Coordinated Development of Farmland Transfer and Labor Migration in China: Spatio-Temporal Evolution and Driving Factors

Abstract

The coordinated development of farmland transfer (FT) and labor migration (LM) is of great efficiency significance to facilitate the development of rural economy and implement the rural revitalization strategy. The study used socioeconomic data from 30 provinces/autonomous regions/municipalities (hereafter referred to as provinces) in China to measure the coupling coordination degree (CCD) of FT and LM. It adopted the coupling coordination degree model (CCDM), exploratory spatial data analysis method (ESDA), and gray relational analysis model (GARM) to investigate the spatial differences in the CCD and its influencing factors. The results indicate the following: (1) Regional differences are evident despite the fact that the comprehensive evaluation level of FT and LM in the various provinces is relatively low and displaying a rising trend. (2) The CCD of FT and LM exhibits a fluctuating upward trend and is at the primary coupling coordination stage, with a significant difference in coupling coordination levels between regions, and a spatial distribution pattern of central region > eastern region > northeast region > western region. (3) The CCD shows a strong global spatial positive correlation with clear fluctuations, demonstrating the agglomeration dispersion development tendency over time; the local spatial agglomeration state emerges and stabilizes. According to the distribution pattern, the Western region exhibits weak agglomeration type, whereas the eastern and central regions exhibit strong agglomeration type. (4) There are significant variations between provinces in terms of the intensity of the CCD of FT and LM, as well as the level of concurrent employment business, the level of non-agricultural industry development, the level of urbanization, the level of agricultural equipment, and the land approval.

1. Introduction

As the key elements of agricultural production, the movement and reorganization of labor and land are not only significance to address problems affecting farmers, rural areas, and agriculture in China, but also beneficial for consolidate progress toward poverty alleviation and achieve common prosperity. However, in light of fundamental national conditions in China, which include a large population and limited land, as well as the institutional arrangement of a household contract responsibility system with output-linked compensation, the problems of the small scale of agricultural operations, the relatively low agricultural labor productivity, and the continuous increase of agricultural surplus labor become extremely prominent. In order to solve the above problems, farmers select different forms of farmland transfer (FT) and labor migration (LM) to optimize the allocation of land and labor factors from production sectors with relatively low marginal productivity to production sectors with relatively higher marginal productivity, so as to continuously improve the income of farmers and achieve the goal of rational allocation of land resources and labor resources [1,2,3]. In fact, influenced by resource endowment [4,5], welfare guarantee function of rural land [6], farmers’ risk awareness [7], land transfer cost [8], family labor allocation [9], the high level of labor migration, and a high level of farmland transfer has not occurred concurrently, and the farmland transfer has a deviation from labor migration in the development process. Such incoordination directly affects the orderly economic and social development, as well as agricultural and rural modernization construction. The issue of coupling and coordinated development of FT and LM has become one of the key concerns of rural economic research in the context of rural revitalization strategy. Therefore, this study intended to focus on the above questions and provide a reference for decision making to advance the mutual promotion of FT and LM in China and establish a positive interaction with organic coupling and coordination between them.

At present, the academic community has accumulated abundant research results related to FT and LM; there are two general categories of existing literature. On the one hand, it explored the mutual influence of FT and LM. The majority of the literature held the view that FT and LM had an inverted “U” relationship [10,11,12]. Concerning the effect that the FT has on LM, the current fragmentation of land prevents the rural labor migration and is not good for agricultural mechanization [13], while farmland transfer and land consolidation may solve this problem by reducing agricultural labor intensity, realizing labor resource reallocation, improving the use efficiency of rural labor force [14,15,16], improving the level of labor migration [17], increasing the income of farmers [18,19], and reducing the urban–rural disparity [20]. The decision-making behavior of farm households to transfer in or out farmland is correlated to the number of non-agricultural labor forces and agricultural labor productivity [21], whereas changes in land use are brought about by large-scale land transactions, while large-scale land transactions lead to changes in land use, both of which have a direct effect on household labor distribution and gender division of labor [22]. In addition, the absence of farmland transfer right [23], household income increases after labor migration [24], a well-functioning land market [25], and the instability of the duration of farmland contractual rights [26] can also significantly affect the level of LM. Due to the fixed time and maturity of crops, labor demand is mainly distributed throughout the growing season [27]; this means that the rural labor force has the possibility of moving out for employment. In terms of the impact of LM on FT, current studies concluded that LM could influence FT [28,29,30,31], specifically, the level of non-agricultural income and the proportion of non-agricultural employment positively affect the farmland transfer [32]: the larger the household labor force, the greatest probability that the peasant household is to transfer to the farmland, and the larger the proportion of non-agricultural labor force in the household labor force, the greater the possibility to transfer out of the farmland [33]; most current literature emphasizes that LM was beneficial to promoting land reallocation and reuse [34] with a phased impact [35]. Meanwhile, the expansion of urban non-agricultural population size has also become the main driving force of FT as the rural labor forces move to cities and towns [36,37].

On the other hand, the relationship between LM and the FT has been the subject of discussion. Existing studies have found an interaction relationship between LM and the FT; with the continuous rise of non-agricultural wages and a large number of rural labor force transferred to cities, the phenomenon of farmland abandonment is increasingly serious [38,39,40,41]; to solve this problem and improve the allocation efficiency of land resources, farmland should be transferred from farmers not willing to manage agriculture or with relatively lower agricultural productivity to those willing to manage agriculture or with higher agricultural productivity [42]. However, for some parts of the transferred labor force, the social pension function of farmland increases the opportunity cost of labor force transfer, thus hindering the transfer of farmland [43]. Some studies suggest that the development of LM and the FT are affected by the same factors, such as farmland certification [44,45,46,47]. In addition, a few existing studies have revealed the coupling and coordinated relation between FT and LM from the theoretical and practical levels. In the coupling theory analysis study, research scholars studied the internal mechanism of FT and LM based on different theoretical perspectives. For example, Wang et al. measured the CCD of FT and LM in Xinjiang, the study found that the CCD of FT and LM in Xinjiang was at the stage of high coupling and primary coordination, and there were large differences between regions [48].

To sum up, the existing literature is not comprehensive in the explanation of the relation between farmland and labor force. In reality, most existing studies have focused on the interaction between FT and LM and lack research on the spatial distribution characteristics and driving factors of the coupling coordination degree (CCD) between the farmland transfer and labor migration. The research units placed emphasis on a province in China, lacking meso-scale provincial comparative analysis. Therefore, it is necessary to conduct detailed and in-depth research on the CCD of FT and LM in China to obtain reliable conclusions, and the research conclusions of this study offer an effective supplement to the current research on resource allocation and the agricultural economy.

The questions that this paper hopes to answer are as follows: What is the coupling coordination degree between the two systems of FT and LM in provinces of China? What are the changes of the coupling and coordination relations between them in different space and time? What are the driving factors affecting the changes in the level of coupling and coordination between them? This study had three main research objectives: (1) Using the data of the panel statistics from 2015 to 2019, applying the linear weighting method to measured farmland transfer and labor migration evaluation index and comprehensive evaluation index in China, and constructing a coupling coordination degree model (CCDM) to measure the CCD of FT and LM in 30 provinces/autonomous regions/municipalities (hereafter referred to as provinces) in China. (2) Applying the exploratory spatial data analysis method (ESDA) investigate the spatial differences in the CCD. (3) Using gray relational analysis model (GARM) to determine the driving factors behind CCD of FT and LM.

This study contribution of this paper is as follows: Reliable evidence regarding the coordinated development of farmland transfer and labor migration was obtained by constructing CCDM. This study also adds evidence to the research framework on the coordinated development of farmland transfer and labor migration and provides guidance to implement the rural revitalization strategy for other regions and provinces.

The rest part of this paper is structured as follows: The second part introduces the data sources, study area and research methods, and the third part presents the econometric analysis results of the coupling coordination, spatio-temporal evolution, and driving factors of FT and LM. The fourth part analyzes and discusses the empirical results, and the fifth part draws the conclusions of this paper.

2. Materials and Methods

2.1. Data Sources

The data of FT and LM used in this paper came from China Rural Management Statistical Annual Report. The social and economic statistics data were taken from the China Statistical Yearbook and the China Rural Statistical Yearbook. The relative indexes are calculated according to the corresponding original data, and the individual data with missing or abnormal data are corrected by the mean replacement method. Moreover, due to the lack of some statistical data in Hong Kong, Macao, Taiwan, and Tibet, this study identified 30 provinces in China as the research units.

2.2. Study Area

Since the reform and opening up, the industrialization and urbanization level has continued to improve in 30 provinces in China, and rural areas have undergone many changes in the rural land system and household registration management system, which have significantly promoted the farmland transfer and labor migration. As recorded by China Rural Management Statistical Annual Report, there is a total of area of 3.70 million hectares of farmland transfer, with a total of 254 million people of labor migration in 2019 in China, and the level of farmland transfer or labor migration had a large increase compared with 2018. However, the improvement of farmland transfer level and labor migration level brings about changes in the extent of farmland use, and problems such as the contradiction between rural labor and farmland are gradually highlighted. At the same time, as China’s farmland transfer and labor migration expand, the level of them varies significantly between provinces. Specifically, only a few provinces such as Inner Mongolia and Liaoning in China have reached the equilibrium state of farmland transfer level and labor migration level. Other provinces have failed to achieve the trend of balanced development, which means that the two have not reached coupled and coordinated development. For details, see Figure 1. Therefore, the evaluation of the CCD of FT and LM is not only significant to improve of the use efficiency of land resource elements and labor resource elements, but also beneficial for the development of agriculture, rural areas, and farmers in China.

2.3. Methods

2.3.1. The Evaluation Index System

The study referred to previous studies [48] and the index system was built according to scientificalness, integrity, and operability to measure the development levels of FT and LM. In this study, we selected 14 indexes from three aspects to represent the degree of FT development, and 13 indexes from three aspects to represent the degree of LM development (

Table 1. Evaluation index system and weight of farmland transfer (FT) and labor migration (LM) in China.

Target layer	Standard Layer	Index Layer	Description	Weight
Farmland Transfer (FT)	Farmland conditions and business conditions	Farmers engaged in agricultural operation situation	The annual rate of increase in the number of farmers working in agriculture	0.0572
		Agricultural planting structure situation	The percentage of food crops planted	0.0952
		The average arable land area	The ratio of the total area of household contracted arable land to the total number of rural households	0.0946
		The level of agricultural scale management	The percentage of total households comprised of large-scale peasant households	0.0988
		The level of land approval	The percentage of all granted land management rights	0.0727
	The intensity of farmland transfer	The rate of FT	The proportion of the area of farmland transfer in the area of farmland contracted by households	0.0553
		The development level of farmland transfer	FT area’s annual growth rate	0.0523
		The participation degree of new business entities	The percentage of the area transferred by the new business entities in the total circulation area	0.0434
		The participation degree of farmers	The percentage of households that have been relocated compared to the total number of households	0.0732
	External conditions	The development level of new business entities	The rate of increase in the number of brand-new agricultural business entities over time	0.0635
		The level of agricultural equipment	Number of mechanical power forces per mu of land	0.0830
		Rural labor productivity	The ratio of agricultural output value and rural labor force	0.0743
		Growth in farmers’ disposable income	The rate of increase in farmers’ disposable income over time	0.0672
		The development level of planting industry	The output value of planting industry accounts for the output value of agriculture, forestry, animal husbandry and fishery	0.0693
Labor Migration (LM)	Rural labor force conditions	The percentage of agricultural labor force	Agricultural labor force accounts for the percentage of rural labor force	0.0942
		Average number of labor force per household	The ratio of the total number of rural labor force to the total number of rural households	0.0775
	The intensity of labor migration	The rate of LM	The proportion of labor migration in the rural labor force	0.0833
		The development level of labor migration	The annual rate of labor migration growth	0.0553
		The level of seasonal labor force transfer	The percentage of seasonal migration in the rural labor force	0.0566
		The level of perennial labor force transfer	The percentage of perennial migration in the rural labor force	0.0895
		The level of part-time job	The percentage of part-time farmers in the total number of rural households	0.0698
		The labor migration breadth	The number of migrations from outside the county accounted for the percentage of the rural labor force	0.0938
	External conditions	Income ratio between residents of urban and rural	The ratio of the disposable income of urban residents to that of rural residents	0.0821
		The ratio of family burden	The ratio of non-working-age population to working-age population	0.0801
		Growth in wage income	Wage income growth rate over time	0.0521
		The development level of non-agricultural industry	Non-agricultural industries’ added value made up a portion of the regional GDP	0.0812
		The rate of urbanization	The proportion of the urban population in the total population	0.0845

).

Before calculating the CCD of FT and LM, the study determined each index’s weight used the mean variance decision method. The specific steps are as follows: Firstly, to find a solution to the issue of significant differences in evaluation indicators, the extreme method is used to standardize the original indexes in the evaluation index system of FT and LM. The formula can be described by Equations (1) and (2):

$$\mathrm{Benefit}\mathrm{Indicator}:{X^{\prime }}_{ij}=\frac{X_{ij}-X_{\min }}{X_{\max }-X_{\min }}$$

(1)

$$\mathrm{Coat}\mathrm{Indicator}:{X^{\prime }}_{ij}=\frac{X_{\max }-X_{ij}}{X_{\max }-X_{\min }}$$

(2)

The $X_{ij}$, ${X^{\prime }}_{ij}$ represent the original and standardized values of the index, and the j index’s maximum and minimum values are respectively represented by X_max and X_min.

The determination of the index weight has an important influence on the accuracy and objectivity of the evaluation results. We select the mean variance decision method in the objective empowerment method to determine the index weight and obtain the weight of each criterion layer and each index. The formula can be described by Equations (3)–(6):

$$E(s)=\frac{1}{n}{\displaystyle \sum _{i=1}^n{Z}_{ij}}$$

(3)

$$\sigma (S_i)=\sqrt{{\displaystyle \sum _{i=1}^n{Z}_{ij}-E({S_i}^2)}}$$

(4)

$$w_i=\sigma (S_i)/{\displaystyle \sum _{j=1}^m\sigma (S_j)}$$

(5)

$$D_i(w)={\displaystyle \sum _{j=1}^m{Z}_{ij}{w}_i}$$

(6)

In the formula, E(s) is the mean of a random variable, σ(S_i) is the mean variance of S_i, w_i is the weight factor of S_i, and D_i (w) is the multi-indicator decision and ranking.

2.3.2. Linear Weighting Method

Based on the standardized weight and value of each index, drawing on the relevant research results [49], the evaluation index of FT and LM is calculated by linear weighting method. The formula can be described by Equations (7) and (8):

$$f(x)={\displaystyle \sum _{\mathrm{i}=1}^m{w}_i{x}_{ij}}$$

(7)

$$g(x)={\displaystyle \sum _{j=1}^n{w}_j{x}_{ij}}$$

(8)

The f(x) and g(x) indicate the evaluation index of FT and LM, w_i and w_j represent the index weights of FT and LM, respectively, and the index value following standardization is x_ij.

2.3.3. CCDM

In order to study the level of CCD between FT and LM, the study referred to relevant research [50], and constructed the coupling coordination degree model (CCDM). The formula can be described by Equations (9)–(11):

$$C={\left\{\frac{f(x)\times g(x)}{{\left[\frac{f(x)+g(x)}{2}\right]}^2}\right\}}^{\frac{1}{2}}$$

(9)

$$D=\sqrt{C\times T}$$

(10)

$$T=\alpha f(x)+\beta g(x)$$

(11)

where C is the coupling degree (CD) of FT and LM, D is the CCD of FT and LM, and T is the comprehensive evaluation index of them. α and β are the pending coefficients of FT and LM, respectively. The study accepts that the two subsystems of FT and LM are similarly significant, so the pending coefficient is α = β = 0.5.

Referring to the correlation study [51], the CCD between FT and LM was divided into 10 grades by using the uniform function distribution method (

Table 2. Classification standards for coupling and coordination degree.

CCD	Type	CCD	Type
0.00–0.09	Extreme coupling disorders	0.50–0.59	Barely coupling coordination
0.10–0.19	Severe coupling disorders	0.60–0.69	Primary coupling coordination
0.20–0.29	Moderate coupling disorders	0.70–0.79	Middle coupling coordination
0.30–0.39	Mild coupling disorders	0.80–0.89	Good coupling coordination
0.40–0.49	Near coupling disorders	0.90–1.00	Quality coupling coordination

).

2.3.4. ESDA Method

When carrying out the overall research on the provinces in China, we ought to follow a mix of overall and local development, propose a top-level design based on the strategic height, and formulate the coordinated development strategy of spatial linkage. Therefore, spatial analysis should also be introduced when analyzing the relevant problems of the provinces in China. According to the relevant literature [52,53], the study used the ESDA method to analyze the CCD, and studied the spatial agglomeration, dispersion, and interaction mechanism by describing and visualizing their spatial layout, which is typically divided into global and local spatial autocorrelations. A global Moran’s I was used to calculate global spatial autocorrelation in order to show how the CCD of FT and LM were distributed across the entire space. The formula is as follows:

$$\mathrm{I}=\frac{\mathrm{M}}{{\mathrm{S}}_0}\times \frac{{\displaystyle \sum _{i=1}^M{\displaystyle \sum _{j=1}^M{w}_{ij}\left(X_{\mathrm{i}}-\overline{X}\right)\left(X_{\mathrm{j}}-\overline{X}\right)}}}{{\displaystyle \sum _{\mathrm{j}=1}^M(X_{\mathrm{i}}-\overline{X}{)}^2}}$$

(12)

where M is the number of the study regions, and X_i and $\overline{X}$ represent the observed value and average value, respectively. The study regions i and j are weighted spatially as w_ij, and the space adjacent is 1 and space non-adjacent is 0. the range of the Moran’s I values is [−1,1], where a value larger than 0 indicates a positive correlation and a value lower than 0 indicates a negative correlation.

To determine the degree of spatial correlation and difference between the CCD of FT and LM in neighboring provinces, the local spatial autocorrelation test was calculated using local Moran’s I. The formula is as follows:

$$I_i=Z_i{\displaystyle \sum _{i=1}^M{W}_{ij}{Z}_j}$$

(13)

where W_ij are the spatial weights, while Z_i and Z_j are the normalized values of the observed values in the study regions i and j, respectively.

2.3.5. GRA Model

The driving factors of CCD of FT and LM were examined using the GRA model in 30 provinces in China [54]. The following is the workflow: Find out the feature sequence and the factor sequence. The characteristic sequence, which is denoted by Y0(m,t), is the CCD of FT and LM. Each driver was selected as the factor sequence and represented by Xi(m,t). Next, the grey correlation coefficients were determined. The formula can be described by Equation (14):

$$r_i(m,t)=\frac{\underset{i,m,t}{\min }\left|Y_0^{\prime }(m,t)-X_i^{\prime }(m,t)\right|+\rho \times \underset{i,m,t}{\max }\left|Y_0^{\prime }(m,t)-X_i^{\prime }(m,t)\right|}{\left|Y_0^{\prime }(m,t)-X_i^{\prime }(m,t)\right|+\rho \times \underset{i,m,t}{\max }\left|Y_0^{\prime }(m,t)-X_i^{\prime }(m,t)\right|}$$

(14)

Among them, Y′0(m,t) and X′i(m,t) represent the feature and factor sequences after standardized treatment, respectively, and the coefficient of resolution is ρ (ρ = 0.5).

Finally, we calculated the grey correlation degree of the panel data by Equation (15):

$$r_i=\frac{1}{M\times T}{\displaystyle \sum _{m=1}^M{\displaystyle \sum _{t=1}^N{r}_i(m,t)}}$$

(15)

In the formula, ri represents the gray correlation degree, where the larger the ri value, the stronger the correlation between the feature sequence and the factor sequence and the weaker the correlation.

3. Results

3.1. The Integrated Level of FT and LM Has Changed over Time in a Time Series

The level of China’s LM evaluation index was relatively high from 2015 to 2019, with an overall slight upward trend but obvious fluctuations, soaring to 0.5082 in 2019, up from 0.4958 in 2015, representing an average annual growth rate of 0.50%. While the index level showed a small decline from 2016 to 2017 and started to rise after reaching a minimum value in 2017. The evaluation index of FT fluctuated from 0.3798 in 2015 to 0.4009 in 2019, with an annual growth rate of 1.11% on average, and showed a good upward trend in all years except for a short decline in 2016. It benefited from the improvement of the level of FT and LM, the comprehensive evaluation index of them in China increased from 0.4378 in 2015 to 0.4546 in 2019, with an annual growth rate of 0.76% on average, and the comprehensive evaluation index during the study period showed a stable upward trend without obvious differences in the rate of change. For details, see Figure 2.

There are great differences in provincial FT, LM, and comprehensive evaluation index levels in China. The national average values of FT and LM evaluation indexes were only 0.3750 and 0.4882, respectively, from 2015 to 2019, whereas the comprehensive evaluation index only had an average value of 0.4316 (Figure 3). Among them, 21 provinces had the evaluation indexes of FT higher than the national average value, showing the spatial distribution characteristics of northeast region > central region > eastern region > western region. In total, 11 provinces had the evaluation indexes of LM higher than the national average value, showing the spatial distribution characteristics of central region > eastern region > western region > northeast region. Under the interaction of FT index and LM index in 30 provinces, there were 16 provinces with comprehensive evaluation index higher than the national average value, forming the spatial distribution characteristics of central region > eastern region > northeast region > western region.

3.2. Spatio-Temporal Evolution of the CCD between FT and LM

The CDs of FT and LM in China from 2015 to 2019 were all higher than 0.970, indicating that the CD of FT and LM reached a high level of coupling and tends to be stable. While the CCD between FT and LM in China showed a tendency of upward and fluctuate, from 2015 to 2019, the CCD increased from 0.6558 to 0.6695, reaching the overall primary coupling coordination; however, the average annual growth rate was only 0.42%, which was relatively slow. The CCD of FT and LM had a large difference among regions, showing a distribution pattern of central region > eastern region > northeast region > western region. Among them, the central region had the highest level of coupling coordination between FT and LM, with an excellent upward trend, and the CCD fluctuated between 0.6692 and 0.6917, with an annual growth rate of 0.67% on average, which was at the middle and late stage of primary coupling coordination. Followed by the eastern region, the CCD decreased from 0.6654 to 0.6599, with an annual growth rate of −0.17% on average, and the CCD showed a slight downward trend. The northeast region ranked the third, with the CCD rising from 0.6420 to 0.6427, with an average annual growth rate of 0.02% and a relatively slow upward trend. The western region had the lowest coupling coordination level, rising from 0.6292 to 0.6346, with an annual growth rate of only 0.17% on average. For details, see Figure 4.

The CD of FT and LM of provinces in China from 2015 to 2019 was consistent with the overall national coupling degree and reached a high level of coupling. Except for Guangdong (0.942), the coupling degrees of FT and LM in all other provinces were above 0.950. In contrast, the CCDs of FT and LM in provinces of China were not high. The national average value was 0.6542, while most provinces had CCDs between 0.60 and 0.70, which was at a primary coupling coordination level (

Table 3. The mean degree of the coupling and coupled coordination of farmland transfer (FT) and labor migration (LM) in China (2015–2019).

Province	CD	CCD	Type	Province	CD	CCD	CD
Beijing	0.9664	0.6578	Primary coupling coordination	Liaoning	0.9945	0.5913	Barely coupling coordination
Tianjin	0.9814	0.6738	Primary coupling coordination	Jilin	0.9922	0.6357	Primary coupling coordination
Hebei	0.9880	0.6271	Primary coupling coordination	Heilongjiang	0.9778	0.7155	Middle coupling coordination
Shanghai	0.9914	0.7450	Middle coupling coordination	Inner Mongolia	0.9920	0.6473	Primary coupling coordination
Jiangsu	0.9876	0.7361	Middle coupling coordination	Guangxi	0.9857	0.6124	Primary coupling coordination
Zhejiang	0.9844	0.6916	Primary coupling coordination	Chongqing	0.9693	0.7003	Middle coupling coordination
Fujian	0.9510	0.6389	Primary coupling coordination	Sichuan	0.9890	0.6644	Primary coupling coordination
Shandong	0.9942	0.6502	Primary coupling coordination	Guizhou	0.9840	0.6112	Primary coupling coordination
Guangdong	0.9420	0.6455	Primary coupling coordination	Yunnan	0.9832	0.5793	Barely coupling coordination
Hainan	0.9839	0.5411	Barely coupling coordination	Shaanxi	0.9910	0.6680	Primary coupling coordination
Shanxi	0.9944	0.6047	Primary coupling coordination	Gansu	0.9908	0.6391	Primary coupling coordination
Anhui	0.9913	0.7107	Middle coupling coordination	Qinghai	0.9784	0.6413	Primary coupling coordination
Jiangxi	0.9753	0.6633	Primary coupling coordination	Ningxia	0.9967	0.6623	Primary coupling coordination
Henan	0.9974	0.6819	Primary coupling coordination	Xinjiang	0.9948	0.6419	Primary coupling coordination
Hubei	0.9894	0.6795	Primary coupling coordination	Mean	0.9844	0.6542	Primary coupling coordination
Hunan	0.9956	0.6693	Primary coupling coordination

). Particularly, there were 15 provinces with CCD of FT and LM higher than the national average value, accounting for 50.00%, among which five provinces, including Shanghai (0.7450), Jiangsu (0.7361), Heilongjiang (0.7155), Anhui (0.7107), and Chongqing (0.7003) had the highest level of coupling coordination, with the average value higher than 0.70, which was at the early stage of the middle coupling coordination, accounting for 16.67% over the country. The CCDs of 10 provinces, including Zhejiang, Henan, and Hubei, were between 0.6578 and 0.6916, which were at the middle and late stage of primary coupling coordination, accounting for 33.33% of the country. There were 15 provinces with the CCD of FT and LM lower than the national average, accounting for 50.00% nationwide, among which 12 provinces, including Shandong, Inner Mongolia, and Guangdong, had the CCD between 0.6047 and 0.6502, and were at the middle and early stage of primary coupling coordination, accounting for 40.00% nationwide. Three provinces including Liaoning, Yunnan, and Hainan, had the CCD between 0.5411 and 0.5913 and were at the level of barely coupling coordination, accounting for 10.00% nationwide. Therefore, at this stage, there are still obvious regional differences in the CCD between FT and LM in 30 provinces in China. Although all of them have reached the coupling coordination, the coordination levels of most provinces are relatively low, and there is still large room for growth.

3.3. Spatial Autocorrelation of the CCD between FT and LM

The global Moran’s I index estimates of the CCD of FT and LM in China from 2015 to 2019 were all positive, demonstrating an overall positive spatial correlation. The significance test was passed by all Moran’s I indexes during the study period, and a highly significant correlation was observed between 2015 and 2019 in Moran’s I indexes. Some years’ low global Moran’s I indexes suggested that there was little clustering and no obvious spatial autocorrelation feature. Among them, the maximum value of 0.3678 was reached in 2015, followed by a cyclic development trend of decreasing firstly and then increasing and decreasing, which reached the minimum value of 0.1773 in 2019. Therefore, the CCD of FT and LM in China shows a development trend of agglomeration–dispersion over time (Figure 5).

Most provinces are located in H-H and L-L agglomeration areas, indicating that both provinces with higher coupling coordination of FT and LM and those with lower coupling coordination have emerged as agglomeration effects and showed a tablet distribution in space. Specifically, from 2015 to 2019, the number of H-H agglomeration areas increased from 8 to 11, and the proportion increased from 26.67% to 36.67%, with this type of area with the greatest proportion. The increase in the number of H-H agglomeration areas indicates that the development level of both FT and LM in China has entered a high level, and it has basically formed a benign coordination situation with mutual promotion (

Table 4. Spatial correlation changes of the coupling and coordination degree (CCD) of farmland transfer (FT) and labor migration (LM) in China from 2015 to 2019.

Years	H-H		L-H		L-L		H-L
	Province	%	Province	%	Province	%	Province	%
2015	Shanghai, Jiangsu, Zhejiang, Anhui, Henan, Hubei, Gansu, Ningxia (8)	26.67	Beijing, Fujian, Shandong, Jilin, Shanxi, Jiangxi, Inner Mongolia, Shaanxi, Qinghai (9)	30.00	Guangdong, Hainan, Liaoning, Guangxi, Sichuan, Guizhou, Yunnan (7)	23.33	Tianjin, Hebei, Heilongjiang, Hunan, Chongqing, Xinjiang (6)	20.00
2016	Shanghai, Jiangsu, Zhejiang, Anhui, Hubei, Beijing, Chongqing, Qinghai (8)	26.67	Gansu, Fujian, Jiangxi, Shandong (4)	13.33	Guangdong, Hainan, Guangxi, Guizhou, Yunnan, Hebei, Shanxi, Inner Mongolia, Liaoning (9)	30.00	Tianjin, Jilin, Heilongjiang, Henan, Hunan, Sichuan, Shaanxi, Ningxia, Xinjiang (9)	30.00
2017	Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Chongqing, Shaanxi (10)	33.33	Fujian, Shandong (2)	6.67	Hebei, Shanxi, Hainan, Inner Mongolia, Liaoning, Jilin, Guangxi, Guizhou, Yunnan, Gansu, Qinghai, Xinjiang (12)	40.00	Beijing, Tianjin, Heilongjiang, Guangdong, Sichuan, Ningxia (6)	20.00
2018	Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, Chongqing, Shaanxi (11)	36.67	Tianjin, Shanxi, Fujian, Guangdong, Ningxia (5)	16.66	Hebei, Inner Mongolia, Liaoning, Jilin, Guangxi, Hainan, Guizhou, Yunnan, Gansu, Qinghai, Xinjiang (11)	36.67	Beijing, Heilongjiang, Sichuan (3)	10.00
2019	Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Chongqing, Shaanxi, Ningxia (11)	36.67	Shanxi, Jilin, Fujian, Shandong (4)	13.33	Beijing, Hebei, Liaoning, Guangxi, Sichuan, Guizhou, Hainan, Yunnan, Gansu, Qinghai, Xinjiang (11)	36.67	Tianjin, Inner Mongolia, Heilongjiang, Guangdong (4)	13.33

). In terms of spatial distribution, it gradually expands in scope, with the development trend from relatively scattered to concentrated, and the distribution range is concentrated in the central-eastern region. In general, the strong–strong clustering type is primarily distributed in the east-central region, and the weak–weak clustering type is primarily distributed in the western region. The spatial coordination of FT and LM in China has significant clustering characteristics and tends to be stable. The distribution and number of clustering types change slightly with time, but generally remain stable.

3.4. Driving Factors of CCD between FT and LM

FT and LM have formed a complex coupled resource–population–economy system through interaction, and the coupled and coordinated development between them is the result of multiple factors. This study identified the factors of land approval (X₁), the level of agricultural equipment (X₂), the level of concurrent employment business(X₃), the level of non-agricultural industry development (X₄), and the level of urbanization (X₅) as the main driving factors and carried out research. As shown in

Table 5. Correlation degree between coupled and coordinated driving factors of farmland transfer (FT) and labor migration (LM) in China.

Region		X1	X2	X3	X4	X5
Eastern Region	Beijing	0.5144	0.6170	0.6538	0.6073	0.5907
	Tianjin	0.6013	0.6784	0.5796	0.5673	0.5135
	Hebei	0.5968	0.6967	0.6501	0.6218	0.6206
	Shanghai	0.6118	0.6057	0.8550	0.7067	0.6402
	Jiangsu	0.6713	0.7415	0.7686	0.5890	0.7343
	Zhejiang	0.6250	0.6826	0.7505	0.6898	0.6026
	Fujian	0.5677	0.5968	0.5827	0.7672	0.7442
	Shandong	0.6085	0.6590	0.8735	0.6172	0.6458
	Guangdong	0.6752	0.6933	0.3535	0.5003	0.5618
	Hainan	0.7769	0.6553	0.6307	0.5264	0.5659
Central Region	Shanxi	0.7456	0.6310	0.7595	0.5814	0.5668
	Anhui	0.5761	0.5128	0.6339	0.6674	0.6446
	Jiangxi	0.5778	0.5165	0.6935	0.7108	0.6567
	Henan	0.7803	0.7008	0.6621	0.7407	0.6542
	Hubei	0.5729	0.6176	0.7895	0.6285	0.5943
	Hunan	0.7397	0.5904	0.7959	0.7347	0.7498
Northeast Region	Liaoning	0.5410	0.5097	0.8450	0.6697	0.6295
	Jilin	0.5785	0.6047	0.7419	0.7486	0.6563
	Heilongjiang	0.7614	0.4789	0.8138	0.7290	0.6821
Western Region	Inner Mongolia	0.6271	0.5436	0.5151	0.6620	0.8374
	Guangxi	0.7180	0.7248	0.7115	0.5512	0.4367
	Chongqing	0.4136	0.6414	0.7915	0.7824	0.6212
	Sichuan	0.3836	0.6116	0.7000	0.5862	0.5901
	Guizhou	0.5761	0.5406	0.6478	0.7028	0.6149
	Yunnan	0.6486	0.4886	0.7278	0.6636	0.6905
	Shaanxi	0.7615	0.5318	0.7759	0.8155	0.6522
	Gansu	0.6209	0.7269	0.7072	0.5898	0.5965
	Qinghai	0.5715	0.6653	0.5939	0.6713	0.6428
	Ningxia	0.5651	0.7501	0.6508	0.5070	0.5835
	Xinjiang	0.5521	0.6091	0.6834	0.5677	0.6163
Mean		0.6187	0.6207	0.6979	0.6501	0.6312

, the drivers that significantly affect FT and LM are, in descending order, the level of concurrent employment business (0.6979) > the level of non-agricultural industry development (0.6501) > the level of urbanization (0.6312) > the level of agricultural equipment (0.6207) > the land approval (0.6187).

4. Discussion

4.1. Temporal Evolution Analysis of Integrated Level

From a temporal perspective, the evolution trend of FT and LM in China from 2015 to 2019 is not obvious due to the influence of agricultural production conditions, the development of non-agricultural industries, and new urbanization, and its evolution trend is relatively stable. From the spatial perspective, in order to clarify rural land contracting relationships, clarify rural land property rights, solve the problems of inaccurate arable land area and the unclear four boundaries operated by peasant households under contract, guide the orderly transfer of rights to manage rural land, and realize large-scale agricultural operations, China has carried out the registration and certification of land contracting rights and the rural land system reform of “separating rural land ownership rights, contract rights, and management rights”, which has increased the enthusiasm of rural labor forces to participate in the transfer of farmland, guided them to complete the transfer of farmland in an orderly manner, concentrated the land in individuals or organizations such as large planters and new agricultural business entities, promoted the moderate scale operation of agriculture and the rational allocation of land resources, and significantly improved the level of FT. The provinces with relatively high LM evaluation index are concentrated in the eastern region, which is due to the higher level of new urbanization and development of non-agricultural industries in the eastern region, which has a certain radiation and driving effect, and is conducive to improving the absorption capacity of cities and towns for rural labor forces and providing sufficient jobs for them. At the same time, the transfer of farmland can promote occupational differentiation within society and division of labor within families, promote the transfer of rural labor forces to cities and non-agricultural industries, realize the flow and reorganization of labor factors, and improve the level of LM. The difference between the overall evaluation levels of FT and LM in provinces is significant, which is due to the unbalanced development levels of the two subsystems in most provinces, and the lower level of FT in areas with a higher level of LM and vice versa, which directly affects the overall evaluation index level of the whole system.

4.2. Spatio-Temporal Evolution Analysis of Coupling Coordination

At the regional level, as a region with a higher economic levels and social development and better agricultural production conditions in China, the eastern region ought to have a higher CCD of FT and LM than other regions; however, it has a lower coupling coordination level that in the central region, which is due to the expansion of demand for land from new urbanization and non-agricultural industry development, so that it has caused some farmland in the eastern region to be converted to non-agricultural use, which directly affects farmland transfer and leads to a small decline in its coupling coordination level. The rate of LM in the central region has increased significantly, and the conditions of agricultural production and operation have improved. The geographical environment conditions are better, which makes the level of FT and LM increase significantly, thus making its coupling coordination level higher than other regions. As the main grain producing area in China, the northeast region has superior land resource endowment conditions, high level of agricultural mechanization and large-scale operation, and should have a higher level of FT, but the level of FT varies significantly within the region. Agriculture still occupies an important position in the region, and the level of LM is relatively lagging behind, so that the CCD of FT and LM in the northeast region is lower than that in the central and eastern regions. Compared with other regions, the western region has relatively backward agricultural production conditions, urbanization level, and non-agricultural industry development level, and the level of FT and LM are not dominant, so its coupling coordination level is lower than other regions. From the perspective of provinces, the provinces with relatively high level of coupling coordination between FT and LM are mainly located in the east and central regions. The reason is that the level of FT and LM in these provinces is at a higher level, which directly affects the other subsystem and enables it to enjoy certain advantages in the coupling coordination development of FT and LM, so it shows a higher degree of coupling coordination. For Shanghai and Jiangsu, the rapid development of economy and society, new urbanization, and non-agricultural industries can provide a large number of employment opportunities, absorb a large number of migrated labor forces, and fully meet the requirements of rural labor force for concurrent employment or non-agricultural employment. While Heilongjiang has comparative advantages in land resource endowment, excellent level of agricultural equipment, high efficiency of agricultural labor production, and better development of farmland transfer market. The level of FT is higher than that of other regions. The provinces with relatively low level of coupling coordination are mainly located in the western region, while the other three regions also involve a few provinces. This is due to the low development level of both subsystems in Hainan, Yunnan, and Liaoning provinces, which directly reduces the level of coupling coordination. The agricultural farming conditions in these provinces are relatively backward, and the rural labor force is more dependent on the land. The degree of fragmentation of farmland is high, such as the “land belonging to one production unit but enclosed in that of another” in Hainan Province. The imperfect development of the transfer market makes the level of FT relatively low. The large population and poor comprehensive quality of the rural labor force in the western region, the outstanding structural contradictions in transferring employment, and the level of development of local non-agricultural industries cannot meet the willingness to transfer locally, which seriously hinders the development of LM.

4.3. Spatial Autocorrelation Analysis

The CCD of FT and LM in China shows agglomeration effects with high and low values, respectively. On the one hand, because the level of urbanization and non-agricultural industry development in the eastern region is better than other regions, which can provide sufficient employment opportunities for LM, the higher level of LM will directly affect the development of FT, which is manifested by the higher CCD of these provinces; thus, the phenomenon of agglomeration in high-value areas has emerged. On the other hand, the agricultural production conditions, urbanization level, and development level of non-agricultural industries in western provinces are relatively backward, so the development level of FT and LM are both low and have not yet achieved coordinated development, resulting in a low degree of coupling coordination between them, thus showing a significant low-value area agglomeration phenomenon.

4.4. Analysis of Driving Factors

(1) The level of concurrent employment business has the greatest influence on the CCD of FT and LM, primarily because of the rise in agricultural production efficiency and the level of agricultural mechanization and because the demand for labor factors in agricultural production and operation activities continues to decrease, from which a significant amount of unneeded labor is released. Under the combined influence of various aspects, such as the heterogeneity of resource endowment of farm households, the comparative income gap between agriculture and non-farm industries, the nature of agricultural production’s seasonality, and the traditional concept of farm households, a large number of concurrent employment farm households have emerged and gradually become the common production status of farm households. However, concurrent employment farmers still have a certain degree of attachment to the land, and the transfer of farmland does not always occur in this production state, which makes the efficiency of farmland transfer not optimal, and the LM under the concurrent employment operation has obvious “migratory bird” characteristics, which directly affects the coupling coordination development of FT and LM.

(2) The level of non-agricultural industry development has the second highest impact on coordination degree of FT and LM. This is due to the significant pull effect of non-agricultural industries on LM; the higher level may help the economic growth of the province and create more jobs for rural surplus labor. The more non-agricultural employment opportunities, the more likely rural laborers will break away from agricultural production to engage in non-agricultural employment. Meanwhile, as rational economic people, income level is the key factor for rural laborers to measure whether to transfer employment. When the level of non-farm income is higher than the income from agricultural operation, laborers will choose to shift to non-farm industries and generate the willingness to shift to self-contracted land management.

(3) The impact of the level of urbanization on coordination degree is in the third place. For rural laborers, the expansion of new urbanization offers numerous employment opportunities, which is conducive to increasing the transfer income of laborers, ensuring their long-term and stable transfer, and increasing the rate of transfer of rural laborers. Meanwhile, in order to meet the increasingly diversified demands of urban residents for agricultural products, the state has introduced corresponding “strong and favorable agricultural” policies to encourage the development of new agricultural business entities and orderly guide capital to the countryside, which has gradually strengthened the demand for land in order to realize large-scale, specialized, and scientific agricultural production and operation, and stimulated the farmland transfer market. It has accelerated the orderly transfer of farmland.

(4) The level of agricultural equipment affects the development of coupling coordination in fourth place. Agricultural machinery can replace some labor factors, reduce labor factor inputs in agricultural production and operation activities, improve agricultural production efficiency, release more surplus agricultural labor, promote occupational differentiation within rural society and division of labor within households, improve the transfer market’s supply–demand relationship, realize stable and efficient farmland transfer, increase the willingness of rural labor to transfer employment, and improve the level of FT and LM.

(5) The influence of the land approval on coupling coordination development is in the fifth position. On the one hand, it is conducive to solving the problem of ambiguous property rights of farmland, urging farmers to participate actively in FT, improving the probability and level of FT, and releasing the bundle of fragmented farmland operation and inefficient agricultural production labor. On the other hand, land approval can enhance the strength of farmland property rights, enhance land rights and interest protection during LM, reduce the cost of LM, make the migrated labor not worry about the risk of land loss, encourage the employment of LM, and improve the long-term and stability of transfer employment.

4.5. Limitations and Future Work

This paper investigated the coordination, spatio-temporal evolution, and driving factors of the coupling between FT and LM in China’s provinces. The results of this study are significant for implementing the rural revitalization strategy, realizing the modernization of agriculture and rural areas, and promoting the development of urban–rural integration, but there is room for further in-depth research. On the one hand, this study took China’s provinces as the geographical unit and conducted in-depth analysis on the coordination, spatio-temporal evolution characteristics and driving factors of the coordination degree of FT and LM. It will be of great practical guidance if long-term provincial panel data or county-level data are used to conduct the study. On the other hand, as farmers are the main decision makers of FT and LM, it is necessary to analyze the coordination degree of FT and LM by using the data of farmers’ survey in the future to further clarify the influence of farmers’ behavior and willingness on the coupling coordination between them.

5. Conclusions

This study measured the coordinated development between FT and LM in 30 provinces in China from 2015 to 2020 and used the ESDA method and GRA model to study the spatial difference characteristics of coordination degree and analyze the driving factors of coupling coordination development. The following are the main findings of the study: (1) From 2015 to 2019, the level of the evaluation index of LM in China was relatively high. The rising trend of the level of FT was more obvious, and their level of comprehensive evaluation continued to rise. The regional differences were obvious, with the central region being higher than the eastern and northeastern regions and the western region being the lowest. (2) From 2015 to 2019, the coordination degree of FT and LM in China showed a fluctuating upward trend and was at the primary coupling coordination stage, among which the difference in the coordination degree of FT and LM between regions was large, showing a distribution pattern of central region > eastern region > northeast region > western region. (3) The coordination degree of FT and LM in China has a significant global positive spatial correlation and exhibits a clear development trend of agglomeration dispersion over time with obvious fluctuations. Local spatial agglomeration appears and tends to be stable, and strong agglomeration types are concentrated in the eastern and central regions, while weak agglomeration types are concentrated in the western region. (4) The driving factors of the CCD of FT and LM are, in descending order, the level of concurrent employment business, the level of non-agricultural industry development, the level of urbanization, the level of agricultural equipment, and the land approval. There are significant differences in the main driving factors in different provinces.