**1. Introduction**

During recent decades, China has seen rapid urbanization as well as intensified crises including farmland abandonment, deficiency of rural land use, and rural decline [1–3]. Since the massive outward rural migration due to rapid urbanization, rural land use has been dramatically affected, especially in cases of farmland abandonment [4,5]. Due to the household contract responsibility system in place in China, even though the rural migrants left the agricultural industry and abandoned their land in the countryside, they could not sell the rural land nor could other farming households obtain more rural land [6]. Under the household contract responsibility system, all residents of a village collective own all the rural land within the village, and the amount of land any household can own depends on its historical numbers of household family members [7]. In fact, this household contract responsibility system stipulates that farming households cannot sell their contracted land even if they intend to leave the countryside permanently, as the farming households only have contractual and usage rights to the contracted land, but not the ownership rights. As a result, the household contract responsibility system increased the levels of farmland abandonment and rural land use deficiency in rural China [1,7]. Thus, land transfer was proposed to solve the rural land use problem in rural areas through the promulgated "separating three property rights" reform [8]. Thanks to the "separating three property rights" reform, the contractual and usage rights of rural land are divided into non-tradable contractual rights and tradable usage rights, which make it possible for farmers who cease

**Citation:** Gao, J.; Zhao, R.; Lyu, X. Is There Herd Effect in Farmers' Land Transfer Behavior? *Land* **2022**, *11*, 2191. https://doi.org/10.3390/ land11122191

Academic Editors: Yongsheng Wang, Qi Wen, Dazhuan Ge and Bangbang Zhang

Received: 11 November 2022 Accepted: 1 December 2022 Published: 2 December 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

engaging in agricultural industry to transfer outward the land usage rights to others, and it also makes it possible for farmers remaining in agricultural industry to transfer inward more rural land to enlarge the scale of their farming operations [9]. Land transfer refers to this inward and outward transfer of rural land usage rights, and is used with that meaning in this study.

Land transfer is nowadays the route one must take to revitalize abandoned rural land resources and develop moderate-scale operations in rural areas in China [10,11]. Reasonable land transfer is vital for developing modern agriculture, to address problems in rural areas [6,7]. The 18th, 19th, and 20th National Congresses of the Communist Party of China all urged efforts to implement the tasks and requirements of land transfer reform, and to obtain rural revitalization through optimal allocation of land elements [12–14]. At present, despite legal protection and central government support, the rural land transfer market still suffers from ineffective information transmission, small scale, and uncoordinated structures [15,16]. Further attempts are required to facilitate rural land transfer and allocate land resources appropriately to support rural economic progress and revitalization. These are key to promoting reform of rural land systems and actualizing agricultural scale management in the new era.

For famers, obtaining land transfer information is fundamental to the land transfer process. With limited information channels broadcasting government policies, instead of spending more time, cost, and energy collecting and interpreting land transfer information, farming households are more inclined to refer to and imitate the behavior of other farmers in their social networks when making decisions on land transfer, showing a herd effect [17,18]. Herd effect refers to the behavior of individuals extracting information from other people's behaviors and imitating them to maximize utility when information is asymmetric or insufficient due to imitations of information-discrimination ability [19]. Although farmers have a general tendency to pursue the maximization of economic benefits and utility, behavioral economics research shows that farmers' individual willingness and behavior are also influenced by the willingness and behavior of other individuals in their groups [20].

As the most important subjective factor, the group psychology of farmers affected by the herd effect plays a key role in the process of land transfer. Due to group psychology, there may be a big difference between an individual's response in the group environment and their response in the independent environment [21–23]. Farmers' land transfer decision-making behavior shows conformity, and in order to avoid being isolated or treated differently by other farmers, they choose to imitate the land transfer behavior of other farmers [24,25]. At the same time, due to the narrow channels of information transmission, farmers tend to rely on decision-making information obtained from other farmers such as acquaintances, relatives, and friends as active reference when obtaining land transfer information [26]. Therefore, in the process of land transfer, due to incompleteness and difficulty in obtaining information, farmers rely on public information when making decisions affecting land transfer behavior, which leads to a herd effect in farmers' land transfer behavior [20–26].

Since land transfer is now protected and recognized by the law, numerous academics have started to investigate the costs, obstacles, and issues regarding land transfer and have put forward helpful policy proposals. In China, farmers live together in villages relying on land resources and maintaining geo-network relationships [18], but little focus has been placed on the geo-network characteristics of the acquaintance society in rural areas, where farmers have few options for getting information about land transfer policies and therefore frequently follow the land transfer behavior of the majority when unsure how to proceed. To examine how geo-networks affect land transfer behavior, this study considers the herd effect, which reflects the actions of people in a group. Therefore, this paper takes herd effect as the starting point, considers the influence on farmers' land transfer behavior of herd effect caused by group psychology, analyze the mechanism of the herd effect in farmers' land transfer behavior, puts forward a research hypothesis, and verifies it through

micro-investigation data, to obtain an effective theoretical and empirical basis for guiding land transfer practice.

The remainder of this paper is structured as follows: Section 2 reviews the existing literature and Section 3 proposes the research objective and research hypotheses. Section 4 displays the data and methodology. Section 5 presents the results of empirical analysis and the discussion of this research. Section 6 presents the conclusions.

#### **2. Review of Literature**

Recently, many studies have performed extensive research on the external factors influencing land transfer behavior, such as the size of the farming household, resource endowment, household income status, the size of the household labor force, agricultural machinery level, awareness of property rights, land transfer policies, the external environment, and so on [1,6,10,13,14,27–32]. It can be seen from the existing literature that there are various factors affecting farmers' land transfer behavior, but the existing studies have paid little attention to the effect of group psychology on farmers' land transfer behavior. China's rural society is characterized by acquaintance society formed by geographic ties, and group psychology is held to have a significant impact on farmers' land use behavior [33]. In reality, access to land transfer information and direction is related largely to farmers' social networks [34]. According to available studies, social networks can significantly influence farmers' decisions on allocating production factors, especially land resources, and may even change a farmer's land transfer transaction mode and lead to lower land transfer prices [35,36]. Furthermore, land transfer relies heavily on invisible commitments made by members of the kin society. To be specific, the transferor reduces or waives rents in exchange for favorable assistance from the transferee, so land transfer is more likely to occur among friends and relatives, featuring low transfer prices or even zero rents [37,38]. Meanwhile, some scholars have found that in areas where farmers have no strong willingness to transfer land, or social networks play a major part, most farmers access land transfer information through communicating with others in their social networks, revealing that the social network mechanism of farmers promotes the development of land transfer to some degree [39,40].

As stated above, China's rural areas are home to an acquaintance-based society with geographic ties [33]. Such a society boasts the advantages of information symmetry and social network access [41], so land transfer information can be transmitted smoothly among acquaintances. Farmers in the same region basically know each other well. Hence, when a farmer is unable to access land transfer information effectively, he tends to consult his acquaintances and farmers in the same group, within the same village, who are experienced in land transfer. If he follows their actions without fully considering his resource endowments and limitations, his land transfer behavior is thus affected by the herd effect [21,22,42,43]. The herd effect has distinct functions of information transfer and demonstration [22,42]. It is an efficient way to transmit information that impacts individual decision-making while also enabling individuals to change their behavior based on information obtained from other subjects in a group [21,42], hence its vital role in disseminating land transfer information. Scholars have previously focused on the herd effect in the stock market, financial investment, securities market, agricultural production, and rural land use [17,18,23,42,44]. This current paper takes into account the specificity of rural geo-networks, links the herd effect with farmers' land transfer behavior, considers the information transmission and demonstration function of the herd effect, and analyzes how the herd effect influences farmers' land transfer practices.

In this research, we focused on geo-networks to observe the herd effect, because Chinese farmers tend to live together in villages where they maintain geo-network relationships [18]. Geo-networks are considered the contractual basis of rural society [45]. Because rural residents live together in villages, they form interpersonal relationships through mutual social activities and exchanges, leading to close ties between farmers in contexts of politics, economy, culture, customs, socializing, and agricultural production. In China's

rural areas, geo-network relationships have long affected economic development and the establishment of new structures [45]. It is precisely because of the existence of geo-networks that villagers in the same village have form general rules for long-term production and life processes, which indirectly affect farmers' land use behavior. Therefore, this study applied the concept of geo-networks to assess the herd effect in farmers' land transfer behavior.

### **3. Research Objective and Hypotheses**

For its research objective, this study begins with the herd effect, integrates the features of acquaintance society with geographic ties in China's rural areas, and considers the influence of group psychology on individual farmers' land transfer behavior. By taking farmers themselves as the channel of disseminating land transfer information, to explore how the geo-network, exerting the herd effect, impacts farmers' individual land transfer behavior. In this research, the IV-Probit model was employed to verify the herd effect of farmers' land transfer behavior. The herd effect based on geo-networks may act as a scientific reference for shareholders to further normalize and direct orderly rural land transfer, solve the problem of fragmented arable land, and facilitate large-scale farming operations.

In order to verify whether there is herd effect in farmers' land transfer behavior, we put forward hypotheses based on theoretical analysis.

In terms of land transfer, the herd effect supports information transfer and provides an example for farmers in the same geo-network to copy [46]. On the one hand, collecting information about land transfer can prove costly, and the traditional land transfer market can fail to match efficiently demand with supply, and as a result, many potential land transfer transactions cannot be realized [1]. Social networks, by contrast, greatly reduce the costs of farmers' information searches [33]. Farmers with abundant social network resources can acquire more useful information quicker and at lower cost that those with less social networks resources. They can also spread land transfer information more effectively and reach land transfer deals more easily [47]. On the other hand, the more that individual farmers identify with the group they belong to in their geo-network, the greater their decision making is influenced by the other farmers in the geo-network. The closer their relationship with the geo-network they belong to, the more likely it is that their decisionmaking on land transfer is influenced by the group's opinions [23,47]. Therefore, farmers who are unable to acquire land transfer information in advance, cannot make decisions on their own and must instead refer to other land transfer behaviors to decide whether to transfer their land. In this process, farmers' inclination to transfer land is inevitably influenced by the actions of their peers in the same geo-networks, thereby exhibiting the herd effect [22].

According to existing studies, village collectives are function as the main channels for the spread of land transfer information, having the innate advantages of releasing land transfer information, and their functions and effects have been recognized by farmers and academics [28,35,37]. There remains a necessity for developing new channels for land transfer information dissemination in rural areas. To this end, this paper examines the interplay between the land transfer behaviors of farmers in a group based on a collective geo-network, and dissects the land transfer behavior of individual farmers with the aid of the information transmission and demonstration functions of the herd effect. This paper proposes the following research hypotheses:

**H1.** *Geo-networks positively impact farmers' land transfer behavior*.

**H2.** *Farmers imitate the land transfer behavior of other farmers in the same geo-network, and a herd effect exists*.

#### **4. Data and Methodology**

*4.1. Data Source and Variables*

#### 4.1.1. Data Source

In recent years, land transfer in Shandong Province has been at the forefront of China's advancements in this field. However, a mature transfer-market mechanism has not yet been formed throughout the province, and all levels of local government lack in-depth understanding of land transfer needs from the perspectives of both supply and demand, and communications platforms for land transfer information are in need of improvement. As one of the prefecture-level cities in Shandong Province, the construction of the land transfer platform for Qufu City started late, and the spread of land transfer information was asymmetric and irregular. Due to the low education levels of farmers, and their lack of awareness of land transfer policies, farmers usually transfer land by oral confirmation. Among acquaintances, even if a land transfer contract is signed between the transferor and the transferee, the terms agreed in the contract are often not clear enough, the land transfer procedures are not complete, and there is no uniform standard for the land transfer price, frequently leading to land transfer disputes and bringing severe challenges to the large-scale management of land in Qufu City. Thus, we conducted the investigation in Qufu City, Shandong Province.

The data used in this paper were gathered from a questionnaire-based survey of farmers in Qufu City, conducted by the author's research group in August 2020. Based on the preliminary investigation and demonstration, the research group took into full account the natural resources, socioeconomic situation, agricultural development, and land transfer practices among towns in Qufu City, and found that Wucun, Shimenshan and Xizou, three typical agricultural towns, are representative in terms of agricultural production and land transfer [48]. Hence these three towns were selected as the research areas. The investigators were assigned four randomly chosen villages in each of the three towns, and a number of farmers in the villages were randomly selected for face-to-face questionnaire-based interviews. The investigators on site were responsible for filling out questionnaires according to the interviews. A total of 359 farmers were surveyed. By reviewing and screening out invalid questionnaires, a total of 337 valid answer sets were obtained, at a survey response rate of 93.8%. The questionnaires covered such aspects as family composition, family livelihood, family contracted land and its transfer, rural land transfer policy cognition, and so on, to reflect fully every farming household's land transfer and land use status.

4.1.2. Variables

• Dependent variable

The dependent variable in this paper is farmers' land transfer behavior (Y), including inward transfer and outward transfer. It was considered whether or not farmers had performed land transfer behavior, be it transferring the land outward to a transferee or inward from a transferor. Assigned values were 1 for "transfer", and 0 for "no transfer".

• Core independent variables

The core independent variables of this study were intended to characterize scientifically the farmers' geo-networks. By referring to existing research [18,49] and considering data availability, this study used the number of farmers in the same village making land transfers (X1) and the number of village cadres in the same village making land transfers (X2) as the core independent variables to reflect the characteristics of the geo-network.

• Instrumental variable

Farmers' land transfer behavior is causally related to the behavior of their peers in the same geo-network, i.e., the endogeneity of the herd effect may occur during estimation. In order to control the estimation bias caused by such endogeneity, the area where farmers are located (IV) was used as an instrumental variable for the number of farmers in the same village making land transfers.

• Control variables

For more accurate estimates of the model, this study included control variables in the model representing farmers' family features, resource endowment features, and cognitive features, with reference to current literature [1,16,17,22,26,28,30]. Variables reflecting farmers' family features included age of the householder (X3), gender of the householder (X4), educational attainment of the householder (X5), and occupation of the householder (X6). Variables for resource endowment features were arable land area (X7), number of land plots (X9), agricultural income (X8), agricultural input–output ratio (X10), changes of unit grain yield in the past five years (X11), and living expenses (X12). Farmers' cognitive features comprise their perception of life and their understanding of policies and regulations. Farmers' perception of life included two variables, their way of accessing information in the village (X13) and their satisfaction with farmland infrastructure (X14), whereas farmers' cognition of policies involved three variables, whether they think contracted land can be inherited by their children (X15), whether farmers are sure that the confirmation and registration of the right to contracted management of rural land are performed in their villages (X16), and farmers' understanding of farmland protection policies (X17). Table 1 describes the symbols and descriptions of the variables.


**Table 1.** Variables and their symbols and descriptions.


**Table 1.** *Cont.*

#### *4.2. Methodology*

As the dependent variable, farmers' land transfer behavior, is a dichotomous choice, this paper employs a Probit model for regression analysis. Also, endogeneity is likely to arise in the analysis of the herd effect in farmers' land transfer behavior. For one thing, environmental factors may cause farmers to perform similar land transfer behaviors against the same backgrounds, resulting in the overestimation of the herd effect. For another, farmers will interact, because when impacted by group behavior they will influence the group behavior, hence invoking mutual causality. Therefore, the herd effect of farmers' land transfer behavior cannot be inferred simply from the fact that farmers' land transfer behavior is influenced by group behavior; the endogeneity issue should be solved first. Based on available research results, the instrumental variable approach was administered to address endogeneity in the model [18]. Considering the dichotomy of the response variable, the IV-Probit model was developed to solve the endogeneity of the herd effect. The formula is:

$$\text{Prob}(Y) = \beta\_0 + \beta\_1 X\_1 + \beta\_2 X\_2 + \sum\_{n=1}^{n} X\_n + \mu + \varepsilon \tag{1}$$

$$X\_i = \gamma\_0 + \gamma\_1 IV + \gamma\_2 \sum\_{n=1}^{n} \beta\_n X\_n + \mu + \omega \tag{2}$$

$$\text{IV}-\text{Probit}(\text{Y}) = \beta\_0 + \beta\_1 X\_1 + \beta\_2 X\_2 + \beta\_2 IV + \sum\_{n=1}^{n} \beta\_n D\_n + \varepsilon \tag{3}$$

In Formulas (1) to (3), Probit (*Y*) denotes farmers' land transfer behavior. *X*<sup>1</sup> and *X*<sup>2</sup> represent the number of farmers in the same village making land transfers and the number of village cadres in the same village making land transfers, respectively, and these two are jointly employed as geo-network variables affecting farmers' land transfer behavior. *Xi* (i = 3, 4, ... , n) denotes a control variable reflecting farmers' family features, resource endowment features, or cognitive features. *IV* means an instrumental variable. *β*<sup>0</sup> is a constant, *β*<sup>1</sup> is the core coefficient, and *ω* and *ε* represent error terms.

#### **5. Results and Discussion**

*5.1. Results*

5.1.1. Farmers' Land Transfer Features

Table 2 details the respondents' land transfer features. In this study, a number of farmers in 12 administrative villages were randomly selected for survey.



Note: Options for transfer recipients include: 1. relatives, 2. other individuals in the same village, 3. groups in the village, 4. individuals from other villages, 5. groups from other villages, 6. others. This question about selection transfer recipients was a multiple choice question, so the total proportions are not always equal to 100%. Numbers inside the parentheses represent the proportion.

The results reveal that there were similarities and differences between villages in terms of the numbers and proportions of farmers making land transfers, transfer price, transfer period, and selection of transfer recipients. Concretely, in terms of the numbers and proportion of farmers involved in land transfer, Dongzhuang South and Beiyuantuan villages had more farmers making land transfers, accounting for 85.71% and 65.52% respectively, while Liuzhuang, Jiangxiahou, Bujiazhuang, and Beixiasong villages had fewer farmers involved in land transfer, with less than 40% in each. In regard to transfer price, land transfer prices varied considerably between villages. In terms of average land transfer price, Beiyuantuan took the first spot, with RMB 595/*mu* (1 *mu* = 0.667 hectare), while Bujiazhuang came in last with RMB 224/*mu*, a gap of around RMB 370/*mu*. This implies

a nonnormalized mechanism of land transfer price in the research areas, and arbitrary price setting. Beixiasong village had the longest average transfer period of 4.40 years, whereas Beiyuantuan had the shortest, 1.20 years. The average transfer periods of the remaining villages ranged from 1 to 3 years. With regard to transfer recipients, except for Dongzhuang South and Beixiasong, the remaining 10 villages comprised 81.81% of the total, with most of their farmers transferring their land to individuals in the same village. In addition, some farmers transferred their land to relatives and groups in the same village, but few transferred their land to individuals and groups in other villages, confirming that the recipients of farmers' land transfers were often acquaintances.

#### 5.1.2. Impact of Geo-Networks on Farmers' Land Transfer Behavior

With the aid of the Probit model, we performed regression analysis of the number of farmers in the same village making land transfers (X1), the number of village cadres in the same village making land transfers (X2), farmers' family features (X3–X6), resource endowment (X7–X12), and cognitive features (X13–X17). Prior to regression analysis, these variables were tested for possible multicollinearity. Only if the variance inflation factor (VIF) value is less than 10 can it be considered that no multicollinearity exists between the variables. The test results confirmed that the explanatory variables all had a VIF of less than 10, thus satisfying the independence principle. The regression results are shown in Table 3.

**Table 3.** Summary of model fitting results.


Note: \*\*\*, \*\* and \* denote significance at 1%, 5% and 10% respectively. Numbers inside the parentheses represent the standard error, the same below.
