In traditional production function research related to agricultural production, regional differences in agricultural labor capacity and information distribution are ignored. In the context of the digital economy (DE), this paper reintroduces information data as a new production factor to analyze the factor allocation mechanism. Based on previous studies, it conducts spatial Durbin model (SDM) analysis at the inter-provincial regional scale. It further studies the regional heterogeneity of land circulation distribution in China’s east, middle, and west. It finds that land circulation is consistent with the theory of the siphoning effect. DE (DE) variables accelerate the circulation of information factors and improve the allocative efficiency of land, technology, and capital factors. Finally, the article proposes suggestions on the role of the DE in improving agricultural production efficiency and narrowing the urban–rural gap.

1. Introduction

“Narrowing the gap between the rich and the poor and promoting common prosperity” is the fundamental demand of people all over the world [1], and the implementation of common prosperity policy is an important way to narrow the gap between the rich and the poor and between urban and rural areas. Inequality of distribution, the gap between rich and poor, and the gap between rural and urban areas have been the problems that governments in various countries need to address [1]. Global economic output has more than tripled since 1990, but the income share of the poorer half of the population has barely changed, with data showing that four out of every five people facing extreme poverty live in rural areas [2], suggesting that the greater disparity between rich and poor is occurring between rural and urban areas. The gap between rich and poor has been further widened especially since the end of 2019 with the outbreak of COVID-19 [3], and by the end of 2022, at least 75 million more people will have been pushed into poverty (living on less than US $1.90 a day) than was expected before the pandemic [4]. Since China’s reform and opening up, it has achieved economic development by leaps and bounds, and people’s income and living standards have been improved, but at the same time the gap between the rich and the poor in China is very serious, the Gini coefficient is an important indicator to measure the gap between the rich and the poor, and data show that China’s Gini coefficient is 0.468 [5], more than the United Nations warning line of 0.4. On the other hand, from 2000 to 2022, China’s urbanization rate increased from 36% to 65.22% [6, 7], which also implies a large loss of rural population, a widening gap between urban and rural areas, and a sharpening conflict between urban and rural areas. To this end, in 2022, the Chinese government proposed that “common prosperity” will be part of the “Chinese path to modernization” and emphasized that common prosperity is the essential requirement of socialism with Chinese characteristics [8–10] to demonstrate the Chinese government’s determination to solve the gap between the rich and the poor and between urban and rural areas.

Land circulation based on the context of the digital economy (DE) is an effective way to achieve common prosperity. China is currently in the era of a DE, the scale of China’s DE reached 6.13 trillion dollars, ranking second in the world, and the scale of the DE accounted for 38.6% of the gross domestic product (GDP)¹ and some studies have shown that the DE significantly contributed to the common prosperity [11, 12]. Moreover, the disparity between the wealthy and the impoverished is particularly pronounced in the context of urban and rural divisions, with individuals residing in rural areas consistently experiencing economic disadvantages. Land resources consistently represent a significant component of rural inhabitants’ income; thus, optimizing the utilization of land resources is of paramount importance. Land circulation encompasses the transfer, leasing, and mortgaging of rural land-use rights. In numerous countries, this phenomenon serves a vital function in fostering agricultural development, augmenting land-use efficiency, and bolstering rural economic growth. Introducing the DE into land circulation can promote rural productivity development, enable optimal allocation of rural land resources, and thus increase rural residents′ income [13, 14], helping to narrow the urban–rural gap and help rural residents achieve the goal of common prosperity. Thus, China’s use of land circulation in the context of a DE-based approach to raising the income of rural residents, narrowing the urban-rural gap and thus achieving common prosperity, is profoundly practical and provides case experiences for other regions of the world to narrow the gap between the rich and the poor and the urban–rural gap.

However, based on past literature examining the DE, land circulation, and common prosperity issues, we found that current academic research has mostly remained at the theoretical level and lacks empirical considerations. So can land circulation in the context of the DE really contribute to common prosperity? Is there spatial heterogeneity in land circulation in the context of the DE and common prosperity? Based on the above research questions, in order to further explore the influence mechanism among the three, this paper introduces data as a new production factor into the production function and introduces spatial measures for discussion through empirical studies.

Therefore, different from previous research, the main marginal contributions of this article are as follows:

(1)
Correcting the traditional Cobb–Douglas production function by introducing data as a new production factor into the production function.
(2)
Expanding the spatial econometric research on land circulation in the context of the DE and common prosperity.
(3)
Exploring the spatial heterogeneity of the impact of collective land as a production factor on the urban–rural gap in China by regional grouping.

2. Literature Review

“Common prosperity” originates from the labor theory of value in Marxist political economy. According to Marx, labor creates value and participates in the creation of use value [15]. Pareto further interpreted this concept as redistributing resources to at least one person without affecting the welfare level of others. However, this may lead to an idealistic state where resource allocation is uneven, resulting in a widening gap between the rich and poor. Therefore, Karl Dahlbeck improved and perfected the connotation of common prosperity, pointing out that the focus of common prosperity lies in the relationship between economic efficiency and economic fairness, which is the problem of income redistribution in economics [16]. This study focuses on improving the efficiency of land circulation based on the background of the DE, that is, improving economic efficiency and promoting economic fairness, thereby increasing the income level of urban and rural residents, reducing income inequality, and enabling all people to live a happy and prosperous life [9, 17].

The goal of “common prosperity” emphasizes the need to narrow the gap between urban and rural areas and raise the income level of the people [18]. Currently, China’s urban–rural gap and regional coordination ability are still relatively low, and the most arduous and difficult task of achieving common prosperity still lies in the rural areas. Land, as an important component of the rural economy, plays a critical role in increasing the income of rural residents, which can help to narrow the urban–rural gap and accelerate the achievement of the goal of common prosperity [19, 20]. In recent years, in order to improve the economic development level of rural areas, China has established a basic rural operating system, which guides rural residents to transfer the land contract management rights to collective organizations through land circulation, thereby increasing the property income of rural residents. At the same time, land circulation can also increase employment opportunities for rural residents, releasing labor from land and enriching the daily lives of the people, promoting rural common prosperity [21, 22]. In past literature, research methods concerning land circulation and common prosperity have primarily focused on case studies, empirical analysis, and theoretical modeling. These methods have aimed to explore the impact of land circulation on rural economic development, narrowing the urban–rural gap and improving social welfare from various perspectives. While previous studies have achieved certain results, some shortcomings remained, such as addressing the singular impact of land circulation, not fully considering the influence of digital economic development, and overlooking regional heterogeneity.

On the other hand, China is currently in the era of the DE, with a DE scale of 39.2 trillion yuan, ranking second in the world, and accounting for 38.6% of the country’s GDP [23]. The rapid development of the DE, represented by big data, artificial intelligence, and blockchain, empowers land transfer. It is mainly reflected in the fact that digital communication technology provides technical conditions for rural land transfer, reducing various difficulties encountered in the land transfer process. At the same time, the DE reduces the cost of the land transfer process through agricultural digitization, agricultural networking, and agricultural intelligence, providing new momentum for the high-quality development of the rural economy [24, 25]. The marginal contribution of this study lies in modifying the traditional Cobb–Douglas production function by incorporating data as a new production factor, which helps to more accurately measure the impact of land circulation on rural economic development and common prosperity in the DE [26, 27]. Furthermore, this study expands spatial econometric research on land circulation and common prosperity in the context of the DE, providing researchers with a new theoretical and methodological framework for a more comprehensive exploration of the relationship between land circulation and common prosperity [28, 29]. Additionally, this study examines the spatial heterogeneity of the influence of collective land as a production factor on the urban–rural gap in different regions of China, revealing the differential effects of land circulation on rural economic development and common prosperity across regions [30, 31].

Through reviewing and evaluating related research, many studies have delved into the theoretical origins of the concept of “common prosperity,” its application in modern Chinese society and economic development, and the importance of enhancing the efficiency of land circulation against the backdrop of the DE. However, current research also has several gaps, such as an excessive focus on the singular impact of land circulation, a failure to fully consider the role of digital economic development, and neglect of regional heterogeneity.

The necessity of this research lies in its response to the current challenges of economic equity and sustainable development faced by China and the world at large. In the context of the rapid development of the DE, exploring how digital technology can optimize land resource allocation and enhance the economic efficiency and fairness of rural areas is crucial for promoting income balance between urban and rural areas and achieving common prosperity. This study introduces data as a new production factor and modifies the traditional production function, aiming to more accurately measure the impact of land circulation on rural economic development and common prosperity in the DE. Furthermore, the research employs spatial econometric methods to examine the heterogeneity of effects across different regions, offering new perspectives on how land circulation impacts common prosperity in various areas.

In summary, this study fills the gaps in existing literature, providing theoretical and methodological innovations on how land circulation can promote rural common prosperity in the era of the DE. This has significant theoretical and practical implications for formulating more effective rural economic development policies, narrowing the urban–rural divide, and advancing common prosperity in China and developing countries.

3. Theoretical Mechanism

The rapid development of the DE has caused a new round of technological revolution and given new momentum to the high-quality development of China’s economy. Firstly, data, as a new type of production factor, can provide a positive correlation independent variable for overall economic development; then, technological progress and changes in production relations can help improve total factor productivity. In addition, the DE will promote the transformation of industrial networking and platform, optimize and improve the organization of production, enhance productivity, and ultimately increase production output. In terms of the mechanism of action, the development of the DE changes the traditional economic production function.

The spatial effects of land circulation on communality and prosperity indices manifest in multiple aspects. Firstly, geographical location and regional development disparities lead to differential impacts of land circulation in various areas. Secondly, the influence of land circulation on farmers′ income, agricultural production, and rural labor force also exhibits spatial variation. Moreover, the policy and institutional environment, rural social relations, and rural ecological environment pertaining to land circulation have spatial effects on communality and prosperity indices. The spatial distribution of the common prosperity is shown in Figure 1.

Details are in the caption following the image — **Figure 1 (a)**
Open in figure viewer PowerPoint

Spatial distribution of communality (a) and prosperity (b).

Therefore, when analyzing the impact of land circulation on communality and prosperity indices, it is necessary to consider various factors, including geographical location, regional development levels, changes in farmers’ income, agricultural production efficiency, labor mobility, policy frameworks, and rural social relations. This approach helps to more accurately assess the influence of land circulation on regional development and provides a basis for policy formulation.

Data are considered a new factor of production, and digital technologies optimize the efficiency of allocation of traditional factor resources, which together change the production functions and cost–benefit relations of traditional factor resources. The “DE” aims to develop information and analytical technologies that increase the share of industrial production through innovation. The innovative development path is characterized by an increased demand for intellectual resources, which leads to a higher concentration of intellectual capital in material production. The modern concept considers intellectual capital as a unity of its components, among which human capital (HC) is considered an active part of intelligence, a carrier of knowledge, skills, and experience. Thus, the market value of HC is determined by the positive side of intelligence, which allows people to transform information into knowledge, knowledge into action, and finally, develop action into a new and innovative commodity product.

Secondly, data, as a core production factor of the DE, can improve the appropriateness of supply and demand, help the industry refine its management and operation, and thus promote better appropriateness of supply and demand and a better flow of the industrial chain. The ability of experts to select production personnel is assessed through one’s perception indicators. For this purpose, various models represent and measure the overall indicators of individual cognitive ability to achieve individually assigned production functions.

Finally, by effectively integrating the supply and demand data of each digital terminal, the traditional knowledge and experience barriers of each industry will be broken, the comparative advantages of different regions will be better utilized, and the efficient synergy of production between different industries and regions will be promoted better to serve the new development pattern of “double cycle.”

The relationship between the DE, land circulation, and common prosperity can be deeply explored from the perspective of the production function. The production function describes how inputs (such as labor, capital, and land) are transformed into outputs under a given production technology. Here, we can use the Cobb–Douglas production function as a foundation, which is a commonly used form of the production function to describe the relative importance of different inputs in the production process.

()

where Y represents the output, such as the quantity of agricultural products. A is the technological advancement parameter, representing production efficiency. L represents labor input. K represents capital input, such as agricultural machinery and equipment. T represents land input. α, β, and γ are production elasticities, describing the relative importance of inputs in the output.

Land circulation can be viewed as a change in the land input T. Through land transfers, farmers can access more land for large-scale agricultural production [32]. This not only enhances production efficiency but also reduces the income disparity between urban and rural areas, as farmers can more easily sell their products to urban markets through agricultural e-commerce. This scale benefit can be represented through an extended Cobb–Douglas production function, which includes a scale benefit parameter.

()

where S represents the scale of production and θ represents the scale benefit.

If θ > 1, this implies that there are increasing returns to scale in agricultural production, meaning that an expansion in the scale of production leads to a more than proportional increase in output. This could be due to more efficient resource utilization, amplification of technological advantages, or other synergistic effects. In such a scenario, promoting land circulation and expanding the scale of production could bring about significant economic benefits. The development and expansion of rural e-commerce in China has become a significant trend in recent years. Especially in the 2010s, as urban markets gradually became saturated, many e-commerce companies began to expand their businesses to rural areas. This shift was not only actively responded to by e-commerce companies but also strongly supported by the Chinese government [33].

Firstly, from the perspective of e-commerce companies, in 2000, several well-known e-commerce companies, such as Taobao, JD.com, Suning, and Pinduoduo, developed various e-commerce platforms. The primary purpose of these platforms was to support farmers in selling their agricultural products to urban areas. This move was confirmed by the China International Electronic Commerce Center (CIECC) in 2010. At the same time, China’s national and local governments also recognized the potential of rural e-commerce and took a series of measures to promote its development in rural areas [34, 35]. In 2018, the Ministry of Commerce of China (MCC) issued a policy emphasizing the importance of developing rural e-commerce to revitalize the rural economy. Moreover, at the beginning of 2018, the country also proposed the “Development of Rural E-Commerce to Aid Poverty Alleviation” strategy, aiming to promote the development of e-commerce in impoverished rural areas. To further support this strategy, in 2010, the country launched the “Introduction of E-Commerce into Rural Pilot Counties” project, which mainly targeted impoverished areas, providing funding to support their e-commerce development.

The development of rural e-commerce fits the trend of Internet development and maximally meets the economic operation needs of rural areas [36]. Against the backdrop of comprehensively implementing the urban–rural coordinated development strategy, the market economy in rural areas is becoming increasingly prosperous, Internet infrastructure is continuously improving, and the consumption patterns of farmers are subtly changing. Digital finance provides farmers with funds, enabling them to purchase more capital inputs K, such as agricultural machinery and equipment. This further enhances production efficiency and supports production expansion.

To further measure the contribution of technological progress and factors of production to economic development, the above C-D functions are logarithmically taken separately to obtain the following equation.

()

In this paper, the DE is studied in agricultural organizations. The digitization of the sectoral economy, including agriculture, involves active investment policies in information technology, consulting, and data processing. Capital investments must be optimally allocated among the factors of production.

In summary, the relationship between the DE, land circulation, and common prosperity can be explained from the perspective of the production function. Digital technologies enhance production efficiency, land circulation enables farmers to engage in large-scale production, and digital finance provides farmers with the necessary funds, supporting production expansion. These factors collectively promote common prosperity.

4. Study Design

(i)
Basic model
This paper uses panel data from 30 provinces from 2011 to 2020 for empirical analysis. This paper uses a spatial econometric model to consider the differences between regions and the characteristics of digital technology with spillover effects. After the Wald and Hausman tests, the spatial Durbin model (SDM) is selected for the econometric analysis study.
(ii)
Data sources and processing
Considering the different data levels and the heteroskedasticity that may arise in the empirical process, all data are processed using normalization, and the missing data are completed using ARIMA interpolation.
The digital financial inclusion index is obtained from the Digital Finance Research Center of Peking University. In contrast, the other data are obtained from the China Statistical Yearbook, the China Population and Employment Statistical Yearbook, and the statistical yearbooks of each province.
(iii)
Variable selection

4.1. Dependent Variable: Common Prosperity

Accurately measuring common prosperity is a hot topic widely discussed in academic circles. In recent years, China’s urbanization rate has been relatively high, especially the massive transfer of the rural labor force, which has led to the shortage of an adequate supply of highly qualified labor required for agricultural production, and more and more relatively low-qualified laborers have started to combine with the land; at the same time, the massive reduction of the rural population has led to a considerable reduction of the agricultural labor force on the same amount of land, resulting in a sizeable urban–rural gap, which is not conducive to promoting common prosperity in rural areas. Therefore, the focus of the study of common prosperity should be on how to reduce the gap between rural and urban areas. However, combing the literature, we found that the current academic communality has not formed a unified view on the indicators to measure the level of common affluence in the present world. Most of them use indicators such as GDP per capita, urban–rural income gap, binary contrast index, and Thayer index to measure the level of common affluence, which mainly use the entropy weight method and factor analysis method, the level of common prosperity. Considering that the measurement of the common prosperity level is not a single-level indicator, it should focus on multiple levels. Therefore, the commonality index uses indicators such as the Gini coefficient, urban–rural income disparity, and urbanization rate, which are entropy-weighted. To maintain consistency in data calculations, the data have been normalized. Additionally, this study refers to the measurement of affluence proposed by Zheng, Zou, and Li [37], which combines the income levels of both rural and urban areas. Similarly, the index has also been normalized for the purposes of this analysis. These indices measure the level of common affluence in 30 provinces from 2011 to 2020 through the entropy weight method.

Overall, the level of prosperity has exhibited an increasing trend over time. As can be seen from Figure 2, the eastern region has a higher level of prosperity, while the level of prosperity in the western region remains relatively low. From a regional perspective, areas with higher levels of economic development have higher levels of prosperity due to their stronger economic strength, while some central and western regions have relatively low levels of prosperity due to differences in resource endowment and geographical location. However, in contrast to the level of prosperity, the level of commonality does not show a clear increasing trend over time and does not exhibit significant high or low values in terms of regional distribution.

4.2. Independent Variable

Land transfer area (TLA) refers to the essential role of land in the production of commodities, serving as the foundation of various production models.

Land circulation refers to the process by which households with land contract management rights transfer the operational rights of their land to other households or economic organizations, while retaining the contract rights.

Land circulation plays a crucial role in improving land-use efficiency. Consolidating fragmented small plots into larger, more manageable units enhances agricultural productivity and competitiveness. ²Moreover, large-scale land operations provide the necessary foundation for introducing advanced agricultural technologies and machinery, which boosts both production efficiency and the quality of agricultural output. ³Research conducted by Li et al. provides empirical evidence that the enhanced intensity of land transfer can reduce multidimensional poverty. This reduction is a crucial factor in achieving common prosperity, as it enhances overall economic conditions and improves welfare levels in rural areas [38]. Macroeconomics often uses quantity to measure the scale of production, and this paper draws on Jiang et al. [38] to study the impact of land circulation on common prosperity using the square meters of land circulation.

4.3. Moderating Variable: DE

DE-enabled land circulation has improved production conditions and provided infrastructure guarantees for common prosperity in rural areas. However, the DE, as a new type of production factor, involves a wide range of fields, and academics have not yet formed a unified standard, primarily measured in terms of digital inclusive finance development index, Internet penetration rate, Internet personnel output efficiency, and telecommunication user usage [39, 40].

Wu and Feng [41] demonstrated that digital financial inclusion significantly impacts China’s provincial economic growth, establishing its importance for economic development. Similarly, Ahmad et al. [42] introduced a digital financial inclusion index based on payment data across 52 developing countries, underscoring fintech’s role in enhancing financial accessibility. Khera et al. [43] further confirmed the influence of economic growth, government policies, Internet access, and credit expansion on digital financial inclusion through spatial econometric analysis in China. Li and Peng [44] and Hu et al. [45] highlighted digital financial inclusion’s role in improving welfare and fostering rural revitalization, respectively, showcasing its breadth in impacting various development aspects. Collectively, these studies indicate that provincial-level digital financial inclusion indices are vital tools for accurately gauging the DE’s health, reflecting their significant contributions to economic growth, welfare improvement, and rural development support.

This paper draws on the work of Zhao et al. [46] and Li et al. [47], who constructed a comprehensive index with five dimensions, mainly including Internet penetration rate, Internet practitioners, interconnection output, number of mobile Internet users, and total digital inclusive finance index, and assigned different weights to each indicator to measure the comprehensive DE development index using the entropy value method.

4.4. Controlled Variables

To ensure the reliability of the study results, it is also necessary to set some control variables that affect common prosperity. In this paper, we refer to the studies by Gao et al. [48], Yan et al. [49], and Gan [50] to select relevant control variables. Based on these references, we identify the following seven control variables. (1) Social consumption (SC) level: The level of SC reflects the macroeconomic impact, affecting the urban–rural income gap, and is expressed as the proportion of total SC goods to regional GDP. (2) Technological innovation (TI): Innovation is the fundamental driving force of economic development and is a crucial indicator of regional economic strength. Promoting high-quality economic development with an innovation drive helps accelerate the pace of achieving common prosperity, mainly expressed using the number of patents granted. (3) HC: The essence of common prosperity is to focus on people, and the improvement of HC level helps to narrow the gap between urban and rural areas, especially the agricultural and technical talents with high education can drive the rural areas to realize common prosperity. In this paper, we use the average years of education, including six years of elementary school, nine years of middle school, 12 years of high school, and 16 years of college and a bachelor’s degree or above, measured by using the ratio of the sum of years of education of employees at each stage to the total number of employees in the region in that year. (4) Unemployment rate (urUR): An increase in the rural employment rate can reduce the urban–rural gap, and the urUR is a crucial indicator reflecting rural economic development. It is expressed as the ratio of the number of unemployed people to the number of people employed in the year. (5) Industrial structure (IS): IS affects high-quality economic development, especially the IS in the DE can help rural areas develop unique industries. It is expressed by the ratio of the tertiary industry to the secondary industry. (6) Foreign trade dependence (TDI): The higher the level of openness to the outside world, the more it is conducive to increasing China’s imports and exports and improving the quality of China’s economic development. It is expressed as the total imports and exports ratio to regional GDP.

(iv)
Descriptive analysis

To further understand the characteristics of the selected variables, as demonstrated in Table 1, descriptive statistical analysis is conducted. This analysis provides an overview of the distribution, central tendency, and data distribution shape of the key variables in the study. The insights gained from this descriptive analysis will lay the groundwork for the subsequent empirical analysis by highlighting potential patterns and relationships among the variables.

Table 1. Descriptive statistical analysis.

Index	Prosperity_degree	Communality_degree	TLA	DE	SC	TI	HC	urUR	IS	TDI
Count	300	300	300	300	300	300	300	300	300	300
Mean	0.256	0.478	0.207	0.200	0.608	0.082	0.338	0.606	0.104	0.168
Std	0.174	0.229	0.198	0.192	0.124	0.126	0.175	0.188	0.165	0.192
Min	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.135	0.288	0.064	0.091	0.536	0.013	0.240	0.529	0.030	0.052
50%	0.222	0.480	0.152	0.136	0.617	0.037	0.320	0.632	0.049	0.087
75%	0.326	0.653	0.294	0.232	0.687	0.087	0.398	0.735	0.083	0.197
Max	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000

The findings presented in Table 2 indicate that the overall differences in the common prosperity and GDP growth rate are insignificant, indicating that China’s comprehensive economic strength has improved significantly. However, there are some differences in TI and TDI, which the differences between regions may cause. Generally speaking, regions with higher levels of economic development have higher technological R&D capabilities under their geographical location and resource endowments, which are conducive to expanding net exports of products and increasing trade dependence. In addition, the level of unemployment in China is relatively high, with an average value of 0.606. On the one hand, the rise of the DE has given rise to the platform economy, which has increased the employment rate of takeaway workers and increased the employment opportunities of several low-skilled workers; on the other hand, the impact of the epidemic has caused some real enterprises to close down, reducing the employment rate of the real economy.

Table 2. Skewness and kurtosis.

Index	Skewness	Kurtosis
Prosperity_degree	1.589	3.452
Communality_degree	0.069	−0.908
TLA	1.693	3.359
DE	2.340	6.179
SC	−0.270	1.776
TI	3.402	15.222
HC	1.184	2.246
urUR	−0.793	0.909
IS	3.211	10.304
TDI	2.131	4.425

These descriptive statistics reveal important distinctions and similarities among the selected variables, which will be further explored in the empirical analysis. By examining these initial patterns, we aim to better understand how these variables interact and contribute to the broader economic outcomes under study.

5. Empirical Analysis

5.1. Endogenous Test

The Hausman test can be used to test for endogeneity. The model needs to be estimated using both fixed effects (FE) and random effects (RE), and then these two models are compared. If the FE model fits the data better, then endogeneity may be present. If the p value of the Hausman test is less than the significance level, then the null hypothesis can be rejected, indicating that the FE model is more appropriate.

The Wu–Hausman test is a statistical test used to compare the consistency between two estimators of the same dependent variable in a regression model. In this context, it is applied to examine the endogeneity of the explanatory variables in the model.

When comparing the two tables, we observe that Table 3 (communality degree) has evidence of endogeneity in the model, while Table 4 (prosperity degree) does not. This suggests that the explanatory variables in Table 3 may be correlated with the error term, potentially leading to biased estimates. In contrast, the variables in Table 4 are not significantly correlated with the error term, indicating more reliable estimates.

Table 3. Wu–Hausman test of communality degree.

	Parameter	Std. Err.	T-stat	p value	Lower CI	Upper CI	F-statistic	p value
TLA	−0.0124	0.0277	−0.4456	0.6562	−0.0670	0.0422
DE	0.1262	0.0599	2.1058	0.0361	0.0082	0.2441
SC	0.1584	0.0335	4.7244	0.0000	0.0924	0.2244
TI	0.5654	0.0454	12.454	0.0000	0.4761	0.6548
HC	0.3271	0.0483	6.7761	0.0000	0.2321	0.4221
urUR	−0.0437	0.0223	−1.9532	0.0518	−0.0876	0.0003
IS	0.5127	0.0529	9.6891	0.0000	0.4086	0.6169
TDI	−0.2604	0.0474	−5.4875	0.0000	−0.3537	−0.1670
SC	0.2861	0.0682	4.1929	0.0000	0.1524	0.4198
TI	0.6917	0.0907	7.6255	0.0000	0.5139	0.8695
HC	0.3394	0.0614	5.5243	0.0000	0.2190	0.4598
urUR	−0.0492	0.0233	−2.1088	0.0350	−0.0949	−0.0035
IS	0.5601	0.1004	5.5768	0.0000	0.3632	0.7569
TDI	−0.3161	0.0575	−5.4950	0.0000	−0.4288	−0.2033
TLA	−0.2891	0.1454	−1.9878	0.0468	−0.5741	−0.0041
Wu–Hausman test							4.434214385	0.036079

Table 4. Wu–Hausman test of prosperity degree.

	Parameter	Std. Err.	T-stat	p value	Lower CI	Upper CI	F-statistic	p value
TLA	−0.0099	0.0756	−0.1307	0.8961	−0.1588	0.1390
DE	−0.0967	0.1634	−0.5916	0.5546	−0.4183	0.2249
SC	0.2991	0.0914	3.2711	0.0012	0.1192	0.4791
TI	−0.0531	0.1238	−0.4286	0.6686	−0.2967	0.1906
HC	0.3661	0.1316	2.7811	0.0058	0.1070	0.6252
urUR	0.2399	0.0609	3.9368	0.0001	0.1200	0.3599
IS	0.2239	0.1443	1.5513	0.1219	−0.0601	0.5079
TDI	0.1565	0.1294	1.2100	0.2272	−0.0981	0.4112
SC	0.2013	0.1662	1.2116	0.2257	−0.1244	0.5270
TI	−0.1498	0.1833	−0.8175	0.4137	−0.5090	0.2094
HC	0.3567	0.1231	2.8985	0.0037	0.1155	0.5979
urUR	0.2442	0.0556	4.3884	0.0000	0.1351	0.3532
IS	0.1876	0.1211	1.5489	0.1214	−0.0498	0.4250
TDI	0.1992	0.1792	1.1115	0.2663	−0.1521	0.5505
TLA	0.2021	0.3405	0.5937	0.5527	−0.4652	0.8694
Wu–Hausman test							0.350036	0.554551

In both tables, some variables are statistically significant, meaning they have a significant effect on the respective dependent variables. It is essential to consider these variables when interpreting the results and making policy recommendations.

By employing the FE model in Table 3 (communality degree), the analysis controls for unobserved time-invariant variables, effectively reducing potential biases in the estimates. This methodology facilitates a more precise interpretation of the relationship between the explanatory variables and the dependent variable within the context of communality degree.

For both tables, researchers ought to consider the significant variables while also exercising caution when interpreting the results. It is essential to take into account statistical significance and potential endogeneity issues during the analysis. Utilizing the FE model in Table 3 (communality degree) enhances the reliability of the results and minimizes susceptibility to bias.

5.2. Spatial Econometric Regression Analysis

The results presented in Figure 3 reveal the strength and statistical significance of spatial autocorrelation in research data. Moran’s I value is 0.319, indicating a certain degree of positive spatial autocorrelation. This suggests that neighboring provinces exhibit some similarity in their levels of prosperity. However, this value is not very close to 1, so the spatial autocorrelation is not particularly strong. The p value is 0.001, which is less than 0.05, indicating that the spatial autocorrelation of “Prosperity_degree” is statistically significant. In other words, the observed positive spatial autocorrelation is unlikely to have occurred by chance.

For the “Communality_degree” variable: As is shown in Figure 3, Moran’s I value is 0.046, which is close to 0, suggesting weak spatial autocorrelation. This implies that neighboring provinces do not exhibit significant similarities or differences in terms of communality degree. However, the p value is 0.002, which is less than 0.05, indicating that the spatial autocorrelation of “Communality_degree” is statistically significant. This means that the observed spatial autocorrelation (albeit weak) is unlikely to have occurred by chance.

The “Prosperity_degree” variable exhibits significant positive spatial autocorrelation, indicating that neighboring provinces have some similarity in their levels of prosperity. The spatial autocorrelation of the “Communality_degree” variable is weak, but statistical significance tests suggest that the observed spatial autocorrelation is unlikely to have occurred by chance. Therefore, neighboring provinces may exhibit some degree of similarity or difference in their communality degrees, although this association is relatively weak.

It should be noted that these interpretations are based on statistical results, and specific conclusions may need to be considered in the context of the actual situation.

By calculating the global Moran index for each of the ten years from 2011 to 2020, we can see that it is significant, and the above is the Moran index for the year 2020. Next, make available multiple matrix tests and select the most significant matrix for the following tests and regressions.

Evidence from Table 5 focuses on the spatial dependence of the communality degree, a key indicator within China’s common prosperity index, across various provinces. The results from the Lagrange multiplier (LM) tests help us determine whether there is a significant spatial relationship between the provinces in terms of this indicator.

Table 5. Communality degree LM test.

Test	Statistic	df	p value
Spatial error:
Moran’s I	−11000.00	1	2.000
Lagrange multiplier	1.337	1	0.248
Robust Lagrange multiplier	2.316	1	0.128

Spatial lag:
Lagrange multiplier	0.004	1	0.951
Robust Lagrange multiplier	0.983	1	0.322

5.2.1. Spatial Error Model (SEM)

Moran’s I: This statistic measures the presence of spatial autocorrelation in the data. With a value of −11000, close to 0, and a p value of 2.000 (greater than 0.05), there is no evidence of significant spatial autocorrelation in the communality degree among Chinese provinces. LM: This statistic tests for the presence of spatial error dependence. With a value of 1.337 and a p value of 0.248 (greater than 0.05), we fail to reject the null hypothesis, indicating that there is no significant spatial error dependence. Robust Lagrange multiplier (R-LM): This more robust statistic has a value of 2.316 and a p value of 0.128 (greater than 0.05), suggesting that we cannot reject the null hypothesis of no significant spatial error dependence.

5.2.2. Spatial Lag Model

LM: This statistic tests for the presence of spatial lag dependence. With a value of 0.004 and a p value of 0.951 (greater than 0.05), we fail to reject the null hypothesis, indicating that there is no significant spatial lag dependence. R-LM: This more robust statistic has a value of 0.983 and a p value of 0.322 (greater than 0.05), suggesting that we cannot reject the null hypothesis of no significant spatial lag dependence.

Based on these results, we find no evidence of significant spatial dependence in the communality degree among Chinese provinces, as indicated by the p values for Moran’s I, LM, and R-LM all being greater than 0.05. This implies that the use of spatial econometric models (spatial error or spatial lag models) may not be necessary for this analysis, and an ordinary least squares (OLS) regression model could be employed instead.

An examination of Table 6 reveals that both the LM statistic and the R-LM statistic for the SEM show statistical significance, so the SEM model is chosen, while the R-LM statistic of the spatial lag model (SAR) is not significant, and further LR and Wald tests are needed. In order to test which effect model is appropriate for the model, LR tests were, respectively, done for personal effects and FE. The result is shown in Table 7.

Table 6. LM statistic test.

Test	Statistic	df	p value
Spatial error:
Moran’s I	97000.00	1.000	0.000
Lagrange multiplier	107.845	1.000	0.000
Robust Lagrange multiplier	46.311	1.000	0.000

Spatial lag:
Lagrange multiplier	87.958	1.000	0.000
Robust Lagrange multiplier	26.424	1.000	0.000

Table 7. Moran’s I test.

	Prosperity_degree	Communality_degree
Moran’ I	0.319	0.0463
p value	0.001	0.002

The above tests conclude that the time-space dual fixed model is preferable when choosing the SEM model. The Hausman test was further used to compare the model effects of FE with RE, and it was found that

()

The data had a negative value using the Hausman test, which is a more complex situation. The simulation analysis revealed that this is mainly because the asymptotic assumptions of the basic assumptions of the RE model could not be satisfied. Therefore, FE should be used in this case. To determine whether the model should use the SDM for the study, a Wald test was conducted, and the results indicated that it is appropriate.

As outlined in Table 8, Prob > chi2 is significant, and the hypothesis that the SDM model degenerates into SEM and SAR models is rejected. Meanwhile, the results of the LR test are consistent with the results of the Wald test, and the SDM cannot degenerate into a SAR model or an SEM model. In summary, we will choose the optimal SDM with FE and double fixation for spatial econometric analysis as described in the above tests. The immense log-likelihood value, as shown in Table 9, indicates a better model fit and a higher confidence level.

Table 8. Wald test and LR test.

	H0 hypothesis: SDM model degenerates into SEM model	H0 hypothesis: SDM model degenerates into SAR model
Wald test	Chi2(9) = 47.44 Prob > Chi2 = 0.00	Chi2(9) = 53.67 Prob > Chi2 = 0.00
LR test	LR Chi2(9) = 43.38 Prob > Chi2 = 0.00	LR Chi2(9) = 49.54 Prob > Chi2 = 0.00

Table 9. Model fit analysis.

Model fit analysis
Within = 0.7241
Between = 0.1778
Overall = 0.0182
Mean of fixed effects = 0.2541
Log-likelihood = 758.7168

5.2.3. Main Effects and Spatial Lag Interaction Effects

Table 10 presents comprehensive regression results from the SDM, revealing key insights into the spatial relationships among variables. Land circulation (TLA) demonstrates a significant negative impact on prosperity across all model settings, suggesting that land circulation may not effectively promote common prosperity within the studied regions. In contrast, the DE exhibits a significant positive influence on prosperity in most models, indicating a constructive role of DE development in enhancing common prosperity. However, the interaction between land circulation and the digital economy (TLA_DE) does not show significant effects in most models, implying that the direct impact of their interaction on common prosperity is unclear. Among the control variables, SC, IS, and high-quality agricultural development (Agri) positively affect prosperity, while TDI and urbanization negatively impact prosperity in certain models. TLA shows a positive spatial lag effect in the model controlling for individual effects, indicating that land circulation in neighboring regions may indirectly positively influence the region’s prosperity. The spatial lag effect of DE is not significant, suggesting that the spillover effects of the DE may not be as pronounced as those of land circulation. In the model controlling for time effects, the spatial lag effect of TLA_DE is significantly positive, indicating a potentially positive spillover effect on prosperity when combining land circulation with the DE in a spatial context.

Table 10. Spatial Durbin model regression results.

	Individual effects	Time effect	Bidirectional fixation
	′Ind	Time	Both′
Main
TLA	−0.119 ^∗∗∗	−0.147 ^∗∗∗	−0.134 ^∗∗∗
(−4.23)	(−5.52)	(−5.08)
DE	0.144 ^∗∗∗	0.117 ^∗∗	0.130 ^∗∗∗
(3.50)	(3.05)	(3.32)
TLA DE	−0.189	−0.134	−0.0279
(−1.13)	(−0.77)	(−0.17)
SC	0.0789 ^∗∗∗	0.110 ^∗∗∗	0.0613 ^∗∗
(3.80)	(4.22)	(3.11)
TI	0.0350	0.317 ^∗∗∗	0.0370
(1.40)	(10.86)	(1.58)
HC	0.00112	−0.00209	−0.0282
(0.04)	(−0.06)	(−0.95)
urUR	−0.0230 ^∗	0.0751 ^∗∗∗	−0.0217
(−2.03)	(5.06)	(−1.91)
IS	0.0993 ^∗∗∗	0.276 ^∗∗∗	0.0699 ^∗∗∗
(4.59)	(9.88)	(3.39)
TDI	−0.297 ^∗∗∗	−0.0405	−0.289 ^∗∗∗
(−10.45)	(−1.32)	(−10.88)
Urbanization	−0.128	0.158 ^∗∗	−0.229 ^∗∗
(−1.68)	(3.12)	(−3.12)
Industry	0.349 ^∗∗∗	0.0685	0.346 ^∗∗∗
(8.25)	(1.67)	(8.49)
Agri	0.428 ^∗∗∗	0.776 ^∗∗∗	0.460 ^∗∗∗
(7.42)	(11.21)	(8.08)
Policy	0.0263 ^∗∗	0.0113	0.0193 ^∗
(2.84)	(0.68)	(2.11)
id	0	0.000000122 ^∗∗∗	0
(.)	(6.03)	(.)
Wx
TLA	0.228 ^∗∗	0.428 ^∗∗∗	0.0822
(2.88)	(4.79)	(1.04)
DE	−0.138	0.170	−0.132
(−1.26)	(1.07)	(−1.06)
TLA DE	1.352 ^∗∗	−0.385	2.098 ^∗∗∗
(3.01)	(−0.66)	(4.58)
SC	−0.0478	0.107	−0.119 ^∗
(−0.90)	(1.43)	(−2.05)
TI	0.161 ^∗	−0.137	0.0997
(2.02)	(−1.07)	(1.15)
HC	0.159 ^∗∗	0.113	−0.0159
(2.59)	(0.98)	(−0.20)
urUR	0.00157	0.0921 ^∗	−0.00857
(0.06)	(2.24)	(−0.28)
IS	0.184	−0.00825	−0.113
(1.65)	(−0.06)	(−0.97)
TDI	0.233 ^∗	0.116	0.0475
(2.34)	(1.13)	(0.39)
Urbanization	0.485 ^∗	−0.570 ^∗∗	−0.655 ^∗
(2.17)	(−2.89)	(−2.42)
Industry	−0.300 ^∗	0.0529	−0.253 ^∗
(−2.39)	(0.43)	(−1.99)
Agri	−0.330 ^∗∗∗	0.452 ^∗	0.113
(−4.53)	(1.99)	(0.66)
Policy	0.0123	−0.133 ^∗	−0.0237
(0.53)	(−2.56)	(−0.82)
id	0	2.13e − 08	0
(.)	(0.30)	(.)
Spatial
rho	0.483 ^∗∗∗	−0.233	−0.0969
(6.72)	(−1.91)	(−0.81)
Variance
sigma2 e	0.000232 ^∗∗∗	0.000899 ^∗∗∗	0.000200 ^∗∗∗
(12.06)	(11.75)	(12.21)
r2	0.340	0.746	0.0242
N	300	300	300

Note: Ind refers to individual effects and Both refers to two-way fixed effects.
^∗p < 0.05.
^∗∗p < 0.01.
^∗∗∗p < 0.001.

5.2.4. Comprehensive Analysis of Spatial Autocorrelation Coefficient (rho) and Policy Intensity

The spatial autocorrelation coefficient (rho) is significantly positive in the model controlling for individual effects, indicating a presence of positive spatial dependency. However, in the model controlling for both time effects and individual effects (two-way FE), the significance of rho weakens, possibly due to the control of a greater number of variables. Policy intensity is significant in both the model controlling for individual effects and the two-way FE model, suggesting that the intensity of policy implementation indeed impacts common prosperity, and the introduction of this variable helps explain the main effects and spatial lag effects. The introduction of policy intensity as a control variable and the changes in significance for direct effects and spatial lag effects suggest that the model successfully isolates some impacts of synchronized policy implementation. Specifically, the change in significance of rho may indicate that introducing policy intensity helps distinguish between genuine spatial economic interactions and pseudo-spatial spillover effects that could arise from synchronized policy implementations. These findings provide a deeper understanding of how land circulation and the DE affect prosperity and offer valuable insights for policy formulation. The importance of controlling for policy intensity in research discussions should be emphasized, highlighting how it enhances the quality and reliability of spatial analysis.

5.3. Siphoning Effect

The study first analyzes the decomposition effect of spillovers, focusing on direct, indirect, and total effects. The results demonstrate that the moderating variables of prosperity are highly significant in all three effects. For the direct effect, a one-unit increase in TDI can lead to a −0.383-unit change in the dependent variable y (prosperity) in the region. Regarding the indirect effect, a one-unit increase in TI in neighboring regions can result in a 0.277-unit change in the dependent variable y (prosperity). As for the total effect, a one-unit change in the moderating variables land circulation and DE across all regions can cause a −0.117 and −0.191 unit change in the explanatory variable y (prosperity) in the region, respectively.

These findings indicate that both the DE and prosperity moderating variables can generate a certain degree of agglomeration effect, thereby accelerating the rationalization of labor and capital factor allocation. This highlights the importance of considering the interdependence between regions when examining the impact of these variables on prosperity.

Detailed examination of Table 11 demonstrates that the moderating variables of common prosperity are highly significant in direct, indirect, and total effects. In the direct effect, a one-unit increase in TDI can cause a −0.383-unit change in the dependent variable y in the region. In the indirect effect, a one-unit increase in TI in neighboring regions can cause a 0.277-unit change in the dependent variable. In the total effect, one unit change in the moderating variables land circulation (TLA) and DE in all regions can cause −0.117 and −0.191 units to change in the explanatory variable y in the region, respectively. This suggests that land circulation lead to a siphoning effect, where the redistribution of land resources results in a net outflow of economic benefits from certain regions, ultimately reducing regional prosperity.

Table 11. Decomposition effects of spillover effects.

Direct	Coefficient	Std. err.	z	P > z	95% conf.	Interval
TLA	−0.134	0.035	−3.810	0.000	−0.204	−0.065
DE	0.066	0.046	1.440	0.150	−0.024	0.156
TLA_DE	0.061	0.206	0.300	0.766	−0.342	0.465
SC	0.085	0.027	3.130	0.002	0.032	0.139
TI	−0.002	0.038	−0.060	0.950	−0.077	0.072
HC	0.025	0.038	0.660	0.508	−0.050	0.100
urUR	−0.018	0.015	−1.220	0.222	−0.047	0.011
IS	0.111	0.026	4.310	0.000	0.060	0.161
TDI	−0.383	0.041	−9.280	0.000	−0.463	−0.302

Indirect
TLA	0.018	0.103	0.170	0.863	−0.184	0.219
DE	−0.257	0.167	−1.540	0.124	−0.584	0.070
TLA_DE	1.803	0.577	3.120	0.002	0.671	2.934
SC	−0.071	0.062	−1.150	0.252	−0.193	0.051
TI	0.277	0.115	2.420	0.016	0.052	0.501
HC	0.020	0.111	0.180	0.856	−0.198	0.238
urUR	−0.022	0.036	−0.630	0.529	−0.092	0.047
IS	0.177	0.111	1.600	0.109	−0.039	0.394
TDI	0.304	0.132	2.310	0.021	0.046	0.562

Total
TLA	−0.117	0.110	−1.060	0.287	−0.331	0.098
DE	−0.191	0.170	−1.130	0.260	−0.523	0.142
TLA_DE	1.864	0.604	3.090	0.002	0.680	3.048
SC	0.014	0.069	0.210	0.837	−0.121	0.150
TI	0.274	0.098	2.810	0.005	0.083	0.466
HC	0.046	0.117	0.390	0.696	−0.183	0.274
urUR	−0.040	0.040	−1.010	0.315	−0.119	0.038
IS	0.288	0.114	2.530	0.011	0.065	0.512
TDI	−0.079	0.137	−0.580	0.564	−0.347	0.189

This indicates that both DE and common prosperity moderating variables can create a certain degree of agglomeration effect and accelerate the rationalization of labor and capital factors allocation. The decomposition of spillover effects provides a more detailed understanding of the factors contributing to regional prosperity and offers insights for policymakers to develop targeted strategies for enhancing common prosperity in various regions.

5.4. Further Discussion

This paper divides 30 Chinese provinces into eastern, central, and western regions to further discuss the variability among regions. Regressions are conducted by constructing two-way FE and changing some control variables. The regression results are shown in Table 9.

As shown in Table 12, the development of the DE and its impact on the prosperity of different regions must be considered against the backdrop of urban–rural disparities. The digital divide between urban and rural areas in China, coupled with the uneven distribution of industries, has led to significant differences in the ability of urban and rural residents to benefit from the advantages offered by the DE. Farmers earn relatively low incomes through land transfers, and their income growth primarily comes from leaving agricultural production to work in cities, where they can find higher-paying jobs created by the DE. This reality indicates that simply promoting land transfers or the development of the DE may not effectively narrow the income gap between urban and rural areas. Without targeted policies and a deep consideration of urban-rural differences, the impact of a single factor could be mixed up or even distorted by other relevant factors, leading to inaccurate empirical conclusions. Therefore, further research has reintroduced urbanization, IS, and high-quality agricultural development as three control variables, conducting a more in-depth regression analysis on the prosperity of the eastern, central, and western regions of China’s 30 provinces.

Table 12. Two-way stationary model regression results by region.

Variables	(1) East	(2) Central	(3) West
Variables	Prosperity	Prosperity	Prosperity
TLA	−0.00210	0.0275	−0.495 ^∗∗∗
TLA	(0.133)	(0.0402)	(0.134)

DE	0.326 ^∗	−0.468 ^∗∗∗	−0.157 ^∗
DE	(0.169)	(0.0850)	(0.0845)

TLA_DE	0.598	0.828 ^∗∗∗	1.801 ^∗∗∗
TLA_DE	(0.803)	(0.238)	(0.631)

SC	0.348 ^∗∗∗	0.0297	0.136 ^∗∗
SC	(0.113)	(0.0320)	(0.0544)

TI	0.103	0.362 ^∗∗	0.629 ^∗∗∗
TI	(0.0702)	(0.147)	(0.236)

HC	0.780 ^∗∗∗	0.125 ^∗	0.0284
HC	(0.122)	(0.0668)	(0.0427)

urUR	−0.0499	−0.0112	−0.0689 ^∗∗∗
urUR	(0.0692)	(0.0191)	(0.0188)

IS	0.262 ^∗∗∗	0.255 ^∗∗	0.331 ^∗∗
IS	(0.0845)	(0.108)	(0.159)

TDI	−0.519 ^∗∗∗	0.209	0.0307
TDI	(0.0883)	(0.136)	(0.0845)

Urbanization	0.515 ^∗∗	1.663 ^∗∗∗	1.496 ^∗∗∗
Urbanization	(0.228)	(0.136)	(0.152)

Industry	−0.0750	0.189 ^∗∗∗	−0.0398
Industry	(0.380)	(0.0652)	(0.0987)

Agri	0.606 ^∗∗∗	0.205 ^∗∗∗	0.206 ^∗∗∗
Agri	(0.0979)	(0.0668)	(0.0715)

Constant	−0.739 ^∗∗∗	−0.908 ^∗∗∗	−0.638 ^∗∗∗
Constant	(0.201)	(0.0928)	(0.0900)

Observations	100	100	100

R-squared	0.943	0.979	0.980

Number of id	10	10	10

Note: Standard errors in parentheses.
^∗p < 0.1.
^∗∗p < 0.05.
^∗∗∗p < 0.01.

Land transfers (TLA) have a nonsignificant impact in the eastern and central regions but show a significant negative effect on prosperity in the western region (−0.495 ^∗∗∗), indicating that land transfers may not be conducive to the development of prosperity in the western region. The DE has a positive but marginally significant impact on prosperity in the eastern region (0.326 ^∗), a significant negative impact in the central region (−0.468 ^∗∗∗), and a marginally significant negative impact in the western region (−0.157 ^∗). This reflects the different impacts of the DE on the prosperity of different regions. The interaction between land transfers and the DE (TLA_DE) shows a significant positive effect in all three regions, especially in the western region (1.801 ^∗∗∗). This indicates that the combination of land transfers and the DE has a synergistic effect on enhancing regional prosperity. Among the control variables, urbanization has a moderately significant positive impact on prosperity in the eastern region (0.515 ^∗∗), with even more significant impacts in the central and western regions (1.663 ^∗∗∗ and 1.496 ^∗∗∗, respectively), demonstrating the positive role of increasing urbanization levels on regional prosperity. The IS (Industry) has a significant positive impact on prosperity in the central region (0.189 ^∗∗∗), while its impact is not significant in the eastern and western regions.

High-quality agricultural development (Agri) has a significant positive impact on prosperity in all three regions, highlighting the importance of improving the quality of agricultural development to enhance regional prosperity.

The further analysis results show that the introduction of urbanization, IS, and high-quality agricultural development as control variables provides a more comprehensive perspective on the determinants of prosperity in different regions. Especially, the interaction between land transfers and the DE demonstrates a significant synergistic effect on promoting regional prosperity, although this effect shows notable differences across regions. Policymakers should consider these differences when formulating strategies to promote regional prosperity and tailor policies to the specific circumstances of each region. Future research should further explore the complex relationships between urbanization, IS, high-quality agricultural development, and regional prosperity to provide deeper guidance for regional development strategies.

In order to increase the robustness of the model, this study replaces the economic distance matrix with the geographical distance matrix and reconducts the empirical study of the SDM. The SDM results presented in Table 13 show that the coefficient for TLA (land circulation) is −0.0372 with a standard error of 0.0282, z-value of −1.32, and p value of 0.187. This implies that land circulation does not have a significant impact on the level of prosperity in the SDM. Moreover, the coefficient for TLA_DE (interaction between land circulation and the regulatory variable DE) is 0.0381 with a standard error of 0.1525, z-value of 0.25, and p value of 0.803, suggesting no significant interaction effect on the level of prosperity. Concerning control variables, SC and IS exhibit significant positive influences on the level of prosperity, while TDI presents a substantial negative impact. On the other hand, TI, HC, and urUR do not significantly affect the level of prosperity.

Table 13. Robustness testing after matrix replacement.

Prosperity∼e	Coefficient	Std. err.	z	P > z	95% conf.	Interval
Main
TLA	−0.03722	0.028234	−1.32	0.187	−0.09256	0.018117
TLA_DE	0.038059	0.152469	0.25	0.803	−0.26077	0.336892
DE	−0.09374	0.039905	−2.35	0.019	−0.17195	−0.01552
SC	0.05611	0.021589	2.6	0.009	0.013797	0.098423
TI	0.020899	0.021768	0.96	0.337	−0.02177	0.063565
HC	−0.00228	0.030169	−0.08	0.94	−0.06141	0.056848
urUR	0.002606	0.011681	0.22	0.823	−0.02029	0.0255
IS	0.089012	0.020639	4.31	0	0.048561	0.129463
TDI	−0.34599	0.026791	−12.91	0	−0.3985	−0.29348

Wx
TLA	0.043139	0.079826	0.54	0.589	−0.11332	0.199595
TLA_DE	−0.57818	0.434128	−1.33	0.183	−1.42906	0.272696
DE	0.607507	0.101491	5.99	0	0.408589	0.806426
SC	−0.06861	0.043139	−1.59	0.112	−0.15316	0.015943
TI	0.245102	0.061437	3.99	0	0.124688	0.365517
HC	0.251073	0.099631	2.52	0.012	0.0558	0.446345
urUR	0.089516	0.035834	2.5	0.012	0.019282	0.15975
IS	−0.02303	0.045324	−0.51	0.611	−0.11186	0.065808
TDI	0.095968	0.056756	1.69	0.091	−0.01527	0.207208

Spatial
rho	0.739562	0.058463	12.65	0	0.624977	0.854148

Variance
sigma2_e	0.000207	1.75E − 05	11.81	0	0.000173	0.000241

For the spatial lag variables (Wx), DE, TI, HC, and urUR demonstrate significant positive impacts on the level of prosperity, while land circulation (TLA), the interaction between land circulation and DE (TLA_DE), SC, and IS do not exhibit substantial effects on the level of prosperity.

In summary, the SDM results reveal that land circulation does not significantly influence the level of prosperity, and the interaction between land circulation and the DE does not present a substantial impact either. Among the control variables, SC and IS display significant positive effects on the level of prosperity, whereas TDI adversely affects it. TI, HC, and the urUR do not show any significant influence on the level of prosperity.

As for the spatial lag variables, DE, TI, HC, and the urUR exhibit significant positive impacts on common prosperity, implying that these factors in one region positively influence the common prosperity of neighboring regions. Land circulation, the interaction between land circulation and DE, SC, and IS do not have significant effects on common prosperity in terms of spatial lag variables.

For robustness checks, the results remain consistent even when the economic distance matrix is replaced with a geographic distance matrix. This further supports the findings from the primary empirical analysis.

Based on the analysis, we can derive the following policy recommendations. Governments should pay attention to the significant positive impact of SC levels and IS on common prosperity. Policymakers can promote common prosperity by enhancing SC levels and optimizing the IS. The significant negative effect of TDI on common prosperity should be considered, and governments should be aware of the influence of the external economic environment on domestic common prosperity. Measures should be taken to reduce reliance on foreign trade and strengthen the resilience of the domestic economy.

Although the impact of land circulation on common prosperity is not significant, land policy remains an essential policy tool. Governments should monitor the implementation outcomes of land circulation policies and make adjustments when necessary. In regional development policies, governments need to consider the impact of spatial lag effects on common prosperity. In particular, regarding DE, TI, and HC, policymakers should pay attention to the transmission of these factors across regions and adopt corresponding measures to promote coordinated development among regions.

5.4.1. Endogeneity Analysis in the Impact of the DE on Land Circulation

Through two-stage least squares (2SLS) regression analysis using digital finance policy and PM2.5 as instrumental variables (IVs) to examine the impact of the DE on land circulation (TLA), we obtained different perspectives and insights (see Appendix A (Tables A1, A2, A3, A4, A5) for details). These analyses are detailed in the appendix, providing an in-depth exploration of the endogeneity issue. Initially, using digital finance policy as an IV for DE, despite the first-stage regression not showing a significant impact of this IV on DE, which may indicate limited effectiveness of this instrument in the current model setup, it still provides important insights into how policy and economic factors influence DE. Specifically, control variables such as SC, TI, and HC demonstrate significant positive effects, while the urbanization rate (urUR) shows a negative effect. Furthermore, the 2SLS regression analysis does not reveal a significant impact of DE on TLA, suggesting a complex relationship.

On the other hand, using PM2.5 as an IV highlights the significant hindrance of environmental pollution to DE development, thus emphasizing the role of environmental factors in economic activities. In this context, DE shows a significant negative impact on TLA, indicating that deterioration in environmental quality may directly and negatively affect land circulation. The comparison of these two IV methods not only reveals the different roles of environmental and policy factors in economic development but also further clarifies the complexity and challenges of addressing endogeneity.

The results of the endogeneity analysis in the appendix show that through these two IVs, we are able to further address the endogeneity issue between DE and TLA. The Sargan test results support the validity of the chosen IVs, although the absence of specific values in the output reminds us to interpret these statistical results with caution. This analysis underscores the importance of selecting appropriate IVs in research studying the impact of the DE on land circulation, as well as how methodological improvements can reveal the dynamics between these variables. By employing these two distinct IVs, we not only address potential endogeneity concerns but also offer valuable insights into how environmental and policy interventions can promote sustainable development.

6. Results

The empirical analysis uncovers key insights into the effects of land circulation and the DE on regional prosperity. The econometric results indicate that land circulation (TLA) has a significant negative impact on prosperity in the western regions, with a coefficient of −0.495, suggesting it may hinder economic growth in these areas. Conversely, the DE shows a generally positive effect on prosperity, particularly in the eastern region, with a coefficient of 0.326, though its impact varies across regions. Additionally, the interaction between land circulation and the DE has a significant positive effect on prosperity, especially in the western region, with a coefficient of 1.801. Spatial econometric analysis also highlights notable spatial dependencies, with neighboring provinces exhibiting similar levels of prosperity. The SDM further confirms that while land circulation’s impact is not significant at the national level, the DE, TI, and HC significantly enhance regional prosperity, both directly and through spatial spillovers.

Furthermore, land circulation may induce a siphoning effect, particularly in regions with high TDI, reducing overall prosperity. In contrast, the DE generally strengthens prosperity by optimizing the allocation of labor and capital. Robustness checks and endogeneity analysis using 2SLS with IVs confirm the consistency and reliability of these findings, underscoring the complex interplay between the DE and land circulation.

7. Conclusion and Outlook

7.1. Conclusion

The empirical analysis reveals distinct effects of land circulation and the DE on regional prosperity. Land circulation is shown to negatively impact prosperity in certain regions, particularly those with higher levels of TDI, where it may contribute to a siphoning effect, leading to diminished economic benefits. In contrast, the DE generally exerts a positive influence on regional prosperity, especially in more developed areas, suggesting its role in enhancing economic outcomes through improved resource allocation. The interaction between land circulation and the DE exhibits a significant positive effect in specific regions, indicating a potential synergistic relationship between these factors. Moreover, the analysis confirms the existence of spatial dependencies, where regional prosperity is influenced by the economic performance of neighboring regions, emphasizing the interconnectedness of regional economies. These findings highlight the nuanced and varied impacts of land circulation and DE development across different regions.

The integration of digital technology into the agricultural sector and land allocation significantly enhances the efficiency of agricultural digitization. Large-scale implementation is crucial as it optimizes technology development, attracts investors, and enables efficient regional agricultural operations. The DE accelerates information flow, reduces acquisition costs, and allows farmers to access timely market information, facilitating real-time adjustments in labor, land, and technology, ensuring a balance between agricultural production and environmental protection. HC in the DE transforms labor into a cognitive production factor, driving innovation and agricultural modernization. The mutual promotion of IS optimization and digital technology invigorates agriculture’s digitization and expands the DE’s scale. Accelerating this development integrates the domestic economy with the global economy, promoting resource sharing. Overall, the development of the DE and its integration with land circulation enhance regional prosperity, necessitating tailored strategies that consider regional characteristics and leverage these synergistic effects for balanced and inclusive growth in China. Future research should further explore these dynamics to inform more effective policy interventions.

7.2. Suggestions and Outlook

This study highlights the critical role of the DE in driving common prosperity, particularly in rural China. To build on these findings, several strategic recommendations are proposed.

First, the continuous promotion of digital infrastructure construction provides the momentum to improve the quality of economic development and help achieve common prosperity in rural areas. The development of the DE has sparked a new technological revolution in China. This has led to the growth of the platform economy, creating employment opportunities for nontechnical personnel. Additionally, data technologies, such as artificial intelligence and blockchain, have driven innovation in technical fields, enhanced China’s research and development capabilities, and improved the quality of economic development [27]. At the same time, the rise of the DE has improved the backward production modes in rural areas. Inclusive finance represented by the DE also provides financing channels for farmers, solving the problem of the shortage of capital chains and helping to drive common prosperity in rural areas.

Secondly, our findings show that the DE has no significant spatial effect on common prosperity. This is related to the fact that the development of the DE is not constrained by spatial limitations [28]. As a result, it is important to emphasize this point and explore its beneficial aspects and suggestions. The DE’s ability to transcend geographic boundaries can be leveraged to promote equal access to resources, information, and opportunities for people in both urban and rural areas. This can help reduce regional disparities and promote more balanced development across different regions.

This paper combines theory and empirical evidence to enrich the study of common prosperity in the context of the DE, which has certain theoretical and practical values. However, this paper is mainly based on province-level research on common prosperity, and the scope of data is still relatively small, while the focus on common prosperity is mainly in rural areas. The conclusions drawn in this paper deserve further expansion. In the future, scholars can expand the sample data and use city-level or county-level data for further research.

In conclusion, the DE has played a significant role in promoting common prosperity in China, particularly in rural areas. The lack of spatial constraints in the DE’s development should be emphasized and used to its advantage in fostering equal opportunities and access to resources for people in both urban and rural regions. By focusing on reducing unemployment and promoting balanced regional development, China can continue to leverage the DE for inclusive growth and common prosperity.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Cong Xu: As the submitting and first author, Cong Xu was responsible for the conceptualization and design of the study, led the data analysis and interpretation of results, and undertook the majority of the writing and revision of the manuscript. Feng Wu: Contributed to data collection and analysis, assisted in developing the modeling and statistical analysis, and provided significant technical support for the methods and results. Jingran Lin: As the corresponding author, Lin handled the literature review, provided integration of the research background and theoretical foundation, and contributed to the drafting of the manuscript. Yie-Ru Chiu: As the corresponding author, Yie-Ru Chiu played a key role in project management, data verification, and the approval of the final version of the manuscript and coordinated the research activities of the team.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors received no financial support for the research, authorship, and/or publication of this article.

Endnotes

¹White Paper on the Development of China’s Digital Economy. (2021, April). CAICT. http://www.caict.ac.cn/kxyj/qwfb/bps/202104/t20210423_374626.htm.

²The Dilemma and Breakthrough Path of Rural Land Circulation (February 20, 2012). Studies on Land System of China. https://illss.gdufs.edu.cn/info/1026/3059.htm.

³Rural Revitalization Must Overcome the Four Major Dilemmas of Land Circulation. (December 26, 2021). Institute of China Rural Studies. https://ccrs.ccnu.edu.cn/List/H5Details.aspx?tid=19677.

Appendix A: Detailed Endogeneity Analysis

Table A1. Two-stage least squares regression analysis of land circulation impact using digital finance policy as an instrumental variable for the digital economy.

First-stage regressions
DE	Coefficient	Std. err.	t	P > t	95% conf.	Interval
SC	0.157358	0.044784	3.51	0.001	0.069213	0.245504
TI	0.12498	0.048684	2.57	0.011	0.029158	0.220803
HC	0.155712	0.06446	2.42	0.016	0.02884	0.282584
urUR	−0.14376	0.028117	−5.11	0	−0.1991	−0.08842
IS	0.236162	0.054145	4.36	0	0.129592	0.342732
TDI	0.41177	0.051431	8.01	0	0.310542	0.512998
Urbanization	0.206358	0.097339	2.12	0.035	0.014771	0.397944
Industry	0.16427	0.077222	2.13	0.034	0.012279	0.31626
Agri	0.321585	0.098184	3.28	0.001	0.128335	0.514835
Policy	−0.06574	0.034116	−1.93	0.055	−0.13289	0.001408
digital_finance	−0.0001	8.78E − 05	−1.17	0.241	−0.00028	6.97E − 05
_cons	−0.15427	0.053305	−2.89	0.004	−0.25919	−0.04936

Instrumental variables 2SLS regression
TLA	Coefficient	Std. err.	z	P > z	95% conf.	Interval

DE	0.966832	2.118658	0.46	0.648	−3.18566	5.119325
SC	0.420436	0.315322	1.33	0.182	−0.19758	1.038456
TI	0.265078	0.24713	1.07	0.283	−0.21929	0.749444
HC	−0.54892	0.362154	−1.52	0.13	−1.25873	0.160884
urUR	0.266443	0.296669	0.9	0.369	−0.31502	0.847905
IS	−0.23457	0.482359	−0.49	0.627	−1.17997	0.71084
TDI	−0.95134	0.953398	−1	0.318	−2.81996	0.917291
Urbanization	0.180205	0.430699	0.42	0.676	−0.66395	1.024359
Industry	−0.13877	0.414551	−0.33	0.738	−0.95128	0.67373
Agri	0.772326	0.70997	1.09	0.277	−0.61919	2.163841
Policy	0.014736	0.199135	0.07	0.941	−0.37556	0.405034
_cons	−0.33581	0.336589	−1	0.318	−0.99552	0.323889

Table A2. Two-stage least squares regression analysis of land circulation impact using PM2.5 as an instrumental variable for the digital economy.

First-stage regressions
DE	Coefficient	Std. err.	t	P > t	95% conf.	Interval
SC	0.157475	0.039248	4.01	0	0.080226	0.234725
TI	0.075349	0.04203	1.79	0.074	−0.00738	0.158074
HC	0.171614	0.059642	2.88	0.004	0.054224	0.289004
urUR	−0.14162	0.02534	−5.59	0	−0.19149	−0.09175
IS	0.197477	0.048374	4.08	0	0.102266	0.292689
TDI	0.494355	0.039005	12.67	0	0.417584	0.571125
Urbanization	0.131623	0.085962	1.53	0.127	−0.03757	0.300817
Industry	0.106808	0.07166	1.49	0.137	−0.03424	0.247851
Agri	0.328277	0.090667	3.62	0	0.149824	0.50673
Policy	−0.13726	0.027218	−5.04	0	−0.19083	−0.08369
PM2_5	−0.00203	0.000287	−7.09	0	−0.0026	−0.00147
_cons	−0.04369	0.050985	−0.86	0.392	−0.14404	0.056665

Instrumental variables 2SLS regression
TLA	Coefficient	Std. err.	z	P > z	95% conf.	Interval

DE	−0.70836	0.288377	−2.46	0.014	−1.27357	−0.14315
SC	0.655423	0.089754	7.3	0	0.479508	0.831338
TI	0.439043	0.09066	4.84	0	0.261352	0.616733
HC	−0.29215	0.129642	−2.25	0.024	−0.54624	−0.03805
urUR	0.038141	0.065026	0.59	0.558	−0.08931	0.165589
IS	0.132746	0.117252	1.13	0.258	−0.09706	0.362555
TDI	−0.20194	0.151072	−1.34	0.181	−0.49804	0.094153
Urbanization	0.46776	0.182291	2.57	0.01	0.110477	0.825044
Industry	0.152105	0.153681	0.99	0.322	−0.1491	0.453314
Agri	1.299543	0.206424	6.3	0	0.89496	1.704126
Policy	−0.13244	0.05948	−2.23	0.026	−0.24902	−0.01586
_cons	−0.58079	0.108529	−5.35	0	−0.79351	−0.36808

Table A3. Prosperity degree SDM with IV.

Prosperity	Coefficient	Std. err.	z	P > z	95% conf.	Interval
Main
TLA	−0.1344354	0.026442	−5.08	0	−0.186261	−0.0826098
DE_hat	0.1343494	0.040485	3.32	0.001	0.0550002	0.2136986
TLA_DE	−0.0278642	0.162662	−0.17	0.864	−0.3466755	0.2909471
SC	0.0048135	0.026881	0.18	0.858	−0.0478725	0.0574996
TI	0.0013956	0.024523	0.06	0.955	−0.046669	0.0494603
HC	0.0455896	0.037862	1.2	0.229	−0.0286179	0.1197971
urUR	−0.0575346	0.015933	−3.61	0	−0.0887619	−0.0263072
IS	0.1014376	0.022175	4.57	0	0.0579756	0.1448996
TDI	−0.1610103	0.044137	−3.65	0	−0.2475165	−0.0745041
Urbanization	−0.2530863	0.074382	−3.4	0.001	−0.3988729	−0.1072997
Industry	0.3644198	0.041914	8.69	0	0.2822707	0.4465689
Agri	0.3566467	0.066561	5.36	0	0.2261889	0.4871045
Policy	0.0173188	0.009172	1.89	0.059	−0.0006576	0.0352951

Wx
TLA	0.0821766	0.078728	1.04	0.297	−0.0721265	0.2364796
DE_hat	−0.1362109	0.128026	−1.06	0.287	−0.3871368	0.1147149
TLA_DE	2.097628	0.457786	4.58	0	1.200384	2.994873
SC	−0.0620114	0.07623	−0.81	0.416	−0.2114198	0.0873969
TI	0.1357961	0.091419	1.49	0.137	−0.043381	0.3149731
HC	−0.09069	0.106243	−0.85	0.393	−0.298923	0.1175431
urUR	0.0277195	0.049376	0.56	0.575	−0.0690559	0.1244949
IS	−0.1449241	0.120873	−1.2	0.231	−0.3818306	0.0919825
TDI	−0.082104	0.181039	−0.45	0.65	−0.4369342	0.2727262
Urbanization	−0.6309432	0.27737	−2.27	0.023	−1.174579	−0.0873075
Industry	−0.2722653	0.128203	−2.12	0.034	−0.5235382	−0.0209924
Agri	0.2182533	0.203242	1.07	0.283	−0.1800933	0.6165999
Policy	−0.021736	0.029264	−0.74	0.458	−0.0790921	0.0356201

Spatial
rho	−0.0968598	0.119351	−0.81	0.417	−0.3307827	0.1370631

Variance
sigma2_e	0.0001996	1.63E − 05	12.21	0	0.0001676	0.0002317

Table A4. Communality degree SDM with IV.

Communality	Coefficient	Std. err.	z	P > z	95% conf.	Interval
Main
TLA	−0.96004	0.340632	−2.820	0.005	−1.62767	−0.29241
DE_hat	−0.69057	0.519648	−1.330	0.184	−1.70906	0.327924
TLA_DE	3.298748	2.081639	1.580	0.113	−0.78119	7.378686
SC	0.143838	0.345627	0.420	0.677	−0.53358	0.821253
TI	−0.16759	0.315459	−0.530	0.595	−0.78588	0.450698
HC	−0.92083	0.484784	−1.900	0.058	−1.87099	0.02933
urUR	0.356093	0.204779	1.740	0.082	−0.04527	0.757454
IS	−0.28853	0.28574	−1.010	0.313	−0.84857	0.27151
TDI	−0.44718	0.568867	−0.790	0.432	−1.56214	0.667775
Urbanization	−0.75965	0.957961	−0.790	0.428	−2.63722	1.11792
Industry	−0.35056	0.538905	−0.650	0.515	−1.4068	0.70567
Agri	0.292199	0.857388	0.340	0.733	−1.38825	1.972649
Policy	−0.01204	0.117647	−0.100	0.918	−0.24263	0.21854

Wx
TLA	−1.6185	1.020804	−1.590	0.113	−3.61924	0.382235
DE_hat	−3.14255	1.651312	−1.900	0.057	−6.37906	0.093962
TLA_DE	4.765224	5.898222	0.810	0.419	−6.79508	16.32553
SC	−0.32936	0.978978	−0.340	0.737	−2.24812	1.589403
TI	−0.99047	1.155674	−0.860	0.391	−3.25555	1.274606
HC	−2.4135	1.365147	−1.770	0.077	−5.08914	0.262135
urUR	0.495662	0.636233	0.780	0.436	−0.75133	1.742656
IS	−4.85414	1.535864	−3.160	0.002	−7.86438	−1.8439
TDI	−8.04155	2.276191	−3.530	0.000	−12.5028	−3.5803
Urbanization	−0.14013	3.527575	−0.040	0.968	−7.05405	6.773788
Industry	−1.62291	1.6336	−0.990	0.320	−4.8247	1.578893
Agri	5.173626	2.595654	1.990	0.046	0.086238	10.26101
Policy	0.189497	0.374966	0.510	0.613	−0.54542	0.924417

Spatial
rho	−0.15668	0.106652	−1.470	0.142	−0.36571	0.052356

Variance
sigma2_e	0.033003	0.0027	12.220	0.000	0.02771	0.038296

Table A5. Digital finance as IV analysis and Sargan test.

Variables	(1)	(2)
Variables	Prosperity_degree	Communality_degree
DE	−3.744 ^∗∗∗	−0.836
DE	(1.188)	(0.913)

TLA	−0.726 ^∗∗∗	−0.149
TLA	(0.212)	(0.163)

SC	1.053 ^∗∗∗	0.257
SC	(0.289)	(0.222)

TI	1.030 ^∗∗∗	−0.101
TI	(0.225)	(0.173)

HC	0.420	−0.00780
HC	(0.275)	(0.211)

urUR	−0.406 ^∗∗∗	0.0284
urUR	(0.155)	(0.119)

IS	1.225 ^∗∗∗	0.305
IS	(0.296)	(0.227)

TDI	1.168 ^∗∗	0.305
TDI	(0.513)	(0.394)

Urbanization	1.251 ^∗∗∗	0.796 ^∗∗∗
Urbanization	(0.393)	(0.302)

Industry	0.824 ^∗∗	−0.277
Industry	(0.388)	(0.298)

Agri	2.428 ^∗∗∗	0.453
Agri	(0.701)	(0.539)

Constant	−1.263 ^∗∗∗	−0.108
Constant	(0.342)	(0.263)

Observations	300	300

R-squared	−1.463	0.158

Sargan test	0	0

Note: Standard errors in parentheses.
^∗p < 0.1.
^∗∗p < 0.05.
^∗∗∗p < 0.01.

Table A6. PM2.5 as IV analysis and Sargan test.

Variables	(1)	(2)
Variables	Prosperity_degree	Communality_degree
DE	1.030 ^∗∗∗	0.455
DE	(0.266)	(0.570)

TLA	0.0170	0.0515
TLA	(0.0554)	(0.119)

SC	0.0901	−0.00291
SC	(0.0783)	(0.168)

TI	0.413 ^∗∗∗	−0.268 ^∗
TI	(0.0675)	(0.145)

HC	−0.155 ^∗	−0.163
HC	(0.0889)	(0.190)

urUR	0.0718	0.158 ^∗
urUR	(0.0440)	(0.0942)

IS	0.306 ^∗∗∗	0.0568
IS	(0.0838)	(0.179)

TDI	−0.801 ^∗∗∗	−0.228
TDI	(0.122)	(0.262)

Urbanization	0.415 ^∗∗∗	0.570 ^∗∗
Urbanization	(0.127)	(0.271)

Industry	−0.288 ^∗∗	−0.578 ^∗∗
Industry	(0.114)	(0.245)

Agri	0.0413	−0.192
Agri	(0.187)	(0.401)

Constant	−0.139	0.196
Constant	(0.0938)	(0.201)

Observations	300	300

R-squared	0.693	0.185

Sargan test	0	0

Note: Standard errors in parentheses.
^∗p < 0.1.
^∗∗p < 0.05.
^∗∗∗p < 0.01.

Open Research

Data Availability Statement

The data supporting the findings of this study are available from the authors upon reasonable request.

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The Impact of Land Circulation on Common Prosperity in the Digital Economy Context

Abstract

1. Introduction

2. Literature Review

3. Theoretical Mechanism

4. Study Design