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Article

Rural Land Circulation and Peasant Household Income Growth—Empirical Research Based on Structural Decomposition

by
Wenwu Zhang
1,
Shunji Zhao
1,*,
Jinguo Wang
1,
Xinyao Xia
1 and
Hongkui Jin
2,*
1
School of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
2
School of Economics and Management, Nanjing University of Finance and Economics, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(16), 6717; https://doi.org/10.3390/su16166717
Submission received: 21 June 2024 / Revised: 31 July 2024 / Accepted: 1 August 2024 / Published: 6 August 2024
Versions Notes

Abstract

:
How rural land transfer affects the growth of non-agricultural income and the changes in its sources are important research topics. This study uses the micro-data from the China Family Panel Studies (CFPS) spanning from 2014 to 2020 and empirically analyzes the impact of rural land transfer on the growth of non-agricultural income, based on a multi-dimensional decomposition of rural household income structure. This study found that (1) land transfer has a significant promoting effect on the growth of non-agricultural income. Transferring out land is conducive to increasing wage income and transfer income, while transferring in land compensates for the decrease in operating income by achieving a higher operating income, ultimately leading to an increase in total income. (2) The effect of land transfer on the growth of non-agricultural income is higher in the Eastern region than in the Central and Western regions. The higher the education level of family members, the greater the income-increasing effect of land transfer on farmers. (3) Mechanism analysis shows that land transfer increases farmers’ opportunities for migrant work and improves farmers’ operational efficiency, which are the main channels for the growth in non-agricultural income. This study demonstrates that land circulation will promote farmers’ income growth and prosperity through rental income, share cooperation and dividends, labor transfer and wage income, industrial chain extension and value-added income, and policy support and subsidies.

1. Introduction

Land transfer serves as a crucial lever for increasing farmers’ income and promoting rural revitalization. According to data released by China’s Ministry of Agriculture, by the end of 2023, the area of rural household contracted land transferred for management rights exceeded 600 million mu (1 mu = 666.67 square meters), accounting for approximately 35% of the total cultivated land area in China. Over the years, large-scale land transfer has effectively facilitated the popularization and application of modern machinery and cultivation techniques, providing strong support for the development of information-based agriculture, accelerating agricultural and rural supply-side structural reform, and enhancing the market competitiveness of agriculture [1]. This has made positive contributions to the industrialization of agriculture and the increase in farmers’ production and income. (In 2021, the per capita disposable income of rural residents will reach CNY 18,931, an increase of 9.7 percent in real terms, 2.6 percentage points higher than that of urban residents.) However, land transfer has also generated some controversies. Among them, the concerns that land transfer activities may widen the income gap between rural households with different characteristics, such as age and education level, leading to income differentiation, have always been the focal point for the government and society at large. What impact does land transfer have on non-agricultural income? What are the specific effects on the structure of income sources?
The impact of land transfer on farmers’ income may have a certain degree of uncertainty. Specifically, if land transfer policies simply encourage farmers to transfer their land to other agricultural operators without considering whether these farmers can obtain other sustainable sources of income after the transfer, it may lead to an irreversible change in the income structure of rural residents. Farmers who lose their land not only lose economic resources but also emotional ties [2]. Some farmers face the challenge of re-adapting to society, but they quickly rely on their own knowhow to find new ways of survival and easily resolve livelihood and economic issues, further improving their living standards. However, among the various components of their total income, agricultural income continues to decline, while non-agricultural income continues to rise. This issue of unbalanced changes in income structure is urgently needed to be addressed in rural land transfers. Meanwhile, as many farmers transfer their land and migrate to cities, the urban population increases, putting greater pressure on urban resources, such as transportation, environment, healthcare, and education [3]. Some farmers may enter the urban workforce without adequate preparation, facing issues such as employment, housing, and their children’s education. If the children of migrant workers want to enroll in urban public schools, they usually need to provide several documents, such as household registration books, temporary residence permits, birth certificates, social insurance, and labor contracts. The processing of these documents is difficult for some migrant workers, especially those migrant families with strong mobility. In addition, the admission process is complicated and cumbersome, which requires a lot of time and energy. However, migrant workers often find it difficult to complete these procedures due to busy work schedules and time constraints. Their living and working conditions may be poor, affecting their rights and quality of life. Furthermore, some farmers, after transferring their land, may be unable to cope with the challenges of society and adapt to a life without farmland due to various personal or family reasons. This may increase their living pressure. Such unbalanced changes in income structure may further exacerbate the socio-economic disparities in rural areas, which are not conducive to rural stability and sustainable development [4]. From the perspective of the central government’s original intention to promote rural land circulation (The Ministry of Agriculture and Rural Affairs of the People’s Republic of China Decree No. 1 of 2021 “Administrative Measures for the Transfer of Rural Land Management Rights” has been reviewed and adopted at its first Executive Meeting of the Ministry in 2021 and is hereby issued and will come into force as of 1 March 2021), its main goal is to drive rural revitalization and improve the living standards of rural residents. If the policy only focuses on increasing rural residents’ income while ignoring the unbalanced changes in their income structure, it will be unable to achieve the expected policy effect. Local governments should also closely observe the changes in rural residents’ income structure. Therefore, given the current comprehensive promotion of the mechanization, scale, and specialization of rural land cultivation, it is of great theoretical and empirical significance to investigate the impact of land circulation activities on rural residents’ income, rural industrial revitalization, and labor mobility. this investigation will help evaluate whether the current land circulation model is sustainable, and whether land circulation can truly improve rural residents’ lives. Scholars have shown that estimates also indicate that a global approach to separability attenuates the significant effect that less-encumbered land transfer rights would have on shadow factor price equalization across households and on allocative efficiency [5].
According to Xinhua news, Xinyuan County has encouraged and guided farmers to become active participants in land circulation and large-scale agricultural operations. Through land circulation, the market has been activated, leading to cost reduction and efficiency enhancement in agriculture, bringing significant benefits to farmers. Recently, at the Karasu Village Committee in Biestobie Township, Xinyuan County, villager Ma Qianjun came to collect the money for his land circulation. Last year, he transferred all his land to a farmer cooperative and opened a shop to increase his income. Ma Qianjun, a villager from Karasu Village in Biestobie Township, Xinyuan County, said, “I have 25 acres of land that I circulated and received 35,000 yuan. I freed up the time I used to spend farming and opened a shop, earning around 60,000 to 70,000 yuan a year. Together with the money from land circulation, I earn around 100,000 yuan annually.” With the circulation of land management rights, farmers not only receive income from the land transfer, but their labor force is also liberated. Farmers who have transferred their land have found opportunities in secondary and tertiary industries, such as working in cities, starting their own businesses, and developing animal husbandry, further increasing their income and contributing to the collective economy of the village. Talebeng Saylik, the deputy village head of Karasu Village in Biestobie Township, Xinyuan County, said, “After the convening of the 10th Party Congress of the autonomous region, our village has unified our thinking and actions with the spirit of the congress. We continue to consolidate the achievements of centralized land circulation and put in sufficient effort in rural revitalization. This year, 310 households in our village transferred over 8000 acres of land to cooperatives, generating an additional income of 1 million yuan for the village collective. Each household has an average increase in income of 5000 yuan, and over 400 laborers have been liberated. This has created a win-win situation for farmers’ income growth, collective benefits, and industrial efficiency” (Xinhua News, 15 December 2022).
The relationship between land circulation and farmers’ income is an important topic in the field of economics. There are three main strands of literature directly related to this paper. The first strand of literature focuses on analyzing the influencing factors of land circulation. Rural land circulation is influenced by government macro policies. Some scholars believe that the government plays a guiding role in land circulation, and there is a significant positive correlation between farmers’ trust in the government and their likelihood of participating in land circulation [6]. Factors such as government support and the standardization of the land circulation market also have a significant impact on the likelihood of land circulation [7]. At the macroeconomic level, considering the significant differences in economic development levels among regions, there are also significant differences in the scale and rate of rural land circulation. The development of rural non-agricultural industries, the mode of land circulation, the degree of land aggregation, farmers’ land circulation behavior, the degree of education of the labor force, the per-capita net income level, the level of social security, and the agricultural production structure all have significant impacts on rural land circulation [8]. On the other hand, land circulation can also be influenced by the farmers’ idiosyncratic factors. For individual farmers, factors such as educational level, occupation, non-agricultural income, possession of non-agricultural employment skills, entitlement transfer rights, and land ownership stability are important influencing factors for land circulation [9]. Farmers’ families will also rationally allocate land resources based on their endowments. When family labor is allocated to non-agricultural sectors, and the relative benefits are greater, farmers develop expectations for land circulation [10]. The economic effects of land circulation may vary significantly among different households. For households with higher production efficiency, they can obtain higher levels of agricultural production and operating income when engaged in agricultural production. Higher agricultural production efficiency also makes these families willing to invest more labor and agricultural capital, including all kinds of funds, machinery, and equipment, as well as their own labor in agricultural production on the land, and less unwilling to rent out their land. On the other hand, for families with lower production efficiency, the lower level of agricultural production and operating income makes them more willing to invest more labor in non-agricultural sectors and obtain higher non-agricultural wage income to increase family income, and they are more willing to rent out their land [11].
There could be several reasons for this behavior. Firstly, the improvement in agricultural production efficiency: when agricultural production is more efficient, farmers can get more output from the land. This is usually due to factors such as technological progress, agricultural mechanization, better planting techniques, or more scientific management. Higher productivity means that the same input of land and labor can produce more agricultural products, thereby increasing farmers’ incomes. Secondly, reinvestment of labor and agricultural capital: as productivity increases, farmers may find it more profitable to continue cultivating their own land rather than renting it out. Farmers are willing to put more labor and agricultural capital in the land to further increase output and income. Thirdly, from the perspective of labor economics: Equilibrium Marginal Product. in economics, the marginal product refers to the amount of output that is increased by one additional unit of input. When inputs are increased, the marginal product may first increase and then decrease because the finite nature of resources causes the efficiency of inputs to gradually decline. Farmer decisions: farmers weigh the marginal costs and marginal benefits of increasing inputs. When productivity increases, marginal returns also increase. As a result, farmers tend to increase their inputs for higher returns, rather than renting out their land. Fourthly, the substitution effect of land leasing: when farmers find that they can increase their income by improving production efficiency, they will think that renting land is not an optimal choice. This is because leased land may only receive a fixed rent, and they will not enjoy the additional benefits brought by increased productivity.
The second strand of literature focuses on analyzing the influencing factors of rural residents’ income structure, which is a complex and multifaceted research topic involving land, industrial structure, policies, and other dimensions. Firstly, diversified land management can not only improve farmers’ income structure and income, but also enhance their ability to cope with natural and market risks [12]. Land circulation has also been a heated topic in promoting the adjustment of rural income structure, and it is considered an important tool for increasing farmers’ income [13]. Secondly, those with sufficient idle funds to engage in non-agricultural operations generally have a higher overall income, but only a small portion of people can earn this income. Overall, the impact of rural industrial structure on income structure is apparent [14]. Reference [15] also found that industrial structure is an important determinant of workers’ income structure. Finally, from 2010 to 2020, the development of urbanization, the promotion of labor transfer, and the implementation of policies to benefit farmers and increase their income have significantly changed the income structure of farmers in China [16]. Additionally, improving farmers’ income structure and raising their income levels must be preceded by a shift in policy orientation, focusing on accumulating farmers’ human capital and providing them with equal employment and social security [17]. Nationally, urbanization has primarily increased the proportion of farmers’ wage and property income, while reducing the proportion of operating and transfer income [18].
The third strand of literature explores the relationship between land transfer and the income of rural residents. Land is one of the most important production factors for rural residents, and the allocation of land resources directly affects the distribution of farmers’ income. After the Chinese government changed its foreign policy, the household contract responsibility system (an agricultural production and management system in which farmers contract land to their families, assume responsibility for their profits and losses, and sell agricultural products at prices and quantities set by the municipal government) was implemented in rural areas. Under the system of equalized land management rights, farmers’ income achieved a relatively balanced growth. Since the implementation of the second round of national land extension and contracting policies, to avoid frequent changes in contracted land and prevent the continuous subdivision of farmland management, the land contracting relationship has followed the principles of “no increase in land for population growth, no decrease in land for population decrease” (“Measures for the Administration of the Transfer of Rural Land Management Rights”) and long-term contracts [16]. Land transfer can help increase farmers’ income. It can affect farmers’ income through mechanisms such as labor division, land property rights, land resource integration, and agricultural scale. Different forms of land transfer have varying impacts on farmers’ income and the types of income they receive [14]. A classified examination of land transfer behavior reveals that the land transfer behavior of households who transfer out land can contribute to increased rental income, wage income, and property income. On the other hand, the land transfer behavior of households who transfer in land has a clear income effect, with the increase in operating income offsetting the decrease in wage income and promoting an increase in total income [19]. The flexibility and targeting of land circulation have brought a differentiated income growth to farmer groups at different income levels. This process constitutes an example of Pareto improvement, where the overall income of farmers has been generally increased without causing any detriment to the interests of any farmer group, thereby positively enhancing the welfare level of all farmers. Therefore, promoting land transfer remains an effective way to increase farmers’ income and improve the existing rural land system [20]. However, land transfer activities have significantly increased the income of households who transfer in land and have a higher initial income, but the impact on the income of households who transfer out land with a lower initial income level is limited, thus widening the income gap among rural residents [21].
In summary, existing research has conducted extensive discussions on how land transfer affects the income of rural residents. However, there are two points worthy of further discussion and expanded analysis. Firstly, the current research lacks a comprehensive analysis of the impact of land transfer on non-agricultural and agricultural income within the income structure of farmers. Secondly, there is a limited number of studies analyzing the impact of land transfer on different types of households [22]. It is urgent to conduct structural decomposition to dissect the impact of land transfer in different scenarios, segmented by different characteristics of families.
Chinese and foreign scholars have conducted extensive and in-depth research on rural land circulation and provided a series of new discoveries. The characteristics of land circulation in the UK are the encouragement of private land purchases, leading to a gradual privatization of land ownership. However, land concentration may result in some farmers losing their land, thereby exacerbating social inequality. In the United States, the private ownership of agricultural land is the norm, with the initial acquisition of such ownership primarily stemming from purchases or government grants without compensation. This could potentially enable large-scale farmers to further expand their land holdings through land circulation, posing challenges for small- and medium-sized farmers. Japan, also adopting a private ownership system for agricultural land, sees most of the land being self-owned and cultivated by farmers, with relatively limited tenant farming. Consequently, there is still room for improvement in the scale, depth, and breadth of land circulation in Japan. Rural household income typically comprises multiple components, including agricultural income, non-agricultural income (such as wage income, business income, property income, etc.), and potentially other transfer incomes. Land circulation can potentially impact all aspects of household income, not just limited to non-agricultural income. In addition, the data used in this paper are cross-section data, and fixed effects cannot be used. Therefore, by solely measuring the impact of land circulation through non-agricultural income in this paper, we overlook changes in other income sources, resulting in certain limitations in the research findings. Men, elderly people, villagers with higher education level and income are more active and willing to participate in land circulation. Age, education level, having a part-time job, and insurance have a positive relationship with the area of land flowing out (the effects on inflow of land are in reverse) [23].

2. Theoretical Analysis and Hypothesis

2.1. The Influence Mechanism of Rural Land Transfer on Transferred Household Income [24]

Firstly, land transfer is conducive to the growth of wage income for rural residents. Firstly, rural land transfer helps to release rural household labor. The traditional way of earning a living in rural families is no longer suitable for the modern society. Initially, farmer households relied on the output from a few acres of farmland, which primarily met their fundamental survival needs—namely, the physiological needs in Maslow’s hierarchy of needs, encompassing access to necessities such as food, water, and shelter. However, as times changed and economies evolved, the income generated from these lands became insufficient to cover the family’s escalating expenses for essential living, encompassing education, healthcare, and aspirations for a better quality of life. When the physiological needs could no longer be fully met, farmer households were compelled to seek the possibility of fulfilling higher-level needs, often reluctantly. They chose to migrate to cities for work, a move that was not only a pursuit of safety needs (such as job stability and financial security) but also implicitly harbored desires for social needs (like connecting with broader social groups) and esteem needs (like gaining social status and recognition through work). Though this transition might entail the pain of separation from family and the challenges of adapting to a new environment, it reflects the courageous endeavors made by farmer households under existing conditions to satisfy their higher-level needs. Moreover, with the rapid urbanization and industrialization of China, cities require a large amount of labor for construction, and factories also need many workers to keep operations running. For factories and construction companies, many qualified rural labors are their best choice. For farmers, the income from urban employment is far higher than that from farming. Secondly, rural land transfer is conducive to unleashing the vitality of rural production factors and promoting rural industrial development. Rural areas possess abundant production factors, and the organic integration (machine integration refers to combining different parts and different attributes into a unified whole, so that the parts constituting the whole are interrelated and coordinated and have inseparable unity [4]. In agricultural production, organic integration is to combine various production factors, such as land, labor, capital, science, and technology, etc., according to certain procedures, to form an efficient, coordinated, and sustainable agricultural production system) of various production factors is conducive to the development of rural enterprises, providing more employment opportunities for farmers and making it easier for them to obtain wage income after transferring their land. In addition, most farmers who migrate to cities for work do not settle there, and the demand for rural labor in urban construction fluctuates. When the quantity of labor flowing out of the countryside cannot meet the demand of the urban labor force, most farmers will choose to return to their hometowns. Rural land transfer can promote rural industrial development and provide temporary employment opportunities, which is beneficial to the growth of their wage income.
Secondly, rural land transfer is conducive to the growth of transfer-out households’ property income. Firstly, the transfer of land directly generates periodic land rent income. Rural households generally have relatively few assets, most of which are concentrated in land, forestry, fishponds, etc. In rural land transfer, the land rent income of transfer-out households is an important source of their property income. The annual rent per mu of land directly affects the property income of rural residents. Secondly, rural land transfer promotes the appreciation of land value and increases farmers’ property income. On the one hand, the rapid development of land transfer has made an increasing number of people want to contract land in rural areas and obtain high returns through large-scale and mechanized farming. Their demand for farmers’ land has increased the value of farmers’ land, raised land rents, and thereby increased farmers’ property income. On the other hand, rural land transfer is beneficial for the development of rural industries, which enables the value of farmers’ assets, such as trees and fishponds, to continue to rise and makes it easier to cash them in, thereby further increasing the property income of rural residents.
Thirdly, rural land transfer is conducive to the growth of transfer-out households’ transfer income. Transfer income for farmers mainly includes pensions from their children or relatives, family support, government subsidies, donations, or compensation. Government subsidies, donations, or compensation are strongly exogenous, being primarily influenced by government policies; thus, rural land transfer will not affect them. Therefore, rural land transfer does not impact the transfer-out households’ receipt of government subsidies, donations, or compensation (the government generally pays farmers based on the amount of land they hold, whether or not they rent it out). However, rural land transfer may have a significant impact on the pensions provided by children or relatives. Most elderly farmers have a strong attachment to their land and are unwilling to transfer it at a low price. Out of concern for their parents’ health, children are willing to regularly provide them with a considerable amount of money to maintain their daily lives. This funding is the main source of farmers’ transfer income.
H0: 
Land transfer can increase farmers’ off-farm income.

2.2. The Effect of Rural Land Transfer on Different Household Income (This Paper Will Verify the Theoretical Analysis through Empirical Test)

Firstly, the land transfer policy is more beneficial for households with a younger weighted age. This is because land transfer helps rural families liberate their labor force, enabling them to engage more in wage-earning jobs and thus increase their household income. One reason is that households with a younger weighted age usually have heavier financial burdens, so they are more inclined to work outside the village to earn more income. In contrast, households with an older weighted age may prefer to stay in rural areas and engage in agricultural work. Therefore, after transferring their land, households with a younger weighted age may earn higher wage income than those with an older weighted age, resulting in greater benefits from land transfer. The second reason is that households with a younger weighted age also have advantages in working outside the village. The jobs farmers engage in when migrating to cities are mainly physical labor, and a younger age means better physical fitness and, thus, greater ability to earn wage income. This ability enables households with a younger weighted age to better adapt to the new economic environment after transferring their land, thereby enhancing the positive effects of land transfer. Additionally, land transfer also helps increase rural families’ property income. Through land transfer, farmers can rent or transfer their land to other agricultural operators, earning rent or transfer fees. These incomes can become an important source of property income for rural families, helping to improve their living standards.
Secondly, land transfer is more beneficial for families with a higher weighted educational level. This is because families with higher educational levels can better adapt to the new economic environment after transferring their land, thereby obtaining more benefits. On one hand, when farmers transfer their land and migrate for work, the higher their educational level and cultural literacy, the more economic income, medical conditions, and employment opportunities they will have. This is mainly because modern construction sites are mostly mechanized, and migrant workers with a higher educational level are more likely to learn how to operate engineering equipment than those with a lower educational level, making it easier for them to adapt to the new work environment and earn higher wages than ordinary workers. Additionally, migrant workers with a higher educational level tend to have more flexible thinking and stronger problem-solving abilities than those with a lower educational level, which makes it easier for them to gain the appreciation of their superiors and further increase their income. On the other hand, families with a higher educational level are more enthusiastic about entrepreneurship after transferring their land than those with a lower educational level. This is because families with a higher educational level typically possess more knowledge and skills, making it easier for them to identify and seize entrepreneurial opportunities. The income generated from entrepreneurship directly enhances the positive effects of land transfer on families, allowing families with a higher educational level to obtain more benefits after land transfer.
Lastly, land transfer is more beneficial for families with a larger household size. This is because when the household size is larger, the family tends to have a relatively abundant labor force. Therefore, these families are more likely to send a portion of their labor force to cities or other regions to engage in wage-earning jobs, thereby obtaining additional income. In contrast, families with a smaller household size may lack sufficient labor resources to effectively capitalize on the opportunity of land transfer to increase their income. Additionally, land transfer often brings along the issues of management and planning, such as land integration, administration, and utilization. For families with a smaller household size, these issues may be more prominent and difficult to resolve. Therefore, for families with a larger household size, land transfer can provide more opportunities and benefits, as they have more labor resources to tap into and are relatively better able to handle the management and planning issues.
H1: 
Families with different education levels, different comprehensive ages, and different regions have different feedback on land transfer.

3. Materials and Methods

3.1. Data Sources

The data for this study were sourced from the China Family Panel Studies (CFPS), conducted by the Institute of Social Science Survey at Peking University. The survey, with 2010 as the base year, is conducted every two years and covers 25 provinces (autonomous regions and municipalities directly under the central government) across the country, involving over 22,000 households. This study utilizes data from CFPS (2014), CFPS (2016), CFPS (2018), and CFPS (2020), matching household financial data with individual self-reported data. Abnormal and missing values were excluded, specifically samples with missing data on individual and parental ages, individual and parental education levels, as well as samples with zero non-agricultural income. For the few samples that could not be excluded, the mean value method was used as a substitute. After review and collation, a total of 12,780 samples were deemed as valid.

3.2. Variable Setting

3.2.1. Dependent Variable

This study takes the non-agricultural income of rural households as the dependent variable. The selection of non-agricultural income as a measure of the benefits of land transfer is primarily based on the following reasons. Firstly, non-agricultural income is an important source of income for farmers and, compared to agricultural income, it can more directly reflect changes in farmers’ earnings. By studying the changes in non-agricultural income, we can more accurately understand the impact of land transfer on farmers’ income. Secondly, compared to agricultural income, non-agricultural income data are easier to obtain and analyze. Studying the changes in non-agricultural income allows for more convenient data collection and analysis, thereby better supporting research on land transfer. Thirdly, it reflects the positive effects of land transfer: land transfer can promote the agglomeration of rural land and large-scale agricultural operations, thereby improving agricultural production efficiency and non-agricultural income. Studying the changes in non-agricultural income can better reflect the positive effects of land transfer. Finally, by studying the changes in non-agricultural income, we can more comprehensively consider farmers’ income sources, thereby gaining a better understanding of farmers’ livelihood and welfare.
In studying the relationship between land transfer and the non-agricultural income of rural households, further classifying non-agricultural income into different types, such as wage income, professional income, and property income, is helpful for systematically examining the impact of land transfer on the non-agricultural income of rural families. According to the definitions of these incomes in CFPS, wage income refers to the after-tax wages, bonuses, and in-kind welfare earned by household members engaged in agricultural work or non-agricultural employment; property income refers to the income obtained by households through renting land, housing, and means of production (capital appreciation of physical property); and transfer income mainly refers to government subsidies and the value of money and goods received by households from social or private donations (donations from other people).

3.2.2. Independent Variable

The land transfer process can be broken down into the land leasing out and land leasing in components. In the model setting, the definition of the variable is presented in the table below. Both land leasing out and land leasing in are used as independent variables. Additionally, considering land leasing out as an independent variable has several advantages. Firstly, land leasing out allows for more efficient land utilization, enabling large-scale and specialized agricultural production, thereby improving agricultural production efficiency. Secondly, both land leasing out and land leasing in can increase farmers’ income. Farmers who transfer out their land can receive land rent or transfer fees, while farmers who transfer in land frame it in terms of economies of scale. Finally, land leasing out contributes to the diversified development of rural economies, optimizing resource allocation and enhancing the overall efficiency of rural economies. Land leasing in, on the other hand, can promote the process of agricultural modernization, introduce advanced agricultural technologies and equipment, enhance the technological content and product quality of agricultural production, and promote the concentrated and contiguous management of land, thereby improving land utilization efficiency and increasing rural grain production. According to the CFPS definition of “land transfer,” “land” includes cultivated land, forestland, pasture, and ponds. Whether a household rents out its land to others or rents land from others is used as the basis to identify whether the household is involved in land transfer.

3.2.3. Control Variable

This paper controls for household characteristics, which include the weighted average age of the household, the weighted education level of household members, household size, wages emitted by migrant workers, land rent, government subsidies, and other incomes. Given that the age and education level of each household member have different impacts on household income, and that the age and education level of the couple, as the mainstay of the household, are obviously more important than those of their parents, the calculation of household age and education level follows the following formulas:
Household age = household head age × 0.3 + spouse age × 0.3 + father age × 0.2 + mother age × 0.2
Being single also reduces a family’s earning power
Household education level = education level of household head × 0.3 + education level of spouse × 0.3 + education level of father × 0.2 + education level of mother × 0.2
The weight of each member in the family is mainly based on the importance of their economic income in the family. In a rural family, the head husband and wife need to support the elderly and raise children at the same time, so their ability to obtain income is the key to the total income of a rural family because they are given a higher weight (it highlights their importance in obtaining household income and is used to analyze the heterogeneity of different household income improvements after land transfer). In rural households, parents mostly have the responsibility of taking care of their offspring, so this paper thinks that their age and education level have little impact on the total income of the family, so they are given a lower weight.
Regarding possible objections to family size, whether it is a family of three generations or a family of less than 4 people, the calculation method I propose is applicable. If there is no one in a family, the weight of the family is set to 0, and the value 0 is brought into the equation to calculate the weighted age of the family. The level of education of the family also reflects the actual situation, and the proposed dilemma will not occur. I found out by calculation that the calculated results are also realistic, and there will be no problem with distorting reality. In addition, I have also made changes in the revised draft based on the questions raised about the calculation formula. All variables are shown in Table 1.

3.3. Specification of Model

This article focuses on the source structure of non-agricultural income and primarily examines the specific effects of land transfer on the growth of non-agricultural income. The following empirical equation is established:
ln Y i = c 0 + α Z i + β D i + ∑ δ i control i + ε 0
When examining the impact of land transfer on farmers’ non-agricultural income, we separately consider the effects of land leasing out and land leasing in on farmers’ non-agricultural income. D and Z are dummy variables. lnYi represents the logarithm of non-agricultural income for household i during the survey period, indicating the logarithmic value of transfer income, wage income, and property income. δi is the coefficient of the control variables to be estimated, such as gender, age, and education level of the head of the household. c0 is the constant term, α and β are the coefficients of the explanatory variables to be estimated, and ε0 is the random error term. In the empirical analysis, regression analyses are conducted separately for households that transferred in land and those that did not, households that transferred out land and those that did not, and households that participated in land transfer and those that did not. Assume that the error terms are independent and equally distributed. (The data have been repeatedly checked).

4. Results

4.1. Analysis of the Impact of Land Transfer on Non-Agricultural Income

We first verify the statistical correlation characteristics between the two core independent variables and the control variables. Then, we incorporate strongly correlated and highly significant feature variables into the regression process to mitigate self-selection bias. Table 2 reports the baseline results of land leasing out and land leasing in with respect to non-agricultural income and its various sources. For non-agricultural income and its component sources, the regression coefficients of the core independent variable, land leasing out, passed the significance test at the 1% level and are positive, indicating a positive impact on the explained variable. This suggests that land leasing out has a significant positive effect on promoting non-agricultural income and its various components, and increasing rural residents’ land leasing out can enhance their non-agricultural income. In terms of the economic effect of the coefficients, for every increase of one-unit standard deviation in land leasing out, the non-agricultural income in the region will increase by 1.31%, clearly supporting the theoretical analysis and aligning with the changing trends in non-agricultural income characteristics in China’s economic development and the evolution of land leasing out policies.
The results for land leasing in as the core independent variable show that its regression coefficients for wage income, property income, and non-agricultural income passed the significance test at the 1% level and are negative, indicating a significant negative effect of land leasing in on non-agricultural income and its various components. After rural residents transfer in land, their non-agricultural income will significantly decrease. In terms of the economic effect of the coefficients, for every increase of one-unit standard deviation in land leasing in, the non-agricultural income in the region will decrease. This also supports the theoretical analysis. Although the absolute value of the 286 regression coefficients observed is of a relatively low order, it should not be ignored that when we consider it in the context of the huge base of rural residents, the cumulative effect of these small coefficient increases shows an exponential growth trend. As a result, the effect of non-agricultural income obtained by rural residents through land transfer has reached a remarkable level. The analysis results are shown in Table 3.

White’s Test

H0: Homoskedasticity
Ha: Unrestricted heteroskedasticity
chi2(74) = 2106.77
Prob > chi2 = 0.0000

4.2. Robustness Test

1. Random Sampling Method
Firstly, the method allows for multiple samplings of different sizes to be conducted, with regression tests performed separately for each sample. By comparing the regression results with the baseline regression results, the stability of the model can be tested. Secondly, randomly sampling from the overall population can reduce the accuracy of the CFPS in selecting surveyed households, thereby enhancing the reliability of the baseline regression results. In this paper, data representing 25% and 75% of the total sample were randomly selected for regression analysis, and the results are presented in Table 4. The regression coefficients of the independent variable, land leasing out, for wage income, property income, and non-agricultural income all passed the significance test at the 1% level and were positive, consistent with the baseline regression results. However, the impact on transfer income was not significant, likely due to the weak correlation between transfer income and land leasing out. As for the core independent variable, land leasing in, it had a significant negative impact on all other income sources except for property income. The primary reason for this is that rural residents with property income are relatively few, accounting for only 12.39% of the total sample, making it difficult to accurately determine the strength of its impact. Overall, the regression results from random sampling are largely consistent with the baseline regression results.
2. Chronological Regressions
To observe the relationship between the independent variables and the explained variable across different years and to understand the changing trends and patterns of the impact of land leasing out and land leasing in on the non-agricultural income of rural residents, it is important to consider that different years represent different external conditions at the same time due to the era and rapid social development. It is necessary to observe whether the impact of independent variables on the explained variable remains significant under different conditions. Therefore, conducting a regression analysis by year is necessary. Since the CFPS database releases data every two years, the overall data for 2014, 2016, 2018, and 2020 used in the previous analysis were separated and analyzed through regression analysis separately. The results are presented in the Table 5. In each year, the core independent variable of land leasing out has a significant positive impact on non-agricultural income, with regression coefficients passing the significance test at the 1% level; this is highly consistent with the baseline regression results mentioned earlier. Similarly, the core independent variable of land leasing in has a significant negative impact on non-agricultural income, with regression coefficients also passing the significance test at the 1% level; this is also highly consistent with the baseline regression results. This indicates that the impact of land leasing out and land leasing in on non-agricultural income does not change over time.
3. Non-Farm Income Changes
Additionally, the method can assist researchers in discovering potential hidden assumptions or limiting conditions within the model. If the impact remains significant after changing the explained variable, it further validates the reliability of the previous baseline regression results. Total income encompasses all economic activities of an individual or household, including agricultural and non-agricultural activities, and it is strongly correlated with non-agricultural income. Therefore, using total income as a substitute for non-agricultural income can capture economic activities more comprehensively, thereby reflecting the actual situation more accurately (I have not learned simultaneous equation modeling yet). Here, we chose to replace the dependent variable of non-agricultural income with the related variable of total income for regression analysis. The regression results, shown in Table 6, indicate that regardless of whether the control variables are included in the regression, the core independent variable of land leasing out has a significant positive impact on total income, with regression coefficients passing the significance test at the 1% level. Simultaneously, the core independent variable of land leasing in has a significant negative impact on total income, with regression coefficients passing the significance test at the 1% and 5% levels, respectively. These results are highly consistent with the baseline regression results mentioned earlier, further enhancing the reliability of the baseline regression findings. The sensitivity analysis results 1 are shown in Table 7.
4. Sensitivity Analysis
Partial R2 of the treatment with the outcome (R2yd.x):
A confounding factor of extreme significance, existing orthogonally to the covariates and accounting for the total residual variance in the outcome, must elucidate a minimum of 0.84% of the treatment’s residual variance to comprehensively justify the observed estimated effect. This threshold is indicative of the robustness value, denoted as q = 1.00 (RV_q), emphasizing the crucial role of such a confounder in influencing the analysis. Sensitivity analysis results 2 and 3 are shown in Table 8 and Table 9.
Unidentified confounding variables, orthogonal to the covariates, with explanatory power exceeding 8.81% of the residual variance in both treatment and outcome, possess sufficient strength to nullify the point estimate (achieving a 100% bias relative to the initial estimate). Conversely, those undetected confounders explaining less than 8.81% of the residual variance in both domains lack the potency to drive the point estimate to zero.
Robustness value, q = 1.00, alpha = 0.05 (RV_qa):
Unobserved confounding factors, which are orthogonal to the covariates, explaining more than 7.21% of residual variance in both treatment and outcome, have the capacity to shift the estimate into a realm where it is no longer statistically discernible from zero (incurring a 100% bias relative to the initial assessment), under an alpha threshold of 0.05. In contrast, those unidentified confounders failing to surpass this 7.21% explanatory threshold are insufficient to alter the estimate’s statistical significance vis-à-vis zero, at the same level of significance.
Bounds on Omitted Variable Bias:
The presented table delineates the upper limit of unobserved confounders’ influence, constrained by multiples of the observed explanatory capacity of the selected benchmark covariate(s) in relation to both the treatment and the outcome.

Endogenous Processing

The baseline regression analysis controlled for the characteristic variables with high significance at both the individual and government governance levels and improved the reliability of the results by merging data from multiple years. However, there may still be some endogenous issues caused by reverse causality, omitted variables, etc. Firstly, land leasing out is influenced by the economic development of the region where the farmers reside. After transferring their land, the increase in their non-agricultural income may also enhance the farmers’ willingness to transfer their land, resulting in a potential reverse causality between the independent variable and the explained variable. Additionally, some variables may be inevitably omitted in the baseline regression, despite the inclusion of numerous control variables, necessitating further processing. Therefore, following the classical approach of existing research, we constructed instrumental variables and addressed the issues using the two-stage least squares (2SLS) method. Specifically, we adopted expenditure on culture, education, and entertainment as the instrumental variable, referencing existing literature (according to the teacher’s unpublished paper). This instrumental variable is correlated with the actual value of the independent variable but is exogenous to the residual term, satisfying the assumptions of relevance and exclusivity. On the other hand, transportation and communication expenses are not directly related to non-agricultural income, as they are more influenced by exogenous factors, such as the family members’ contact with the outside world and the convenience of transportation in their residential areas, exhibiting a considerable degree of exclusivity.
Table 10 reports the regression results using the two-stage least squares method with instrumental variables, where the control variables are included in the regression sequentially for classification. Column (1) represents the IV test without the participation of any control variables. As can be seen, the coefficients of the regression results passed the significance test at the 5% level. Columns 2 to 6 gradually add variables such as family age and agricultural machinery, land assets, government subsidies and money received from others, migrant remittances and social insurance, family education level, and family size. The coefficients of the regression results all passed the significance test at the 1% level. The results of the VI test using transportation and communication expenses as an instrumental variable indicate that land leasing out still has a significant positive effect on promoting non-agricultural income.

4.3. Heterogeneity Test

4.3.1. Regional Inspection

By comparing the differences in the impact of land transfer on the non-agricultural income of rural residents in different regions and further analyzing the reasons and impacts that cause these differences, we can better understand the impact of economic, social, and schooling aspects in different regions on the effect of land transfer. This provides a solid basis for the government to formulate targeted policies and measures. According to the traditional regional division method, regions are divided into three major parts: East, Central, and West. Heilongjiang, Jilin, and Liaoning, three major grain-producing provinces, are analyzed separately as the Northeast region. The 10 Eastern provinces (cities) include Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan, but the data for Hainan and Shanghai are missing. The six Central provinces include Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan. The 12 Western provinces (regions, cities) include Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang, but the data for Inner Mongolia, Tibet, Qinghai, Ningxia, and Xinjiang are missing. Although the impact of land leasing out on non-agricultural income is significant in all four regions, the intensity of the impact varies. The results of regional regression are shown in Table 11. The impact of land leasing out on non-agricultural income gradually increases from the Central, Eastern, to Western regions. This can be attributed to the Eastern region typically having a higher level of economic development, while the Central region has a relatively lower level of economic development. The policy support and market environment in the Eastern region attract more investment and talent, promoting the rapid development of land cultivation on a larger scale and mechanization, enabling farmers to obtain more non-agricultural income after transferring their land. The Western region’s economy is significantly weaker than that of the Eastern region, and the local farmers’ income from farming is also relatively low. This makes the percentage increase in income from farmers migrating to other provinces for work after transferring their land higher than that in the Eastern region. Therefore, the positive correlation between land leasing out and non-agricultural income is stronger in the Western region than in the Eastern region. The positive impact of land leasing out on non-agricultural income in the Northeast region is significantly stronger than in other regions. This may be because the Northeast is a major grain-producing area with highly mechanized and scaled planting, greatly increasing land output. In addition, the agricultural structure in the Northeast is diversified, encompassing traditional grain crop cultivation, as well as the development of new agricultural models, such as specialty agriculture, green agriculture, and ecological agriculture (much of the data are hard to come by), which have improved land utilization efficiency and economic benefits.

4.3.2. Group Regression by Median Age

Since family age may be associated with various aspects, such as family members’ career choices, educational level, and income structure, age-stratified regression analysis can also help us understand and test whether the relationship between land transfer and non-agricultural income differs among families of different age groups, further examining the impact of family age on the influencing factors. To ensure that our analysis can more accurately capture the potential heterogeneity among families of different age groups, the overall sample was carefully grouped based on family age. According to the data, the median weighted age of the families in the sample is 51.5. Therefore, families with an age greater than 51.5 are classified as high-age families, while the rest are considered low-age families. The regression results are shown in Table 12. Although both land leasing out and land leasing in, the core independent variables, have significant impacts on non-agricultural income, whether in mature families or new-generation families, the positive impact of land leasing out on non-agricultural income is stronger in mature families compared to new-generation families. This may be because the members of mature families tend to have more skills and experience due to their older age, resulting in stronger earning capabilities after transferring their land and thus higher increases in non-agricultural income. Conversely, the negative impact of land leasing in on non-agricultural income is higher in new-generation families compared to mature families. This may be because younger families are more adaptable to modern agricultural production and more willing to embrace large-scale mechanized farming, such as using advanced machines such as drones for cultivation. Therefore, when they acquire land, the operational income earned through managing the contracted land is higher than that of mature families.

4.3.3. Group Regression by Median Level of Education

The level of family education is an important factor influencing rural residents’ non-agricultural income after transferring out their land. It is generally believed in society that families with higher educational qualifications have stronger earning capabilities and stronger adaptability after transferring out their land. Classifying rural household samples based on different levels of education can, on one hand, more intuitively demonstrate the importance of education for farmers engaged in non-agricultural activities, and on the other hand, indicate the significant connotations of promoting rural education development. To investigate the differences in the impact of land leasing out and land leasing in on non-agricultural income among families with different educational levels, the overall sample was grouped according to the family’s educational level, and regression analysis was conducted separately. According to the data, the median weighted educational level of the families in the sample is 2.2. Therefore, families with a value greater than 2.2 are classified as highly educated families, while the rest are considered less-educated families. As shown in Table 13, both in highly educated and less-educated families, the core independent variables of land leasing out and land leasing in have significant impacts on non-agricultural income, and the regression coefficients passed the significance test at the 1% level. However, there are differences in the strength of the impact. The increase in non-agricultural income caused by land leasing out in highly educated families is higher than that in less-educated families. This may be because after transferring out their land, the family members of highly educated families can rely on their high educational qualifications to find jobs with higher income, better treatment (high-income equivalent), and more stable cash flow compared to those found by less-educated families. Therefore, the impact of land leasing out is stronger in highly educated families compared to less-educated families. In highly educated families, the negative impact of land leasing in on non-agricultural income is only slightly higher than that in less-educated families. This may be because the level of education has little impact on managing land, and less-educated families can rely on experience to compensate for some of the gaps in educational qualifications.

4.3.4. Age Segmentation Analysis

The results of regression analysis for specific age categories are shown in Table 14.

4.4. Mechanism Test

Based on the analysis of the theoretical framework, rural residents obtain a substantial and objective income after transferring their land. As their income increases, this prompts them to purchase vehicles, leading to a qualitative improvement (innovative points based on China’s national conditions) in the convenience and timeliness of their travel in rural areas, where public transportation is underdeveloped. Consequently, their daily lives and work scopes expand, and their interactions with others become closer, which enhances their earning capabilities and opportunities compared to those without vehicles, thus resulting in an increase in non-agricultural income. Moreover, when rural families transfer their land, family members are compelled to engage in non-agricultural work, further contributing to an increase in the family’s non-agricultural income. However, when rural residents acquire land, most farmers do not possess sufficient funds to cover expenses such as land rent, agricultural machinery rental, and fertilizer purchases. This necessitates borrowing from banks or other sources, and the resulting financial costs lead to a decline in non-agricultural income. Additionally, when rural families acquire land, it increases the number of those engaged in agricultural employment while reducing the number of family members engaged in non-agricultural employment, ultimately resulting in a decrease in the family’s non-agricultural income. To analyze these mechanisms, we utilize an endogenous mediation-effect test model. This model effectively mitigates estimation biases associated with ordinary causal identification methods under the two-stage least squares estimation of instrumental variables. It also assists in correcting the regression errors caused by non-randomly selected variables.
Columns (1) and (2) in Table 15 present the regression results for the travel improvement mechanism and the non-agricultural employment mechanism, respectively. According to the results in column (1), the regression coefficient for land transfer is positive and passes the 1% significance test, indicating a clear promotional relationship. The coefficients of the above variables on non-agricultural income are significantly positive at the 1% level, suggesting that a considerable portion of the increase in non-agricultural income resulting from rural residents’ land transfer can be attributed to the mediating effect of improved travel modes. In other words, rural residents purchase vehicles after transferring their land, which alleviates the adverse impacts of underdeveloped rural public transportation on rural residents, creates more earning opportunities, and ultimately increases their non-agricultural income. In the non-agricultural employment mechanism, the regression coefficient for land transfer also passes the 1% significance test and is positive, indicating a clear promotional relationship.
Columns (1) and (2) in Table 16 present the regression results for the funding cost mechanism and the agricultural employment mechanism, respectively. As can be seen from the results in column (1), when the main explanatory variables and mediating variables are introduced into the regression equation, the coefficients of these variables on non-agricultural income are significantly negative at the 1% level. This indicates that a considerable portion of the decrease in non-agricultural income resulting from rural residents’ land acquisition can be attributed to the mediating effect of funding costs. In other words, the funds borrowed by rural residents to cover the costs of contracting land and subsequent expenses have a significant impact on their non-agricultural income, causing a significant burden for those acquiring land. In the agricultural employment mechanism, the regression coefficient for land acquisition also passes the 10% significance test and is positive, indicating a clear promotional relationship. When the main explanatory variables and mediating variables are introduced into the regression equation, the coefficients of these variables on non-agricultural income are significantly negative at the 1% level. This suggests that an increase in the number of agricultural workers in rural households significantly leads to a decrease in their non-agricultural income.

5. Discussion

The promotion of the transfer of rural land contractual management rights is an important policy of the Chinese government regarding rural land, which may become an effective path to address the problems of rural decline and industrial revitalization caused by the massive outflow of rural labor. This article focuses on the issue of changes in the non-agricultural income of rural residents after the transfer of land, conducting empirical research from a micro-perspective. It examines and explains the changes in the actual income and living standards of rural residents during the process of promoting rural land transfer in China. Furthermore, using data from the China Family Panel Studies (CFPS) spanning from 2014 to 2020 and employing the classic ordinary least squares regression estimation, this study divides land transfer into land transfer-out and land transfer-in to examine their respective effects on the non-agricultural income of rural residents.
This study found that the transfer of rural land has a significant positive impact on the non-agricultural income of farmers. The heterogeneity analysis showed that, geographically, compared to the cities with lower grain production, the positive impact of land transfer on farmers’ non-agricultural income is stronger in major grain-producing regions such as Northeast China, where the fertile black soil and vast plains are more suitable for large-scale and mechanized planting. At the household level, different types of households exhibit significant differences in sensitivity to land transfer. Land transfer has a significant promoting effect on the non-agricultural income of both mature and new-generation families, especially for families with a combined age of over 50 years, who are most sensitive to increases in non-agricultural income. Compared to families with lower educational attainment, families with higher educational attainment are more likely to obtain non-agricultural income after land transfer, which is consistent with the general pattern of income acquisition for families with different educational backgrounds.
The mechanism tests revealed that the transfer of rural land prompts rural residents to improve their travel modes and increases the number of family members engaged in non-agricultural employment, enabling more household members to engage in non-agricultural work and promoting the growth of non-agricultural income. However, in the process of contracting rural land, if families raise funds through banks or other channels, the cost of these funds can significantly affect their non-agricultural income, leading to a significant decrease in non-agricultural income.

6. Conclusions

Against the backdrop of rural revitalization, this article provides research support and policy insights for local governments to further optimize land transfer policies and promote farmers’ income growth. Firstly, it is necessary to unwaveringly promote land transfer in rural areas across the country, increase the mechanization and scale of cultivated land, and make land transfer an important measure for China to improve the land food output rate, land use rate, and achieve rural revitalization. Secondly, when promoting the transfer of rural land contractual management rights, local governments should conduct thorough investigations into the local aging rate and illiteracy rate and provide certain government subsidies to illiterate families and younger families to overcome the negative impacts of land transfer on less-educated and young families and to narrow the gap between rich and poor among rural residents. Thirdly, in regions where major grain-producing provinces are located, efforts should be intensified to promote land transfer, so that the local economy does not overly rely on agricultural production. Finally, it is necessary to appropriately reduce the loan interest rates of township banks or issue targeted land contract loans, reduce the risks and costs of land contracting, encourage more local farmers to contract land, introduce external funds (it refers to reducing the capital cost of contracting land) and advanced equipment, and build various facilities that are compatible with large-scale farming.
To implement the policy suggestions, the first step is to increase the mechanization and scale of farmland. The government can provide financial subsidies and tax incentives to encourage agricultural mechanization and large-scale operation, as well as establish demonstration projects to showcase the advantages of large-scale and mechanized planting. The second step is to consider the regional aging rate and illiteracy rate: conduct regional research to understand the impact of aging and illiteracy on land transfer; provide targeted training and education programs for affected families to enhance their adaptability. The third step is to intensify land transfer efforts: in major grain-producing provinces, promote policies to encourage land transfer and agricultural modernization; establish a land transfer market to improve the transparency and efficiency of land transfer. The fourth step is to lower loan interest rates or issue targeted loans: cooperate with financial institutions to provide low-interest loan products for land contracting; set up risk funds to alleviate the economic pressure brought by land transfer to farmers.
The implementation of these policies may lead to the following impacts. First, it will enhance agricultural production efficiency. Mechanization and large-scale operation will increase agricultural production efficiency and food output. Second, it will increase the farmers’ income. Optimizing land transfer policies will help farmers increase their income through non-agricultural employment and other income sources. Third, it will promote regional economic development. Land transfer and agricultural modernization will contribute to the diversification of regional economies and reduce their dependence on a single agricultural production.
When considering the proposal of “promoting land transfer in rural areas nationwide”, it is indeed necessary to comprehensively assess multiple criteria, not just the direct impact on farmers’ income. Evidence from international studies shows that agricultural mechanization, industrialization, and the development of large-scale agriculture may, to a certain extent, bring economic benefits and efficiency improvements, but they also have many negative impacts.
Firstly, large-scale agriculture and excessive mechanization and industrialization may lead to the overexploitation and misuse of land resources, resulting in soil degradation, water scarcity, and environmental damage. These issues not only affect the sustainability of agricultural production, but also pose a threat to the health of the entire ecosystem. Secondly, large-scale agriculture often accompanies the migration of rural labor to cities, potentially leading to the decline of rural communities and the loss of traditional culture. In addition, unfair phenomena that may occur during the land transfer process, such as forced land expropriation and deprivation of farmers’ rights, may also trigger social dissatisfaction and conflicts. In future research, we must pay more attention to the national differences and regional characteristics of land transfer, to provide beneficial references for the formulation and practice of China’s rural land transfer policy.
Therefore, when promoting rural land transfer, the authorities need to comprehensively consider the following criteria:
  • Ecological and environmental protection: ensure that land transfer and agricultural development activities do not cause irreversible damage to the environment and promote the harmonious coexistence of agricultural production and the environment.
  • Social fairness and justice: protect farmers’ legitimate rights and interests during the land transfer process, avoid unfair phenomena, and ensure social harmony and stability.
  • Economic development and sustainability: promote rural economic development, increase the farmers’ income levels, and pay attention to the sustainability and long-term benefits of agricultural production.
  • Rural community and cultural protection: emphasize the development of rural communities and the protection of traditional culture, avoiding the decline of rural communities and the loss of traditional culture.

Author Contributions

Investigation, J.W.; data curation, X.X.; writing—original draft, S.Z.; writing—review & editing, H.J.; supervision, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Variable definitions and descriptive statistics.
Table 1. Variable definitions and descriptive statistics.
Variable TypeVariable NameVariable DefinitionMeanStd. Dev.
dependent
variable		household non-farm incomeThe logarithm of non-farm household income10.5521.305
wage incomeThe logarithm of wage income9.8972.568
property
income		The logarithm of property income0.9342.531
transfer incomeThe logarithm of transferred income5.7313.248
independent variableland transferFlow = 1, uniflow = 00.2790.448
land transfer outRoll-out = 1, not roll-out = 00.1520.359
land transfer inIn = 1, not in = 00.1270.333
control variableageFamily weighted age (this study is based on the family unit, so we use a summary measure)55.03323.710
degree of educationIlliteracy/semi-illiteracy = 1, primary school = 2, junior high school = 3, senior high school = 4, junior college = 5, undergraduate = 6, graduate and above = 72.2920.793
family sizeNumber of family members5.5361.910
value of agricultural equipmentThe logarithm of the value of the tool or equipment3.9454.292
outside work incomeIncome from farmers attending outside work3.4994.798
real estate propertiesThe logarithm of land assets8.0384.258
other revenueThe logarithm of transferred income1.5093.174
public subsidyThe logarithm of government aid3.7963.529
interpersonal expenditureThe cost of travelling between rural residents7.0582.523
Table 2. Effects of land circulation on non-agricultural income.
Table 2. Effects of land circulation on non-agricultural income.
Wage IncomeProperty IncomeTransfer IncomeNon-Agricultural Income
transfer of land0.357 ***5.464 ***0.02300.310 ***
(0.061)(0.046)(0.060)(0.027)
land transfer−0.234 ***−0.124 ***−0.144 **−0.191 ***
(0.058)(0.043)(0.057)(0.026)
family weighted age0.0010.0010.009 ***0.001 ***
(0.001)(0.001)(0.001)(0.001)
degree of education0.251 ***0.137 ***0.0330.178 ***
(0.026)(0.019)(0.026)(0.011)
family size0.193 ***0.021 ***0.153 ***0.129 ***
(0.010)(0.007)(0.010)(0.004)
value of agricultural equipment−0.0060.003−0.006−0.007 ***
(0.005)(0.003)(0.005)(0.002)
outside work income0.230 ***−0.019 ***−0.076 ***0.062 ***
(0.004)(0.003)(0.004)(0.002)
other income−0.012 **0.0010.162 ***0.0002
(0.005)(0.004)(0.005)(0.002)
the logarithm of land assets−0.047 ***0.067 ***0.028 ***−0.030 ***
(0.005)(0.004)(0.005)(0.002)
governmental subsidy−0.087 ***−0.009 *0.669 ***−0.020 ***
(0.006)(0.004)(0.006)(0.002)
interpersonal expenditure0.052 ***0.037 ***0.037 ***0.039 ***
(0.008)(0.006)(0.008)(0.003)
constant term7.100 ***−0.964 ***1.089 ***8.903 ***
(0.151)(0.112)(0.147)(0.066)
observed quantity12,78012,78012,78012,780
R20.1950.5420.5210.162
Note: * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 3. Cameron and Trivedi’s decomposition of IM-test.
Table 3. Cameron and Trivedi’s decomposition of IM-test.
Sourcechi2dfp
Heteroskedasticity2106.77740.0000
Skewness749.93110.0000
Kurtosis104.2110.0000
Total2960.91860.0000
Table 4. Random regional regression.
Table 4. Random regional regression.
RegionNon-Farm Income
(1)(2)
land transfer out0.253 ***0.316 ***
(0.0545)(0.0323)
land transfer into−0.277 ***−0.177 ***
(0.0513)(0.0305)
control variablecontrolcontrol
constant term8.609 ***8.927 ***
(0.166)(0.0759)
observed31539586
R20.1840.161
Note: *** p < 0.01.
Table 5. The table shows the results of yearly regression analysis.
Table 5. The table shows the results of yearly regression analysis.
YearNon-Farm Income
2014201620182020
land leased out0.150 ***0.188 ***0.331 ***0.327 ***
(0.041)(0.036)(0.058)(0.063)
land leased in−0.078 **−0.085 ***−0.266 ***−0.224 ***
(0.034)(0.031)(0.059)(0.069)
control variablecontrolcontrolcontrolcontrol
constant term6.272 ***5.131 ***7.358 ***9.210 ***
(0.145)(0.153)(0.238)(0.133)
observed3043316333943004
R20.3170.4030.2500.145
Note: ** p < 0.05, *** p < 0.01.
Table 6. Correlation analysis between total income and land transfer.
Table 6. Correlation analysis between total income and land transfer.
VariableTotal Income
land leased out0.261 ***0.245 ***
(0.025)(0.024)
land leased in−0.068 ***−0.058 **
(0.024)(0.023)
control variablenoyes
constant term10.66 ***9.064 ***
(0.010)(0.061)
observed12,58912,401
R20.0100.121
Note: ** p < 0.05, *** p < 0.01.
Table 7. Sensitivity analysis result 1.
Table 7. Sensitivity analysis result 1.
TreatmentCoef.S.E.t(H0)R2yd.xRV_qRV_qa
land transfer out0.28740.027610.40150.00840.08810.0721
Table 8. Sensitivity analysis result 2.
Table 8. Sensitivity analysis result 2.
Bound R2dz.xR2yz.dxCoef.S.E.t(H0)Lower CIUpper CI
1.00×Real estate properties0.00050.00660.28180.027510.22840.22780.3358
2.00×Real estate properties0.00100.01330.27610.027510.05470.22230.3299
3.00×Real estate properties0.00150.01990.27050.02749.87990.21680.3241
Table 9. Sensitivity analysis result 3.
Table 9. Sensitivity analysis result 3.
Extreme Bound R2dz.xR2yz.dxCoef.
1.00×Real estate properties0.00051.00000.2181
2.00×Real estate properties0.00101.00000.1894
3.00×Real estate properties0.00151.00000.1674
Table 10. Instrumental variable test results for land transfer.
Table 10. Instrumental variable test results for land transfer.
VariableNon-Farm Income
(1)(2)(3)(4)(5)(6)
land leased out14.08 **10.85 ***10.73 ***10.36 ***11.63 ***9.763 ***
(5.505)(3.244)(3.107)(2.894)(3.753)(3.279)
family age 0.00811 ***0.00810 ***0.00739 ***0.00645 **−0.00246
(0.00243)(0.00240)(0.00225)(0.00257)(0.00257)
agricultural equipment 0.0879 ***0.0838 ***0.0815 ***0.0943 ***0.0742 **
(0.0324)(0.0287)(0.0269)(0.0351)(0.0300)
land assets 0.01290.01610.02230.00371
(0.0143)(0.0142)(0.0187)(0.0154)
government subsidies −0.0147−0.0186−0.0190 **
(0.0101)(0.0114)(0.00960)
other revenue −0.0434 **−0.0554 **−0.0516 **
(0.0181)(0.0229)(0.0200)
migrant income 0.0629 ***0.0644 ***
(0.00825)(0.00693)
interpersonal expenditure −0.0449−0.0247
(0.0322)(0.0272)
family education level −0.0138
(0.0718)
family population Size 0.172 ***
(0.0203)
observations12,55312,55312,55312,55312,55312,553
Note: ** p < 0.05, *** p < 0.01.
Table 11. Significance analysis by region.
Table 11. Significance analysis by region.
RegionNon-Farm Income
Central RegionEastern RegionWestern RegionNortheastern Region
Land leased out0.245 ***0.269 ***0.297 ***0.390 ***
(0.0450)(0.0555)(0.0490)(0.0979)
Land leased in−0.0717−0.138 **−0.264 ***−0.237 ***
(0.0463)(0.0586)(0.0422)(0.0781)
Control variableParticipative regressionParticipative regressionParticipative regressionParticipative regression
Constant term8.935 ***9.038 ***8.795 ***8.293 ***
(0.116)(0.141)(0.111)(0.307)
Observed3756274449011313
R20.1700.1430.1570.216
Note: ** p < 0.05, *** p < 0.01.
Table 12. Regression by age group.
Table 12. Regression by age group.
Non-Farm Income
Mature FamilyNew-Generation Family
Land leased out0.337 ***0.277 ***
(0.038)(0.039)
Land leased in−0.146 ***−0.244 ***
(0.036)(0.037)
Control variableParticipative regressionParticipative regression
Constant term9.007 ***8.984 ***
(0.089)(0.144)
Observed64106401
R20.1560.171
Note: *** p < 0.01.
Table 13. Points of educational regression.
Table 13. Points of educational regression.
Non-Farm Income
High-Education FamiliesLow-Education Families
Land leased out 0.325 ***0.271 ***
(0.0367)(0.0415)
Land leased in−0.204 ***−0.177 ***
(0.0374)(0.0364)
Control variableParticipative regressionParticipative regression
Constant term9.150 ***8.672 ***
(0.115)(0.103)
Observed68775903
R20.1450.162
Note: *** p < 0.01.
Table 14. Supplement to the previous analysis of family age heterogeneity.
Table 14. Supplement to the previous analysis of family age heterogeneity.
Non-Farm Income
16–45-Year-Olds45–70-Year-Olds
Land leased out0.305 ***0.310 ***
(0.0493)(0.0337)
Land leased in−0.118 **−0.231 ***
(0.0460)(0.0322)
Control variableParticipative regressionParticipative regression
Constant term8.949 ***8.478 ***
(0.210)(0.119)
Observed39888546
R20.1600.160
Note: ** p < 0.05, *** p < 0.01.
Table 15. Test results of land transfer out mechanism.
Table 15. Test results of land transfer out mechanism.
(1)(2)
Travel Improvement MechanismNon-Agricultural Employment Mechanism
Purchase a VehicleNon-Farm IncomeOff-Farm WorkNon-Farm Income
Land leased out0.0847 ***0.333 ***0.185 ***0.268 ***
(0.0246)(0.0624)(0.0537)(0.0571)
Purchase a vehicle 0.296 ***
(0.0463)
Off-farm work 0.484 ***
(0.0194)
Sample size3004300430043004
R20.0830.1540.1550.290
Note: *** p < 0.01.
Table 16. Test results of land transfer-in mechanism.
Table 16. Test results of land transfer-in mechanism.
(3)(4)
Capital Cost MechanismAgricultural Employment Mechanism
Arrears DueNon-Farm IncomeAgricultural EmploymentNon-Farm Income
land transferred in0.188 ***−0.258 ***0.125 *−0.0794 ***
(0.0315)(0.0689)(0.0698)(0.0179)
agricultural employment −0.277 ***
(0.0686)
arrears due −0.106 ***
(0.0398)
sample size3004300430043004
R20.0250.1400.3540.139
Note: * p < 0.10, *** p < 0.01.
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Zhang, W.; Zhao, S.; Wang, J.; Xia, X.; Jin, H. Rural Land Circulation and Peasant Household Income Growth—Empirical Research Based on Structural Decomposition. Sustainability 2024, 16, 6717. https://doi.org/10.3390/su16166717

AMA Style

Zhang W, Zhao S, Wang J, Xia X, Jin H. Rural Land Circulation and Peasant Household Income Growth—Empirical Research Based on Structural Decomposition. Sustainability. 2024; 16(16):6717. https://doi.org/10.3390/su16166717

Chicago/Turabian Style

Zhang, Wenwu, Shunji Zhao, Jinguo Wang, Xinyao Xia, and Hongkui Jin. 2024. "Rural Land Circulation and Peasant Household Income Growth—Empirical Research Based on Structural Decomposition" Sustainability 16, no. 16: 6717. https://doi.org/10.3390/su16166717

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