Does Monocentric Spatial Structure Narrow the Urban-Rural Income Gap? A Case Study of Northeast China

Abstract

The impact of spatial structure on the Urban-Rural income gap varies across regions and scales. Much of the current literature focuses on urban agglomerations, highlighting the need for more in-depth exploration of specific regions. Using LandScan population data, this paper investigates urban spatial structure from the perspective of monocentricity and polycentricity, investigating their respective impacts and mechanisms on the Urban-Rural income gap in Northeast China. The findings are as follows: (1) The development of a monocentric spatial structure in Northeast China significantly reduces the Urban-Rural income gap, a conclusion verified through robustness testing. (2) A mediation effect test confirms that this spatial structure reduces the gap by increasing urban labor productivity. (3) The urban spatial structure of Northeast China also has positive spillover effects, reducing income inequality between urban and rural areas in neighboring regions. Further exploration of the relationship between Urban-Rural income inequality and spatial structure is crucial for achieving shared prosperity. Additionally, this research provides policy support for developing a novel framework for land use planning and conservation.

1. Introduction

Narrowing the income gap between rural and urban populations is essential for promoting balanced regional economic development [1]. The government attaches increasing importance to promoting equity and balance in the advancement of urban and rural regions. Narrowing the income gap between urban and rural areas remains a central challenge in the process of achieving common prosperity in China. As China moves into a new stage of development, the promotion of coordinated regional growth and spatial equity has become a key policy objective. The structure of urban spaces plays an increasingly important role in shaping economic opportunities and in determining access to employment, public services, and infrastructure across different regions. In recent years, scholars in China have explored how spatial configurations affect income disparities between urban and rural areas, particularly at the provincial scale and within city clusters. However, at the city level, it remains unclear whether monocentric or polycentric structures are more effective in reducing this gap, as research on this issue is limited. Polycentric spatial development has gained increasing attention in recent years. In this context, it is important to evaluate how monocentric and polycentric urban forms influence the income gap between urban and rural areas. This analysis can contribute to improving urban planning approaches and advancing high-quality, balanced regional development.

Existing studies related to this topic primarily concentrate on two main areas. The first involves studying the characteristics and effects of urban forms. This existing research primarily analyzes urban spatial structures, highlighting their multidimensional nature and regional variations [2,3,4,5]. In recent years, increasing attention has been directed toward the influence of urban structure [6]. With respect to economic development, research indicates a significant positive association between urban structure and growth performance, where spatial agglomeration acts as a major contributing factor [7]. Several studies employing dynamic panel threshold models have revealed a nonlinear association between spatial configurations and environmentally sustainable economic development. This relationship is shaped by factors including the efficiency of resource distribution, coordination among industries, and the region’s ecological carrying capacity [8]. Additionally, findings show that different dimensions of urban form affect growth in distinct ways. For instance, urban sprawl tends to foster economic development, while greater urban compactness has a negative effect [9]. In terms of the environment and urban ecology, findings are less consistent. Some studies indicate that dispersed urban structures contribute to poorer air quality and higher PM_2.5 levels [10,11,12], while others argue that polycentric spatial structures can help reduce carbon emissions [13,14]. In summary, most existing research focuses on how spatial configuration influences economic development [15] and environmental impact [16], with relatively little attention to its impact on the Urban-Rural income gap, particularly at the prefecture-level city scale.

The second area of research examines the spatial and temporal evolution of Urban-Rural income inequality and its influencing factors, revealing multidimensional heterogeneity and increasingly prominent spatial spillover effects [17,18,19]. However, most of these studies adopt a factor-driven approach, focusing on variables such as industrial development [20], the digital economy [21,22], urbanization levels [23], urban sprawl [24], agricultural inputs [25], and other factors that affect the Urban-Rural income gap. Limited research has investigated how urban structure influences this form of income inequality. The existing literature that addresses the relationship between spatial organization and the Urban-Rural income divide tends to concentrate on urban form, with analyses mostly conducted at the provincial level or across metropolitan clusters [26].

Overall, existing research on urban form in the context of Urban-Rural income inequality primarily focuses on cities as a whole [27], without thoroughly addressing how spatial configurations within cities affect income disparities between urban and rural areas. In addition, the impact of spatial structure is scale-dependent and varies regionally. Most studies analyze provincial areas, city clusters, and other larger scales. There is no clear consensus on whether a monocentric or polycentric urban development model is more effective in reducing Urban-Rural income inequality at the prefecture-level city scale. Additionally, there is a lack of sufficient reasoning and analysis on the mechanisms driving these effects. Among the possible mechanisms, the role of urban labor productivity as a mediator remains largely underexplored. Labor productivity reflects the efficiency of resource allocation and employment matching and may serve as a key pathway through which urban spatial structure influences income disparities.

To address these research gaps, this study selects Northeast China as a representative case, given its distinct spatial structure and pronounced regional disparities. As an important old industrial base, Northeast China has played a vital role in the economic development and modernization of the country. In the past, rapid industrialization and urbanization contributed to it maintaining a relatively narrow income gap between urban and rural areas compared with other regions. However, as the region encounters mounting challenges such as resource depletion, industrial decline, and substantial population loss, an in-depth investigation into the Urban-Rural income gap becomes essential for promoting the revitalization of Northeast China and accelerating the achievement of common prosperity.

Building on this, this study addresses the following three questions: How does urban space structure affect income disparities between urban and rural areas in Northeast China? What are the mechanisms through which urban spatial structure affects the Urban-Rural income gap? Moreover, does the spatial structure of cities in Northeast China produce spillover effects that impact the Urban-Rural income gap?

To address these questions, this study proposes an integrated analytical framework that links urban spatial structure, labor productivity, and the Urban-Rural income gap. The analysis focuses on both the direct and indirect mechanisms through which spatial configurations affect income inequality, with particular emphasis on Northeast China. Specifically, this study begins by evaluating the urban form of Northeast China using LandScan population data. A mediation effect model is subsequently used to analyze the pathway through which spatial structure affects the Urban-Rural income gap via labor productivity. In the final step, a spatial panel econometric approach is adopted to evaluate the spillover impact of urban structure on income inequality across regions.

The theoretical innovations of this study are threefold. First, it offers a new perspective on how urban form influences equity, particularly the Urban-Rural income gap, which is an aspect that has often been overlooked in existing research focusing primarily on economic growth and environmental outcomes. Second, by introducing urban labor productivity as a mediating variable, this study provides a theoretical explanation of how spatial configurations affect income distribution through resource allocation efficiency and labor market matching. Third, this study emphasizes the spatial spillover effects of urban spatial structure on surrounding regions, underscoring the importance of incorporating inter-city interactions into both theoretical modeling and policy formulation. Together, these contributions enhance the understanding of spatial mechanisms underlying income inequality and offer a conceptual framework for advancing integrated urban and rural development.

The organization of this paper is outlined as follows. Section 2 sets out the theoretical framework, focusing on how urban spatial structure influences Urban-Rural income disparity through direct effects, mediating pathways, and spatial spillover mechanisms. Section 3 provides an overview of the study area, outlines the data sources, and describes the construction of the empirical model. Section 4 examines the spatial characteristics of both urban structure and Urban-Rural income disparities in Northeast China. This section also investigates the empirical relationships among key variables, incorporating direct, mediating, and spatial spillover effects. Section 5 presents the conclusions and recommendations of the study. It summarizes the main empirical findings, elaborates on their practical and policy implications, and identifies the limitations of the study while outlining possible directions for future research. Section 6 provides an in-depth discussion of the theoretical contributions and the broader significance of the findings.

2. Theoretical Hypothesis

Urban spatial structure describes how different functional zones are distributed and organized within a city during the process of urbanization. It reflects the spatial relationships and interactions among land use, population distribution, and industrial layout. This structure is a crucial aspect of urban form, significantly influencing the efficiency and direction of urban development. In a monocentric urban spatial structure, resources, functions, and population are concentrated in a single central area, while other areas remain dispersed and dependent on this core zone [28]. The core urban area often functions as the center for economic, administrative, and cultural activities, drawing in a significant share of the population and industrial inputs. In contrast, peripheral zones depend on the core for access to key resources and services.

The dual Urban-Rural structure theory posits that disparities in income between urban and rural residents primarily stem from the unequal allocation of key resources and production factors [29]. This imbalance leads to differences in both the pace and scale of income growth. Moreover, migration from rural to urban areas serves as a critical driver of economic transformation. The configuration of urban space, through its spatial layout and functional organization, directly shapes the direction and magnitude of population flows, thereby promoting the optimal allocation of labor. In monocentric cities, the high degree of concentration of capital, technology, and innovation fosters agglomeration effects that operate through three mechanisms: sharing, matching, and learning [30]. The clustering of enterprises and industries in central urban areas generates economies of scale and industrial synergy. Access to shared market information and technological resources enables firms to identify development opportunities more efficiently and to create more employment, thereby expanding job availability for both urban and rural residents. The co-location of diverse enterprises also results in a wider range of labor demands and skill requirements. Beyond high-end technical positions, a substantial number of service and manufacturing jobs are created, offering employment opportunities across multiple skill levels. This environment allows rural workers to find jobs that match their skillsets, facilitating their transition into the urban labor market.

At the same time, agglomeration enhances the diffusion of innovation. The accelerated flow of knowledge and technology contributes to skill development among rural laborers, improving their competitiveness and further encouraging migration to urban areas. According to labor market theory, the balance between labor supply and demand directly affects income levels for both urban and rural populations. As population and industry become increasingly concentrated in urban cores due to agglomeration, the intensity of labor demand and the quality of labor matching improve [31]. This fosters fuller employment among rural workers and contributes to rising rural incomes. Consequently, rural income levels may rise at a faster pace than urban incomes, which contributes to reducing the absolute gap between urban and rural areas.

Additionally, effective coordination and the outreach capacity of the urban core are essential for achieving balanced development across the city. Although some scholars suggest that polycentric development can improve resource allocation and reduce transaction costs [32], monocentric structures tend to exhibit more pronounced advantages at smaller spatial scales [33]. The urban core area remains a primary destination for rural laborers. However, inefficient polycentricity within a city can undermine the core area’s dominant position, reducing its ability to attract resources from rural areas and hindering the positive spillover effects [34]. If the economic advantages of urban areas are not effectively utilized, a polycentric spatial configuration may lead to an excessively dispersed urban form. This dispersion can diminish the positive externalities typically associated with concentrated urban development, thereby intensifying income inequality between urban and rural regions.

Based on these considerations, the first hypothesis of this study is formulated as follows:

Hypothesis 1 (H1): 

The monocentric spatial arrangement at the prefecture-level city scale in Northeast China contributes to a reduction in the Urban-Rural income disparity.

Under a monocentric spatial configuration, economic functions and resources tend to be densely clustered in the central urban area, leading to advantages in terms of economic efficiency, transportation accessibility, and infrastructure development [35]. This monocentric development significantly impacts urban labor productivity and residents’ incomes, with agglomeration economies serving as an important intermediary in shaping the Urban-Rural income gap.

According to the Petty–Clark theorem, economic development is accompanied by a structural shift in labor and production resources [36]. Labor gradually transitions from agriculture to industry and eventually to services. In the early stages of development, the majority of the labor force is concentrated in the primary sector. As the economy advances, labor moves toward the industrial sector. In more mature stages, the service sector absorbs a larger share of labor, contributing to overall income growth in both urban and rural areas. Monocentric agglomeration accelerates this transformation by concentrating labor demand in secondary and tertiary industries. It promotes the creation of diverse employment opportunities through mechanisms such as specialization, labor market matching, and knowledge sharing [37,38]. These forces stimulate the outflow of surplus rural labor and expand income sources for rural households.

At the same time, agglomeration enhances productivity by facilitating the diffusion of knowledge and technology. These spillovers improve both enterprise efficiency and individual labor performance. Rising labor productivity contributes to industrial upgrading and attracts high value-added sectors, thereby promoting overall income growth [39,40]. Furthermore, the transfer of excess labor from rural regions enhances agricultural productivity and boosts output in the primary industry. This in turn fosters further gains in productivity and raises rural income levels. Collectively, these processes help to reduce the income disparity between urban and rural areas.

Building upon these insights, the second hypothesis is advanced in this study:

Hypothesis 2 (H2): 

The urban spatial structure in Northeast China reduces the Urban-Rural income disparity by boosting urban labor productivity.

Tobler’s First Law of Geography states that the connections between geographical phenomena strengthen as distance decreases [41]. This suggests that the influence of urban spatial form on Urban-Rural income disparity extends beyond the local context and demonstrates notable spatial spillover effects. Research by Fujita and Krugman [42] indicates that spatial agglomeration can drive the development of neighboring areas through positive spillover effects, while negative externalities may have adverse impacts. The interaction between these positive and negative externalities creates an uncertain effect of urban spatial structure on the Urban-Rural income gap in adjacent regions. Positive externalities, such as through the diffusion of knowledge and technology, can enhance labor productivity and income levels in neighboring areas, thereby narrowing the Urban-Rural income gap. Conversely, negative externalities may exacerbate resource shortages and environmental pressures in those areas, hindering economic development and widening the income gap [21,43]. Accordingly, the effect of urban spatial structure on the Urban-Rural income gap extends beyond individual cities, indirectly shaping income disparities in adjacent areas due to spatial interdependence. As a result, it is crucial to account for this complex spatial linkage and the resulting spillover effects when analyzing the relationship between urban spatial structure and income inequality.

In light of these insights, the third hypothesis is proposed in this study:

Hypothesis 3 (H3): 

The urban spatial structure affects the Urban-Rural income gap in Northeast China in a spatially relevant manner, directly impacting local income disparity while also impacting neighboring areas indirectly through spillover effects.

3. Data and Methodology

3.1. Study Area

The study area is Northeast China, which includes Heilongjiang, Jilin, and Liaoning Provinces, along with four prefecture-level divisions in the eastern part of the Inner Mongolia Autonomous Region: Hulunbuir, Hinggan League, Tongliao, and Chifeng. The research sample comprises 37 prefecture-level municipalities, excluding Daxing’anling Prefecture, Yanbian Korean Autonomous Prefecture, and Hinggan League due to missing data. Figure 1 presents the spatial location of the selected study region.

Northeast China has undergone a distinct urbanization process and economic transformation, shaped by its historical role as a traditional industrial base and its strategic position in national development agendas. In recent years, the region has become a primary focus of policy initiatives promoting revitalization and balanced regional growth, making it an appropriate case for examining the spatial dimensions of Urban-Rural disparities.

3.2. Variables

3.2.1. Explained Variable: Urban-Rural Income Gap

The Urban-Rural income gap in this study is measured by the ratio of per capita disposable income between urban and rural residents, where a higher ratio indicates a greater disparity between urban and rural incomes.

3.2.2. Core Explanatory Variable: Municipal Spatial Structure Index

Existing studies often rely on census or employment data to evaluate urban spatial structures, using key quantitative measures such as the Pareto index, Herfindahl index, primacy index, monocentricity index, and relative dispersion coefficient [44,45,46]. This study builds on related research [47], which employs logarithmic transformation to linearize the rank-size rule for characterizing the degree of monocentricity and polycentricity in urban spatial structures. The formula is presented below:

$$\ln L_i=\ln L_1-q\ln R_i$$

(1)

where i denotes the number of administrative units in the city area, R_i represents the rank of the i-th administrative zone based on its population size, L_i indicates the total population size of the i-th administrative zone, L₁ signifies the maximum population size within the city, and q is the index of the city’s spatial structure, notated as Uss. A value of Uss > 1 indicates a tendency toward a monocentric structure, while Uss < 1 suggests a polycentric structure.

Additionally, this study employs the first-place weight index method to evaluate the city’s spatial structure, aimed at testing the robustness of the estimation results. The index, denoted as Pri, is calculated as follows:

$$Pri={\displaystyle \frac{S_1}{S}}$$

(2)

where Pri denotes the first-place weight index of the city, S₁ represents the population size of the top-ranked subunit within the region, and S represents the total population of the entire city region. A higher Pri value indicates a stronger tendency toward a monocentric structure, while a lower Pri value indicates a shift toward a polycentric structure.

3.2.3. Other Variables

Urban labor productivity is used as the mediating variable in this study, measured by the ratio of real GDP to employment [48].

• Urbanization rate: An increase in urbanization may have an uncertain impact on Urban-Rural income disparity [49]. This is measured by the ratio of the non-agricultural population to the total population, denoted as urban.
• Urban economic growth: Urban economic growth directly reflects regional economic dynamics and plays a key role in altering income levels between urban and rural areas [23]. Per capita GDP is used to measure regional economic growth, denoted as grow.
• City size: City size is measured by the proportion of the population living within the city district compared to the total population [50], denoted as size.
• Industrial structure: Changes in industrial structure influence the Urban-Rural income gap by shifting employment opportunities for rural residents [20]. This is measured by the ratio of GDP from the tertiary sector to GDP from the secondary sector, denoted as stru.
• Fixed asset investment: The level of fixed asset investment reflects the completeness of infrastructure, which facilitates resource flow between urban and rural regions and can increase rural income [51]. This is measured by the ratio of investment to GDP, denoted as inv.
• Degree of external openness: Existing research suggests that external openness indirectly affects Urban-Rural income disparity through enhanced employment efficiency, shifts in income distribution, and industrial upgrading [52]. This is measured by the ratio of total foreign imports and exports to GDP, denoted as open.

3.3. Modeling

To test Hypothesis 1, the following panel regression model is established:

$$Ga{p}_{it}={\alpha }_0+{\alpha }_{1}Us{s}_{it}+{\alpha }_2{X}_{it}+c_i+u_t+{\epsilon }_{it}$$

(3)

Gap_it stands for the gap in income levels between urban and rural residents, Uss_it denotes the municipal spatial structure index, and X_it refers to a series of control variables, while i denotes the city and t represents time; c_i is the individual city-specific effect, u_t refers to the time effect, ε_it denotes the error term, α₀ denotes the constant term, α₁ represents the coefficient of the core explanatory variables, and α₂ indicates the coefficient for the control variables.

To examine the impact of urban spatial structure on the Urban-Rural income gap and to test Hypothesis 2, this study employs a mediating effect model. This approach explores the potential mediating role of urban labor productivity in reducing the Urban-Rural income gap within a monocentric spatial structure. The specific mediating effect model is outlined as follows:

$$Labo{r}_{it}={\beta }_0+{\beta }_{1}Us{s}_{it}+{\beta }_2{X}_{it}+c_i+u_t+{\epsilon }_{it}$$

(4)

$$Ga{p}_{it}={\lambda }_0+{\lambda }_{1}Us{s}_{it}+{\lambda }_{2}Labo{r}_{it}+{\lambda }_3{X}_{it}+c_i+u_t+{\epsilon }_{it}$$

(5)

where Labor_it represents the mediating variable, which is labor productivity, while the other variables remain consistent with those in Equations (4)–(6). Along with the baseline model, these equations form the mediating effect model used in this study.

To explore the spatial influence of urban spatial structure on the Urban-Rural income gap and to test Hypothesis 3, the following spatial panel econometric model is established:

$$Ga{p}_{it}={\gamma }_0+\rho {\displaystyle \sum }_{i=1}^{n}WGa{p}_{it}+{\gamma }_{1}Us{s}_{it}+{\rho }_1{\displaystyle \sum }_{i=1}^{n}WUs{s}_{it}+{\gamma }_2{X}_{it}+{\rho }_2{\displaystyle \sum }_{i=1}^{n}W{X}_{it}+c_i+u_t+{\epsilon }_{it}$$

(6)

where W denotes the spatial weight matrix, which is assessed using both geographic distance and economic distance matrices. The coefficients ρ, ρ₁, and ρ₂ represent the spatial effects of the respective variables, while the remaining other variables are consistent with those in Equation (4).

3.4. Data Sources

Data on urban spatial structure are obtained from the LandScan Global High-Resolution Population Raster Database, produced by the Oak Ridge National Laboratory under the U.S. Department of Energy. This dataset offers detailed global population distribution information, enabling an accurate representation of regional population patterns. It has been extensively applied in research concerning urban and regional spatial configurations. Urban and rural income data, along with other explanatory variables, are primarily obtained from the China Urban Statistical Yearbook for various years, as well as from the statistical yearbooks and reports of provinces and prefecture-level cities. The study covers the period from 2003 to 2020. Missing annual data points are supplemented using interpolation.

Table 1. Statistical description characteristics of each variable.

Variable	N	Min	Max	Mean	SD
Gap	666	0.188	1.717	−0.762	0.198
Uss	666	−0.608	1.003	0.495	0.298
Labor	666	6.107	8.824	7.800	0.455
urban	666	−1.374	−0.124	−0.591	0.271
grow	666	8.180	11.903	10.250	0.693
size	666	−2.354	−0.113	−1.071	0.503
stru	666	−0.691	−0.022	−0.209	0.141
inv	666	−3.381	0.805	−0.691	0.539
open	666	−7.346	0.345	−2.762	1.306

provides descriptive statistics for the indicators, with variables transformed using logarithmic scales to mitigate the effects of heteroscedasticity. To further illustrate the distribution characteristics of the key variables, standardized boxplots are presented in Figure 2. In addition, Figure 3 presents the Pearson correlation matrix of the standardized explanatory variables to examine potential multicollinearity. The results show that most correlations are below 0.5, and none exceed 0.8, suggesting that multicollinearity is not a significant concern.

4. Empirical Analysis

4.1. Characterization Facts

The evolution of urban spatial structure (Figure 4) and the Urban-Rural income gap (Figure 5) in Northeast China is analyzed across three time points: 2003, 2012, and 2020. The analysis reveals the following key characteristics:

First, from 2003 to 2020, the municipal structure index for Northeast China experienced an initial decline followed by a subsequent increase. Nonetheless, the urban spatial structure in most prefecture-level cities within the area continues to exhibit a preference for monocentric development. This suggests that spatial agglomeration of economic activities remains a dominant feature in the majority of these cities. Second, calculations and decompositions of the income index for urban and rural residents demonstrate a decrease in the Urban-Rural income gap in Northeast China over the same time period. Third, the Urban-Rural income disparity displays patterns of spatial agglomeration and distribution. Smaller income disparities are primarily found in the northeastern and southern areas, whereas larger gaps are concentrated in the western and central regions. Finally, among the four major central cities in Northeast China, Changchun City exhibits the most significant Urban-Rural income gap, although it has exhibited a consistent downward trend over time.

4.2. Benchmark Regression

Prior to implementing the baseline regression, a Hausman test was conducted, yielding a p-value below 0.01. This outcome supports the rejection of the null hypothesis and suggests that the fixed effects model is more appropriate than either the random effects or pooled regression models. The fixed effects estimation results for the benchmark analysis are reported in

Table 2. Baseline regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Uss	−0.626 ***	−0.722 ***	−0.696 ***	−0.658 ***	−0.620 ***	−0.631 ***	−0.753 ***
	(0.197)	(0.196)	(0.198)	(0.197)	(0.198)	(0.198)	(0.199)
Urban		−0.453 ***	−0.436 ***	−0.476 ***	−0.530 ***	−0.567 ***	−0.531 ***
		(0.111)	(0.112)	(0.113)	(0.116)	(0.121)	(0.120)
Grow			−0.090	−0.098	−0.158 *	−0.133	−0.219 **
			(0.082)	(0.082)	(0.087)	(0.091)	(0.093)
Size				0.275 **	0.237 **	0.250 **	0.244 **
				(0.118)	(0.120)	(0.120)	(0.119)
Stru					0.595 **	0.550 *	0.584 *
					(0.300)	(0.303)	(0.301)
Inv						−0.035	−0.036
						(0.034)	(0.034)
Open							−0.087 ***
							(0.024)
_cons	−2.157 ***	−2.553 ***	−1.694 ***	−1.369 ***	−0.823 ***	−1.124 ***	−0.534 ***
	(0.191)	(0.301)	(0.334)	(0.343)	(0.385)	(0.432)	(0.137)
N	666	666	666	666	666	666	666
R2	0.781	0.803	0.804	0.810	0.815	0.816	0.831

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

. Column (1) displays the regression results without control variables, while columns (2) to (7) show the results with control variables introduced in stages.

The findings indicate that the estimated coefficients consistently remain significantly negative, regardless of whether control variables are included. The results are statistically significant at the 1% level. Specifically, the coefficients without and with the inclusion of all control variables are −0.626 and −0.753, respectively. This implies that a 1% increase in the municipal spatial structure index is associated with a reduction of 0.626 and 0.753 units in the Urban-Rural income gap. This suggests that an increase in the spatial structure index in municipal areas correlates with a decreasing Urban-Rural income disparity in Northeast China, having a negative impact on the growth of the Urban-Rural income ratio. These results confirm the hypothesis that monocentric spatial structures help narrow Urban-Rural income disparity. By concentrating economic activities and public resources within a central area, monocentric cities enhance rural labor integration, improve employment accessibility, and strengthen agglomeration effects, thereby fostering more equitable income growth across regions. This provides empirical support for H1.

This study also includes a concise interpretation of the estimated coefficients for the control variables. The coefficient associated with the variable urban is significantly negative, suggesting that the concentration of resources and economic opportunities during the urbanization process in Northeast China has contributed to improving rural income levels. In contrast, the coefficient for size is significantly positive, suggesting that the expansion of city size exacerbates the Urban-Rural income gap. In larger cities, the concentration of high-skilled labor tends to benefit the higher-income urban population, while rural areas and low-income groups struggle to access these advantages. In addition, the coefficient for open is significantly negative, highlighting that openness policies, particularly those aimed at attracting foreign investment and technology, have facilitated more balanced economic development between urban and rural areas, thereby contributing to the reduction of the income gap.

4.3. Mediation Model Regression Results

Table 3. Regression results of mediating effect model.

Variable	(1) Gap	(2) Labor	(3) Gap
Uss	−0.225 ** (0.100)	0.094 ** (0.045)	−0.190 ** (0.095)
Labor			−0.377 *** (0.084)
Control Variables	YES	YES	YES
N	666	666	666
R2	0.501	0.482	0.567

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

presents the regression results of the mediation effect model. In model (3), the regression coefficient for the municipal spatial structure index is significantly negative, consistent with earlier findings. Model (2) reveals a regression coefficient of 0.094 for the municipal spatial structure index, indicating a significant positive relationship with urban labor productivity. Conversely, model (3) reveals a regression coefficient of −0.377 for labor productivity, significant at the 1% level, suggesting that higher labor productivity can significantly reduce the Urban-Rural income gap. By combining the regression results from models (1) to (3) to evaluate the mediating effect of urban labor productivity, it is evident that the regression coefficients for the municipal spatial structure index are significant in columns (1) and (2). Additionally, the coefficients for labor productivity are significant in column (3), confirming the presence of a mediating effect. These results confirm the presence of a mediation effect. In other words, labor productivity serves as a significant channel through which urban spatial structure influences income disparity between urban and rural areas.

On the one hand, an efficient spatial layout and well-developed transportation networks facilitate the concentration of economic activities, enabling labor to participate more effectively in high value-added industries. This process promotes labor reallocation and enhances overall productivity. On the other hand, improvements in labor productivity directly affect income levels. The stronger agglomeration effects associated with monocentric cities contribute to the creation of more job opportunities and higher wage levels. As urban spatial structure becomes more optimized, rural laborers gain greater access to urban employment, improving rural income distribution and contributing to the gradual convergence of per capita income levels between urban and rural populations. In summary, a monocentric spatial structure helps reduce the Urban-Rural income gap by enhancing labor productivity. These findings provide empirical support for Hypothesis 2.

4.4. Analysis of Spatial Spillover Effects

Prior to constructing the spatial panel model, a spatial autocorrelation test is required.

Table 4. Spatial correlation test of spatial structure in city regions and Urban-Rural income gap.

Year	Uss		Gap		Year	Uss		Gap
	Moran	Z-Score	Moran	Z-Score		Moran	Z-Score	Moran	Z-Score
2003	0.315 ***	2.950	0.463 ***	4.156	2012	0.360 ***	3.371	0.493 ***	4.494
2004	0.310 ***	2.905	0.314 ***	2.968	2013	0.349 ***	3.294	0.527 ***	4.750
2005	0.308 ***	2.884	0.391 ***	3.596	2014	0.284 ***	2.692	0.443 ***	4.166
2006	0.323 ***	3.031	0.380 ***	3.491	2015	0.263 ***	2.593	0.493 ***	4.565
2007	0.328 ***	3.083	0.429 ***	3.926	2016	0.287 ***	2.702	0.278 ***	2.707
2008	0.339 ***	3.171	0.406 ***	3.778	2017	0.302 ***	2.863	0.329 ***	3.129
2009	0.337 ***	3.163	0.301 ***	2.848	2018	0.296 ***	2.805	0.297 ***	2.893
2010	0.338 ***	3.179	0.263 ***	2.595	2019	0.259 ***	2.685	0.283 ***	2.780
2011	0.349 ***	3.272	0.368 ***	3.452	2020	0.269 ***	2.600	0.288 ***	2.870

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

reports the global Moran’s I statistics for both the urban spatial structure and the Urban-Rural income gap variables. The results demonstrate that the global Moran index values for these variables in Northeast China from 2003 to 2020 are statistically significant at the 1% level. This significance justifies proceeding with the subsequent spatial econometric analyses.

This study determines the most suitable spatial econometric model through a series of applicability tests, the results of which are detailed in

Table 5. Test of spatial panel econometric model.

Test Methods Purpose	Statistic	p Value	Test Methods Purpose	Statistic	p Value
LM_Error	49.872	<0.001	Hausman	48.640	<0.001
RLM_Error	3.433	0.004	LR_SAR	70.150	<0.001
LM_Lag	85.003	<0.001	LR_SEM	70.230	<0.001
RLM_Lag	38.564	<0.001	LR-id	54.430	<0.001
Wald_SAR	23.07	<0.001	LR-time	313.14	<0.001
Wald_SEM	21.83	<0.001

Note: LM and RLM denote the Lagrange multiplier test and its robust test, respectively, LR denotes the likelihood ratio test, and Hausman denotes the Hausman test.

. The LM test and robust LM test are significant at the 1% level, indicating that the spatial panel Durbin model is appropriate. Additionally, both the Wald test and LR test show significance at the 1% level, indicating that the spatial panel Durbin model cannot be reduced to either the spatial panel lag model or the spatial panel error model. Furthermore, the Hausman test and LR test provide strong evidence in favor of using the individual and time-fixed effects model. Therefore, this study selects the spatial panel Durbin model with individual and time-fixed effects for further analysis.

The spatial panel Durbin model incorporates lagged variables, which implies that the coefficients for the explanatory variables may not fully reflect their actual effects on the Urban-Rural income gap, as noted in previous studies [53]. This study differentiates between the direct and indirect effects of urban spatial structure on the Urban-Rural income gap using the partial differentiation method, with the results presented in

Table 6. Spatial effect decomposition of spatial Durbin model based on panel data.

Variable	Geographic Distance Matrix		Economic Distance Matrix
	Direct Effect	Indirect Effect	Direct Effect	Indirect Effect
Uss	−0.246 *** (2.590)	−0.058 ** (2.050)	−0.170 ** (2.280)	−0.050 ** (2.390)
Urban	−0.146 ** (2.160)	0.316 ** (2.280)	−0.081 ** (2.460)	0.171 * (1.860)
Grow	−0.001 ** (2.010)	0.001 (1.320)	−0.001 ** (2.110)	0.001 ** (2.480)
Size	0.065 ** (2.120)	−0.200 (1.490)	0.065 ** (2.010)	−0.005 (0.080)
Stru	−0.038 (1.090)	−0.208 (1.300)	−0.006 (0.180)	−0.004 (0.060)
Inv	−0.011 ** (2.210)	−0.099 ** (2.390)	−0.016 ** (2.120)	−0.004 * (1.920)
Open	−0.039 ** (1.970)	−0.106 (1.390)	−0.042 ** (2.160)	−0.056 (1.360)
ρ	0.378 *** (2.990)		0.178 ** (2.090)
Log L	1638.847		1620.152
N	666		666

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

. The analysis shows that the direct impact of urban spatial structure on the Urban-Rural income gap in Northeast China is significant; specifically, a higher municipal spatial structure index correlates with a reduction in the Urban-Rural income ratio. This implies that the monocentric urban spatial structure plays a pivotal role in reducing the Urban-Rural income gap in the region. In addition, the results indicate a significant indirect effect, demonstrating that the monocentric urban spatial structure contributes to reducing the Urban-Rural income gap through spillover effects, which also benefit neighboring areas and help reduce their Urban-Rural income disparities. By fostering a monocentric municipal structure, Northeast China enhances agglomeration effects, increases scale and industrial efficiency, and raises income levels for both urban and rural populations. This structure also boosts demand for rural labor in cities, facilitates the integration of local and neighboring rural workers into urban job markets, and improves the mobility of resources between urban and rural regions, further reducing the Urban-Rural income disparity.

With respect to the control variables, the analysis yields several noteworthy results. To begin with, the urbanization rate demonstrates a significant negative direct impact on the Urban-Rural income gap, while its indirect effect is significantly positive. This dual impact implies that while urbanization in Northeast China contributes to narrowing the income gap within the region, it simultaneously slows the reduction of this gap in surrounding areas. Given that Northeast China initiated its urbanization from a relatively high base and has reached an advanced level of development, the migration of numerous rural laborers to urban centers allows them access to valuable market information. This migration results in greater employment opportunities and improved income levels, thereby aiding in the reduction of the Urban-Rural income gap. However, the increasing urbanization rate amplifies local agglomeration effects, resulting in the creation of a ‘siphoning effect’ that hampers similar processes in neighboring areas, thereby exacerbating the Urban-Rural income disparity. Second, the positive direct effect of city size on the Urban-Rural income gap, along with a minimal spatial spillover effect, indicates that an increasing city size intensifies regional income disparities. In Northeast China, alterations in city size have a positive influence on income inequality between urban and rural areas. As cities expand, the resulting agglomeration effect raises the average per capita income of urban residents, further widening the income gap between urban and rural areas. Third, enhancing openness to external markets can help reduce the Urban-Rural income disparity in Northeast China. This expansion accelerates the transfer of key innovative resources, such as talent, technology, and investment. The expansion of innovative enterprises promotes employment growth, which helps absorb surplus rural labor and improve rural income levels, thereby reducing the income disparity between urban and rural areas. Finally, fixed asset investment shows a significantly negative effect on the Urban-Rural income gap, both directly and indirectly. This indicates that fixed asset investment not only reduces the income disparity within the region but also generates positive externalities that help reduce the Urban-Rural income gaps in surrounding areas.

Based on the preceding analysis, the confirmation of Hypothesis 3 indicates that the relationship between urban spatial structure and the Urban-Rural income gap in Northeast China is characterized by spatial dependence. Monocentric urban development not only reduces the income gap within individual cities but also exerts an indirect influence on neighboring cities, narrowing their Urban-Rural income gaps through spillover effects.

4.5. Robustness Tests

4.5.1. Replacement of Core Explanatory Variables

To ensure the reliability of the conclusions, a robustness test was conducted using a variable substitution method. The explanatory variables are substituted with the first-place weight index for regression analysis. The robustness check results of the benchmark regression are reported in

Table 7. Robustness test of the benchmark regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Pri	−0.619 ***	−0.582 ***	−0.592 ***	−0.632 ***	−0.637 ***	−0.639 ***	−0.663 ***
	(0.119)	(0.105)	(0.114)	(0.120)	(0.120)	(0.124)	(0.119)
Urban		−0.426 ***	−0.414 ***	−0.458 ***	−0.509 ***	−0.537 ***	−0.501 ***
		(0.111)	(0.112)	(0.113)	(0.116)	(0.121)	(0.121)
Grow			−0.064	−0.072	−0.136	−0.117	−0.195 **
			(0.085)	(0.084)	(0.091)	(0.094)	(0.096)
Size				0.292 **	0.256 **	0.266 **	0.265 **
				(0.118)	(0.120)	(0.121)	(0.120)
Stru					0.572 *	0.540 *	0.581 *
					(0.304)	(0.307)	(0.305)
Inv						−0.027	−0.026
						(0.034)	(0.034)
Open							−0.074 ***
							(0.024)
_cons	−3.002 ***	−3.567 ***	−2.919 ***	−2.515 ***	−1.869 ***	−2.091 ***	−1.639 ***
	(0.109)	(0.271)	(0.300)	(0.312)	(0.372)	(0.414)	(0.231)
N	666	666	666	666	666	666	666
R2	0.778	0.798	0.799	0.806	0.810	0.810	0.821

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

, whereas

Table 8. Robustness test of panel spatial Durbin model.

Variable	Geographic Distance Matrix		Economic Distance Matrix
	Direct Effect	Indirect Effect	Direct Effect	Indirect Effect
Pri	−0.261 ** (2.370)	−0.012 ** (2.540)	−0.214 *** (3.380)	−0.030 ** (2.130)
Urban	−0.149 ** (2.330)	0.348 ** (2.570)	−0.077 ** (2.440)	0.022 ** (2.470)
Grow	−0.001 ** (1.990)	0.001 * (1.660)	−0.001 ** (1.980)	0.001 ** (2.150)
Size	0.066 ** (2.210)	−0.120 (0.910)	0.067 ** (2.070)	−0.007 (0.120)
Stru	−0.045 (1.300)	−0.195 (1.220)	−0.013 (0.400)	−0.014 (0.200)
Inv	−0.012 ** (2.430)	−0.099 ** (1.990)	−0.018 ** (2.400)	−0.010 ** (1.960)
Open	−0.037 ** (2.070)	−0.094 (1.330)	−0.041 ** (2.380)	−0.050 (1.030)
ρ	0.399 *** (3.080)		0.169 ** (1.980)
Log L	1639.726		1618.849
N	666		666

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

displays the outcomes of the robustness test based on the spatial Durbin model. The findings indicate that, regardless of changes in the method of measuring urban spatial structure or the application of different spatial weights, the conclusions remain consistent with the previous empirical analysis, thereby affirming their robustness.

4.5.2. Tests for Mediating Effects

To verify the robustness of the mediating effect, this study employs both the Sobel test and the Bootstrap test to examine the influence of labor productivity. The regression results show that the Sobel test significantly rejects the null hypothesis, while the Bootstrap test produces a confidence interval of [0.0254082, 0.097614], with no values crossing zero, confirming the mediating effect of labor productivity. Additionally, to address the potential endogeneity of the mediator variable, this study uses the first-order lag of the mediator as an instrumental variable in a system GMM analysis. The AR (2) p-value of 0.160 and the Hansen test p-value of 0.352 indicate proper model specification and the validity of the instrumental variables. After adjusting for endogeneity, the mediation effect remains significant, with no change in direction, further validating the robustness and reliability of the mediation pathway between the explanatory variable and dependent variables.

4.5.3. Endogeneity Analysis

Addressing endogeneity is essential to ensure the robustness of regression outcomes and to strengthen the credibility of the research conclusions [54]. Regions with more balanced Urban-Rural development tend to have relatively better conditions for optimizing industrial layout and resource allocation, which in turn affects urban spatial structure. As a result, there may be a mutual influence between Urban-Rural income disparity and urban spatial structure. To mitigate potential bias caused by reverse causality, this study employs a lagged model to address endogeneity concerns. Specifically, the one-year lagged urban spatial structure index is used as an instrumental variable in the regression. GMM estimation confirms the validity of the instrumental variable, as shown in

Table 9. Results of difference GMM estimation and system GMM estimation.

Variable	Difference GMM Estimation	System GMM Estimation
Uss	−0.117 *** (0.044)	−0.193 *** (0.067)
Control Variables	YES	YES
Time fixed effects	YES	YES
Individual fixed effects	YES	YES
AR (2) p-value	0.718	0.619
Hensen test p-value	0.642	0.552

Note: Significance levels are marked by *, **, and *** for 10%, 5%, and 1% respectively, with standard errors provided in parentheses.

. The Hansen test results demonstrate no over-identification issues. The findings indicate that, even after addressing endogeneity concerns, the conclusion remains that monocentric urban spatial structure development continues to contribute to narrowing the Urban-Rural income disparity.

5. Conclusions and Recommendations

5.1. Conclusions

This study analyzes 37 prefecture-level cities in Northeast China, using LandScan population data to construct an urban spatial structure index. It applies the mediating effect model and spatial panel Durbin model to quantify how changes in urban spatial structure influence the Urban-Rural income gap. Additionally, this study investigates the spatial effects and mechanisms behind these changes. The study derived the following conclusions. First, the monocentric urban spatial organization in Northeast China significantly reduces the Urban-Rural income gap. This finding holds true even after adjustments to the measurement method for urban spatial structure and considerations of endogeneity, highlighting that monocentric spatial structure provides a solid foundation for achieving balanced Urban-Rural development at the prefecture level. Second, mediating effect analysis confirms that a monocentric urban spatial structure in Northeast China can significantly narrow the Urban-Rural income disparity by improving labor productivity. Specifically, a 1% rise in the municipal spatial structure index leads to a 0.094% gain in labor productivity and a 0.190% reduction in the Urban-Rural income ratio. Third, results from the spatial panel Durbin model reveal that urban spatial structure has significant spatial spillover effects, where monocentric development in local cities helps reduce income disparities both within the cities and in neighboring areas. This finding suggests that the benefits of monocentric development extend beyond individual cities, contributing to a broader regional reduction in income inequality.

5.2. Policy Recommendations

The results indicate that a coherent spatial development strategy that emphasizes the strengthening of central cities, the improvement of labor mobility, and the enhancement of regional coordination can play a significant role in reducing income disparities between urban and rural areas and in promoting inclusive regional revitalization. Drawing on the empirical findings of this study, the following policy recommendations are proposed to support more equitable Urban-Rural development in Northeast China through spatial restructuring.

First, it is essential to strengthen the guiding role of monocentric urban spatial structures in narrowing the Urban-Rural income gap. Monocentric development promotes factor agglomeration, improves employment access, and enhances the economic spillover capacity of core cities. Policymakers should prioritize spatial planning strategies that reinforce the dominant position of central urban areas, ensuring that these areas can attract resources, generate employment, and serve surrounding regions. This can be achieved through infrastructure upgrades, public service concentration, and coordinated land-use planning that supports high-density, high-efficiency development in urban cores.

Second, improving labor productivity is a key mechanism through which urban spatial structure affects income distribution. In order to unleash this potential, targeted measures are needed to facilitate the orderly migration of rural populations to urban areas, especially small and medium-sized cities. Specific strategies include reforming the household registration system, improving access to urban social services for migrant workers, and developing affordable housing and vocational training programs. These efforts would help integrate rural labor into urban labor markets and increase income opportunities for rural residents.

Third, the spatial spillover effects of urban spatial structure call for enhanced interregional coordination. Urban planning should not only focus on individual cities but also address the broader urban system across Northeast China. Establishing cross-regional infrastructure networks, promoting shared public services, and coordinating industrial development among adjacent cities can amplify the positive externalities of monocentric development. Regional development policies should emphasize functional complementarity and institutional collaboration to reduce disparities across administrative boundaries.

5.3. Research Limitations and Prospects

This study focuses on the evolution of urban spatial structure and the Urban-Rural income gap from 2003 onward. The choice of this starting point reflects both policy relevance and data availability. In 2003, the Chinese government launched the Revitalization Strategy for Northeast China and Other Old Industrial Bases, marking a pivotal moment in the development of the region. This policy initiative brought significant changes to spatial patterns through industrial restructuring, infrastructure development, and regional coordination. Furthermore, consistent and comparable income data for urban and rural residents at the prefecture level have been available only since 2003, which defines the temporal scope of this analysis.

Future research may consider extending the study period by incorporating alternative data sources, such as historical GIS data, remote sensing imagery, or reconstructed income measures. These approaches may help capture longer-term spatial dynamics and provide deeper insights into the historical evolution of income inequality. Moreover, since this study focuses on the municipal level, future research could adopt a multiscale framework to account for spatial heterogeneity and scale dependence. Expanding the analysis to provincial or national levels would help clarify how spatial configurations influence income distribution across broader geographic contexts.

6. Discussion

This study finds that a monocentric urban form plays a significant role in narrowing the income gap between urban and rural areas in Northeast China. These results highlight the distributive effects of spatial concentration and offer new insights into the relationship between urban configuration and income equality. Existing research has mostly examined how urban spatial patterns relate to economic efficiency and environmental sustainability. However, this study extends the analysis to equity outcomes, thus broadening the scope of urban spatial planning research.

The negative association between the spatial structure index and the Urban-Rural income gap confirms the redistributive potential of monocentric urban systems. In cities with a concentrated spatial layout, economic activities and public resources are primarily located in the urban core. This configuration improves access to employment opportunities and services for rural migrants. The centralization of enterprises and labor demand facilitates better matching in the labor market and reduces spatial mismatches. These advantages are especially important for rural workers transitioning into urban labor markets.

Moreover, the mediation analysis shows that labor productivity serves as a key mechanism through which urban form influences income disparity. A more compact spatial organization enhances labor productivity by improving resource allocation, supporting industrial upgrading, and promoting efficient transportation and infrastructure use. As a result, higher productivity not only increases citywide income levels but also creates broader opportunities for rural residents to access better-paying jobs. This dynamic contributes to a more balanced pattern of income growth across urban and rural populations.

In the case of Northeast China, these findings are particularly relevant. The region, historically known as a major industrial base, is undergoing industrial restructuring while facing population decline and economic pressures. Most cities in the region already display monocentric spatial configurations that are theoretically favorable for agglomeration. However, the full benefits of this urban form may not yet be realized due to constraints such as shrinking labor pools and weak interregional coordination. Strengthening the core areas of these cities may enhance their capacity to attract labor and investment. By improving spatial cohesion and productivity, such development can help bridge Urban-Rural income divides and support inclusive regional revitalization.

In addition, the spatial econometric analysis confirms that the effects of urban form are not confined to individual cities. When a city adopts a more monocentric structure, the economic benefits extend to nearby areas through spillover effects. These include the diffusion of labor, capital, and knowledge across city boundaries. The results emphasize the importance of coordinated spatial planning across regions. Local governments should adopt strategies that foster integration and collaboration, rather than isolated development efforts within administrative borders.

In summary, the study provides strong empirical evidence that urban spatial configuration significantly influences income distribution. It contributes to a more nuanced understanding of how urban form, labor dynamics, and regional disparities are interconnected. These findings offer a valuable perspective for shaping urbanization strategies that promote both growth and equity.