regional development, and improving PES efficiency is an effective means to achieve this goal. In this research, we use the DEA Malmquist index model to measure China’s PES efficiency from 2012 to 2022 and use the Dagum Gini coefficient and kernel density estimation to further explore its regional differences and dynamic evolution, and then examine the impact of PES efficiency on the URIG. Accordingly, both research hypotheses H1 and H2 proposed in this study were validated. It is found that, firstly, the improvement of PES efficiency can significantly narrow the URIG; secondly, the impact of PES efficiency on the URIG has a spatial spillover effect, and the spatial spillover effect is much greater than the local effect.

5.2. Policy Recommendations

On the basis of the research findings, this study proposes the following policy recommendations:

Firstly, improving the system of PES and comprehensively enhancing service efficiency. The five-level network of PES at the provincial, municipal, county, street (township) and community (village) levels should be optimized, the layout of PES should be coordinated in accordance with the characteristics of the district, the characteristics of the population, and the radius of service, etc., and the system of PES should be improved to foster the balanced growth of PES in different areas. In addition, modern information technology means such as the Internet, big data and cloud computing should be utilized to build a nationally unified employment information resource database and the platform for PES so as to realize efficient and convenient collection, sharing and use of employment information. This would also promote the integration of online and offline services, providing workers with a variety of convenient service methods, such as appointment services, door-to-door services and agency services, in order to comprehensively enhance the efficiency of PES and create conditions for narrowing the URIG.

Secondly, reversing the bias of PES and narrowing the URIG. By adhering to the direction of integration and gradually changing the PES bias of “emphasizing towns and cities over rural areas”, a network of PES covering both urban and rural areas can be built to promote the extension of PES to rural areas, ensure the basic needs of rural residents for PES, and promote high-quality employment for rural residents. Moreover, taking the “rural revitalization” strategy as a development opportunity, we will increase financial support for PES in rural areas, expand the coverage of PES institutions, and improve the professional quality of PES personnel, so as to enhance the efficiency of PES in rural areas, promote the accumulation of human capital of the rural workforce, and alleviate the irrationality of income distribution between urban and rural areas.

At last, innovating the model of PES to expand the spatial spillover impact. On the one hand, drawing on the model of PES alliances including Beijing, Tianjin, Hebei, Shanxi, NeiMenggu and Shandong, we would establish partnerships with neighboring regions to jointly discuss and formulate the development direction and policy measures for PES, promote inter-regional information exchange and resource sharing, and enhance the efficiency and quality of PES. On the other hand, it would establish a “demonstration imitation” mechanism for PES, create PES demonstration districts, showcase the results of efficient PES practices, provide examples for other districts to learn and imitate, and lead to the improvement of PES efficiency in other districts. By expanding the spatial spillover impact of PES efficiency through these two innovative service models, the employment opportunities and incomes of rural residents in the region and neighboring areas can be increased, thereby narrowing the URIG in different regions.

6. Limitations and Future Prospects

The research theme of this study involves a number of disciplinary fields such as sociology, public administration and econometrics. This research not only deepens our understanding of the issue of income disparity between urban and rural areas but also helps to promote the integration and exchange between disciplines, and to promote the development of cross-disciplinary research. By measuring China’s PES efficiency and analyzing its regional differences and dynamic evolution, this research provides an in-depth study of the impact of PES efficiency on the URIG and its spatial spillover effects. However, there are still certain shortcomings that require attention and improvement in future research endeavors. On the one hand, the provincial panel data used in this study, while providing a valuable perspective at the macro level, may not be able to adequately capture the more nuanced intra-regional structures and differences. On the other hand, pure PES fiscal expenditure statistics are currently unavailable from official statistics, and the fiscal inputs of PES, as a key part of the social security and employment system, are often closely related to social security and employment expenditures. Therefore, this research follows the academic practice of using “social security and employment expenditures” as an alternative indicator to reflect the fiscal inputs of PES. In view of this, it would be more explanatory if future studies could use the panel data of municipalities or counties as well as the statistics of individual fiscal expenditures of PES.