Discrete Dynamics in Nature and Society
Volume 2019 (2019), Article ID 9631701, 13 pages
https://doi.org/10.1155/2019/9631701

Research Article

Impact of Livelihood Assets on Farmland-Transferred Households’ Willingness to Urbanism and Policies Implications for Farmland Transfer: Evidence from Zhejiang, China

Heyuan You, Xiaowei Hu, Chenmeng Bie, and Deshao Zhou

School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou, China

Correspondence should be addressed to Heyuan You; youheyuan@gmail.com

Received 28 August 2018; Revised 27 January 2019; Accepted 14 February 2019; Published 10 March 2019

Academic Editor: Francisco R. Villatoro

Copyright © 2019 Heyuan You et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The rural households who transfer their farmland are more likely to migrate into urban cities in China. Understanding their willingness to urbanism can provide references for promoting China’s new-type urbanization. Using structural equation modeling (SEM), this study analyzes the effect of livelihood assets on farmland-transferred households’ willingness to urbanism in Zhejiang province in China. The results show that there is no relationship between natural capital (NC) and willingness to urbanism (UI). Statistically significant negative relationships are identified between human capital (HC), financial capital (FC), and social capital (SC), respectively, and UI. Physical capital (PC) has a statistically significant and positive relationship with UI. The findings demonstrate that the farmland transfer characteristics have different effects on farmland-transferred households’ livelihood assets. Benefit of farmland transfer (BFT) has a statistically significant positive influence on HC, FC, and SC. Meanwhile, PC, FC, and SC are positively affected by openness in farmland transfer (OFT). Based on these profiles, Chinese local governments should design more livelihood-oriented policies to help farmland-transferred households raise willingness to urbanism in the process of operation right transfer under Three Rights Separation Policy for farmland.

1. Introduction

China has the largest rural-to-urban migration in the world after over 30 years of the opening-up reform [1, 2]. But many migrants cannot really merge into urban cities because of hukou (household registration) system, taxation, land use system, and urban planning policy [3–5]. They face a number of obstacles to obtain sustainable livelihoods in urban cities [6]. Therefore migrants have various willingness to urbanism. China’s planning for a new type of urbanization seeks to raise the urbanization rate and reduce the rural-to-urban migration restriction. It suggests that there are millions of migrant workers that should become permanent migrants [7]. The migrants’ willingness to urbanism is important in the process of rural-to-urban migration. Therefore, understanding their complicated willingness to urbanism can provide essential references for promoting the new type of urbanization in China.

The migration of farmers from rural areas to urban areas is driven by increasing off-farm employment opportunities and high nonagricultural income [8, 9]. Since the adoption of the household responsibility system in the early 1980s in rural China, rural collectives have contracted farmland to peasant households for agricultural production [10]. The household responsibility system has improved the agricultural production efficiency dramatically. However, on average, the farmland size that is distributed to a peasant household is too small under household responsibility system. The peasant households are hardly to get rich by farming. Expectation for higher incomes stimulated the movement of labor from farm production to nonagricultural industries. During a long period, Chinese government forbids farmers to leave farmland uncultivated [11]. Article 37 of Land administration law of the People’s Republic of China stipulates that all units and individuals are forbidden to leave farmland uncultivated [12]. Therefore the peasant households who cannot farm by themselves transfer their farmland to other households in rural China, particularly economically developed areas. The Rural Land Contracting Law also allowed land use right to be transferred in 2002 [13]. The households who transfer their redundant farmland most likely migrate to urban areas since there are more off-farm employment opportunities in urban areas. The farmland-transferred households’ willingness to urbanism affects their migration intention and the dispose of land property rights. Understanding farmland-transferred households’ willingness to urbanism helps households to seek strategies for building livelihoods in urban areas and raise urbanization rate.

The influencing factors of farmland-transferred households’ willingness to urbanism reveal the reasons that result in differences among households’ willingness to urbanism. It is one of key issues that are fundamental to understanding farmland-transferred households’ willingness to urbanism. Many studies have analyzed the influencing factors of willingness to urbanism. Todaro [14] found that the high nonagricultural income in urban sectors and a related migration future stimulated the willingness to rural-urban migration. Huang et al. [15] revealed the social ties of migrants that are categorized into two types: interactive social ties and supportive social ties played an important role in migrants’ permanent settlement intention. Xie and Chen [16] found that rural migrants who lived in better housing conditions and enjoy housing support were more determined to increase willingness to settle in urban areas. Khoo et al. [17] examined the impact of geographic and institutional contexts in the place of destination on the migrants’ settlement intention. Zhao [18] found that urban villages generated transient urbanism of rural migrants since the urban villages have not been integrated into the urban systems in China. Mohabir et al. [19] analyzed the effects of notions of gender, age, and sense of belonging on the return migration from the urban back to the rural. From the above, the influencing factors of willingness to urbanism include micro and macrolevel factors. These influencing factors involve many aspects of livelihood. The farmland-transferred households also require a range of livelihood assets which are the essential livelihood resources facilitating the achievement of livelihoods for peoples to turn into permanent migrants in nonrural areas. Therefore a linkage should be expected between livelihood assets and farmland-transferred households’ willingness to urbanism. But few researches have studied the impact of livelihood assets on farmland-transferred households’ willingness to urbanism. Based on the sustainable livelihood approach [20], this study attempts to assess the relationship among livelihood assets, farmland-transferred households’ willingness to urbanism, and farmland transfer.

Willingness to urbanism has been operationalized as a polarized (i.e., yes or no) question in previous researches [15]. In other words, many aspects of willingness to urbanism such as migration regions and urbanization types (i.e., settle temporarily or settle permanently) are hardly analyzed in detail in previous researches. It is necessary to describe farmland-transferred households’ willingness to urbanism from all angles. Therefore the farmland-transferred households’ willingness to urbanism is difficult to be defined as one variable which can be observed quantitatively. Meanwhile, according to the sustainable livelihood approach, the livelihood assets also should be classified into several different assets to understand the specific aspects of farmland-transferred households’ strengths [20, 21]. Therefore the structural equation modeling (SEM) which is widely used in multivariate analysis is selected in this study [22].

Using the case of Zhejiang, one of the economically developed provinces in China, this article aims to (1) identify the variables for livelihood assets, farmland-transferred households’ willingness to urbanism, and farmland transfer; (2) quantify the relationships between livelihood assets and farmland-transferred households’ willingness to urbanism; (3) analyze the characteristics of farmland transfer on the households’ livelihood assets; and (4) provide suitable policies to improve farmland- transferred households’ livelihood assets to raise their willingness to urbanism.

2. Hypotheses

Department for international development (DFID) puts forward a livelihood framework to help understand the livelihoods [20, 23]. Chambers and Conway [21] defined assets as a collection of stores, resources, claims, and access. DFID identifies five different capital endowments to compose the livelihood assets, including natural capital, human capital, physical capital, financial capital, and social capital [20]. Erenstein et al. [24] revealed that poverty incidence was associated with the livelihood asset in Indo-Gangetic Plains in India. Belay and Bewket [25] examined the influence of ownership of livelihood assets in farmers’ decisions to use cattle manure as land improvement technology in northwestern highlands of Ethiopia. Li et al. [26] found that household livelihood assets had an important impact on the households’ willingness to select the economic compensation pattern for cultivated land protection. According to the livelihood framework, the livelihood assets are classified into natural capital, human capital, physical capital, financial capital, and social capital.

Specifically, the variables of farmland transfer comprise benefit of farmland transfer and openness in farmland transfer. The benefit of farmland transfer reveals the characteristics of farmland-transferred households’ income from farmland transfer. The openness in farmland transfer reveals transferee and farmland transfer tenure in the process of farmland transfer.

An investment regret mitigation effect has existed in the process of farmland transfer. The investment regret mitigation effect results when greater transfer rights make households more willing to make investments [27]. It means that farmland transfer can increase the agricultural investment in the farmland. Heerink et al. [28] found chemical fertilizers (phosphorus and potassium) were used in larger quantities on rented plots in Jiangxi province. Consequently, farmland transfer can improve farmland- transferred households’ natural capital such as farmland quality. Based on these observations, the following is suggested:

H1: The benefit of farmland transfer (BFT) is positively related to the natural capital (NC).

H2: The openness in farmland transfer (OFT) is positively related to the natural capital (NC).

There is a significant direct effect of farmland transfer on long-term capital accumulation of household including years of education completed in India [29]. But the migrant farmers who transfer farmland suffer the conflicting impacts on their health. For one thing, the farmers have more opportunities to access to improved health care infrastructure in cities. For another, they are exposed to health risks including work stress and pollution [30]. Based on these observations, the following is suggested:

H3: The benefit of farmland transfer (BFT) is positively related to the human capital (HC).

H4: The openness in farmland transfer (OFT) is positively related to the human capital (HC).

In rural China, the rural residential buildings’ quantity and quality are largely determined by the economic status of the households [31]. And the farmland transfer has significant impact on the economic status of the farmland-transferred households [32]. Therefore it can be assumed that there is a relationship between farmland transfer and physical capital. Based on these observations, the following is suggested:

H5: The benefit of farmland transfer (BFT) is positively related to the physical capital (PC).

H6: The openness in farmland transfer (OFT) is positively related to the physical capital (PC).

The agricultural income of household decreases since the household transfers their farmland [33]. The nonagricultural income of household can increase because of growing off-farm employment opportunities for labour force of household [34]. And the nonagricultural income plays a key role in the financial capital of most farmland-transferred households. Based on these observations, the following is suggested:

H7: The benefit of farmland transfer (BFT) is positively related to the financial capital (FC).

H8: The openness in farmland transfer (OFT) is positively related to the financial capital (FC).

The farmland transfer also has relationship with social capital since the absence of a formal system that can guarantee permanent farmland property right for households, and social capital can substitute for the formal system by using a nonmarket solution [35]. Consequently, social capital can be increased in the process of farmland transfer since the nonmarket solutions are applied frequently. Based on these observations, the following is suggested:

H9: The benefit of farmland transfer (BFT) is positively related to the social capital (SC).

H10: The openness in farmland transfer (OFT) is positively related to the social capital (SC).

There is a relationship between natural capital and the willingness to urbanism. Due to the existing arrangement of land management, the households have not been willing to migrate permanently to urban areas [1]. The following hypothesis is offered:

H11: The natural capital (NC) is negatively related to the willingness to urbanism (UI).

The willingness to urbanism also has significant relationship with households’ human capital. The human capital determines what kinds of job opportunities are available for the households [36]. These job opportunities may have different impacts on the farmland-transferred households’ willingness to urbanism. The farmland-transferred households can get employment in rural areas or get employment in urban areas. The following hypothesis is offered:

H12: The human capital (HC) is negatively related to the willingness to urbanism (UI).

The greater quantity and higher quality of households’ residential buildings can reduce the willingness of households for settling in cities and towns permanently. Transportation is also an insignificant determinant of migration [37]. The following hypothesis is offered:

H13: The physical capital (PC) is negatively related to the willingness to urbanism (UI).

There are the inconsistent results found in the literature about the effect of the financial capital on the willingness to urbanism. In the household sample of one study [38], most poor famers do not migrate. But in the household sample of the other study, the poor famers tend to migrate due to local few off-farm work opportunities (Rozelle et al., 2005). The following hypothesis is offered:

H14: The financial capital (FC) is negatively related to the willingness to urbanism (UI).

The social capital plays an important role on the migration decisions. Zhao [39] found that the migrant networks have impact on the process of migration. Experienced migrants have a positively significant effect on subsequent migration, but return migrants do not. The following hypothesis is offered:

H15: The social capital (SC) is negatively related to the willingness to urbanism (UI).

3. Data and Methods

3.1. Data Collection

Zhejiang province, situated to the south of Taihu Lake, is located in China’s southeastern coast. Zhejiang is one of the most economically developed provinces in China, and the peasant households in Zhejiang tend to transfer their farmland since they have many opportunities for higher income nonfarm employment. It is estimated that approximately one-third of farmland is being transferred in Zhejiang [40]. Data for empirical study came from a questionnaire survey of 318 peasant households in Zhejiang province in 2014. The surveyed locations included Xiaoshan district in Hangzhou, Nanhu district in Jiaxing, and Jiangshan city in Quzhou (Figure 1). The selected villages in surveyed locations were determined by local government staff. The interviewees were household heads or key members of households. The questionnaire interviewers visited 360 households, but 20 interviewees refused to be interviewed. Since the 22 questionnaires were eligible for data analysis, the final sample was comprised of 98 farmland-transferred households in 10 villages located in Xiaoshan district, 110 farmland-transferred households in 9 villages located in Nanhu district, and 110 farmland-transferred households in 10 villages located in Jiangshan city.

Figure 1: Map of surveyed locations.

3.2. Selected Variables

The purpose of this study is to identify the interrelationships between livelihood assets and farmland-transferred households’ willingness to urbanism when the households transfer their farmland. Therefore the influences of farmland transfer on livelihood assets and willingness to urbanism were also investigated in SEM. There are 8 latent variables in the whole model. Specifically, the latent variables of farmland transfer comprise benefit of farmland transfer and openness in farmland transfer. The latent variable of farmland-transferred households’ willingness to urbanism is willingness to urbanism. The latent variables of livelihood assets consist of natural capital, human capital, physical capital, financial capital, and social capital. Those latent variables are measured by observed indicators (Table 1).

Table 1: Selected variables used in SEM.

Latent variables
Manifest variables
Definition

Farmland transfer

    Benefit of farmland transfer (BFT)
Transfer income (TI)
Income of farmland transfer

Transfer area (TA)
Area of transferred farmland

    Openness in farmland transfer (OFT)
Transferee (TF)
Transferee of farmland

Farmland transfer tenure (FT)
The period of time that farmland is used by transferee

Willingness to urbanism

    Willingness to urbanism (UI)
Urbanization type (UT)
Urbanization type of household

Migration region (MR)
The region that the household migrates in

Livelihood assets

    Natural capital (NC)
Farmland area (NC1)
Area of farmland belonged to the household

High quality farmland (NC2)
Area of high quality farmland belonged to the household

    Human capital (HC)
Education level (HC1)
Years of education completed of the head of household

Labour force (HC2)
Number of labour force in the household

Health status (HC3)
Health status of the household head

    Physical capital (PC)
Residential quantity (PC1)
Number of residential buildings belonged to the household

Residential quality (PC2)
Residential quality of household

Public transportation (PC3)
Distance between residential house and bus passenger station

    Financial capital (FC)
Non-agricultural income (FC1)
Non-agricultural income of household last year

Capacity to borrow (FC2)
Difficulty level of borrowing money

    Social capital (SC)
Relationship with friends (SC1)
Relationship between household and friends

Relationship with relatives (SC2)
Relationship between household and relatives

Relationship with village cadres (SC3)
Relationship between household and village cadres

Benefit of farmland transfer is measured by the factors that reveal the characteristics of farmland-transferred households’ income from farmland transfer. Two manifest variables including transfer income and transfer area are selected [41]. Openness in farmland transfer, one latent variable, is presented in terms of two manifest variables: transferee and farmland transfer tenure. Willingness to urbanism is measured by urbanization type and migration region. Farmland-transferred households were asked to perceive which type they select to settle in the migration region, how to deal with their land contractual right, whether they are willing to migrate to nonrural areas, and where they want to migrate. According to the literature, natural capital is described by area of farmland belonged to the household and area of high quality farmland belonged to the household [42]. Human capital is measured by years of education completed of the head of household, number of labour force in the household, and health status of the household head [43]. Physical capital is measured by number of residential buildings belonging to the household, residential quality, and convenience of public transportation [44]. Financial capital is measured by nonagricultural income of household last year and difficulty level of borrowing money [20]. Social capital is measured by relationships among household, friends, relatives, and village cadres [35]. The relationships are measured by three items on 5-point scales that ranged from 1 (not at all intimate) to 5 (not at all intimate). Statistical description of manifest variables is shown in Table 2.

Table 2: Statistical description of manifest variables.

Manifest variable
Unit or variable assignment
Min.
Max.
Mean
Std.dev.

TI
10⁴Yuan
1.000
5.000
2.097
0.936

TA
Mu
0.200
6.500
2.478
1.387

TF
relative=1, friend=2, village collective=3, migrant worker=4, enterprise=5
1.000
5.000
2.906
1.340

FT
flexible tenure=1, <1 year =2, 1 year =3, 2-5 years=4, > 5years=5;
1.000
5.000
2.770
1.518

UT
unwilling to migrate =1; willing to migrate, settle temporarily =2; willing to migrate, settle permanently and hold the land contractual right=3; willing to migrate, settle permanently and give up land contractual right=4
1.000
5.000
2.280
0.770

MR
village or town=1, country town=2, prefectural-level city=3, provincial capital=4, Beijing/Shanghai/Guangzhou=5
1.000
5.000
2.343
1.137

NC1
Mu
0.200
13.000
3.456
2.366

NC2
Mu
0.000
13.000
2.792
2.401

HC1
0-6 years=1; 7-9 years =2, 9-11 years =3, 11-15 years =14, >15 years =5
1.000
5.000
3.079
1.128

HC2
Person
0.000
5.000
2.484
0.969

HC3
very poor=1, poor=2, fair=3, good=4, very good=5
1.000
5.000
3.333
1.093

PC1
House
0.000
5.000
1.770
0.934

PC2
very poor=1, poor=2, fair=3, good=4, very good=5
1.000
5.000
2.739
1.105

PC3
Km
1.000
10.000
2.596
1.411

FC1
0-50000 Yuan=1, 50001-100000 Yuan=2, 100001-150000 Yuan=3, 150001-200000 Yuan=4, >200000 Yuan=5
1.000
5.000
2.447
1.096

FC2
very difficult=1, difficult =2, fair=3, easy=4, very easy=5
1.000
5.000
2.660
0.869

SC1
not at all intimate =1, not so intimate =2, somewhat intimate=3, very intimate=4, extremely intimate=5
1.000
5.000
2.786
1.148

SC2
not at all intimate =1, not so intimate =2, somewhat intimate=3, very intimate=4, extremely intimate=5
1.000
5.000
2.994
1.014

SC3
not at all intimate =1, not so intimate =2, somewhat intimate=3, very intimate=4, extremely intimate=5
1.000
5.000
3.116
1.024

Abbreviations: TI, Transfer income; TA, Transfer area; TF, Transferee; FT, Farmland transfer tenure; UT, Urbanization type; MR, Migration region; NC1, Farmland area; NC2, High quality farmland; HC1, Education level; HC2, Labour force; HC3, Health status; PC1, Residential quantity; PC2, Residential quality; PC3, Public transportation; FC1, Nonagricultural income; FC2, Capacity to borrow; SC1, Relationship with friends; SC2, Relationship with relatives; SC3, Relationship with village cadres.

3.3. Estimation Procedures

SEM is very flexible, because it can be viewed as a sort of a combination of factor analysis with structural models (e.g., regression analysis or path analysis) [45]. SEM allows for complex relationships between one or more independent variables and one or more dependent variables. The variables are divided into manifest variables and latent variables. A manifest variable is defined as a variable that is directly observed and measured, and a latent variable is defined as a variable that cannot be directly observed and measured but is rather inferred from manifest variables by using a mathematical model. SEM allows for ease of interpretation of latent variables [46]. Voon et al. [47] analyzed the determinants of willingness to purchase organic food by using SEM. Le Dang et al. [48] assessed the farmers’ adaptation intention to climate change in the Mekong Delta in Vietnam based on a SEM. Masud et al. [49] found that status of residents’ livelihoods has a positive effect on environmental conservation attitudes in Tioman Island Marine Park area by applying SEM. Therefore the SEM is selected to assess the relationship among livelihood assets, farmland-transferred households’ willingness to urbanism, and farmland transfer.

A two-step procedure for SEM is proposed by Anderson and Gerbing [50]. The first step concerns the measurement model validation and aims to discover the validity of the manifest variables in relation to the latent variable. Goodness of fit testing is conducted in the first step. Goodness of fit allows the adequacy of the tested SEM to be evaluated. Specifically, goodness of fit reflects the extent to which the tested SEM fits the current sample under investigation. Multiple goodness of fit indices have been put forward to assess the goodness of fit of the measurement model since there are no concrete rules about which goodness of fit index is best. The second step evaluates the extent to which the hypothesized relationships between the latent variables are supported within the current sample.

3.4. The Proposed SEM

The hypothesized structural equation model based on the selected variables and the hypothesized relationships among latent variables between is presented in Figure 2.

Figure 2: Hypothesized structural equation model. Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; TI, Transfer income; TA, Transfer area; TF, Transferee; FT, Farmland transfer tenure; UI, Willingness to urbanism; UT, Urbanization type; MR, Migration region; NC, Natural capital; NC1, Farmland area; NC2, High quality farmland; HC, Human capital; HC1, Education level; HC2, Labour force; HC3, Health status; PC, Physical capital; PC1, Residential quantity; PC2, Residential quality; PC3, Public transportation; FC, Financial capital; FC1, Nonagricultural income; FC2, Capacity to borrow; SC, Social capital; SC1, Relationship with friends; SC2, Relationship with relatives; SC3, Relationship with village cadres.

To test the hypothesized structural equation model, AMOS 21.0 was employed. And the maximum likelihood estimation was used to examine the proposed hypothetical model. Multiple goodness of fit indices revealed that the hypothesized structural equation model did not adequately fit to the data. Given the complexity of SEM, there are no common methods to improve the fit of a proposed model which is poor [51]. In this study, the process for improving model fit includes two steps. The variables whose measurement error variances are negative are deleted in the first step [52]. Then the paths whose P values are higher than 0.1 are deleted in the second step [53]. The revised SEM is presented in Figure 3. It resulted in a moderate fit to the data (x²/df = 2.543, GFI = 0.906, RMSEA = 0.070, PCFI = 0.714, PNFI = 0.69, PGFI=0.647) (Table 3).

Table 3: Model fitness index for the hypothesized and alternative models.

Index
Abbreviation
Value
Criteria (acceptable) value

Chi-square/df
x²/df
2.543
<5.00

Goodness-of-fit index
GFI
0.906
>0.90

Root-mean-square error of approximation
RMSEA
0.070
<0.08

Parsimony adjustment to the CFI
PCFI
0.714
>0.50

Parsimony adjustment to the NFI
PNFI
0.669
>0.50

Parsimony goodness of fit index
PGFI
0.647
>0.50

Figure 3: Final structural equation model. Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; TI, Transfer income; TA, Transfer area; TF, Transferee; FT, Farmland transfer tenure; UI, Willingness to urbanism; UT, Urbanization type; MR, Migration region; NC, Natural capital; NC1, Farmland area; NC2, High quality farmland; HC, Human capital; HC1, Education level; HC2, Labour force; HC3, Health status; PC, Physical capital; PC1, Residential quantity; PC2, Residential quality; PC3, Public transportation; FC, Financial capital; FC1, Nonagricultural income; FC2, Capacity to borrow; SC, Social capital; SC1, Relationship with friends; SC2, Relationship with relatives; SC3, Relationship with village cadres.

The reliability of the manifest variables in relation to the latent variable was tested (Table 4). A manifest variable can be considered as a defining part of that latent variable when the absolute value of the loading of the manifest variable is greater than 0.3. Therefore these latent variables are treated as the relevant parts for the latent variables when the SEM is established. Cronbach’s alpha (Cronbach’s α) is one important measure of internal consistency. Cronbach’s α value that is greater than 0.5 is usually acceptable. Cronbach’s α values are greater than 0.5 in the test. Therefore the manifest variables in relation to the latent variable have high internal consistency. Average variance extracted (AVE) is a common measure of the amount of variance. AVE value that is above 0.5 is usually acceptable. AVE values are above 0.5 in the test. Therefore the manifest variables in relation to the latent variable have high discriminant validity. Additionally, construct reliability (CR) is a less biased estimate of reliability than Cronbach’s α. CR value that is greater than 0.6 is usually acceptable. CR values are greater than 0.6 in the test. Therefore the manifest variables in relation to the latent variable have high reliability.

Table 4: Reliability of the manifest variables.

Latent variables
Manifest variables
Loadings
Cronbach’s α
AVE
CR

Farmland transfer

    BFT
TI
0.88
0.630
0.538
0.690

TA
0.55

    OFT
TF
0.76
0.759
0.617
0.763

FT
0.81

Willingness to urbanism

    UI
UT
0.83
0.682
0.569
0.723

MR
0.67

Livelihood assets

    HC
HC1
0.99
0.724
0.561
0.768

HC2
0.33

HC3
0.77

    PC
PC1
0.40
0.702
0.511
0.739

PC2
0.94

PC3
0.70

    FC
FC1
0.90
0.638
0.545
0.700

FC2
0.53

    SC
SC1
0.65
0.792
0.579
0.803

SC2
0.79

SC3
0.83

Cronbach’s α is Cronbach’s alpha. AVE is average variance extracted. CR is construct reliability.

Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; TI, Transfer income; TA, Transfer area; TF, Transferee; FT, Farmland transfer tenure; UI, Willingness to urbanism; UT, Urbanization type; MR, Migration region; NC, Natural capital; NC1, Farmland area; NC2, High quality farmland; HC, Human capital; HC1, Education level; HC2, Labour force; HC3, Health status; PC, Physical capital; PC1, Residential quantity; PC2, Residential quality; PC3, Public transportation; FC, Financial capital; FC1, Nonagricultural income; FC2, Capacity to borrow; SC, Social capital; SC1, Relationship with friends; SC2, Relationship with relatives; SC3, Relationship with village cadres.

The discriminant validity is measured as the square root of AVE compared to the construct correlations [54, 55]. The correlation matrix and discriminant validity assessment are shown in Table 5. Values in diagonal are the square roots of AVE in Table 5, and the other values in corresponding rows and columns are the construct correlations. The square root of AVE of a latent variable is greater than the construct correlations of that latent variable with other latent variables. Overall, the latent variables exhibited high discriminant validity in final SEM.

Table 5: Correlation matrix and discriminant validity assessment.

BFT
OFT
UI
HC
PC
FC
SC

BFT
0.733

OFT
0.000
0.785

UI
-0.117
-0.025
0.754

HC
0.260
0.000
-0.166
0.749

PC
0.000
0.130
0.188
0.000
0.715

FC
0.242
0.175
-0.172
0.063
0.023
0.738

SC
0.358
0.200
-0.146
0.093
0.026
0.122
0.761

Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; UI, Willingness to urbanism; NC, Natural capital; HC, Human capital; PC, Physical capital; FC, Financial capital; SC, Social capital.

4. Results

Figure 4 displays the outcomes of the final structural equation model with standardised parameters. Table 6 displays the estimated parameters. There is no relationship between NC and UI. PC has a statistically significant and positive relationship with UI (path coefficient=0.19). And there are statistically significant negative relationships between HC, FC, and SC, respectively, and UI (path coefficients=-0.15, -0.15, and -0.12, respectively). The SEM also reveals that the characteristics of farmland transfer have different effects on farmland-transferred households’ livelihood assets. There is no relationship between NC, PC, and BFT. BFT has statistically significantly positive influence on HC, FC, and SC (path coefficients=0.26, 0.24, and 0.36, respectively). The relationships between NC and HC, respectively, and OFT are also insignificant. PC, FC, and SC are positively correlated with OFT (path coefficients=0.13, 0.18, and 0.20, respectively). Therefore hypotheses including H1, H2, H4, H5, H11, and H13 are not supported. Hypotheses including H3, H6, H7, H8, H9, H10, H12, H14, and H15 are supported.

Table 6: Parameter estimated.

Hypothesis
Path
Estimate
Estimate
S.E.
C.R.
P

(Standardized)
(Unstandardized)

H1
BFT→NC
-
-
-
-
-

H2
OFT→NC
-
-
-
-
-

H3
BFT→HC
0.26
0.429
0.119
3.607
0.000

H4
OFT→HC
-
-
-
-
-

H5
BFT→PC
-
-
-
-
-

H6
OFT→PC
0.13
0.105
0.058
1.833
0.067

H7
BFT→FC
0.24
0.464
0.137
3.395
0.000

H8
OFT→FC
0.18
0.141
0.058
2.405
0.016

H9
BFT→SC
0.36
0.519
0.113
4.581
0.000

H10
OFT→SC
0.20
0.122
0.045
2.717
0.007

H11
NC→UI
-
-
-
-
-

H12
HC→UI
-0.15
-0.11
0.049
-2.262
0.024

H13
PC→UI
0.19
0.126
0.044
2.877
0.004

H14
FC→UI
-0.15
-0.099
0.053
-1.867
0.062

H15
SC→UI
-0.12
-0.102
0.061
-1.682
0.093

P<0.1, P<0.05, P<0.01.

S.E. is standard error, and C.R. is composite reliability.

Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; UI, Willingness to urbanism; NC, Natural capital; HC, Human capital; PC, Physical capital; FC, Financial capital; SC, Social capital.

Figure 4: Structural equation model showing the relationships between farmland transfer, livelihood assets, and willingness to urbanism. Abbreviations: BFT, Benefit of farmland transfer; OFT, Openness in farmland transfer; TI, Transfer income; TA, Transfer area; TF, Transferee; FT, Farmland transfer tenure; UI, Willingness to urbanism; UT, Urbanization type; MR, Migration region; HC, Human capital; HC1, Education level; HC2, Labour force; HC3, Health status; PC, Physical capital; PC1, Residential quantity; PC2, Residential quality; PC3, Public transportation; FC, Financial capital; FC1, Nonagricultural income; FC2, Capacity to borrow; SC, Social capital; SC1, Relationship with friends; SC2, Relationship with relatives; SC3, Relationship with village cadres.

5. Discussion

5.1. Effect of Livelihood Assets on Willingness to Urbanism

The results of SEM indicate that the farmland-transferred households are more likely to enhance willingness to urbanism when they obtain more physical capital. But the willingness to urbanism is reduced when they get more human capital, financial capital, and social capital. More specifically, the farmland-transferred households tend to migrate from rural areas to big cities and settle down permanently in nonrural areas when they have more physical capital. More quantity and higher quality of residential buildings may lead to households settling down in nonrural areas permanently since farmland-transferred households who have more quantity and higher quality of residential buildings may have residential buildings in towns and cities. And the convenient public transportation helps farmers travel to and from rural areas and urban areas. In contrast, when the farmland-transferred households have higher education level, increased labour force, or improved health status, they decrease their willingness to urbanism. The main reason is that the households with higher human capital have more opportunities to work in the rural areas in developed provinces. And rural areas in developed provinces also can provide opportunities for households with higher human capital to improve livelihoods. The findings are consistent with the conclusions from the reported research [56]. The farmland-transferred households who have high nonagricultural income and strong capacity to borrow decrease their willingness to urbanism. A possible reason is that although households gain more nonagricultural incomes, it does not improve the access of public services that are provided by governments, such as education and public health service. Therefore households choose to settle down in cities temporarily or migrate to a nearby nonrural area. These findings are consistent with what has been proposed in previous research [57]. Meanwhile, the farmland-transferred households also decrease their willingness to urbanism when they strengthen the relationship between households and their friends, relatives, and village cadres. This implies that the improved social capital raises the households’ willingness of living in the rural areas since the peasant households are sentimentally attached to their native land in China [58].

Results of SEM reveal that increasing benefit of farmland transfer can help farmland-transferred households increase human capital, financial capital, and social capital. The increasing benefit of farmland transfer induces the households to transfer more farmland, and they have more time and income to improve their education level and health status. Income of farmland transfer is the part of the nonagricultural income of household. And capacity to borrow money is strengthened when the nonagricultural income of household increases. Therefore the increasing benefit of farmland transfer helps the households get more financial capital. Meanwhile, the increasing benefit of farmland transfer implies that more and more farmland transfer occurred in the rural land market. The households’ friends, relatives, or village cadres play a key role in the process of farmland transfer. Therefore the increasing benefit of farmland transfer boosts social capital. Results of SEM also reveal that increasing openness in farmland transfer can improve the households’ physical capital, financial capital, and social capital. The increasing benefit of farmland transfer increases the households’ physical capital. The main reason is that farmland-transferred households pursue the culture of towns and cities, and they particularly emphasize the residential quality in rural areas. The increasing openness in farmland transfer has identical effect on the households’ financial capital. Generally speaking, farmland-transferred households tend to ask for higher transfer income when the farmland is transferred to the unfamiliar transferee of farmland in a long period of time. The increasing openness in farmland transfer implies unfamiliar transferee of farmland and long period of time that farmland is used by transferee. In such farmland transfer process, the households are inclined to strengthen their social relationships.

5.2. Policies Implications for Farmland Transfer

The policies of farmland transfer in China now ask for paying more attention to strengthening livelihood sustainability of farmland-transferred households. Raising the farmland-transferred households’ willingness to urbanism can help farmland-transferred households promote diversity of livelihood activities by migrating into urban cities [59]. In order to promote large-scaled farmland transfer, Chinese government adopt Three Rights Separation Policy to separate the households’ farmland contractual operation right into a contractual right (right of disposal) and an operation right [60]. But the changes of households’ livelihood assets that can raise households’ willingness to urbanism are not directly beneficial from large-scaled farmland transfer under Three Rights Separation Policy. Chinese local governments should design more livelihood-oriented policies to help farmland-transferred households raise willingness to urbanism in the process of operation right transfer. For example, the governments ought to provide more support for increasing farmland-transferred households’ residential buildings in towns and cities. At present, households ought to be allowed to sell their homesteads in rural areas to buy residential buildings in towns and cities [61]. But the households’ livelihood assets which are closely connected with households’ rural living should be lessened since these livelihood assets can reduce the households’ willingness for settling down permanently in nonrural areas. For example, the local governments should encourage households to transfer farmland in the rural land market and provide more training opportunities and high-quality public health for migrators to compensate transferred farmland. When farmland-transferred households migrate to urban areas, these policies can help them obtain sustainable livelihoods in urban areas. Meanwhile, the governments should consider the necessity of livelihood assets for households’ urbanization in rural land system reform [62]. Specifically, the access to livelihood assets which benefits households to settle down in nonrural areas permanently should be emphasized.

6. Conclusion

The present paper applies the SEM to examine the effect of livelihood assets on farmland-transferred households’ willingness to urbanism in Zhejiang province of China. It presents the selected variables which include latent variables and manifest variables to identify livelihood assets, willingness to urbanism, and characteristics of farmland transfer. And the revised SEM that fits to the data of household sample well is proposed in this study.

Results show that the farmland-transferred households are more likely to enhance willingness to urbanism when they get more physical capital. But the willingness to urbanism is reduced when they get more human capital, financial capital, and social capital. The findings demonstrate that the characteristics of farmland transfer have different effects on farmland-transferred households’ livelihood assets. Increasing benefit of farmland transfer can help farmland-transferred households increase human capital, financial capital, and social capital. Meanwhile, increasing openness in farmland transfer can improve the households’ physical capital, financial capital, and social capital. Finally, the governments should provide more livelihood-oriented policies in farmland transfer process to improve farmland-transferred households’ livelihood assets to raise their willingness to urbanism.

Based on the results and discussion, the current study has several implications for urban policies. Increasing key livelihood assets from the perspective of sustainable urbanization is a potential solution to raise peasant households’ willingness to urbanism. Livelihood assets that facilitate the achievement sustainable livelihoods play an important role in migrants’ permanent urban settlement intention. Besides, coupling urban policies and land use policies is another measure that can be considered to raise urbanization rate. Peasant households worried about the disposition of their contracted land. Land use policies should pay more attention to effects of contracted land disposition on peasant households’ livelihood assets and their willingness to urbanism.

Data Availability

All data included in this study are available upon request by contact with the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research received financial support from the Zhi-Jiang Young Scholar Program of Social Science of Zhejiang Province (16ZJQN026YB), the National Natural Science Foundation of China (71874151, 71403235), the Humanity and Social Science Foundation of Ministry of Education of China (18YJA630134), and the Zhejiang Provincial Natural Science Foundation of China (LY18G030031).

References

Y. Zhao, “Leaving the countryside: rural-to-urban migration decisions in China,” American Economic Review, vol. 89, no. 2, pp. 281–286, 1999.

H. You, “Impact of urbanization on pollution-related agricultural input intensity in Hubei, China,” Ecological Indicators, vol. 62, pp. 249–258, 2016.

K. W. Chan, “China's urbanization 2020: a new blueprint and direction,” Eurasian Geography and Economics, vol. 55, no. 1, pp. 1–9, 2014.

H. You, “Quantifying the coordinated degree of urbanization in Shanghai, China,” Quality & Quantity, vol. 50, no. 3, pp. 1273–1283, 2016.

S. Su, J. Pi, C. Wan, H. Li, R. Xiao, and B. Li, “Categorizing social vulnerability patterns in Chinese coastal cities,” Ocean & Coastal Management, vol. 116, pp. 1–8, 2015.

H. You, “Characterizing the inequalities in urban public green space provision in Shenzhen, China,” Habitat International, vol. 56, pp. 176–180, 2016.

D. Ou and A. Kondo, “In search of a better life: the occupational attainment of rural and urban migrants in China,” Chinese Sociological Review, vol. 46, no. 1, pp. 25–59, 2013.

W. A. Lewis, “Economic development with unlimited supplies of labour,” The Manchester School, vol. 22, no. 2, pp. 139–191, 1954.

S. Su, X. Zhou, C. Wan, Y. Li, and W. Kong, “Land use changes to cash crop plantations: crop types, multilevel determinants and policy implications,” Land Use Policy, vol. 50, pp. 379–389, 2016.

J. Y. Lin, “The household responsibility system reform in China: a peasant's institutional choice,” American Journal of Agricultural Economics, vol. 69, no. 2, pp. 410–415, 1987.

H. Xie, P. Wang, and G. Yao, “Exploring the dynamic mechanisms of farmland abandonment based on a spatially explicit economic model for environmental sustainability: a case study in Jiangxi Province, China,” Sustainability, vol. 6, no. 3, pp. 1260–1282, 2014.

H. You, X. Hu, and Y. Wu, “Farmland use intensity changes in response to rural transition in Zhejiang province, China,” Land Use Policy, vol. 79, pp. 350–361, 2018.

K. Deininger and S. Jin, “Securing property rights in transition: lessons from implementation of China's rural land contracting law,” Journal of Economic Behavior & Organization, vol. 70, no. 1, pp. 22–38, 2009.

M. P. Todaro, “A model of labor migration and urban unemployment in less developed countries,” American Economic Review, vol. 59, no. 1, pp. 138–148, 1969.

X. Huang, Y. Liu, D. Xue, Z. Li, and Z. Shi, “The effects of social ties on rural-urban migrants' intention to settle in cities in China,” Cities, vol. 83, pp. 203–212, 2018.

S. Xie and J. Chen, “Beyond homeownership: housing conditions, housing support and rural migrant urban settlement intentions in China,” Cities, vol. 78, pp. 76–86, 2018.

S.-E. Khoo, G. Hugo, and P. Mcdonald, “Which skilled temporary migrants become permanent residents and why?” International Migration Review, vol. 42, no. 1, pp. 193–226, 2008.

Y. Zhao, “Rural migrants in urban China: enclaves and transient urbanism,” The China Quarterly, vol. 220, pp. 1153–1155, 2014.

N. Mohabir, Y. Jiang, and R. Ma, “Chinese floating migrants: rural-urban migrant labourers’ intentions to stay or return,” Habitat International, vol. 60, pp. 101–110, 2017.

I. Scoones, “Sustainable rural livelihoods: a framework for analysis,” Working Paper 72, Institute for Development Studies, Brighton, UK, 1998.

R. Chambers and G. R. Conway, “Sustainable rural livelihoods: practical concepts for the 21st century,” IDS Discussion Paper 296, Institute of Development Studies, Brighton, UK, 1992.

T. Hu and C. Xie, “Competition, innovation, risk-taking, and profitability in the chinese banking sector: an empirical analysis based on structural equation modeling,” Discrete Dynamics in Nature and Society, vol. 2016, Article ID 3695379, 10 pages, 2016.

H. You and X. Zhang, “Sustainable livelihoods and rural sustainability in China: ecologically secure, economically efficient or socially equitable?” Resources, Conservation & Recycling, vol. 120, pp. 1–13, 2017.

O. Erenstein, J. Hellin, and P. Chandna, “Poverty mapping based on livelihood assets: a meso-level application in the Indo-Gangetic Plains, India,” Applied Geography, vol. 30, no. 1, pp. 112–125, 2010.

M. Belay and W. Bewket, “Farmers' livelihood assets and adoption of sustainable land management practices in north-western highlands of Ethiopia,” International Journal of Environmental Studies, vol. 70, no. 2, pp. 284–301, 2013.

G. Li, C. Fang, D. Qiu, and L. Wang, “Impact of farmer households' livelihood assets on their options of economic compensation patterns for cultivated land protection,” Journal of Geographical Sciences, vol. 24, no. 2, pp. 331–348, 2014.

M. R. Carter and Y. Yao, “Specialization without regret: transfer rights, agricultural productivity and investment in an industrializing economy,” World Bank Policy Research Working Paper, 1998.

N. Heerink, F. Qu, M. Kuiper, X. Shi, and S. Tan, “Policy reforms, rice production and sustainable land use in China: a macro-micro analysis,” Agricultural Systems, vol. 94, no. 3, pp. 784–800, 2007.

K. Deininger, S. Jin, and H. K. Nagarajan, “Land reforms, poverty reduction, and economic growth: evidence from India,” The Journal of Development Studies, vol. 45, no. 4, pp. 496–521, 2009.

P. Gong, S. Liang, E. J. Carlton et al., “Urbanisation and health in China,” The Lancet, vol. 379, no. 9818, pp. 843–852, 2012.

M. Hu, H. Bergsdal, E. Van Der Voet, G. Huppes, and D. B. Muller, “Dynamics of urban and rural housing stocks in China,” Building Research and Information, vol. 38, no. 3, pp. 301–317, 2010.

J. K.-S. Kung, “Off-farm labor markets and the emergence of land rental markets in rural China,” Journal of Comparitive Economics, vol. 30, no. 2, pp. 395–414, 2002.

S. P. S. Ho and G. C. S. Lin, “Emerging land markets in rural and urban China: policies and practices,” China Quarterly, vol. 175, pp. 681–707, 2003.

R. Tan, F. Qu, N. Heerink, and E. Mettepenningen, “Rural to urban land conversion in China - How large is the over-conversion and what are its welfare implications?” China Economic Review, vol. 22, no. 4, pp. 474–484, 2011.

E. G. Katz, “Social capital and natural capital: a comparative analysis of land tenure and natural resource management in Guatemala,” Land Economics, vol. 76, no. 1, pp. 114–132, 2000.

A. de Haan, “Livelihoods and poverty: the role of migration ‐ a critical review of the migration literature,” The Journal of Development Studies, vol. 36, no. 2, pp. 1–47, 1999.

S. Rozelle, L. Guo, M. Shen, A. Hughart, and J. Giles, “Leaving China's farms: survey results of new paths and remaining hurdles to rural migration,” China Quarterly, vol. 158, pp. 367–393, 1999.

Y. Du, A. Park, and S. Wang, “Migration and rural poverty in China,” Journal of Comparative Economics, vol. 33, no. 4, pp. 688–709, 2005.

Y. Zhao, “The role of migrant networks in labor migration: the case of China,” Contemporary Economic Policy, vol. 21, no. 4, pp. 500–511, 2003.

S. Rozelle, J. Huang, and L. Zhang, “Emerging markets, evolving institutions, and the new opportunities for growth in China's rural economy,” China Economic Review, vol. 13, no. 4, pp. 345–353, 2002.

H. You, “Effect of farmland regulation on farmland rental in China: an empirical study of peasant households,” International Journal on Advances in Information Sciences and Service Sciences, vol. 4, no. 17, pp. 467–476, 2012.

P. Ekins, “Identifying critical natural capital conclusions about critical natural capital,” Ecological Economics, vol. 44, no. 2-3, pp. 277–292, 2003.

J. Bingen, A. Serrano, and J. Howard, “Linking farmers to markets: Different approaches to human capital development,” Food Policy, vol. 28, no. 4, pp. 405–419, 2003.

C. Rakodi, “A capital assets framework for analysing household livelihood strategies: implications for policy,” Development Policy Review, vol. 17, no. 3, pp. 315–342, 1999.

J. J. Hox and T. M. Bechger, “An introduction to structural equation modelling,” Family Science Review, vol. 11, pp. 354–373, 1998.

J. B. Schreiber, F. K. Stage, J. King, A. Nora, and E. A. Barlow, “Reporting structural equation modeling and confirmatory factor analysis results: a review,” The Journal of Educational Research, vol. 99, no. 6, pp. 323–337, 2006.

J. P. Voon, K. S. Ngui, and A. Agrawal, “Determinants of willingness to purchase organic food: an exploratory study using structural equation modeling,” International Food and Agribusiness Management, vol. 14, no. 2, pp. 103–120, 2011.

H. L. Dang, E. Li, I. Nuberg, and J. Bruwer, “Understanding farmers' adaptation intention to climate change: a structural equation modelling study in the Mekong Delta, Vietnam,” Environmental Science & Policy, vol. 41, pp. 11–22, 2014.

M. M. Masud, F. B. Kari, S. R. Binti Yahaya, and A. Q. Al-Amin, “Impact of residents' livelihoods on attitudes towards environmental conservation behaviour: an empirical investigation of Tioman Island Marine Park area, Malaysia,” Ocean & Coastal Management, vol. 93, pp. 7–14, 2014.

J. C. Anderson and D. W. Gerbing, “Structural equation modeling in practice: a review and recommended two-step approach,” Psychological Bulletin, vol. 103, no. 3, pp. 411–423, 1988.

D. Hooper, J. Coughlan, and M. R. Mullen, “Structural equation modelling: guidelines for determining model fit,” The Electronic Journal of Business Research Methods, vol. 6, no. 1, pp. 53–60, 2008.

L. Bank, T. Dishion, M. Skinner, and G. R. Patterson, “Method variance in structural equation modeling: living with glop,” in Depression and Aggression in Family Interaction, R. G. Patterson, Ed., pp. 247–279, Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1990.

K. M. Gates, P. C. M. Molenaar, F. G. Hillary, N. Ram, and M. J. Rovine, “Automatic search for fMRI connectivity mapping: an alternative to granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM,” Neuroimage, vol. 50, no. 3, pp. 1118–1125, 2010.

A. Serenko, N. Bontis, and B. Detlor, “End-user adoption of animated interface agentsin everyday work applications,” Behaviour & Information Technology, vol. 26, no. 2, pp. 119–132, 2007.

C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobserved variables and measurement error,” Journal of Marketing Research, vol. 18, pp. 39–50, 1981.

S. Démurger and H. Xu, “Return migrants: the rise of new entrepreneurs in rural China,” World Development, vol. 39, no. 10, pp. 1847–1861, 2011.

T. Hesketh, X. J. Ye, L. Lu, and H. M. Wang, “Health status and access to health care of migrant workers in China,” Public Health Reports, vol. 123, no. 2, pp. 189–197, 2008.

B. Hsiung, “Guanxi: personal connections in Chinese society,” Journal of Bioeconomics, vol. 15, no. 1, pp. 17–40, 2013.

Q. Tian, L. Guo, and L. Zheng, “Urbanization and rural livelihoods: a case study from Jiangxi Province, China,” Journal of Rural Studies, vol. 47, pp. 577–587, 2016.

Q. Wang and X. Zhang, “Three rights separation: China's proposed rural land rights reform and four types of local trials,” Land Use Policy, vol. 63, pp. 111–121, 2017.

H. You, “Quantifying megacity growth in response to economic transition: a case of Shanghai, China,” Habitat International, vol. 53, pp. 115–122, 2016.

S. Su, C. Wan, J. Li et al., “Economic benefit and ecological cost of enlarging tea cultivation in subtropical China: characterizing the trade-off for policy implications,” Land Use Policy, vol. 66, pp. 183–195, 2017.