Land Certificated Program and Farmland “Stickiness” of Rural Labor: Based on the Perspective of Land Production Function

Abstract

The instability of farmland rights is the fundamental reason for the decrease in the “stickiness” of farmland in China. The Land Certificated Program (LCP) plays an important role in clarifying the ownership of land and stabilizing the property rights of land, as well as enhancing the land production function. Most existing literature focuses on the impact of the LCP on non-agricultural labor participation, while research on agricultural labor participation is scarce. This paper analyzes the impact of the LCP on farmland “stickiness” based on the perspective of land production function. This paper also applies propensity score matching (PSM) using CLDS data from 2016 and 2018 to evaluate the policy effect of the LCP on farmland “stickiness”, and conducts heterogeneity analysis and the robustness test. In addition, this paper examines the mechanism of the influence of LCP on farmland “stickiness” by using the mediating effect model. The results of this analysis showed that: (1) The impact of the LCP on farmland “stickiness” is significant, as the rate of agricultural labor participation has increased by 4.8% to 4.9%. (2) The incentive effect is heterogeneous, and has significant impacts on non-professional households, as well as on small and medium-sized of farms. (3) The sensitivity test revealed that unobservable factors do not have an impact on the LCP estimation results, and the results of the PSM estimation were robust. (4) The policy effect of the LCP at the village level also confirms the robustness of the promotion effect and the mechanism. (5) Land production function has a partial mediating effect on the impact of the LCP on farmland “stickiness”. Given these results, we must begin to consolidate, expand and make good use of the results of the LCP, support the connection between smallholders and modern agriculture, and enhance the land production function in order to stabilize agricultural production and realize agricultural modernization.

1. Introduction

Land, as a factor of production, has a production function, as it provides jobs and income for producers. In rural areas, the economic activities basically revolve around the relationship between land and labor. This is particularly true in traditional “rural China” society, which is based on agriculture and living on land; therefore, rural labor “sticks” to land [1]. In order to meet household subsistence needs and secure economic income, rural labor is engaged in agricultural production, which is behaviorally reflected in the dependence of rural labor on the land. However, with the development of the market economy in rural areas, land has come to serve multiple other functions, such as security and property functionalities [2]. This is also the case in the process of China’s structural transformation, as the functional orientation of land, as well as the strengths and weaknesses of the land production function of farmland, change with these new developments. This in turn loosens the relationship between labor and land, thereby altering the “stickiness” of land [3]. According to the labor value theory, agricultural labor participation is the enacted form of land production function. Specifically, as land is an indispensable resource in agricultural production, the utilization of land production function and the realization of economic value must be condensed in human labor. Therefore, as land “stickiness” refers to the dependence of rural labor on land, the production function of land determines the “stickiness” between farmland and rural labor, in turn, the land “stickiness” is expressed by the degree of agricultural labor participation.

Since the China’s reformation and opening up to the world, under the influence of the land system and the urban–rural dual system, the function of land in China’s rural areas has changed, the production function has gradually decreased, and the land “stickiness” of rural labor has gradually weakened. In order to track these new developments, this paper constructs an analytical framework that analyses the weakening of land “stickiness” (Figure 1). China’s current rural land system adheres to collective ownership, which is an internal member’s right, and grants all members of the village equal rights to enjoy the village’s land [4]. Therefore, in order to achieve equity in land ownership, regular and irregular adjustments according to changes in household size are necessary [5]. Regular adjustments refer to land reallocation at the end of the contract period. In 1983, land-use rights were allocated to the households in a village for a period of 15 years each. In 1998, the contract period of land-use rights was increased from 15 to 30 years. In 2008, the land contract period was further increased from 30 years to an unspecified “long-term” period. In 2017, farmers’ land-use right contracts were extended by yet another 30 years upon expiration. Irregular adjustments, meanwhile, refer to land reallocation due to population changes. Using egalitarian principles, the size of land assigned to a household was determined by the number of household members and/or labors [6]. This led to frequent land reallocations within villages to correct for demographic changes that occurred within the contract period. For example, Brandt et al. [7] found that land was reallocated 1.7 times on average per village from 1982 to 1995. Meanwhile, Ren et al. [8] found that 33% of the villages experienced land reallocation after the 1998 land contracting round. These adjustments brought instability to land property rights, which in turn led to the inefficient allocation of land and labor and a reduction in agricultural returns [9,10]. These decreases in land production function weakened land “stickiness” and decreased agricultural labor participation. At the same time, due to the influence of the urban–rural dual system, the non-permanent transfer of “leaving the countryside without leaving the land” has become the main rural–urban pattern in China. [11]. However, migrating laborers still retain rural land or rent out land to relatives and acquaintances [12]. As land’s main function has transitioned to that of security, agricultural production efficiency has remained low, the “stickiness” of the land has weakened, and the agricultural labor participation rate has decreased. The corresponding low agricultural labor participation has brought a series of problems between urban and rural areas, such as an increase in urban–rural income disparity, rural poverty, and rural recession [13].

Through the above analysis, it can be found that the external manifestation of the weakening of land “stickiness” is the low degree of agricultural labor participation; the internal motivation is the instability of land rights and the weakening of land production functions caused by the land system and urban–rural dual system.

Therefore, in order to increase the participation of agricultural labor, it is urgent to stabilize rural land property rights, improve farmers’ agricultural production expectations, strengthen land production function, and enhance land “stickiness”.

In order to stabilize rural land property rights, the Chinese central government has twice implemented the Land Certificated Program (LCP), first in 2003 and then in 2009. The aim of the LCP is to guarantee land property security and provide a legal definition of contractual management rights [14]. In 2003, the government initially implemented the LCP through the Rural Land Contracting Law. This initial program, which lasted through 2007, was called the first round of the LCP. The first round was applicable to over 94.6% of all rural households [15]. However, the LCP implementation during this period was sketchy, with unclear land information on plot size and boundaries [16]. In 2009, the Chinese central government began the new round of the LCP, which was called the latest national LCP. This time, all households with land in a village were required to participate. The implementation of the latest round of the LCP was divided into three stages. The first stage was known as the small-scale village-level pilot stage. Beginning in 2009, small-scale village-level pilot work for the LCP was carried out in eight provinces and municipalities under the direct supervision of the central government. The second stage involved nationwide piloting at the county level. This stage of LCP piloting began in 1 to 3 counties (cities and districts) that displayed strong representation in each province. In all, the process, which took place in March 2011, involved a total of 12,150 villages in 710 townships across 50 counties (cities and districts). In the third and final stage a province-wide pilot phase was implemented. The province-wide piloting started in the three provinces of Shandong, Anhui, and Sichuan, while 27 whole-country pilots were carried out in 2014. Nine additional provinces were added as whole-province pilot units in 2015, and the whole-county pilots were expanded in other places. By 2017, the LCP had been extended to cover almost the whole country. In November 2020, the registration and certification of contracted rural land was basically completed, as the certification rate exceeded 96%. The most important role of this program was to establish clearly registered ownership rights, contract rights, and operational rights, confirm that rural households had the right to the possession, use, mortgage, and benefit of their respective contract land, and validate that the rural household could transfer these property rights to others in legitimate ways. According to the requirement of central government, the process of the latest LCP mainly included public mobilization, land survey, results announcement, signature confirmation, and issuing of certification [17]. The goals of the latest LCP in 2009 were to (a) ensure that county governments could effectively solve the issues left over from history, including inaccurate contracted land size and unclear spatial location; (b) fundamentally guarantee governmental safeguarding of farmers’ land-management rights and contract rights in the form of legally valid certificates in order to reduce farmers’ worries of losing their land; and (c) clarify the transaction parties of land property rights through the LCP to optimize the rural land transaction market and reduce the land transaction cost [18].

The latest national Land Certificated Program (LCP) stabilized the contracting relationship, clarified the land property rights, and strengthened the integrity of land rights by providing land contractual management rights certificates. Therefore, the aims of this study are twofold. The first is to investigate the effect of LCP on land “stickiness” through theoretical analysis and empirical tests. The second aim is to explore how the LCP affects the land “stickiness” of rural labor based on the perspective of land production function.

2. Literature Review

Existing academic research on the policy effect of LCP has mainly focused on the development of the agricultural industry and the livelihood of farmers. First, in terms of the agricultural industry, the LCP affects the input of land, labor, investment, and technology, which in turn promotes the improvement of agricultural production efficiency and the development of agriculture [14,16,19,20,21,22]. Second, in terms of farmers’ livelihoods, the LCP has changed farmers’ choices of livelihood strategies and narrowed the gap in welfare levels among farmers [23,24,25].

Thus, as agricultural labor participation is the external manifestation of land “stickiness”, this paper focuses on the factors involved in rural labor and discusses the impact of the LCP on rural labor participation.

There have been many studies on the LCP and labor participation, and their views can be roughly divided into three different categories. First, the LCP promotes non-agricultural labor participation or rural out-migration [26,27,28]. As the LCP clarifies land property rights, reduces the risk of land loss for farmers, and accelerates land transfer, it thereby promotes the transfer of rural idle labor to non-agricultural industries. Second, the LCP reduces the expected losses caused by the adjustment of farmland and stimulates the enthusiasm of farmers to invest in agricultural production, thereby inhibiting the transfer of rural labor to off-farm employment [29]. Specifically, the irregular adjustment of farmland is akin to levying random taxes on farmers [30], which means that in the unforeseen future, farmers’ land and medium- and long-term investments attached to the land will inevitably suffer losses. The more frequent the adjustment, the greater the expected loss of farmers. On the contrary, the stability created for farmland by the LCP will reduce the random taxes levied against farmers and reduce their expected losses. This should in turn enhance their enthusiasm for engaging in agricultural production, and reducing the transfer of labor to off-farm employment. Third, the LCP has no significant impact on the non-agricultural labor participation of the rural labor because the off-farm employment of rural labor is closely related to labor’s human capital, local social conditions, and other factors [31].

By reviewing the existing literature, we found that: First, there is no consensus among scholars on the impact of the LCP on rural labor. Second, most of the existing studies focus on the impact of the LCP on rural labor off-farm employment or rural out-migration, while the literature on the policy effect of LCP determination on agricultural labor participation is relatively lacking. Due to the heterogeneity of the endowment of rural labor, not all rural labor will realize the transfer from agricultural to non-agricultural work. On the one hand, farmers have formed high asset specificity in long-term agricultural production, such as agricultural machinery and technology. In order to avoid economic losses after abandoning agricultural production, these farmers will be encouraged to continue engaging in agricultural production after experiencing the stability brought by the LCP. On the other hand, due to the constraints of human capital, some rural labor can only stay in the agricultural field [32]. In addition, the LCP mainly promotes the off-farm employment of rural idle labor groups [33]. Therefore, it is particularly important to evaluate the policy effect of the LCP on the participation of agricultural labor. Thirdly, the heterogeneity of the policy effect of the LCP among different rural household types and different farm sizes also requires further analysis. With the development of agricultural modernization, rural household types are beginning to diversify as the co-existence of professional and non-professional households becomes more common. Meanwhile, the market of farmland transfer is constantly developing, and this results in changes in farm size. This begs the question, does the relationship between the LCP and land “stickiness” change due to differences in rural household types and changes in farm size? That is, does the policy effect of the LCP have preferences in terms of household types and farm size? Without further research into this question, it will continue to be difficult to fully estimate the policy effect of the LCP on the land “stickiness” of rural labor.

To fill these knowledge gaps, we address three main questions through empirical research: (1) Does the LCP enhance or weaken the land “stickiness” of rural labor? (2) Can it be applied equally among the different rural household types and the different farm sizes? (3) How does the LCP and land production function affect land “stickiness”?

The answers to the above questions have important theoretical value and practical significance. The results of this research could serve to protect farmers’ rights, promote the development of agricultural production, improve the level of agricultural modernization, and achieve the goal of rural revitalization in China.

Hence, based on the perspective of land production function, this paper firstly constructs the conceptual framework of “LCP–land production function–land ‘stickiness’” to theoretically analyze the mechanism of the property rights exclusion effect and the incentive effect of LCP on the land “stickiness” of rural labor via land production function. Secondly, using mixed cross-sectional data from the 2016 and 2018 China Labor-force Dynamics Survey (CLDS), the propensity score matching (PSM) model is used to empirically test the effect of LCP on land “stickiness”. Heterogeneity analysis and robustness tests are also conducted. Furthermore, we introduce a mediation effect model to test the mechanism of the effect of the LCP on the land “stickiness” of rural labor. Finally, we propose policy recommendations to provide references for stabilizing the property rights of farmland, enhancing its production functions, and promoting agricultural development.

3. Theoretical Framework

3.1. Property Rights Exclusion Effect, Land Production Function, and Land “Stickiness”

The Land Certificated Program has a property rights exclusion effect. The property rights exclusion effect is the exclusivity of contractual rights in farmland. The LCP measured each plot with detailed information, including location, size, and boundaries. The disputes with unclear land information on plot size and boundaries were addressed. Consequently, the LCP was able to guarantee the exclusivity of farmland property rights by clarifying the four areas of farmland and specifying property rights ownership [34]. Now, it continues to promote the improvement of farmland production functions and the redistribution of agricultural labor.

In the past, when farmland property rights were found to be unclear, they were frequently adjusted. On the one hand, farmers could not obtain exclusionary rights through legal empowerment under this system. On the other hand, the exclusionary rights granted by village rules and regulations were not mandatory; therefore, the exclusivity of farmland property rights was weakened [35]. Therefore, in order to avoid the possible risk of land loss, farmers had to bear certain exclusion costs to maintain their contractual rights while going out to work [36]. These farmers were often willing to operate farmland at low cost, or even to transfer farmland to acquaintances for free. As a result, the production function of land and the land “stickiness” of rural labor were weakened.

Therefore, the purpose of implementing the LCP was to permanently determine contractual rights to farmland by measuring each plot. It strengthens the exclusivity of contractual rights to farmland, reduces the risk of land loss, and strengthens the production function of land. This in turn affects the labor allocation decisions of rural households. Laborers are now more inclined to engage in agricultural production, increase the degree of agricultural labor participation, and enhance land “stickiness”. At the same time, due to historical legacy problems, such as the poorly defined property rights of agricultural land and the operational flexibility of local governments in the process of implementing the LCP, farmers inevitably pay more attention to the productive function of the land. This enables them to preserve their contractual rights by increasing their labor input, increasing their labor participation, and strengthening the “stickiness” of the land. In this way, farmers can obtain the most favorable “standard of certificated land” and maximize their interests.

3.2. Property Rights Incentive Effect, Land Production Function, and Land “Stickiness”

The Land Certificated Program also has a property rights incentive effect. The property rights incentive effect is the incentive of farmland returns. The LCP formalized and legalized contract rights though the issuance of land certificates. At the same time, it strengthened farmers’ residual claims to farmland returns by stabilizing long-term land contract relationships [37]. Hence the farmers can use the tenure security to mortgage land, stimulate the improvement of farmland production functions and reallocate agricultural labor.

Prior to the program, with the instability of farmland property rights, farmers’ residual claims to farmland returns were not stable. This meant that once the farmland was reallocated to adjust to population needs, both the medium- and long-term investments in agriculture attached to the farmland were lost, and household income from agriculture was reduced. This in turn led to the inefficient use of farmland and agricultural production. Therefore, in order to avoid the possible risk of farmland adjustment, farmers’ demand for land production function decreases, thus reducing agricultural labor input and weakening land “stickiness”. Driven by comparative interests, farmers then choose to engage in non-agricultural production in urban areas in order to relieve the pressure of survival and improve quality of life, further weakening the land production function and land “stickiness”.

The LCP provides an institutional guarantee for agricultural operators to stabilize agricultural production and enhance the land production function by ensuring the stability of farmland property rights and guaranteeing farmers’ residual claims to farmland returns. It stimulates and enhances farmers’ enthusiasm and stability in agricultural production [29], and the land production function is strengthened. This stabilization of residual claims to farmland and higher expected returns from agricultural operations can, to a certain extent, encourage farmers to increase their participation in agricultural labor, thereby increasing the supply of agricultural labor and land “stickiness” of rural labor.

In summary, the LCP has a property rights exclusion effect and an incentive effect. On the one hand, the LCP confirms contractual rights and ensures farmland exclusivity through the land survey. On the other hand, the LCP guarantees the stability of land property rights and ensures farmers’ residual claims to farmland returns through the issuance of certificates. This improves the land production function, thus promoting the reallocation of agricultural labor, increasing the degree of agricultural labor participation, and increasing the land “stickiness” of rural labor. The specific influence path is shown in Figure 2.

Based on these observations and previous studies, we hypothesize that the LCP has a significantly positive effect on the land “stickiness” of the rural labor, and land production function plays an important role in it. In other words, the LCP exerts a significantly positive effect on land “stickiness” via its influence on land production function.

4. Data, Variables, and Models

4.1. Data

The data used in this paper come from the 2016 and 2018 China Labor-force Dynamics Survey (CLDS) data provided by the Social Science Survey Center of Sun Yat-sen University. This survey is conducted once every two years, with the labor force aged 15–64 as the target population. It utilizes a multi-stage, multi-level sampling method proportional to the size of the labor force. Since the CLDS2018 household samples are all new samples, we were unable to compose panel data. Thus, this paper uses two rounds of surveys consisting of mixed cross-sectional data from 2016 and 2018. This paper focuses on studying the impact of the Land Certificated Program (LCP) on rural households’ agricultural labor participation. In order to do so, rural households were used as the research subjects, from which mainly household and community-level data were used. The survey data were then processed and screened to obtain a valid sample of 3936 rural households. The sample farm households covered 247 villages in 26 provinces across China, and the regional distribution of the sample farm households showed that there were 1399, 1217, and 1320 farm households in the eastern, central and western regions, respectively.

The process of sample selection was as follows: (1) construct the variables needed for this study based on the variables already available in the database; e.g., the variable of the number of rural household members was obtained by using the coding count of each member of the farm household; (2) delete the samples of rural households that were non-agricultural, and keep only those samples whose household type was agricultural; (3) delete the samples of rural households that did not possess farmland; (4) delete the missing values of key variables, e.g., delete the missing value of question F6.2a “Has the household received the Certificate of Rural Land Contract Management Rights” and delete the missing value of question F6.4a “The number of people engaged in agricultural production in your household last year”.

4.2. Variables

1. Dependent variable: land “stickiness” of rural labor. According to the above analysis, agricultural labor participation is the external expression of land “stickiness”. Thus, this paper adopts the agricultural labor participation rates to represent the land “stickiness” of rural labor. This was determined by calculating the ratio of labor participated in farming activities [38]. On average, the ratio of agricultural participation was 44.3% in this sample.

2. Independent variable: Land Certificated Program (LCP). The CLDS2016 and CLDS2018 surveys provide data on the agricultural production of rural households in 2015 and 2017, respectively. In this period, the LCP was being promoted in the form of a province-wide pilot or county-wide pilot, and had not yet even been extended to the whole country in 2015. Although the LCP had been extended across the entire country by 2017, not all rural households had carried out certificated land work simultaneously for the same pilot unit, which provides a good opportunity for this paper to study the policy effects of the LCP.

This paper draws on the studies of Zhou et al. [39] to measure “whether the farm household has received the Certificate of Rural Land Contract Management Rights” in order to determine whether the farmland was certificated. If the answer to this question was “yes”, then the value of LCP was “1”; if not, the value was “0”. Using this processing method, the number of observations in the sample of households that had received the Certificate of Rural Land Contract Management Rights was 2335, while the number of observations in the sample of households that had not yet received the Certificate of Rural Land Contract Management Rights was 1601. Thus, the overall certification rate of the sample was 59.3%, which is similar to the results of previous studies [17].

3. Control variables. We also controlled for the characteristics of farmers individuals, households, and villages in our analysis.

The individual characteristics were gender, age, age squared, education, political outlook, and health of the household heads. The social and cultural norms were the leading causes of gender inequality [40]. Females’ household responsibilities (such as caretaking children, household chores, and so on) limited both their available labor and time to farm [41]. It was therefore expected that males were more inclined to engage in agricultural production. The age of the household head was used as a proxy for the family’s farming experience [42]. Therefore, older farmers were expected to be more productive in agriculture. However, as their age increases, the physical ability of farmers who have consistently engaged in agricultural production will naturally be restricted. Hence, the square of age was added to the model to capture the possible non-linearities in its impact [43,44]. The average age of household head is 53.877 years. Household heads with more education were assumed to have more off-farm skills and therefore be more likely to engage in off-farm employment. Zhang et al. [45] found that rural individuals with more education had increasing access to off-farm jobs. In addition, De Rrauw and Rozelle [46] estimated an average return to education of 6.4% in off-farm wage employment in rural China. Thus, it was expected that the education would have a negative impact on farm employment. The average number of years of education was generally low, at approximately 7 years. Finally, political outlook and individual health played an important role in improving human capital and enhancing the labor market outcomes. Earlier studies found that having a political cadre in the family increased the family members’ off-farm employment [47,48]. Shu Lei [44] found that health was an important factor affecting agricultural production. Thus, political outlook and health were also included in the model. On average, the percentage of household heads who were not party members was 8.3%, and 50% of the household heads reported their health status as “healthy”.

The household characteristics were total family income, land per capita, labor ratio and the types of farming households. Total family income was determined with respect to household wealth, which may have a negative impact on farm employment, as participation in off-farm employment may require a minimum level of assets [49]. For outcomes measured in terms of monetary value (for example, productivity, value of credit received, and consumption), studies usually reported treatment effects on the scale of the natural logarithm [20]. In order to induce normality in skewed income distribution, this paper treated total family income logarithmically [49]. Land per capita was the ratio of the farmland size and the number of people living in the same family. Large land per capita ratios were expected to increase households’ probability of agricultural production, as more agricultural labor was needed to farm more land [50]. The mean of the land per capita was 2.533 mu. Labor ratio was the share of labor population between the ages of 15 and 64. Household labor availability also has an impact on household labor allocation between farm employment and off-farm employment [51]. It was expected that the labor ratio would have a negative impact on farm employment, since the limited amount of arable land per capita in China forced any surplus labor forces to be more inclined to engage in off-farm employment. As for farming type, households were divided into professional households and non-professional households, which accounted for 89.58% and 10.42% of the samples, respectively. The professional households were expected to have more farming skills and therefore to be more productive in agriculture.

The village characteristics were the proportion of the village population engaged in agriculture, the existence of non-agricultural economy in the village, the index of village support services, the distance of the village from the township government, and the topography of the village. As seen in

Table 1. Definitions and Descriptions of Variables.

Variable Types	Variable Names	Variable Definitions	Mean	SD	Min	Max
Dependent variable	Land “stickiness”	The ratio of labor participating in farming activities (%)	0.443	0.248	0	1
Independent variable	LCP	1 if a farmer owns a land certificate, and 0 otherwise	0.593	0.491	0	1
Individual characteristics	Gender	1 if the head of household is male, and 0 otherwise	0.869	0.337	0	1
	Age	Age of the head of household (years)	53.877	10.915	18	89
	Age squared	Age ∗ Age/100 (years)	30.219	11.863	3.24	79.21
	Education	Years of formal education of the head of household (years)	7.186	3.154	0	16
	Political outlook	1 if the head of household is party member, and 0 otherwise	0.083	0.276	0	1
	Health	1 if the head of household is healthy, and 0 otherwise	0.500	0.500	0	1
Household characteristics	Income	Total household income in 2015 or 2017 (yuan, logarithm)	9.709	1.853	0	14.914
	Land per capita	The ratio of the farmland size and the number of family members (mu)	2.533	7.124	0.01	250
	Labor ratio	Share of labor population aged 15–64	0.810	0.315	0	1
	Household type	1 if the household is professional, and 0 otherwise	0.104	0.306	0	1
Village characteristics	Population engaged in agriculture ratio	The ratio of the village population engaged in agriculture (%)	71.919	31.084	0	100
	Non-agricultural economy	1 if the village has non-agricultural economic, and 0 otherwise	0.182	0.386	0	1
	Support service index	The total number of farmers enjoying village support services	2.090	1.396	0	6
	Distance of the village from the township	The distance of the village from the township government (km)	6.330	5.936	0	50
	Village topography	1 if the village is plain, and 0 otherwise	0.477	0.500	0	1

, the mean of the proportion of the village population engaged in agriculture was 71.919, indicating that approximately 72% of the farmers in the village were involved in agriculture. Where there was a higher proportion of the village population engaged in agriculture, there was less non-agricultural economy in the village, which indicates that the level of economic development in the village was low. When this is the case, the farmers can only engage in agricultural production to maintain demand. On the contrary, the existence of a non-agricultural economy in a village indicates that the level of economic development in the village is high, and that farmers tend to regularly engage in non-agricultural production. In our sample, 18% of villages had a non-agricultural economy. The index of village support services represented the level of support and security for agricultural production at the village level. Village support services are likely to increase the ratio of agricultural production, as it can reduce the sunk costs and realize increasing returns [52]. In this paper, support services mainly included unified irrigation and drainage services, machine plowing services, unified pest prevention services, unified purchase of production materials services, planting planning services, and organization of farmers for agricultural production. Each service available to the farmers was assigned a value of “1”, and each service not available was assigned a value of “0”. The values were added together to obtain the village support service index. The distance of the village from the township government was also a key factor influencing farmers’ agricultural production, as the rural households located further away from the township government were more likely to participate in agricultural production than households located nearby [53]. The average distance of the village from the township was 6.330 km in our sample. According to Xie et al. [54], we controlled for whether a laborer was from a plain village. If the village was a plain village, it was given a value of “1”; if not, it was given a value of “0”.

The specific definitions of the above variables and the results of the descriptive statistical analysis are shown in Table 1.

4.3. Models

4.3.1. Propensity Score Matching (PSM) Method

In order to test the impact of the LCP on the land “stickiness” of rural laborers, this paper constructs an empirical model of land “stickiness” in the following form.

$$Y_i=a_0+a_1{R}_i+a_2{X}_i+{\epsilon }_i$$

(1)

where $Y$ denotes the labor land “stickiness” of farming households $i$; $R_i$ denotes whether the LCP was implemented; $X_i$ is a series of control variables, including individual characteristics, household characteristics, and village characteristics; $a_1$ and $a_2$ are the coefficients to be estimated for LCP and control variables, respectively; $a_0$ is a constant term; and ${\epsilon }_i$ is the error term.

Notably, as the LCP was gradually implemented using a moderated piloting system, “selective” bias might arise as a result. The LCP was affected by its circumstances, such as the social, historical, or economic conditions [17]. On the one hand, the local governments of the initial pilot regions were required to invest copious human, material, and financial resources into supporting the LCP, which placed stress on these regions in terms of their economic development. On the other hand, after the LCP, the farmland would no longer be adjusted, which ultimately affected the existing interest pattern in rural areas and even hinder the implementation of the policy [55]. Therefore, the promotion of the LCP inevitably put certain demands on the strength of a village’s collective organizations. Thus, in order to ensure the effectiveness of the LCP pilots, the government tended to select the areas with relatively high levels of economic development and the villages with stronger rural collective organizations or less traditional farmland adjustment as the pilot areas for the LCP. This ultimately resulted in the problem of sample selection bias faced in this study. At the same time, there was a level of heterogeneity in land “stickiness”, individual characteristics, household characteristics, and village characteristics between the groups with and without LCP (see

Table 2. Average individual, household, and village characteristics by LCP status.

Variable Types	Variable Names	LCP	Non-LCP	Diff: (1)-(2)
		(1)	(2)
Dependent variables	Land “stickiness”	0.468 (0.255)	0.407 (0.232)	0.061 ***
Individual characteristics	Gender	0.877 (0.329)	0.858 (0.349)	0.019 *
	Age	53.307 (10.701)	54.710 (11.170)	−1.403 ***
	Age squared	29.561 (11.537)	31.178 (12.263)	−1.618 ***
	Education	7.288 (3.129)	7.037 (3.185)	0.251 **
	Political outlook	0.093 (0.290)	0.069 (0.253)	0.024 ***
	Health	0.534 (0.499)	0.450 (0.498)	0.084 ***
Household characteristics	Income	9.708 (1.941)	9.710 (1.717)	−0.002
	Land per capita	2.886 (5.827)	2.020 (8.650)	0.866 ***
	Labor ratio	0.810 (0.305)	0.811 (0.329)	−0.001
	Household type	0.123 (0.329)	0.076 (0.265)	0.047 ***
Village characteristics	Population engaged in agriculture ratio	72.614 (30.570)	70.905 (31.800)	1.710 *
	Non-agricultural economy	0.170 (0.376)	0.200 (0.400)	−0.030 **
	Support service index	2.139 (1.376)	2.018 (1.423)	0.121 ***
	Distance of the village from the township	6.501 (6.203)	6.154 (5.516)	0.422 **
	Village topography	0.471 (0.499)	0.485 (0.500)	−0.014
Observation		2335	1601	—

Note: Standard deviations are shown in parentheses. ***, **, and * denote significance at 1% level, 5% level, and 10% level, respectively. Significance levels are obtained from t-tests or chi-square tests, depending on whether the variable is categorical or continuous.

). Therefore, if a simple regression analysis were used to estimate the policy effect of LCP on the land “stickiness” of rural laborers, the estimation results may be biased.

Based on the possible “selective” bias of the sample and the heterogeneity of individual characteristics, household characteristics and village characteristics, this paper adopted the propensity score matching (PSM) method to estimate the effect of the LCP on the land “stickiness” of rural labor. The basic idea of this method was to construct a sample of uncertificated farmland for the sample of certificated farmland by introducing a counterfactual framework, and to ensure that the characteristics of both samples are similar except for the farmland rights [56]. The difference of land “stickiness” between the two samples can be regarded as the result of two different experiments (with and without LCP) on the same individual. Therefore the difference was the policy effect of the LCP on labor land “stickiness”.

Specifically, in this paper, the land “stickiness” of farmer i with confirmed farmland rights ($R$ = 1) was set as $Y_i^R$, which was the treatment group; the land “stickiness” of farmer i with unconfirmed farmland rights ($R$= 0) was set as $Y_i^{NR}$, which was the control group. The effect of farmland rights on labor land “stickiness” was thus determined as:

$$T_i=Y_i^R-Y_i^{NR}$$

(2)

In Equation (2), since it was not possible to observe the land “stickiness” of farmer $i$ both before and after the farmland was certificated, the counterfactual framework was constructed in this paper as:

$$\begin{array}{ll}{T}_i& =E(Y_i^R|R=1)-E(Y_i^{NR}|R=0)\\ & =E(Y_i^R|R=1)-E(Y_i^{NR}|R=0)+E(Y_i^{NR}|R=1)-E(Y_i^{NR}|R=1)\\ & =E[(Y_i^R-Y_i^{NR})|R=1]+[E(Y_i^{NR}|R=1)-E(Y_i^{NR}|R=0)]\end{array}$$

(3)

In Equation (3), $E\left[\left(Y_i^R-Y_i^{NR}\right)|R=1\right]$ is the average treatment effect on the treated (ATT) of LCP on land “stickiness”, and $\left[E\left(Y_i^{NR}|R=1\right)-E\left(Y_i^{NR}|R=0\right)\right]$ is the selection bias.

In the above analysis, the certificated farmland group and the uncertificated farmland group were not randomly assigned, so they had a level of selection bias. The PSM method constructs the control group with similar characteristics to the treatment group as much as possible by matching the scores of the whole sample. In this way, we were able to effectively reduce the sample’s selection bias and obtain the effective estimate of the average treatment effect on the treated (ATT). The main estimation steps were as follows.

First, we used a logit model to estimate the conditional probability fitted value of rural households to carry out certificated farmland, i.e., the expression of the propensity score value is

$$PS=Pr(R=1|X_i)=E\left(R=0|X_i\right)$$

(4)

In Equation (4), $PS$ is the propensity score value; $R$ = 1 indicates certificated farmland farmers; $R$ = 0 indicates certificated farmland farmers; and $X_i$ indicates observable individual, household, and village characteristics.

Next, the treatment group was matched with the control group. To verify the robustness of the matching results, two methods, nearest neighbor matching (NNM) and kernel-based matching (KBM), were selected for matching in this paper.

Then, the common support test and the balance test for propensity score estimation were performed. The common support test was utilized to determine whether the treatment and control groups had a common support region and whether there was any partial overlap in the range of values. The balance test was used to determine the matching quality by comparing whether there was a significant difference between the treatment and control groups in terms of explanatory variables.

Finally, the ATT was calculated to determine the effect of the LCP on the land “stickiness” of rural laborers.

4.3.2. Mediation Effect Model

In order to further study the influence path of the LCP on rural labor land “stickiness”, and test whether there was a mediating effect of land production function between the LCP and labor land “stickiness”, we constructed the following model.

$$M_i=b+{\beta }_2{R}_i+{\lambda }_2{X}_i+{\epsilon }_{2i}$$

(5)

$$Y_i=c+{\beta }_3{R}_i+{\beta }_4{M}_i+{\lambda }_3{X}_i+{\epsilon }_{3i}$$

(6)

where the meanings of $Y_i$ and $R_i$ are kept consistent with Equation (1); $M_i$ is the mediating variable, i.e., the land production function; ${\beta }_1$, ${\beta }_2$, ${\beta }_3$ and ${\beta }_4$ are parameters to be estimated; $a$, $b$ and $c$ are constant terms, and ${\epsilon }_{1i}$, ${\epsilon }_{2i}$ and ${\epsilon }_{3i}$ are random error terms.

5. Analysis and Results

5.1. The Estimation Results of the PSM Model

5.1.1. Propensity Score Estimation

The most direct way to measure the impact of the LCP on labor land “stickiness” is to compare the land “stickiness” of certificated rural households with that of non-certificated rural households. However, these two categories (certificated and uncertificated) are mutually exclusive, which makes it impossible to observe the effects of the same household’s “certificated” and “uncertificated” farmland at the same time. Therefore, this paper constructed a logit model and a marginal effects model with certificated farmland as the dependent variable, and then estimated individual, household, and community characteristics to obtain the propensity scores of each variable. The estimation results are shown in

Table 3. Logit model results of factors determining LCP.

Variable Types	Variable Names	Logit		Marginal Effect
		Coefficient	SE	Coefficient	SE
Individual characteristics	Gender	0.086	0.100	0.020	0.023
	Age	0.058 **	0.024	0.014 **	0.006
	Age squared	−0.060 ***	0.022	−0.014 ***	0.005
	Education	0.006	0.011	0.001	0.003
	Political outlook	0.310 **	0.127	0.073 **	0.030
	Health	0.311 ***	0.069	0.073 ***	0.016
Household characteristics	Income	−0.020	0.019	−0.005	0.004
	Land per capita	0.028 ***	0.009	0.007 ***	0.002
	Labor ratio	−0.100	0.110	−0.024	0.026
	Household type	0.480 ***	0.116	0.113 ***	0.027
Village characteristics	Population engaged in agriculture ratio	0.001	0.001	0.000	0.000
	Non-agricultural economy	−0.172 **	0.087	−0.040 **	0.020
	Support service index	0.091 ***	0.024	0.021 ***	0.006
	Distance to township government	0.013 **	0.006	0.003 **	0.001
	Village topography	−0.115 *	0.069	−0.027 *	0.016
Constant		−1.313 **	0.653	—	—

Note: ***, **, and * denote significance at 1% level, 5% level, and 10% level, respectively.

. The explanatory variables all had variance inflation factors (VIFs) of less than ten, except for age and age squared. Although the VIFs of age and age squared were more than ten, according to Allison’s research, multicollinearity will have no adverse consequences when high VIFs are caused by the inclusion of powers or products of other variables [57]. Hence, the estimated model was free of any serious multicollinearity.

From Table 3, we can see that age, political outlook, and health status among individual characteristics had significant effects on the LCP. There was an inverted “U”-type relationship between age and the LCP. This indicates that as the age of the head of the household increased, so did the probability of the household participating in the LCP. However, when the age increased to a certain degree, this same probability decreased in turn. Political outlook and health status had positive effects on the LCP and were significant at the 5% and 1% statistical levels, respectively. In terms of household characteristics, land per capita and household type had positive effects at the 1% statistical level, and the probability of the LCP increased by 0.7% for each unit increase in land per capita. Compared with non-professional households in agricultural production, the probability of the LCP was 11.3% higher for professional households. In terms of village characteristics, the non-agricultural economy had a negative effect on the LCP, and this was significant at the 5% level. This means that villages with a developed non-agricultural economy had less dependence on farmland, and a low probability of participating in the LCP to secure farmland property rights. The support service index and distance to township government had positive effects on LCP, as they were significant at the 1% and 5% levels, respectively. The topography of the village had a negative effect on the LCP at the 10% statistical level, and the probability of the LCP in plain areas was lower than that in non-plain areas. This is because the LCP is easier to promote and implement in the non-plain areas as these farmers are less dependent on land.

5.1.2. Matching Quality Tests: Common Support Test and Balance Test

The common support test aimed to determine whether there was a significant difference between the propensity score values of the treatment group (the certificated farmland group) and the control group (the uncertificated farmland group). If the common range of propensity scores of the two sample groups was large, it indicated a good matching result. Otherwise, it would lead to biased estimation results. The results of the common support test were matched using the nearest neighbor matching and kernel-based matching methods. As shown in Figure 3, most of the observations were within the common support region and only a small number of samples were not in the common support region after matching. Therefore, the common support test was satisfied by the matching estimation using the nearest neighbor matching and kernel-based matching methods in this paper, indicating a good matching effect.

The balance test aimed to examine whether there are significant systematic differences in the variables of individual characteristics, household characteristics and village characteristics between the treatment groups and the control groups. If there was no significant difference after matching, this indicated that individuals with the same characteristics could be found to match between the two groups and the matching effect was good; if not, the matching effect was poor.

In

Table 4. PSM quality indicators before and after matching.

Variables	Unmatched Matched	Nearest Neighbor Matching			Kernel-Based Matching
		Bias (%)	|Bias| Reduction (%)	t-test (p>|t|)	Bias (%)	|Bias| Reduction (%)	t-test (p > |t|)
Individual characteristics
Gender	U	5.4		0.092 *	5.4		0.092 *
	M	1.9	65.2	0.509	0.2	96.5	0.946
Age	U	−12.8		0.000 ***	−12.8		0.000 ***
	M	4.8	62.6	0.090 *	0.3	98.0	0.927
Age squared	U	−13.6		0.000 ***	−13.6		0.000 ***
	M	5.2	61.9	0.061 *	0.5	96.3	0.858
Education	U	8.0		0.014 **	8.0		0.014 **
	M	−2.8	64.7	0.322	0.5	96.6	0.859
Political outlook	U	8.7		0.008 ***	8.7		0.008 ***
	M	2.4	73.0	0.442	0.8	91.2	0.804
Health	U	16.8		0.000 ***	16.8		0.000 ***
	M	−8.5	49.3	0.004 ***	−0.7	95.8	0.809
Household characteristics
Income	U	−0.1		0.968	−0.1		0.968
	M	−5.1	−3786.9	0.071 *	−0.7	−394.1	0.826
Land per capita	U	11.7		0.000 ***	11.7		0.000 ***
	M	7.9	32.7	0.000 ***	8.4	28.5	0.000 ***
Labor ratio	U	−0.3		0.938	−0.3		0.938
	M	−4.3	−1606.5	0.128	1.8	−630.5	0.520
Household type	U	15.8		0.000 ***	15.8		0.000 ***
	M	−3.3	79.1	0.314	2.1	86.8	0.513
Village characteristics
Population engaged in agriculture ratio	U	5.5		0.090 *	5.5		0.090 *
	M	−1.3	76.0	0.647	0.8	84.5	0.770
Non-agricultural economy	U	−7.8		0.015 **	−7.8		0.015 **
	M	−1.2	84.5	0.670	−0.2	98.0	0.956
Support service index	U	8.6		0.008 ***	8.6		0.008 ***
	M	2.6	70.2	0.388	−1.8	79.2	0.548
Distance to township	U	7.2		0.029 **	7.2		0.029 **
	M	−1.2	83.4	0.706	−1.0	85.7	0.736
Village topography	U	−2.8		0.380	−2.8		0.380
	M	−4.5	−56.5	0.128	−0.6	78.4	0.834

Note: ***, **, and * denote significance at 1% level, 5% level, and 10% level, respectively.

it is shown that after applying the nearest neighbor matching method, the standardized deviation of each variable after matching was controlled within 10%. Following this, the standardized deviation of most of the control variables decreased compared with that before matching. Except for the variables of total household income, proportion of working population, and village topography, the deviation of all variables after matching decreased substantially. In addition, the variables including gender, education, and political outlook in individual characteristics, and type of farm household in household characteristics, as well as the variables of population engaged in agriculture ratio, non-agricultural economy, support services index, and distance to township government in village characteristics, were significantly different between the treated and control group samples before matching, and there were also no systematic differences after matching. These indicate that the differences in characteristics between the treated and control groups were basically eliminated, and the two groups of samples became more similar and comparable.

Further, similar results were also obtained using the kernel-based matching method. Compared with before matching, the standardized deviations of each variable were significantly reduced, and all variables were controlled within 10%. Except for income and labor ration, the percentage of deviation reduction in all variables decreased significantly. These indicate that the systematic difference changes between the treatment and control groups before and after matching were consistent with the nearest neighbor matching.

Meanwhile, according to the distribution of standardized deviations used for both methods (see Figure 4), the distribution of standardized deviations of each variable before matching was relatively discrete. This indicates that the individual characteristics, household characteristics, and village characteristics of the certificated farmland group and the uncertificated farmland group before matching were significantly different. After applying the nearest neighbor matching and kernel-based matching methods, the standardized deviations of each variable were less than 10%, and most of them were concentrated around 0, which shows a significant reduction compared with the pre-matching period. This also indicates that the matched group of the LCP and the group without the LCP were better balanced at the level of control variables, and there was no longer a significant difference.

In summary, the results of the balance test using the two matching methods of nearest neighbor matching and kernel-based matching remained consistent. This indicates that the sample matching passed the balance test.

5.1.3. Analysis of Matching Results

The nearest neighbor matching and kernel-based matching methods were used to assess the effect of LCP on the land “stickiness” of rural labor (see

Table 5. The ATT of LCP on land “stickiness”.

Matching Methods	Unmatched Matched	Mean		ATT	SE	T Value
		T	C
Nearest neighbor matching	U	0.468	0.407	0.061 ***	0.008	7.65
	M	0.468	0.419	0.049 ***	0.010	4.70
Kernel-based matching	U	0.468	0.407	0.061 ***	0.008	7.65
	M	0.467	0.419	0.048 ***	0.008	5.95

Note: Treatment group (T) and control group (C). *** denote significance at 1% level.

). In general, the ATT values obtained by both matching methods were relatively close to each other, indicating that the analysis results were robust. In the nearest neighbor matching, the ATT value of LCP was 0.049. Meanwhile, in the kernel-based matching, the ATT value of LCP was 0.048. Both ATT values were statistically significant at the 1% level. In other words, the implementation of the LCP will contribute to an increase in the agricultural labor force participation rate by about 4.8% to 4.9%. This indicates that the LCP has a positive effect on agricultural labor participation and enhances the land “stickiness” of rural labor.

In addition, the unmatched ATT value of 0.061 was slightly higher than the matched result. This suggests that sample selection bias and variability in sample characteristics can overestimate the policy effect of LCP, and that simple regression model estimates are biased.

5.2. Heterogeneity Analysis

With the development of urbanization and industrialization, rural society has become divided. The main agricultural operators present a situation of co-existence of professional and part-time households. At the same time, with the improvement of the farmland rented market, farmland resources can be reallocated in the rural market, which in turn provides the possibility for professional agricultural production households to increase their farm sizes. To this end, this section examines whether the policy effects of the LCP on labor land “stickiness” are heterogeneous in terms of the rural household types and farm size. Does the policy effect of the LCP have preferences in terms of household types and farm size?

In this paper, the types of rural households are divided into professional and non-professional households in agricultural production. At the same time, drawing on the idea of grouping in Opler’s study [58], the samples of farm size less than 25% are defined as small scale, the samples of farm size between 25% and 75% are defined as medium scale, and the samples of farm size more than 75% are defined as large scale.

From

Table 6. Heterogeneity analysis results.

Matching Methods	Unmatched Matched	Rural Household Types		Farm Size
		PH	NPH	Small	Medium	Large
Nearest neighbor matching	U	0.041 (0.027)	0.061 *** (0.008)	0.058 *** (0.016)	0.070 *** (0.012)	0.036 ** (0.015)
	M	0.043 (0.038)	0.032 *** (0.011)	0.042 * (0.021)	0.060 *** (0.015)	0.032 (0.020)

Note: Standard errors are shown in parentheses. Professional households (PH) and non-professional households (NPH). ***, **, and * denote significance at 1% level, 5% level, and 10% level, respectively.

, we can see that in terms of rural household type, the promotional effect of the LCP on agricultural labor participation of non-professional household groups was significant, while the promotional effect on agricultural labor participation of professional household groups was not significant. In terms of farm size, the promotional effect of the LCP had the largest effect on medium-sized farms, with an ATT value of 0.060, and it was significant at the 1% statistical level; the second largest effect was on small-sized farms, with an ATT value of 0.042, and it was significant at the 10% level; and the effect on large-sized farms was not significant.

The above analysis shows that the policy promotion effect of the LCP on land “stickiness” of rural labor is mainly reflected in the group of non-professional agricultural production households, medium-sized farms, and small-sized farms, while the promotion effect on professional agricultural production households and large-sized farms is not significant.

5.3. Robustness Tests

5.3.1. Robustness Test I: Sensitivity Analysis

The key underlying assumption of the PSM method is that a farmer’s decision to participate in LCP is solely dependent on observed factors [59]. However, the real-life decisions of farmers on whether to participate in the LCP are also affected to some extent by unobservable factors. As such, this section uses Rosenbaum bounds estimation for sensitivity analysis [60]. When γ = 1, this indicates that rural households are equally likely to participate in LCP. When different values are assigned to γ, Rosenbaum bounds estimates give the upper and lower significance levels of the impact of LCP at different levels of variation in likelihood, the Hodges–Lehmann point estimates of the upper and lower bounds, and the confidence intervals of the upper and lower bounds. These act as indications of whether heterogeneity in unobserved factors significantly alters the estimates. If unobservable heterogeneity significantly alters the estimation results, this indicates that the PSM method based on observable heterogeneity is not suitable for estimating the policy effects of the LCP.

According to the

Table 7. Rosenbaum bounds sensitivity analysis.

γ	Sig+	Sig−	t-hat+	t-hat−	CI+	CI−
1.0	0.0000	0.0000	0.4167	0.4167	0.4167	0.4167
1.1	0.0000	0.0000	0.4167	0.4167	0.4000	0.4333
1.2	0.0000	0.0000	0.3958	0.4333	0.3889	0.4500
1.3	0.0000	0.0000	0.3875	0.4500	0.3750	0.4500
1.4	0.0000	0.0000	0.3750	0.4500	0.3750	0.4583
1.5	0.0000	0.0000	0.3750	0.4583	0.3667	0.4625
1.6	0.0000	0.0000	0.3667	0.4583	0.3667	0.4762
1.7	0.0000	0.0000	0.3667	0.4762	0.3500	0.5000
1.8	0.0000	0.0000	0.3500	0.5000	0.3500	0.5000
1.9	0.0000	0.0000	0.3500	0.5000	0.3429	0.5000
2.0	0.0000	0.0000	0.3485	0.5000	0.3333	0.5000

Note: sig+: upper bound significance level. sig− lower bound significance level. t-hat+: upper bound Hodges–Lehmann point estimate. t-hat−: lower bound Hodges–Lehmann point estimate. CI+: upper bound confidence interval (a = 0.95). CI−: lower bound confidence interval (a = 0.95).

, even though there was more than twice the likelihood of a difference in the LCP due to unobservable heterogeneity, the effect of the LCP on land “stickiness” of rural labor was still positive, with significance levels below 1%. The Hodges–Lehmann point estimates and confidence intervals were greater than 0 at the 5% significance level. This indicates that the LCP had a significant positive effect on land “stickiness” of rural labor, and unobservable heterogeneity did not affect the estimation results. This suggests that the results obtained by the PSM method are robust.

5.3.2. Robustness Test II: Replacement of LCP Variables

The issuance of certificates is the last part of the confirmation of the LCP, and at the same time, the land certificate is an important legal document to define the property rights of farmland. However, the latest LCP has problems such as the relative lag of the titling process [61]. Specifically, though the confirmation of farmland property rights in villages has been completed, the progress of issuing certificates is inconsistent among villages and some farmers have not yet even obtained farmland certificates. Firstly, due to the complex situation and unclear land boundaries in some areas, a resulting high error rate in land measurement has led to obvious differences in land area before and after titling. These discrepancies have slowed down the progress of certificate issuance. Secondly, due to historical legacy issues, the cadastral information of rural households has changed drastically since the second round of contracting. Conflicts concerning land among rural households, disputes between family members, and inter-generational conflicts are constant and often result in the temporary hold of certificate issuance. Thirdly, there is conflict in the objectives between farmers who go out to work and dedicate their time to apply for certification, the farmers who do not receive the certificates in time, and the certificates ending up being temporarily kept by the village collective.

In response to the inconsistent progress with the issuing of certificates to farmers within villages, this paper used the variable of “village level LCP” as a proxy variable for the LCP. The aim was to eliminate intra-village differences in rural household certificate holdings and to test whether the village level LCP has an impact on land “stickiness”. Drawing on Sun et al. [62], this paper defined a village as a “certificated land village” if the certificate issuance rate of farmers was greater than or equal to 60%; otherwise, it defined a village as a “certificated land village”. The nearest neighbor matching and kernel-based matching methods were also used to assess the policy effects of village level LCP on labor land “stickiness”. The empirical results are shown in

Table 8. The ATT of village level LCP on land “stickiness”.

Matching Methods	Unmatched Matched	Mean		ATT	SE	T Value
		T	C
Nearest neighbor matching	U	0.481	0.395	0.085 ***	0.008	10.88
	M	0.481	0.406	0.074 ***	0.011	7.03
Kernel-based matching	U	0.481	0.395	0.085 ***	0.008	10.88
	M	0.481	0.407	0.074 ***	0.008	8.90

Note: Treatment group (T) and control group (C). *** denote significance at 1% level.

.

The results show that village level LCP had a positive contribution to the “stickiness” of rural labor. These results are consistent with the empirical results at household levels. Specifically, the matched ATT of village level LCP was 0.074, and both were statistically significant at the 1% level, as the promotion effect of village level LCP on land “stickiness” was 7.4%.

5.4. Mechanism Analysis: How LCP Affect Land “Stickiness”

Through our theoretical analysis, the theoretical framework of “LCP–land production function–land ‘stickiness’” was constructed. In this part, the mediating effect model was used to verify whether there was a mediating effect of land production function.

Drawing on the Equations (5) and (6) to test the mediating effect in turn, it was assumed that the LCP enhanced the land “stickiness” of rural labor by strengthening the land production function, and the LCP significantly enhanced the land production function. Conversely, the land production function had no mediating effect. In this paper, we used “agricultural business income” as the proxy variable of land production function, and the higher agriculture income, the more significant the land production function. In the inverse scenario, the productive function of the land was weakened.

In

Table 9. Results of the test for mediating effects of land production function.

Variables	Model Ⅰ	Model Ⅱ	Model Ⅲ	Model Ⅳ	Model Ⅴ	Model Ⅵ
	Land Production Function	Land “Stickiness”	Land Production Function	Land “Stickiness”	Land Production Function	Land “Stickiness”
LCP	0.629 ***	0.049 ***	—	—	—	—
	(0.120)	(0.009)	—	—	—	—
Village level LCP	—	—	0.957 ***	0.068 ***	—	—
	—	—	(0.120)	(0.009)	—	—
Village level LCP rate	—	—	—	—	1.684 ***	0.128 ***
	—	—	—	—	(0.196)	(0.014)
Land production function	—	0.010 ***	—	0.010 ***	—	0.009 ***
	—	(0.001)	—	(0.001)	—	(0.001)
Individual characteristics	Y	Y	Y	Y	Y	Y
Household characteristics	Y	Y	Y	Y	Y	Y
Village characteristics	Y	Y	Y	Y	Y	Y
Observation	3915	3915	3915	3915	3915	3915

Note: Standard errors are shown in parentheses. *** denote significance at 1% level.

, model Ⅰ shows that the LCP significantly enhanced land production function, and model Ⅱ shows that the effect of the LCP and land production function on land “stickiness” was significant at 1% statistical level. The results show that the LCP had a significantly positive effect on the land “stickiness” of rural labor, and land production function played an important role in it. In other words, the LCP exerted a significant influence on land “stickiness” via its influence on land production function. The research hypothesis is verified.

In addition, the mediating effect of the land production function was examined using two proxy variables, “village level LCP” and “village level LCP rate” (Table 9, Model III–Model VI), and the results are consistent with the above. Although there were differences in the titling status of rural households within villages, and some households had not yet received farmland certificates, rural households in these areas still had higher expectations of the effect of the LCP in terms of stabilizing contractual relationships. Thus, the property rights exclusion and incentive effects of the LCP strengthened the land production function and increased the land “stickiness” of rural labor.

6. Discussion

In this section, we discuss the potential contributions, interesting results and limitations of this research.

The first discussion concerns the major contributions to the existing literature. This paper contributes to the current studies in four ways. (1) We constructed an analytical framework of land “stickiness”. Our analysis showed that the insecurity of land property rights is the internal cause of the weakening of the land “stickiness” of rural labor. We went on to explain the necessity of implementing the LCP in China from the perspective of land production function. (2) Though previous studies have examined the effect of the LCP on off-farm employment or rural out-migration [26,27,28], there is a relatively small amount of literature concerning the policy effect of the LCP on agricultural labor participation. Since agricultural labor participation is the external manifestation of land “stickiness”, our paper is the first attempt to estimate the policy effect of the LCP on the land “stickiness” of rural labor. (3) Against the background of rural social division, we studied the heterogeneity of the policy effects in different rural household types and different farm sizes. In other words, we verified whether the policy effect of the LCP has preferences in terms of household types and farm size. (4) Although there are some studies investigating the impact of the LCP on labor reallocation, the mechanism of its influence remains unclear. In order to fill in the literature gap, in this study, we explored the effect of the LCP on land “stickiness” of rural labor with theoretical analysis and empirical tests, as well as the mediating role of land production function. Additionally, in the robustness tests section, we conducted a Rosenbaum bounds sensitivity analysis to determine the influence of unobservable factors on the policy effect of the LCP. Given the inconsistency in the progress of the LCP, this paper used the village level LCP to examine the policy effect and mechanism of the LCP on the land “stickiness” of rural labor.

The following discussion concerns the interesting findings. Firstly, using the PSM method, this paper found that the LCP has a significantly positive effect on land “stickiness” of rural labor and that the LCP promotes the agricultural labor participation. A few studies have shown that the LCP can promote the off-employment and migration of rural labor. For instance, de Janvry et al. [63] found that under the Mexican Land Certificated Program from 1993 to 2006, “households obtaining certificates were subsequently 28% more likely to have a migrant member”. However, this does not contradict the conclusions of this paper. Due to the heterogeneity of the endowment of rural labor, not all rural labor will undergo the transition from agricultural production to off-farm employment. (1) During the long-term agricultural production, some farmers have formed high asset specificity, such as agricultural machinery and technology. If they abandon agricultural production, they will face high “sunk costs”, and the stronger the asset specificity, the higher the sunk costs [64]. For this reason, these farmers will continue to engage in agricultural production after the LCP. (2) Some rural labor faces the constraints of human capital, such as aging and low education, and as such, they cannot leave the agricultural field [32]. Furthermore, Li [33] found that the LCP mainly promotes the off-farm employment of rural idle labor groups.

Secondly, based on the heterogeneity analysis, we found that the policy promotion effect of the LCP on labor land “stickiness” is mainly reflected in the group of non-professional agricultural production households, medium-sized farms, and small-scale farms, while its promotional effect on professional agricultural production households and large-scale farms is not significant. The possible reasons for this are as follows. (1) Professional agricultural production households have more advanced agricultural production machinery and scientific management methods, and their demand for agricultural labor input is lower than that of non-professional households. (2) Large-scale farms overcome the limitation of farmland fragmentation and increase the input of agricultural machinery, which in turn increases the mechanization level of agricultural production and has a certain substitution effect on agricultural labor input [65]. These findings also contribute to the LCP in other developing countries with plenty of smallholders.

The final discussion is about the limitations of this study. (1) We used mixed cross-sectional data from 2016 and 2018. Due to data limitations, it was not possible to compose the panel data, and, therefore, this paper does not strictly reflect the dynamic changes of land “stickiness”, especially against the background of the COVID-19 pandemic and the global grain crisis, during which the land “stickiness” of rural labor may have changed. In future research, we aim to use the latest panel data as the basis for detailed research to further explore the effect of the LCP on land “stickiness”. (2) According to our analysis, the promotional effect of LCP on land “stickiness” is the result of the combined effect of property rights exclusion effect and incentive effect. However, due to the research data, this paper cannot respectively distinguish the extent of the property rights exclusion effect and the incentive effect on land “stickiness”. In the future, we will continue to deepen the effect mechanism of LCP. Specifically, we will estimate the extent of the impact of the property rights exclusion effect and incentive effect, respectively. (3) Due to the data limitations, we could not locate the data on the land quality in the CLDS data. We therefore used village topography as a proxy for the land quality variable. Meanwhile, the initial land allocation under the Household Contract Responsibility System (HCRS) was primarily egalitarian, according to the proximity, fertility, irrigation, and other conditions of plots. As a result, there were no significant differences in land quality between households within villages. In addition, this paper used the PSM method. The basic idea of matching was to find in a large uncertificated farmland group whose samples are similar to the certificated farmland group in all relevant pre-treatment individual characteristics, household characteristics, and village characteristics. Thus, it could correctly evaluate the pure policy effect of the LCP on farmland “stickiness”. Therefore, the issue of omitted variable can be ignored in the case of this study.

7. Conclusions and Policy Implications

7.1. Conclusions

In this paper, we started from the premise that the “stickiness” of farmland is weakening. We went on to show that there is an urgent to improve the efficiency of agricultural production by stabilizing the property rights of farmland, improving its production function, and enhancing land “stickiness”. Firstly, based on the perspective of land production function, this paper analyzed the LCP impact on land “stickiness”. Secondly, the PSM method was applied to estimate the policy effects of the LCP on the land “stickiness” of rural labor. Thirdly, heterogeneity analysis of rural household type and farm size, as well as the necessary robustness tests, were also conducted. Finally, the mediating effect model was applied to examine the mechanism of the LCP on land “stickiness” at the household and village levels.

The results of our study revealed the following: (1) The LCP had a positive promoting effect on the land “stickiness” of rural labor, which in turn increased the agricultural labor participation rate by 4.8~4.9%. (2) The heterogeneity analysis of rural household types showed that the policy promoting effect of the LCP on land “stickiness” had a great impact on non-professional households compared to professional households. In terms of farm size, the promotional effect of the LCP on medium-sized farms was the largest, followed by small-sized farms, while the promotion effect on large-sized farms was not significant. (3) The robustness results confirmed that: first, unobservable factors did not affect the estimation results of the effect of the LCP; second, the policy promotion effect was still significant after adopting the village level LCP variables, and the estimation results were robust. (4) The mediating effect on land production function was significant. The LCP enhanced the “stickiness” of rural labor through enhancing land production function. Meanwhile, the village level LCP variables also further verified the mechanism of land production function.

7.2. Policy Implications

Given the results of our analysis, the necessity of consolidating the results of the LCP, promoting the resolution of historical problems, and continuously strengthening the protection of farmers’ contracted land management rights is even more clear. We must work to expand the application of the results of the LCP, improve the management of contracted land, provide institutional guarantees for the second round of land extension, and ensure the stability of farmers’ original contracted land.

Secondly, our research verified that policy and financial supports are biased towards small and medium-sized farms. Studies have shown that the promotional effect of the LCP on non-professional households and small and medium-sized farmers is significant. Therefore, we should increase policy supports for them and accelerate the construction of a policy system to support the development of smallholders, as well as improve their agricultural production, management capacity, and production efficiency. We should encourage cooperation and interaction between smallholders and new agricultural operators, and realize the linkage between smallholders and modern agriculture in order to accelerate the modernization of agriculture and rural areas.

Thirdly, enhancing the land production function will have a major impact on efficiency. In order to achieve this, we must improve the construction of agricultural infrastructure, such as water conservation and maintenance of field roads. We must also implement the Land Consolidation Program to alleviate the fragmentation of farmland and decrease the gaps in plot quality. Finally, we must work to accelerate the construction of an agricultural information technology platform, integrate the information on agricultural production factors, and promote the efficient allocation of labor, land, and other factors to maximize the production function.