Can the Return of Rural Labor Effectively Stimulate the Demand for Land? Empirical Evidence from Sichuan Province, China

Abstract

Promoting moderate-scale land management is a crucial pathway for achieving the transformation of agricultural modernization in China. Whether migrant workers with the advantage of human capital can effectively promote moderate scale management is a problem worthy of in-depth discussion. Based on survey data from three counties in Sichuan Province in 2024, this paper empirically analyzes the impact of migrant workers’ return on farmers’ land transfer-in behavior by constructing IV-Probit and IV-Tobit models. The results show that (1) the return of migrant workers significantly promotes the land transfer-in of rural households by enhancing their risk tolerance and increasing the participation of cooperative organizations; (2) however, there is some heterogeneity in these results. The effect of the return of migrant workers in plain areas and economically developed villages on land transfer-in is stronger than that in mountainous areas and economically weak villages. Based on these findings, this paper suggests that differentiated policies should be formulated according to the natural conditions and economic foundations of different regions, making full use of the human capital advantages of returning migrant workers to effectively promote the realization of moderate-scale management among farmers.

1. Introduction

China is characterized by a large population and fragmented arable land, which has become a significant constraint on the modernization of agriculture and rural areas [1]. Small agricultural production units, scattered land management, weak anti-risk ability, poor adaptability to the market environment, and other difficulties have become the main shackles inhibiting the improvement of agricultural production efficiency and agricultural economic development [2,3,4]. The urbanization and non-agricultural employment of rural labor have exacerbated the structural contradictions in rural land allocation. The most prominent issue is the decline in the quality of agricultural labor and the increased opportunity cost of agricultural production, which has led farmers to exit agricultural production, resulting in the degradation of arable land and land abandonment [5,6,7]. This, in turn, has further inhibited the efficiency of agricultural technology and machinery inputs [8].

The Chinese government has made extensive explorations on how to improve or avoid the above-mentioned dilemmas of the increasingly tense human–land relationship. Promoting the development of the rural land transfer market has become the main policy direction. With a series of policy supports and market incentives, China’s land transfer market has developed steadily [9]. Statistics show that in 2004, the area of transferred rural contracted land in China was only 58 million mu, while by 2022, the area of transferred family-contracted arable land had exceeded 576 million mu. However, despite the overall increase in China’s land transfer level, only 403 million mu of the transferred land had formal transfer contracts, meaning that 25% of the transferred land was still informal [10]. It is evident that the actual effect of China’s land transfer has not met expectations, and further exploration is needed to effectively promote land transfer in China’s rural areas. Based on the general recognition that rural labor force is the main driving force of rural land transfer market, it is an important historical issue to explore whether the return of migrant workers can effectively increase farmers’ demand for agricultural land transfer under the background that the current trend of the return of migrant workers in China is gradually obvious.

2. Literature Review

Regarding the cultivation and development of the land transfer market, the academic circle has carried out systematic research on its dynamic mechanism, constraint conditions, and practice path, and formed a wealth of theoretical exploration and empirical analysis results. The natural attributes of land, institutional factors, farmers’ social networks, and the mobility of labor all influence farmers’ decision-making behaviors regarding land use [11,12,13]. However, most studies argue that the migration and mobility of household labor are the primary driving forces behind land transfer [14,15]. After experiencing labor loss, rural households often choose to transfer land in order to reconstruct a more rational human–land relationship, thereby balancing family income and risk [16]. The decision-making regarding land transfer is also differently affected by factors such as the distance of employment, gender, and migration patterns. For instance, labor that is employed nearby can return home to continue agricultural production during the busy farming season, and thus these households have a lower willingness to transfer land [17]. Males generally have a relatively higher status in household resource allocation decisions, and households with male labor migrating for employment are more likely to transfer contracted land compared to those with female labor migrating [18]. The development of the agricultural social service market can alleviate the shortage of agricultural labor caused by labor migration, thereby enhancing farmers’ confidence in transferring in more land.

In recent years, China’s labor flow presents a new trend, and the phenomenon of labor return has gradually attracted attention. Driven by the real demand, relevant studies are constantly enriched, mainly focusing on the motivation of labor return and its impact on the economy and society. Since 2008, the acceleration of industrial transfer and transformation has led to an increase in job opportunities in central and western regions, attracting migrant workers who previously sought employment elsewhere to return to their hometowns [19]. Factors such as persistently high housing prices, rising urban living costs, unequal provision of public services, and emotional attachment to their hometowns have collectively contributed to the decision of migrant workers to return home [20,21]. Waves of rural migrant laborers, who once worked or engaged in business in major cities or coastal developed areas across the country, have been returning to their hometowns with skills, projects, and capital. This has not only created a wave of returning migrant workers but has also fostered a growing group of entrepreneurial returnees [22,23]. According to the 2023 Migrant Worker Monitoring Survey Report, the proportion of migrant workers working outside their home provinces has decreased from 63% in 2010 to 59.3% in 2023, while the proportion of local migrant workers has increased from 37% to 41%. Additionally, the proportion of migrant workers employed within their home provinces has risen from 49.7% to 61.8% [24].

As an important means to resolve the gap of rural human capital, the trend of rural labor return is becoming gradually obvious, which has a profound impact on rural development, especially on the allocation of rural land resources [25]. The return of migrants can compensate for the labor demand in regions experiencing labor outflows, and the dilemma of insufficient agricultural labor caused by rural–urban migration will diminish as migrant workers return to their hometowns. Under conditions of relatively sufficient labor supply, the probability of farmers expanding their land management scale to increase household income will rise [26]. Furthermore, whether returning passively or actively, the fact is that migrant workers have lost their non-agricultural employment opportunities in cities. Upon returning, they naturally reconnect with agricultural production spatially, which means the likelihood of engaging in agricultural production increases after their return [27]. At the same time, the reality that non-agricultural income remains relatively higher than agricultural income persists and is unlikely to change in the foreseeable future. During the “window period” of non-agricultural employment after their return, rational households, aiming to maximize income, will allocate all or part of the returned labor resources to agricultural production to avoid waste of labor resources [28].

The above studies have laid a solid foundation for paying attention to the issue of rural labor return, but in terms of the impact of labor return on the development of rural land market, relevant studies are still insufficient, especially from the perspective of farmers; empirical studies on the impact of household return of migrant workers on land transfer-in behavior are relatively scarce. Rural labor force is not only the basic factor of agricultural production, but also an important carrier of other production factors. The return of labor force will inevitably lead to the adjustment of the allocation of agricultural production factors, which will have a profound impact on rural social and economic development, among which the most critical is the change of human–land relationship. This prompts us to think: Does labor return promote or inhibit farmers’ land transfer-in behavior? What is the path of this influence? Under different natural environment and economic conditions, does labor migration have a different effect on the modernization transformation of smallholder production? These problems need to be further explored.

3. Theoretical Analysis and Hypothesis

The direct impact of the return of migrant workers on family agricultural production is the increase in labor force in agricultural production, which changes the “equilibrium” state of man–land allocation in the absence of farmers’ original labor force. In order to avoid redundant labor input per unit of land and improve the production efficiency of the labor force, farmers will inevitably choose to transfer-in to land. In addition to the sheer increase in labor quantity brought by the return, labor return also plays a positive role in enhancing the human capital of rural households. It is well known that after rural household labor migrates from villages to cities, rural families inevitably face multiple challenges in human capital accumulation, such as delayed accumulation, loss of incremental growth, structural imbalances, and low quality. Non-agricultural employment of rural labor is a relatively optimal decision made by rational households after weighing benefits and risks. During their engagement in non-agricultural industries in urban sectors, laborers cultivate their own human capital and accumulate economic capital. Therefore, labor return brings incremental injections of both economic and human capital to rural households. Specifically, urban work experience enhances their ability to accept new ideas and concepts, thereby promoting the willingness of returned laborers to innovate agricultural production models and become new-type professional farmers [29]. After transitioning from non-agricultural to agricultural employment, the returned group seeks to improve the marginal productivity of land or labor to bridge the income gap between the two types of employment, thereby expanding the potential market demand for land. Based on this, this paper proposes the following research hypothesis:

H1: 

The return of migrant workers will promote farmers’ land transfer-in.

In his classic 1964 book Transforming Traditional Agriculture, Schulz argued that traditional agriculture is not “inefficient” or “irrational” as the prevailing wisdom suggests; it is a “poor but efficient” equilibrium within the constraints of given resources and technology [30]. The growth of traditional agriculture by small farmers depends on the introduction of new factors, especially the growth of human capital. Human capital plays a vital role in agricultural production. The further optimization of family human capital will improve the whole family’s risk-bearing ability and enhance the scientific family decision-making. Combined with the realistic background of “China on the move”, the periodic migration of migrant workers creates a unique path of risk tolerance: rural migrant workers enter the urban sector for employment, and improve their risk tolerance through explicit ways such as technology and capital accumulation and implicit ways such as new ideas. In addition, the New Economics of Labor Migration breaks through the traditional paradigm of individual decision-making and extends the unit of analysis to the level of household organization [31]. Based on this theory, the family’s agricultural production behavior is essentially the family’s resource allocation strategy to cope with systemic risks. Considering the production risk and market risk of agricultural production, the level of risk tolerance becomes the key variable of family agricultural decision-making. Specifically, in terms of household land resource allocation, one important manifestation is that the return of migrant workers enhances the comprehensive level of household risk tolerance, which will undoubtedly improve the ability and expectation of farmers to expand land production scale [32]. Rational farmers will comprehensively apply the improvement of risk tolerance to the optimization of agricultural resources, so as to choose to transfer-in more land under the basic decision logic of expanding family income sources. Therefore, this paper proposes the following research hypothesis:

H2: 

The return of migrant workers will promote farmers’ land transfer-in by enhancing their risk tolerance.

The return of migrant workers is more than just a spatial shift, but also a process of reconfiguration of production factors. In this process, returnee workers have accumulated rich capital, technology, and market experience during their migrant work, which has significantly improved their expectations and confidence in agricultural production. However, under the realistic background of “small farmers in big countries”, scattered small farmers often have a difficult time achieving effective integration of resources independently. As a form of organization that can integrate resources and provide technical support and market access, cooperatives have a strong appeal to those farmers who are eager to expand their production scale and become an important bridge between returning labor and land markets. On the one hand, participation in cooperatives can effectively alleviate the information asymmetry faced by farmers in the land transfer market. Through the organizational advantages of cooperatives, farmers can reduce the information collection costs, negotiation costs, and risk costs generated in the process of land transfer, thus enhancing their confidence and ability to transfer-in more land. On the other hand, the collective credit endorsement of cooperatives can significantly reduce farmers’ concerns about the risk of land transfer-in, thereby increasing their likelihood of transferring to more land. In addition, the technical support and market access provided by cooperatives can help returnee labor make better use of modern agricultural technologies, improve agricultural production efficiency, and reduce the cost of selling agricultural products. With the support of cooperatives, those farmers who are eager to earn more income have more incentive to maximize their family returns by expanding the scale of land production. Therefore, cooperatives are not only a platform for resource integration, but also an important help for the return labor force to achieve agricultural modernization. Therefore, this paper proposes the following research hypothesis:

H3: 

The return of migrant workers will promote land transfer-in by joining cooperatives.

Rural areas under different topographical conditions exhibit significant disparities in production efficiency, land endowments, and infrastructure development, which inevitably influence household decision-making regarding labor and land resource allocation. Specifically, in hilly and mountainous regions, land fragmentation and poor soil quality are more pronounced compared to plains, leading to higher agricultural production costs. Consequently, households in these areas have lower expectations of increasing income through expanded production. In contrast, plain regions benefit from better land endowments, higher accessibility for agricultural machinery, and generally superior transportation infrastructure, which shortens market distances for agricultural products. These factors reduce production costs and enhance market access, increasing the likelihood of land expansion in plains areas. Therefore, this paper proposes the following research hypothesis:

H4: 

The effect of the return of migrant workers on farmers’ land transfer-in in plain areas is stronger than that in hilly areas.

On the one hand, higher levels of rural economic development exert a stronger pull on non-agricultural labor, increasing the willingness of migrants to return and engage in local employment or entrepreneurship under the combined incentives of economic and social development [33]. On the other hand, differences in economic development levels reflect varying degrees of factor mobility. Generally, regions with higher economic development exhibit greater factor mobility, which in turn further stimulates rural economic vitality. Households in economically developed areas have higher expectations for agricultural income compared to those in hilly regions, and their overall economic capacity is stronger. Consequently, the costs and benefits of land transfers are often more optimized in these areas. Therefore, this paper proposes the following research hypothesis:

H5: 

The effect of the return of migrant workers on farmers’ land transfer-in in villages with strong economic development is stronger than that in villages with weak economic development.

Based on the above analysis, this paper utilizes survey data collected from 1167 households across three counties in Sichuan Province in 2024 to construct econometric models. The aim is to explore the impact of migrant worker return on land transfer-in, providing insights for the rational allocation of labor and land resources under the new trend of labor return.

4. Materials and Methods

4.1. Data Sources

Sichuan is a typical agricultural province with the sixth largest cultivated land area in China and superior agricultural production conditions. For a long time, Sichuan has been a major province of agricultural products such as grain, oil, and pigs in China. However, limited by topography (large span, with plains, hills, and mountains), the contradiction between people and land in Sichuan is prominent. Mountains and hills are the main terrain of Sichuan Province, accounting for about 90% of the total area. The per capita cultivated land area is small (only 1.12 mu/person), and small-scale farmers have extensive and long-term production. In addition, Sichuan is a province with a large population, but limited by the level of economic development, many young labor forces are driven by economic interests to go out for work. There is a large number of rural labor non-agricultural migration, which makes the trend of agricultural land abandonment intensified; farmers’ income is difficult to increase, and rural development is weak. In recent years, migrant workers from outside the province began to return to their hometown for employment. In 2024, the number of migrant workers in the province reached 15.4759 million, an increase of 349,800. As the trend of labor return becomes more and more obvious, whether the contradiction between people and land can be further alleviated has become a concern of political scholars.

The data used in this study mainly came from a questionnaire survey of farmers in Dayi County, Lu County, and Muchuan County, Sichuan Province, conducted by our research group in July 2024 (Figure 1: in the upper right corner, the main terrain of the three counties is shown by legend, and in the lower image, the difference in elevation within each county is shown by the depth of color). The contents of the questionnaire mainly include family structure, land use, family labor return, agricultural production, and so on. The survey determines samples by combining stratified equal probability sampling with random sampling. The specific sampling steps are as follows: the first step is to divide 151 counties in Sichuan Province except plateau terrain into three groups based on the level of economic development and topography, and a county is randomly selected from the three groups to obtain plain County Dayi County with a high economic development level, Lu County with a moderate economic development level, and Lu County with a low economic development level. In the second step, the villages and towns of the selected sample counties are divided into three groups according to the level of agricultural economic development, one town is randomly selected from each group, and nine sample towns are finally determined. The third step, using the method of random sampling, consists in randomly selecting 6 administrative villages in each sample township, and identifying 54 sample villages; Finally, 20–25 farmers are randomly selected by using the village roster and random number table. Through the above process, and finally, according to the recycling situation and questionnaire data cleaning, 1167 sample farmer observations were obtained.

4.2. Method

4.2.1. Variable Settings

(1)

Dependent Variables

With comprehensive reference to Udimal (2020), Xu et al. (2020), and other studies, the explanatory variable of this paper is farmers’ land transfer-in, which mainly has two dimensions: (1) the decision of land transfer-in, that is, whether farmers choose to transfer-in land; (2) The scale of land transfer-in, that is, the area of land transfer-in by farmers [15,34].

(2)

Independent Variables

The core independent variable studied in this paper is the return of migrant workers, as referred to by Xu et al. (2019). Xiao & Luo et al. (2024) studied the ratio of family labor return (the number of family labor return/the total number of family labor) [6,35]. Among them, the return of migrant workers refers to the experience of the labor force engaged in non-agricultural work outside the country (for half a year or more), and has returned to the place of residence for more than half a year; In addition, the definition of migrant worker return in this study is not completely limited to the spatial return, but further extended to the nature of the return, that is, to engage in agricultural production after returning to their hometown. One of the core explanatory variables of this study is the return farming of the household head because the household head has a strong and even decisive voice in the decision-making of family affairs, and is the main decision-maker of family agricultural production and resource allocation.

(3)

Control variables

This paper refers to the studies of Wang et al. (2023) and Yu et al. (2020), and controls the characteristic variables of household head, family, and village [23,36]. The characteristic variables of the head of household include the age, gender, and education level of the head of household. The variables of family characteristics include the number of family members, the number of family labor force, the number of elderly people engaged in agriculture, the proportion of family dependency, per capita income, productive investment in family agriculture, and household housing assets. Village characteristic variables include economic development status of row villages, village topography, and so on.

(4)

Instrumental variables

This study noted that the return of migrant workers and land transfer-in may be mutually causal and correlated, that is, on the one hand, the return of migrant workers may promote farmers’ land transfer-in, and on the other hand, farmers’ land transfer-in may affect household labor resource allocation. Therefore, the core independent variable of the study may be endogenous. In order to further test the robustness of the model estimation results and consider that there may be a mutual causal relationship between the explanatory variables and the explained variables, refer to the studies of Deng et al. (2018) and Kung (2002) [37,38]. The study uses the average return migration rate of rural labor from other villages within the same sample county (excluding the focal village) as an instrumental variable, that is $IV Return=\left(Retur{n}_1+Retur{n}_2+\dots +Retur{n}_{n-1}\right)/\left(n-1\right)$. Subsequently, the IV-Probit model was used to explore the impact of labor return on farmers’ land transfer-in behavior, and the IV-Tobit model was used to explore the impact of labor return-in on land transfer scale. As for the instrumental variables, on the one hand, the pull end of labor return is usually the development of county economy and hometown policies for returning home to start businesses, etc. Therefore, the above instrumental variables are correlated with the return of household labor force. On the other hand, research shows that farmers’ land transfer behavior often occurs in their acquaintances or close social networks [39], and labor return behavior in other villages and towns has no direct impact on farmers’ family land transfer. To sum up, our tool variable selection is appropriate.

(5)

Mediating variables

How the return of family labor force, especially the return of household head to farming, affects the scale management of family land, and its internal mechanism and path, need to be further analyzed. After comprehensively referring to the studies of Xiao & Luo (2023), this study uses risk tolerance and cooperative participation as intermediary variables to test the impact mechanism of migrant workers’ return on farmers’ land transfer [40]. Among them, the risk tolerance of farmers is obtained by summating two self-scores in the questionnaire: “The degree of adoption of new technologies and new business models” and “the willingness to invest in investments with higher risk and return”. In the questionnaire, “Whether the labor force will participate in the cooperatives of the village after returning home 0 = yes; 1 = no” is also considered.

4.2.2. Method Selection

For the analysis of the impact of labor return on land transfer-in, the Probit model is used because land transfer-in is a dichotomous variable. For the scale of labor return and land transfer-in, since the land transfer-in area represents left merge data with a large number of zero values, the Tobit model is intended to be adopted in this study. The econometric model of this study is set as follows:

$$R_i={\beta }_0+{\beta }_{1}Retur{n}_i+{\beta }_i{X}^{\prime }+{\epsilon }_i$$

(1)

$$A_i={\beta }_0+{\beta }_{1}Retur{n}_i+{\beta }_i{X}^{\prime }+{\epsilon }_i$$

(2)

In the model, $R_i$ and $A_i$ represent the land transfer-in behavior and land transfer-in scale of the i households, respectively. $Retur{n}_i$ represents the proportion of labor return of the $i$ household and whether the household head returns to farming, and the $X^{\prime }$ vector represents a series of control variables; ${\epsilon }_i$ represents the random disturbance term. ${\beta }_0$ is a constant term; ${\beta }_{1 }$ is the estimated coefficient of the variable.

After introducing the instrumental variable, the model estimation formula for the impact of labor return on farmers’ land transfer is as follows:

$$Rent\_i{n}_i={\alpha }_0+{\alpha }_{1}IVRetur{n}_i+X^{\prime }\theta +{\omega }_p$$

(3)

$$Area\_i{n}_i={\alpha }_0^{*}+{\alpha }_1^{*}IVRetur{n}_{ip}+X^{\prime }{\theta }^{*}+{\omega }_p^{*}$$

(4)

In the formula, the meaning of each variable is similar to that in (1) and (2).

In order to further test whether the return of migrant workers affects farmers’ land transfer through the increase in risk tolerance and the strengthening of cooperative participation, this paper constructs the intermediary effect model:

$$R=aX+{\epsilon }_1$$

(5)

$$M=bX+{\epsilon }_2$$

(6)

$$R=c^{\prime }X+\beta M+{\epsilon }_3$$

(7)

R represents the transfer of farmers’ land, $X$ represents the return of labor, and $M$ is the intermediary variable in this paper, that is, the risk tolerance and cooperative participation. The entire process estimation implementation uses Stata 15.0.

5. Results

5.1. Descriptive Statistical Analysis

Table 1. Variable definition and sample descriptive statistics.

Variable	Definition and Coding	N	Mean	sd
Transfer in	Whether the rural households have land transfer-in (1 = Yes; 0 = No)	1167.00	0.47	0.50
Area in	The area of land transfer-in by rural households (mu a)	1167.00	55.94	274.96
Labor_return_ration	The ratio of return laborers to the total household laborers	1167.00	0.13	0.29
H_return_farm	Whether the household head returns to farming (1 = Yes; 0 = No)	1167.00	0.18	0.38
Age	Age of household head in years (year)	1167.00	59.71	9.99
Gender	Gender (0 = male; 1 = female)	1167.00	0.89	0.31
Edu	Education level of household head (year)	1167.00	6.64	3.44
Health	Health of household head (1 = very well; 2 = good; 3 = general; 4 = difference; 5 = very poor)	1167.00	2.21	1.08
Labor-size	Total labor force of rural households (number)	1167.00	2.39	1.23
Family_Size	Total family members of rural households (number)	1167.00	4.14	1.71
old farm	People over 64 years old engaged in agriculture (number)	1167.00	0.60	0.81
House	Household property assets b (CNY 10,000)	1167.00	2.31	1.49
Farm_investment	Agricultural productive investment b (CNY 10,000)	1167.00	10.29	95.53
inper_income	Log of per capita income	1167.00	0.44	1.30
Dependency ratio	Family dependency ratio (%)	1167.00	0.25	0.28
Terrain	Terrain (1 = Plain; 2 = Hills; 3 = Mountain)	1167.00	1.93	0.77
Distance	Distance between village and county seat (km)	1167.00	28.75	16.84
Village economy	Village economy is weak (1 = Yes; 0 = No)	1167.00	0.27	0.44

Note: ^a 1 mu = 667 m² or 0.067 ha; ^b USD 1 = CNY 7.05 in 2023.

shows the results of descriptive statistical analysis of the variables. As shown in Table 1, among the 1167 sample farmers, 47% of them chose land transfer, with an average transfer area of 55.94 mu. With the joint efforts of the government and the market, the overall development level of the land transfer market continues to improve. From the perspective of labor return, the average rate of labor return in rural households is 13%, and 18% of household heads return to their native place and choose to continue farming. From the characteristics of the head of the household, the average age of the head of the household is 59.71 years old, the average years of education of the head of the household is 6.64 years, and the average health status of the head of the household is 2.21, which shows that the overall health of the head of the household is good. The average size of rural households is 4.14, and the average labor force per household is more than two people. The average family dependency ratio is 25%; in addition, in terms of village characteristics, the average distance between the village committee and the county seat is 28.75 km, and the overall economic development level of 28.75% villages is weak.

5.2. Results of Basic Regression

Table 2. Results of the econometric model of the impact of labor return on land transfer-in.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
labor_return_ration	3.52 ***	3.62 ***	417.93 *	484.61 **	0.43 **
	(0.12)	(0.12)	(218.35)	(220.89)	(0.18)
H_return_farm						2.65 ***	2.70 ***	413.94 *	484.03 **	0.43 **
						(0.07)	(0.07)	(217.96)	(225.22)	(0.18)
Age		0.00		−6.89 ***	−0.01 ***		0.01 *		−5.39 ***	−0.00 ***
		(0.00)		(2.22)	(0.00)		(0.00)		(2.07)	(0.00)
Gender		−0.00		57.40	0.05		−0.16 *		20.86	0.02
		(0.10)		(44.80)	(0.04)		(0.09)		(49.18)	(0.04)
Edu		−0.00		−4.53	−0.00		−0.02 **		−7.71	−0.01
		(0.01)		(4.62)	(0.00)		(0.01)		(5.27)	(0.00)
Health		0.04		14.54	0.01		0.03		13.40	0.01
		(0.03)		(11.02)	(0.01)		(0.03)		(11.09)	(0.01)
labor_num		0.18 ***		−17.41	−0.02		0.03		−34.65 *	−0.03 *
		(0.05)		(21.58)	(0.02)		(0.05)		(20.28)	(0.02)
Family_Size		−0.01		23.75 **	0.02 **		0.07 **		36.24 ***	0.03 ***
		(0.03)		(11.04)	(0.01)		(0.03)		(12.82)	(0.01)
old_farm		0.08		−10.73	−0.01		−0.01		−22.20	−0.02
		(0.05)		(21.53)	(0.02)		(0.05)		(22.03)	(0.02)
House		−0.02		7.96	0.01		0.01		12.79	0.01
		(0.02)		(8.79)	(0.01)		(0.02)		(9.50)	(0.01)
Farm_investment		−0.00		0.24	0.00		0.00		0.30	0.00
		(0.00)		(0.20)	(0.00)		(0.00)		(0.21)	(0.00)
inper_income		0.03		153.78 ***	0.14 ***		0.04		150.50 ***	0.13 ***
		(0.04)		(27.68)	(0.01)		(0.03)		(27.43)	(0.01)
Dependency ratio		0.71 ***		78.60	0.07		0.06		1.20	0.00
		(0.16)		(75.81)	(0.07)		(0.18)		(64.82)	(0.06)
Terrain		−0.15 ***		−15.61	−0.01		−0.10 *		−29.87	−0.03
		(0.05)		(28.46)	(0.03)		(0.06)		(32.92)	(0.03)
Distance		0.00		2.42 **	0.00 **		0.00		2.21 *	0.00 **
		(0.00)		(1.14)	(0.00)		(0.00)		(1.14)	(0.00)
Constant	−0.47 ***	−1.09 ***	−182.02 ***	49.15		−0.48 ***	−0.89 ***	−201.89 ***	30.43
	(0.02)	(0.29)	(46.20)	(145.18)		(0.02)	(0.28)	(56.07)	(154.08)
χ2	877.47 ***	1356.63 ***	35.78 ***	59.93 ***	59.93 ***	1614.26 ***	1723.68 ***	34.97 ***	56.48 ***	56.48 ***
Wald χ2	8.48 ***	15.97 ***	2.90 ***	5.42 **	5.42 **	9.17 ***	29.69 ***	3.48 *	5.92 ***	5.92 ***
First-stage F	57.22 ***	50.39 ***	57.22 ***	50.39 ***	50.39 ***	34.46 ***	30.02 ***	34.46 ***	30.02 ***	30.02 ***
Weakiv	51.98 ***	87.30 ***	4.38 **	37.38 ***	37.38 ***	52.04 ***	87.46 ***	4.41 **	37.24 ***	37.24 ***
N	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the quantitative regression results of the impact of labor return on farmers’ land transfer-in. Among them, Model 1–Model 5 are the model results of the impact of labor return on farmers’ land transfer-in. Among them, Model 2 is the IV-Probit result of adding control variables on the basis of Model 1, Model 4 is the IV-Tobit result of adding control variables on the basis of Model 3, and Model 5 is the marginal effect of IV-Tobit. Model 6–Model 10 are the model results of the impact of household main return farming on farmers’ land transfer-in, and the correlation distribution is similar to the above.

As shown in Table 2, several key findings can be summarized. First, based on the Wald χ² statistics, all results are significant at the 10% level or higher, indicating that the explanatory variables in the model are endogenous, and the choice of the instrumental variable (IV) approach is appropriate. Second, the first-stage F statistics are all greater than 10, suggesting that the selected instrumental variables are valid. Third, the results of the weak instrument test show that all statistics are significant at the 1% level, confirming that there is no weak instrument problem and that the selected instrumental variables are effective. In terms of model fit, after employing a stepwise regression approach by gradually adding control variables, the results demonstrate that there are no substantial changes in either the fitted correlation coefficients or the significance levels, indicating the robustness of the findings.

Regarding the specific results, labor return migration significantly and positively influences both land transfer-in behavior and land transfer-in area. Both the proportion of labor return migration and the household head’s return migration show a positive and significant effect on land transfer-in behavior at the 1% significance level. For the land transfer-in area, after including control variables, the effect remains positive and significant at the 5% level. Additionally, other control variables, such as the age of the household head and terrain characteristics, exhibit a negative and significant relationship with the land transfer-in area.

5.3. Robustness Test Analysis

In order to further ensure the robustness of the estimation results of the impact of labor return on farmers’ land transfer-in, this paper adopts the method of replacing explanatory variables, that is, the proportion of households returning to agriculture (number of returning farmers/total household population), the proportion of households active returning to agriculture, and the proportion of passive returning to agriculture as explained variables to further verify the robustness of the results. As shown in

Table 3. Robustness test-1 of the result of labor return to farmers’ land transfer-in.

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Active_Return	9.63 ***	43.45
	(0.91)	(48.24)
Passive _Return			5.27 ***	17.00
			(0.23)	(12.84)
Ration_Return					5.08 ***	12.08
					(0.20)	(6.88)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Wald χ2	8.45 ***	8.45 ***	14.73 ***	14.73 ***	20.62 ***	20.62 ***
First-stage F	92.20 ***	92.20 ***	96.68 ***	96.68 ***	39.50 ***	39.50 ***
Weakiv	86.92 ***	86.92 ***	87.44 ***	87.44 ***	87.03 ***	87.03 ***
N	1167.00	1167.00	1167.00	1167.00	1167.00	1167.00

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, Model 1 to Model 6 respectively represent the measurement results of the proportion of returning agricultural population, the proportion of active returning, and the proportion of passive returning on household land transfer-in. Model 2, Model 4, and Model 6 are the marginal effects of the model. From the results of the Wald χ² test, the first-stage F-value, and the weak instrument variable test, it is evident that the introduction of instrumental variables is valid and effective. Apart from differences in the magnitude of the correlation coefficients, the relevant variables still have a positive and significant impact on land transfer-in, thereby further confirming the robustness of the previous results. However, it should be noted that there are differences in the impact of active and passive returnees on land transfer-in. Both in terms of correlation coefficients and the marginal effects of the models, the influence of voluntary returnees on household land transfer-in decisions is undoubtedly stronger.

5.4. Mediation Effect Analysis

(1) The return of migrant workers, family risk tolerance, and farmers’ land transfer-in. The result report in

Table 4. Analysis of the impact mechanism of labor return on farmers’ land transfer-in (risk tolerance and cooperative).

Variables	(1)	(2)	(3)	(5)	(6)
	Transfer_in	Risk	Transfer_in	Cooperative	Transfer_in
labor_return_ration	3.61 ***	4.37 **	3.62 ***		3.56 ***
	(0.12)	(2.00)	(0.11)		(0.13)
Risk tolerance			0.04 **
			(0.02)
Cooperative				2.89 ***
				(0.54)
					0.06 **
					(0.03)
Control	Yes	Yes	Yes	Yes	Yes
F	50.39 ***	13.03 ***	88.89 ***	11.92 ***	97.50 ***
Endogenous Wald χ2	18.35 ***		13.90 ***		15.33 ***
Weakiv_test	87.30 ***	90.46 ***	84.60 ***	54.14 ***	90.91 ***
N	1167.00	1167.00	1167.00	1167.00	1167.00

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

shows that household risk tolerance is used as an intermediary variable to analyze the impact of labor return on land transfer-in. The return of migrant workers enhances the family risk tolerance of farmers. After adding the intermediary variables, the direction of the influence of the return of migrant workers on the land transfer-in of farmers remains unchanged, indicating that the return of labor force enhances the family risk tolerance of farmers and thus increases the probability of farmers choosing to expand the production scale.

(2) The return of migrant workers, the participation of cooperatives, and the farmers’ land transfer-in. As shown in Table 4, the effects of labor return on farmers’ land transfer-in, cooperative participation on land transfer-in, and the effects of labor return on farmers’ land transfer-in after adding intermediary variables are all significant at the level of 1% or above. After the participation of cooperatives as the intermediary variable, the influence of labor return on farmers’ land transfer-in remains unchanged, indicating that labor return improves farmers’ organizational ability and thus increases the probability of farmers choosing to expand production scale through cooperative participation.

5.5. Heterogeneity Analysis

(1) Terrain. As shown in

Table 5. Heterogeneity analysis of the impact of labor return on rural households’ land transfer-in (terrain).

Variables	Flatlands		Hilly Regions		Variables	Flatlands		Hilly Regions
	Model 1	Model 2	Model 3	Model 4		Model 1	Model 2	Model 3	Model 4
labor_return_ration	4.31 ***	22.98		3.40 ***	H_return_farm	3.07 ***	127.63		2.59 ***
	(0.30)	(37.19)		(0.12)		(0.16)	(425.76)		(0.07)
Wald χ2	5.32 **	5.32 **		5.03 **	Wald χ2	3.00 **	3.00 **		9.92 ***
Control	Yes	Yes	Yes	Yes	Control	Yes	Yes	Yes	Yes
Group difference	Flatlands—hilly regions: 0.057 ***				Group difference	Flatlands—hilly regions: 0.052 ***
N	391.00	391.00	776.00	776.00	N	391.00	391.00	776.00	776.00

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, the impact of return migration of rural labor on land transfer-in by households in plain areas is greater than that in hilly regions, both in terms of correlation coefficients and marginal effects. This difference may be attributed to the fact that plain areas generally have a higher level of overall economic development compared to hilly regions. The endowment of land resources and the level of horizontal division of labor in agriculture are higher in plain areas. Therefore, after returning to their hometowns, migrant workers in plain areas have higher expectations for developing agriculture to increase their family’s overall income, which in turn increases their interest in expanding production scale.

(2) Village economic development level. As shown in

Table 6. Heterogeneity analysis of the impact of labor return on rural households’ land transfer-in (economic development level).

Variables	Weak Economy Village		Strong Economy Village		Variables	Weak Economy Village		Strong Economy Village
	Model 1	Model 2	Model 3	Model 4		Model 1	Model 2	Model 3	Model 4
labor_return_ration	0.28	0.10	0.38 ***	0.14 ***	H_return_farm	0.10	0.03	0.58 ***	0.21 ***
	(0.18)	(0.06)	(0.13)	(0.04)		(0.28)	(0.10)	(0.16)	(0.06)
Wald χ2	33.82 ***	33.82 ***	93.16 ***	93.16 ***	Wald χ2	31.54 ***	31.54 ***	97.03 ***	97.03 ***
Control	Yes	Yes	Yes	Yes	Control	Yes	Yes	Yes	Yes
Group difference	Weak economy—Strong economy: 0.015 **				Group difference	Weak economy—Strong village: 2.38 **
N	315.00	315.00	852.00	852.00	N	315.00	315.00	852.00	852.00
N	315.00	315.00	852.00	852.00	N	315.00	315.00	852.00	852.00

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, there are differences in the impact of return migration of rural labor on land transfer decisions by households in villages with different levels of economic development. Both correlation coefficients and marginal effects indicate that in villages with better economic development, the impact of return migration on land transfer-in by households is greater than that in villages with weaker economic development.

5.6. Further Analysis

The above multi-dimensional empirical study investigated the impact of migrant workers’ return on farmers’ land transfer-in, and concluded that migrant workers’ return can significantly promote the probability that farmers choose to transfer-in the land to expand the scale of family management. But can the return of migrant workers really promote the transformation of small farmers into large-scale business households? Further research is needed. This paper makes comprehensive reference to the study of Xu et al. (2021), divides the land scale under management of farmers into three categories: less than 10 mu, 10–30 mu, and more than 30 mu, and then carries out econometric empirical analysis, respectively [41]. As shown in the results reported in

Table 7. Influences of the return of migrant workers on farmers of different operation scales.

Variables	<10 mu	10–30 mu	>30 mu
H_return_farm	−2.38	0.78	2.52 ***
	(1.66)	(1.53)	(0.21)
Control	Yes	Yes	Yes
Wald χ2	0.04	0.36	8.63 ***
Endogenous Wald χ2	1167.00	1167.00	1167.00
N	−2.38	0.78	2.52 ***

Note: 1 mu = 667 m² or 0.067 ha. Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, the return of migrant workers has no significant impact on the operation scale below 10 mu, but the direction is negative. The operation scale of 10–30 acres has no significant impact, and it is most likely to form a business scale of more than 30 acres.

6. Discussion

The core of economic development lies in the mobilization and allocation of factors. The key driver for advancing agricultural and rural development is optimizing the allocation of labor and land, the two fundamental elements of agricultural production. Cultivating large-scale farming entities and enhancing the marginal productivity of land and labor are universal patterns and experiences among leading agricultural nations. However, in the specific context of China, both labor and land present unique conditions that distinguish China from other agricultural powers. The United States, as one of the most agriculturally advanced countries, benefits from a relaxed human–land relationship, which serves as a prerequisite for its extensive use of policy subsidies and industrial support to develop family farms [42]. Meanwhile, the decline of smallholder farming and the expansion of agricultural operations have been primary drivers of sustained growth in U.S. agricultural productivity [43]. In Europe, the coexistence of land purchases and land leasing as alternative options has effectively promoted the continuous expansion of agricultural operations [44]. In contrast, China’s basic national condition of a large population and limited land dictates that it cannot completely phase out smallholder farming as the U.S. has done. According to the Ministry of Agriculture and Rural Affairs, in 2022, there were 193 million households managing less than 10 mu of farmland, accounting for 41% of all farming households [45]. Additionally, China’s land system, where farmers only possess land contracting and management rights while ownership remains with village collective economic organizations, precludes land sales as an alternative for optimizing land use. Furthermore, international experiences of labor migration in developed countries such as those in Europe and North America typically involve a one-time rural-to-urban migration, completing the urbanization of farmers. Labor return in these contexts often focuses on international return migration [46]. In China, however, labor migration is influenced by institutional factors such as the household registration system and rural land policies, resulting in a pendulum-like or migratory pattern. Migrant workers who fail to achieve urbanization often return to their hometowns and re-engage in agricultural production, dynamically affecting the allocation of rural labor and land resources. The long-standing rural-to-urban migration model has exacerbated the contradiction between human and land resources in rural China. In recent years, the trend of labor return has become increasingly evident. Whether this return can effectively alleviate the shortage of rural labor and the imbalance in human–land resource allocation, and whether the comparative advantage in human capital gained from migrant work can stimulate effective demand for rural land transfers, are pressing and necessary issues for China’s current agricultural and rural development.

This study makes two marginal contributions in exploring the impact of labor return on rural land transfers. First, in measuring the variable of labor return, this study not only focuses on spatial return-migrants returning from their workplaces to their registered hometowns, but also extends the concept to include return to agricultural work. Specifically, whether the household head returns to engage in farming is used as a core explanatory variable. Over the 40 years since China’s reform and opening-up, a large number of rural laborers have left agricultural production to work in cities, leading to the hollowing out of rural areas, abandoned farmland, and economic stagnation. Under the combined influence of new economic, social, and policy conditions, some migrant workers have begun returning to their hometowns. However, whether they re-engage in agricultural production and thereby alleviate the shortage and declining quality of agricultural labor directly affects the allocation of land resources. Moreover, as the household head plays a central role in family decision-making, examining the impact of their return to farming on land transfers aligns more closely with real-world logic, yielding conclusions that are more scientifically robust and policy-relevant. Second, this study utilizes samples from three counties in Sichuan Province with varying topographies and economic development levels, and further conducts heterogeneity analysis across these samples, ensuring that the conclusions are more comprehensive and scientifically sound.

At the same time, this study shares similarities and differences with existing research on related topics. The academic circle has made a multi-dimensional investigation on how to promote the development of China’s land transfer market. For example, from the perspective of the endowment of land itself, due to land fragmentation, the cost of farming and production of farmers is increased, and the expected return of capital and manpower input is low. This type of land is in a disadvantageous position in the land transfer market, and farmers often give up the production of land in the form of desert or transfer [47]. Concentrated large or adjacent plots are transferred to large households, while scattered small express deliveries mainly flow to small farmers, with the heterogeneity of plots [48]. From the perspective of farmers who are the subject of land transfer, farmers’ cognition of land transfer policies will affect their psychological construction of land use decisions and ultimately affect whether family land is transferred or not [49]. However, as the main production factor of agricultural production, labor force is generally regarded as the core factor of farmers’ land transfer. On the one hand, the empirical results demonstrate that labor migration significantly influences the allocation of rural land resources, corroborating findings by Démurger & Xu (2011) and Long et al. (2016) [14,50]. Labor return alleviates the shortage of agricultural labor, supplements household human capital, and enhances the input of agricultural production factors, thereby promoting land transfer-in [51]. On the other hand, this study argues that labor return, particularly the return of household heads to farming, increases the likelihood of land transfer-in by boosting agricultural fixed asset investment and reconstructing and expanding social networks. This finding contrasts with studies by Kang et al. (2014) and Wang et al. (2020), which suggest that labor return may delay or inhibit land transfers [52,53]. In addition, this study believes that compared with villages with better economic development in plain areas and villages with weak economic development, labor return in hilly areas and villages with weak economic development has a weaker promoting effect on farmers’ land transfer-in, which may be caused by differences in land endowments and market development degree. The land in hilly areas and villages with weak economic development is often more fragmented, and the accessibility of transportation and information is weak, which leads to higher costs for farmers to choose to transfer to land, thus increasing the risk of expanding production scale.

This study has several limitations. First, it only explores the impact of labor return to farming on rural land transfers, based on the premise that returning laborers supplement agricultural human capital and are potential new entities for transforming smallholder farming. However, reality may differ. Given the significant productivity gap between agricultural and non-agricultural sectors, where non-agricultural work offers higher expected income, returning laborers may experience employment differentiation even within county boundaries. Different occupational choices post-return may lead to varied land transfer decisions, limiting the scope of this study. Additionally, this study focuses on the demand side of land transfers, examining household-level decisions on labor and land resource allocation post-return. However, the core of any market lies in the relationship between supply and demand. As both market participants and suppliers in the land transfer market, households may adjust land transfers based on new human–land dynamics and land parcel conditions. Future research could explore land transfers from the supply side, examining land transfer-out decisions at the parcel level. Finally, this study focuses on Sichuan Province, selecting three counties as sample areas to ensure representativeness across different natural and economic conditions. However, given China’s vast population, complex natural conditions, and diverse local economic, social, and policy environments, future research could expand the sample size and utilize national-scale data to yield more reliable and replicable conclusions.

7. Conclusions and Policy Implications

7.1. Conclusions

Based on the above analysis, the research mainly draws the following three conclusions: in general, both the proportion of household labor returning and the return of household heads to farming have a positive and significant impact on whether farmers have land transfer-in, and also have a positive and significant impact on the scale of farmers’ land transfer-in. Furthermore, the return of migrant workers has a significant positive impact on the operation scale of farmers over 30 mu. Both labor return and household head return farming increase the probability of farmers choosing to have land transfer-in to expand the scale of agricultural production by enhancing household risk tolerance and increasing the participation of cooperatives. Finally, the effect of labor return on land transfer-in in plain areas is stronger than that in hilly areas. In villages with strong economic development, the effect of labor return on farmers’ land transfer-in is stronger than that in villages with weak economic development.

7.2. Policy Implications

China is a large agricultural country, and the rise and fall of rural areas has a bearing on the overall development. As the core production factors of rural development, especially agricultural development, how to pay attention to and make good use of the human capital advantages of returning groups in the context of the new trend of labor flow is an important issue to stimulate the vitality of rural economic development and realize the modernization of agriculture and countryside by optimizing and reconstructing the new-type man–land relationship and gradually eliminating the problems of small scattered and weak agricultural production caused by the prominent contradiction of man–land relationship. This study makes a micro-empirical analysis of the effects of labor return and return farming on the land resource allocation of rural households. Based on the above empirical results, the following suggestions are made on how to promote the development of the rural land transfer market and how to guide the rural population return in an orderly manner:

(1) Pay attention to the returning population in rural areas, shape new business entities, and further leverage the effective demand for land transfer. In recent years, under the influence of comprehensive reasons such as the in-depth promotion of the rural revitalization strategy, the increase in rural employment opportunities, and the continuous optimization and adjustment of industrial structure, rural labor forces who go out to work have begun to return to their hometowns, and this trend has gradually become obvious. During the period of migrant work, migrant workers have obtained a lot of income and technology accumulation, and at the same time enhanced the ability to capture market economic information. Compared with the older and lower education level of the labor force left in the countryside, this relatively large and high-quality group returns to the countryside, which undoubtedly injects a strong dose of energy into the development of rural economy and society. Therefore, it is necessary to establish a dynamic monitoring mechanism for the returning population, encourage the returning population to expand the scale of agricultural production when migrant workers return home, and revitalize idle and abandoned land in rural areas. Support returnee groups to innovate agricultural production patterns and improve the marginal production efficiency of labor and land in agricultural production. Through the promotion of agricultural insurance, risk management training, and other ways to establish a risk compensation mechanism, further strengthen the risk tolerance of farmers, so as to enhance the confidence in expanding agricultural production.

(2) We will promote the development of cooperatives and enhance their service capacity. The government should increase support to cooperatives, cooperative organization construction, and technology upgrading, improve the ability of absorbing cooperatives for farmers. Support cooperatives to use the Internet and big data technology to establish land transfer information platforms to enhance information transparency and transfer efficiency. Through digital means, we can help farmers obtain land circulation information more conveniently and reduce the risk caused by information asymmetry. In addition, farmers who transfer land through cooperatives and sign transfer agreements will be given certain financial incentives; using cooperatives as a platform to carry out training on land integration strategies. Strengthen legal services for cooperatives, such as one-stop services for contract review and dispute mediation.

(3) Implementation of regional differentiation policy support labor backflow to the land into the promoting effect of regions and economically developed villages in the plain. This shows that the natural conditions and economic development level in different regions have different impacts on land circulation. Therefore, farmers in plain areas and economically developed villages should be encouraged to expand the scale of land transfer and realize the scale and modernization of agricultural production by means of land transfer subsidies and scale management incentives. We will support plain areas and economically developed villages in developing specialized agriculture and agricultural product processing industries, extend the agricultural industry chain, raise the added value of agricultural products, and increase farmers’ confidence and expectations in expanding their operations.