Influencing factors of farmers’ willingness to withdraw from rural homesteads: A survey in zhejiang, China

Land Use Policy 68 (2017) 524–530

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Influencing factors of farmers’ willingness to withdraw from rural homesteads: A survey in zhejiang, China

MARK

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Hongxia Chena, , Luming Zhaob, Zhenyu Zhaoa a b

Department of Public Management, Ningbo University, Ningbo 315211, China Department of Applied Mathematics, Ningbo University, Ningbo 315211, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Withdrawal from rural homesteads (WRH) Willingness of homestead withdrawal Influencing factors Differentiation of peasants Zhejiang

The purpose of the paper is to analyse the influencing factors on farmers’ willingness of withdrawal from rural homesteads (WRH). Methods that included field surveys, factor analyses and case studies were used for this paper. The results show that farmers have a strong willingness to own the property on which their homesteads sit. There are deviations between homestead systems and execution. It is common that the area of a homestead exceeds the lawful standard, and one peasant family owns two or more homesteads. Peasant families have many concerns about WRH, which include: reduction of employment, lack of supporting social security, high expenditure of new houses, low compensation criteria for homesteads, decline in standard of living and inconvenience in agricultural production. Among all means of compensation, peasants prefer allocated residences, and they pay more attention to public service facilities and fundamental facilities. The authors argue that implementing employment and social security policies and setting up linkage mechanisms of WRH could help to smoothly push forward WRH.

1. Introduction With the accelerating pace of urbanization and industrialization in China, there have been many changes in population structures, employment opportunities, ideas of value, and living styles and standards (Unger, 2002; Mukherjee and Zhang, 2007; Tilt, 2008; Goodman, 2008; Zandbergen and Ignizio, 2010; Li et al., 2014). A large amount of rural population has moved into towns. Rural out-migration is an important driver of local land use and land cover change (Kates and Parris, 2003; GLP, 2005; Lambin and Meyfroidt, 2011; Seto et al., 2012). Massive rural out-migrations have transformed China from an agricultural society into an urban and industrial society (Long et al., 2012). Most of these people work and live in towns while continuing to own rural homesteads. Although the rural population is dropping, the total area of rural construction land is rising (Long et al., 2012). Some farmers even own more than one house in their rural hometowns (Xu and Guo, 2012). These phenomena have led to continuous enlargement of rural homestead areas in China instead of decreasing synchronously; this has led to a serious waste of the land (Liu et al., 2014). China's rural population has declined by about 13 percent, yet the rural homestead areas have increased by about 4 percent from 1997 to 2007. The per capita land area even reaches as high as to 229 square meters, thus showing the reverse development trend (Zhang, 2013). However, the

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Corresponding author. E-mail address: [email protected] (H. Chen).

http://dx.doi.org/10.1016/j.landusepol.2017.08.017 Received 13 July 2016; Received in revised form 10 August 2017; Accepted 14 August 2017 0264-8377/ © 2017 Elsevier Ltd. All rights reserved.

vacancy rate of rural homesteads in China has reached 10–15 percent (Han, 2008). Thus, the reform of rural land use in China is different and more complicated than in many Western nations. Withdrawal from rural homesteads(WRH) is a good approach to solve this problem. Therefore, research on WRH has become a hot topic. Scholars have studied “system reform”, “policy arrangement”, and “protection of farmers’ rights” (Hu and Zhang, 2013; Li et al., 2015; Tang et al., 2012). As the process of WRH should be carried out after the agreement of farmers, there were some studies focused on “withdrawal willingness”. The influencing factors of farmers’ WRH vary from farming type, family type, present living conditions and the regional economy (Chen et al., 2009). Survival, economy and social benefits are the focus of farmers’ decision-making regarding land circulation (Zhou, 2013). The influencing factors on farmers’ willingness of WRH are employment, pension, the cost of living and agricultural production. The farmers’ locations, ages and insurance conditions have also certain influence on their willingness of WRH (Peng and Fan, 2012). The inconvenience caused by changing jobs, high prices of urban houses, high living cost, farmers’ ages, and the compensation policies of WRH also affect the implementation of WRH (Wang et al., 2015). In fact, farmers’ ages, education backgrounds, family incomes, the numbers of the older people who need support and the diversity of compensation mode options have positive effects, while the job-changing frequency of family

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According to the enforcing agencies of the WRH, we can divide WRH into four modes: government-led, village-spontaneous, enterprise-pushed and market-allocated. Among the four types, the government-led type was the largest proportion (Xu et al., 2012). To achieve the rational use of land, local governments have tried their best to guide farmers and push WRH voluntarily by village relocation and a combination of modes, including building new houses intensively and encouraging farmers to move into towns. However, some local governments have made inroads on the land rights of farmers with the aid of public power. On 28 December 2011, the Ministry of Land and Resources issued a statement and stipulated local government should fully respect farmers’ willingness to relocate. Any compulsory relocation, compulsory construction, forcing farmers to move into apartment buildings and so on are forbidden (MLR, 2011). WRH must be based on farmers’ willingness.

members, number of children and present living space negatively affect farmers’ willingness for WRH (Chen, 2012). Moreover, farmers’ understanding of land rights also affects implementation of WRH (Peng, 2013). The above studies showed various influencing factors that affect WRH. However, few studies have highlighted the importance of these factors and ranked them. The objective of this paper was to determine the influencing factors on farmers’ willingness of WRH and rank them based on an investigation of Zhejiang Province. The findings may hopefully contribute to the policy making and accelerate the process of WRH. 2. Study area and definition 2.1. Study area Located on the southeast coastline of China and south of the Yangtze River Delta, Zhejiang Province is one of China's most developed provinces. It comprises an area of 105.52 thousand km2, of which the construction land occupies 12.66 thousand km2, accounting for only 12.0 percent (ZJDLRC, 2013). The conflict between increasing demand for construction-used land and serious protection of farmland is on the rise. Even though the rural population has decreased since 1996, the total area of rural housing is increasing (Long et al., 2012). In 2012, the average living space per capita in China was 37.1 square meters, while it was 61.5 square meters in Zhejiang Province (ZJBSC, 2012). The process of urbanization is reducing the rural population at a rate of 491.6 thousand people per year, which means that a large number of farmers will withdraw from rural homesteads, making Zhejiang Province a good example for studying farmers’ willingness of rural homestead withdrawal. Zhejiang Province has 11 cities. To reflect the investigated farmers’ real willingness fully and accurately, we selected areas using the methods of stratified sampling and random sampling. First, we used the stratified sampling method to find areas with different social economic development and urbanization. Then, we chose different counties and districts in each prefecture-level city according to the proportion of industrial structure and per capita income. Finally, we selected samples and sent investigators to conduct in-home interviews and distribute questionnaires. According to the above principles, the sample points of the survey were Xiaoshan District of Hangzhou, Yuyao County of Ningbo, Cangnan County of Wenzhou, Xinchang County of Shaoxing, Jiashan County of Jiaxing, Dongyang County of Jinhua, Wenling County of Taizhou and Suichang County of Lishui (Fig. 1).

3. Methods 3.1. Questionnaire and survey design Because of the diversity of farming types, economic levels and regions, the progress of rural homestead withdrawal shows obvious characteristics in different regions in China. Different modes apply to regional variations: the replacement of rural homesteads, the circulation of rural homesteads and the virtualized trade of the rural homesteads index (Cai and Xu, 2012). The questionnaire was designed on the basis of relative studies and former research. It contained three parts. Basic information was designed to obtain interviewees' gender, age, education, average yearly income members of their family and so on. The second part was to evaluate the interviewees' understanding of rural homestead use correctly. The last part was to measure the willingness of rural homestead withdrawal. It was concluded from the second part of the questionnaire how farmers understood the rural homestead use right they owned. With the data from the third part of the questionnaire, the influencing factors and their importance were acquired by factor analysis. The number of samples was decided by the household size of each county and district. Two or three village households were chosen randomly for the survey. Each sample area stood for different urbanization levels and economic development levels in Zhejiang Province. There were problems such as one household owning more than one homestead and land area exceeding the standard and the unused land area; this meant that there was a large potential space for WRH. After a preliminary investigation, we modified the questionnaire and implementation plan. We conducted formal investigations in July and August 2014 and January and February 2015. The number of farming families we investigated is shown in Table 1. Among eight surveyed areas, the sample households were distributed in eastern Zhejiang (Xiaoshan and Xinchang), western Zhejiang (Dongyang), southern Zhejiang (Wenling, Cangnan and Suichang) and northern Zhejiang (Jiashan and Yuyao). The surveyed samples reflected circumstances of the entire Zhejiang Province. In this study, interviewees were first asked whether they were willing to join the investigation about the willingness of WRH. Those who answered “yes” were invited to take part in the investigation and answer the questionnaire.

2.2. Withdrawal from rural homesteads There are two forms of land ownership in China, state-owned land and collective land. Unused rural homesteads increased rapidly with the emergence of a large quantity of rural out-migrations. According to ‘Land Management Law of China’, farmers, as members of collaborative economic organizations, have the right to own a piece of residential land. At the same time, urban citizens are not allowed to buy houses in rural area. Therefore, when farmers become rural-urban migrants or live permanently in urban areas, their houses remain vacant (Long et al., 2007). WRH means that farmers give up their use right to have rural homesteads, under the guidance of local government or rural collective economic organizations, and acquire money or new houses as compensation (Peng and Fan, 2012). WRH is one of the most important solutions for opening up the stock of rural construction land, thereby using land more economically and intensively and keeping the dynamic balance of farmland. According to practices in China, WRH could be classified into two types. Formal WRH is the passive withdrawal under land acquisition and internal transfer within rural collective organizations. Informal WRH mainly refers to the withdrawal pushed by policies such as the replacement of rural homesteads, reorganization of rural homesteads, village relocation or a combination (Ouyang et al., 2009).

3.2. Methodology The data were analysed using SPSS software. Descriptive statistics were used to analyse averages and standard deviations. To guarantee minimum data loss, the basic idea of factor analysis was to study internal relationships among variants by reducing dimensionality and identifying the relevant variants. Factors with good relativity fell into one category. Finally, there were some hypothetical variants. The relativity between different kinds of variants was very low. These 525

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Fig. 1. Location of the case study area.

influencing factors were added to Table 2. The test value of KMO was 0.793, and the significance level was 0.000; thus indicated that these data were fit for factor analysis. It was calculated that when there were 11 principal components, the cumulative proportion was above 85 percent; this meant that these principal components could fundamentally include the information of all measured indexes. Eleven principle components were extracted from 19 variables. It was difficult to extract concentrated and effective information from these 11 principal components because of scattered distribution of factors. So, it was necessary to make further deletion. The variance of former variables contained two kinds of information. One was communality, which was used to describe the explained ratio of the variance of variables made by all common factors and showed the contribution of factors to variables. The other was contributions of variance made by special factors, which was not explained by all factors. The smaller the variance of a variable was, the less information was lost. Then, variables with smaller variance were deleted, which left variables that were more beneficial to factor analysis. The variances of common factors are shown in Table 3. When five variables with smaller variances were deleted, the result of factor analysis improved. The simplified relative influencing factors are shown in Table 4. Through data verification, the test value of KMO was 0.784, and the significance level was 0.002; this meant that the simplified data were still suitable for factor analysis. The answer was acquired by solving factor loading matrix with SPSS. After many tests, the cumulative proportion was up to 85.230 percent when the factor number was 7. Here, variance with large contribution had a larger influence. The specific cumulative proportion, which was sorted according to its size, is shown in Table 5.

hypothetical variants were referred to as common factors, which contained a lot of information about original variants. Supposing the number of the hidden influencing common factors is m. M, in turn, influenced observable variables (p) and formed a linear relation with p. At the same time, the relationship between common factors and observable variables was linear. Therefore, factor analysis was performed using the following mathematical model.

⎧ X1 = a11 F1 + a12 F2+⋯+a1m Fm + ε1 ⎪ X2 = a21 F1 + a22 F2+⋯+a2m Fm + ε2 ⋯ ⎨ ⎪ Xp = ap1 F1 + ap2 F2+⋯+apm Fm + εp ⎩ Here, ε1, ε2,..., εp were special factors, which presented factors other than common factors and could not be contained by common factors ahead. Fj was a common factor of X. aij, called factor loading, which reflected the degree of Fj correlation between Xi and The task of factor analysis was to solve factor loading. On the basis of the answer, Sj, the cumulative proportion of Fj to X, was solved. It can be shown by the following equation: p

Sj =

∑ aij2

j = 1, ...., p

i=1

To keep the method feasible, it was necessary for factor analysis to pass the proper data check, KMO and significance test. When the contribution of variance was up to 85 percent, principal components were extracted from all variables and named with suitable description. 3.3. Analysis process To obtain influencing factors of farmers’ willingness of WRH, all Table 1 Distribution, households and proportion of investigation samples. Distribution

Eastern Zhejiang

Northern Zhejiang

Western Zhejiang

Southern Zhejiang

Region

Xiaoshang

Xinchang

Yuyao

Jiashan

Dongyang

Cangnan

Wenling

Suichang

Totality

Households (household) Proportion (%)

239 21.74

70 6.36

132 12.00

88 8.00

132 12.00

210 19.09

192 17.45

37 3.36

1100 100

526

Whole province

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Table 2 Relative influencing factors promoting WRH. Homestead

New houses

X1difficulty degree of agricultural working X2living standard X3compensation for old homestead X4expenditure of new houses

X5difficulty degree of transportation X6road facility of residence X7natural disaster X8housing security

X9employment opportunities X10degree of agricultural industrialization X11environmental condition X12living expenditure X13 distance of farming X14 cost of ecological management

Table 3 Variance of common factors.

X15public service facilities X16fundamental facilities X17recreational and sports activities X18transport location X19communication with relatives

Table 5 Contributions of variances.

Variable

X1

X2

X3

X4

X5

X6

X7

Initial Extraction

1.000 0.816

1.000 0.648

1.000 0.789

1.000 0.787

1.000 0.829

1.000 0.821

1.000 0.727

Variable Initial Extraction

X8 1.000 0.776

X9 1.000 0.710

X10 1.000 0.753

X11 1.000 0.702

X12 1.000 0.753

X13 1.000 0.745

X14 1.000 0.696

Variable Initial Extraction

X15 1.000 0.833

X16 1.000 0.802

X17 1.000 0.693

X18 1.000 0.799

X19 1.000 0.714

A factor loading matrix was acquired by Kaiser rotation. After the collection of data, factor loading and the specific index of common factors are shown in Table 6. Here, the bigger factor loading means that it had more influence on common factors. According to the meaning of a specific factor, common factors were named again. The influence of seven common factors was ranked from the largest to the smallest in Table 6, combined with the contribution of principle components. For example, the common factor F3 was named “Productive and Living Factor” because it showed large factor loading on three aspects, such as increase in living cost, inconvenient agricultural production and increased distance of farming.

Sequence number

Eigenvalues

Contribution of variance (%)

Cumulative proportion (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

4.210 1.882 1.805 1.581 1.140 0.700 0.615 0.463 0.424 0.339 0.319 0.290 0.285 0.234

30.072 13.446 12.890 11.291 8.140 5.001 4.390 3.305 3.026 2.419 2.277 2.072 1.216 1.671

30.072 43.518 56.408 67.699 75.839 80.840 85.230 88.535 91.561 93.980 96.257 97.493 98.329 100.000

junior middle school diplomas, followed by high school diplomas, college degrees and above. The number of surveyed farmers who had middle school diplomas was the lowest. The per capita annual income of surveyed farming families ranged from 9000 Yuan to 21,000 Yuan. In the form of family income, except a few predominantly agricultural families, about 50 percent to 80 percent of farming families' incomes was non-agricultural income.

4. Results and discussions 4.2. Cognition of rural homestead use rights 4.1. Essential features of samples Farmers’ understanding of the rural homestead use rights has an influence on willingness of WRH. Through the investigation of the farmers’ recognition of land ownership, we found that most farmers had little understanding to land ownership rights. Of the farmers surveys, 21.84 percent regarded land belonged to the government, 21.36 percent regarded it as belonging to the collective, 51.46 percent regarded it as belonging to individual farmers and the remaining 5.34 percent did not know. In response to the question “What rights do farmers have of the rural homesteads (multiple choice)?", 65.02 percent of farmers thought their land could be inherited, 57.14 percent believed that their land could be sold, 38.42 percent considered their land could be leased and 46.31 percent of farmers insisted that their land could be mortgaged. Further, 37.31 percent of farmers thought that rural homesteads (or housing) could be sold to people in urban area, to which 42.29 percent of farmers disagreed, and 20.40 percent said it had no importance. Generally, the farmers surveyed had a strong desire to own land property rights.

A total of 1100 questionnaires were sent during face-to-face interviews in the research area, with a response rate of 93.1 percent. The relatively low non-response rate of 6.82 percent indicated that the questionnaire was successful. The valid questionnaires 1025 were sufficient for statistical analysis. Among the surveyed farmers, there were 502 farming families who came from urban or rural integration of the countryside, accounting for 49 percent; 523 farming families came from previously urban areas, accounting for 51 percent. The per capita land area of surveyed farmers was 49.92 square meters, and the per capita rural homestead block of surveyed farmers was 1.07 blocks. In the effective samples from the investigation, there were 628 men and 397 women. Among the surveyed farmers, the youngest was 18 years old, and the oldest was 76 years old. Most of the surveyed farmers were between age 30 and 65 years, and the average age was 41.46 years. Regarding education level, most of the surveyed farmers had Table 4 Simplified relative influencing factors promoting WRH. Homestead X1difficulty degree of agricultural working X3compensation for old homestead X4expenditure of new houses

New houses X5difficulty degree of transportation X6road facility of residence X7natural disaster X8housing security

X10degree of agricultural industrialization X12living expenditure X13 distance of farming

527

X15public service facilities X16fundamental facilities X18transport location X19communication with relatives

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Table 6 Short list of factor loading matrix and name of common factors . Common Factor Fi

Factor loading

Specific Index

Name of common factors

F1

X5 : 0.865 X6 : 0.855 X3 : 0.914 X 4 : 0.852 X12 : 0.914 X1: 0.832 X13 : 0.626 X15: 0.896 X16: 0.849 X8: 0.777 X7: 0.765 X10: 0.847 X18: 0.607 X19: 0.869

Difficult degree of transportation of old homesteads Road facility of homesteads residence Compensation for old homesteads Expenditure of new houses Living expenditure of new houses Difficult degree of agricultural working of old homesteads Distance of farming of new houses Public service facilities of new houses Fundamental facilities of new houses Housing security of old homesteads Natural disaster of old homesteads Degree of agricultural industrialization of new houses Transport location of new houses Communication with relatives living in new houses

Transportation Factor

F2

F3

F4 F5 F6

F7

Economic Factor Productive and Living Factor

Environment Factor Security Factor Potential Factor Communication Factor

percent of the farmers held the view that rural homesteads could be bought randomly if you had enough power or money. About 70 percent of the farmers considered that the “one household, one homestead” policy was not strictly implemented or even being carried out in their hometowns.

Misunderstanding rural homestead use rights results in the misuse of rural homesteads. Regarding the national rural homestead policy ‘one household, one homestead’, 38.73 percent of farmers did not understand it. When asked about how to obtain a rural homestead, 14.98 percent of farmers thought that rich families can buy more and 4.83 percent thought local officials could obtain more. Regarding implementation of the policy “one household, one homestead”. 60.89 percent of farmers argued that there was loose implementation in their local area, 8.42 percent thought that the local government did not implement the policy at all, and only 30.69 percent stated that the policy was strictly implemented in their area. When asked ‘Did the government reclaim unused rural homesteads?”, 4.39 percent of farmers answered that this happened in “a lot of cases’, 36.59 percent in ‘some cases’, 16.59 percent in “a few cases”, and 42.44 percent answered in “no cases”. For the transfer or rent of local rural homesteads, 62.89 percent of farmers answered that there were “many” instances, and only 37.11 percent farmers said there were “few” instances. When the farmers were asked whether they were willing to take land circulation, 63.4 percent answered “yes”, but only 46.4 percent chose WRH. Obviously, for farmers, they still have more concerns about WRH. The economic development level of counties in Zhejiang Province is high. According to farmers’ income structures, the proportion of income directly from agricultural activities (about 40 percent) is much lower than their salaries from working in urban industries, which accounted for about 60–80 percent. Farmers mainly chose to work in their native counties or villages; 71 percent of farmers chose to work in Zhejiang Province, and only 9 percent chose to work in other provinces. Hence, most farmers were satisfied with their current income and living conditions and were not willing to make changes, although some of the farmers had purchased houses in towns to provide better education for their children or for better jobs. However, most of the farmers would refuse to leave their home villages if they need to give up their homesteads in exchange. The present rural homestead system has not been strictly implemented and promoted in certain regions. According to Article Nine, Chapter Two, of the Rural Residential Land Management Regulation of Zhejiang Province, the area standard for rural homesteads should be: If there are three (or fewer) members in one farming family, the area of their homestead should not exceed 75 square meters; for a four-member family, the area should not exceed 100 square meters; for a fivemember family, the area should not exceed 110 square meters; for a family with six members or more, the area should not exceed 125 square meters. In other words, the rated maximum per capita for a rural homestead area is 25 square meters. However, according to the statistical calculation, the per capita rural homestead area for the farmers surveyed was 49.92 square meters, which was two times more than the standard. Of the surveyed farmers, 38.73 percent did not know the homestead policy “one household, one homestead”, and about 20

4.3. Analysis of the importance of influencing factors “Transportation factor” is the most important factor influencing WRH. With the development of society, transportation and residences with road access has become increasingly essential. Currently, transportation and road facilities in rural areas lag behind most urban areas. Hence, out of necessity for living, production and communication, farmers strongly desire to improve their living conditions and are likely to agree to being relocated into newly constructed central villages with convenient transportation. It is also an effective external strategy for the government to encourage farmers to withdraw from rural homesteads (Zhang et al., 2011; Wu, 2010). The second factor is “economic factor”, including compensation for old homesteads and expenditure for new houses. Most farmers depend on single economic forms of income, which are not very high. In 2016, the per capita disposable income of rural residents in Zhejiang Province was 22866 Yuan, compared with a per capita net income of 47237 Yuan, for urban residents (ZJBSC, 2017). In fact, Zhejiang is a developed province, and the per capita net income of farmers in Zhejiang is much higher than that of the rest of China, which is only 12363 Yuan (NBSC, 2017). Farmers who will give up their rural homestead use rights are eager to share the profits from land. Low compensation for old houses would result in their refusal to WRH. Further, immature land use right system is the source of land conflict, especially when land rent increased quickly (Alston et al., 1999). Dissatisfaction sometimes also causes serious conflict when local governments push WRH forcibly. Numerous papers have suggested that housing prices have a relationship to consumer spending (Iacoviello, 2004; Campbell and Cocco, 2007; Frappa and Mésonnier, 2010). It could be concluded that scientific and rational value calculations and fair compensation for rural homesteads is a useful way to avoid economic dispute, eliminate worries and push WRH smoothly. In the process of WHR, a large rural surplus workforce chose to work in towns. The farmers were divided into three basic types (Fu, 2014): non-agriculture-oriented farmers, full-time farmers and parttime farmers. However, some of them still chose to stay in rural area and engage in farming after WRH. Therefore, “productive and living factor”, including the cost of new houses, difficulty of farming old homesteads and the distance between farms and new houses, is the issues they consider and what affect their decisions about WRH. It is conceivable that some policies could eliminate farmers’ concerns about WRH, such as providing employment opportunities, encouraging labour 528

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5. Conclusions

transfer, promoting agricultural mechanisation and so on. The forth factor is “environmental factor”. Farmers want convenience. Their demands for public service facilities and fundamental facilities in new houses have increased quickly. Therefore, completed infrastructural facilities and good public services would be attractive for farmers and would improve the process of WRH. Housing security and natural disasters affecting old homesteads were renamed as “security factors”. Now, some old houses built on homesteads in remote locations are in disrepair. In case of fire or natural disasters, firefighters and rescuers sometimes cannot arrive in time, to prevent enormous losses. When improvements are made in security awareness, farmers pay more attention to the hidden dangers of old houses, and security factors becomes essential for farmers’ decisions on WRH. The degree of agricultural industrialization and the location of new houses also influence WRH. These are called “potential factor”. High agricultural industrialization could increase not only income for farmers, but also employment opportunities. Good transport locations are helpful to economic development, which is why farmers think about these factors in the process of WRH. The last factor, “communication factor”, illustrates farmers’ demands for social communication. Even if farmers change their traditional lifestyles, their social communication networks still manifest the characteristics of isolation and homogenization of rural society, which is far different from open and diversified urban social communication based of occupation relationships (Wang, 2013).

The survey adopted factor analysis to measure farmers’ willingness to WRH. Some interviewees, most of whom were elderly, had no incentive to withdraw from their rural homesteads, never thinking that they would never leave their land. But others expressed that they were eager to, or would agree to, withdraw from a rural homestead. From these questionnaires, 19 specific indexes were listed and calculated. Specific indexes were grouped into seven common factors by factor loading, and listed in the descending order of importance. These factors were transportation, economic, productive and living, environment, security, potential, and communication. The importance of the factors affecting the willingness of farmers’ WRH obtained from this study are quite significant. The resulting information recorded both the direction and strength of the respondents' preferences. The results provide implications for convincing researchers and decision makers to select better methods and policies to improve WRH. More studies of this kind are needed due to a growing demand for a higher efficiency of land use in terms of the willingness of WRH. Acknowledgement This work was supported by the Key Project of Social Science Planning of Zhejiang Province (15NDJC008Z), National Social Science Fund of China (16BSH042) and Soft Science Research Program of Ningbo City (2017A10107).

Appendix A See Table A1

Table A1 Factor loading matrix after rotation. Variable

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14

Component 1

2

3

4

5

6

7

0.119 0.139 0.044 0.865 0.855 0.282 0.205 0.163 0.126 0.205 0.062 0.029 0.029 0.079

0.202 0.914 0.852 0.119 0.085 0.193 0.140 −0.030 0.064 0.016 −0.083 0.022 0.125 0.144

0.832 0.074 0.185 0.094 0.064 0.173 0.103 0.075 −0.057 −0.285 0.187 0.329 0.914 0.626

0.014 −0.026 0.059 0.124 0.185 0.107 0.043 0.896 0.849 0.168 0.082 0.133 0.099 0.015

0.143 0.076 0.163 0.157 0.209 0.765 0.777 0.046 0.068 0.024 0.115 0.099 0.079 0.131

0.154 0.010 0.014 0.159 −0.036 0.306 −0.106 0.095 0.102 0.607 0.155 0.847 0.108 0.096

0.114 −0.039 −0.024 −0.039 0.156 −0.135 0.369 −0.135 0.288 0.370 0.869 0.071 0.122 0.160

Han, K., 2008. The three major contradictions of the rural homestead system. Peoples Tribune 14, 38–39 (in Chinese). Hu, Y.G., Zhang, M., 2013. Study on migrant workers’ compensation willingness and influencing factors of housing sites exit—based on questionnaire of wuhan city circle. J. Huazhong Agric. Univ. 4, 90–95 (in Chinese). Kates, R.W., Parris, T.M., 2003. Long-term trends and a sustainability transition. Proc. Natl. Acad. Sci. U. S. A. 100 (14), 8062–8067. Lambin, E.F., Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. U. S. A. 108 (9), 3465–3472. Li, Y.R., Liu, Y.S., Long, H.L., Cui, W.G., 2014. Community-based rural residential land consolidation and allocation can help to revitalize hollowed villages in traditional agricultural areas of China: evidence from Dancheng County, Henan Province. Land Use Policy 39, 188–198. Li, T.T., Long, H.L., Liu, Y.Q., Tu, S.S., 2015. Multi-scale analysis of rural housing land transition under China’s rapid urbanization: the case of Bohai Rim. Habitat Int. 48, 227–238. Liu, Y.S., Fang, F., Li, Y.H., 2014. Key issues of land use in China and implications for policy making. Land Use Policy 40, 6–12.

References Alston, L., Libecap, G., Mueller, B., 1999. Theory and applications: a model of rural conflict: violence and land reform policy in Brazil. Environ. Dev. Econ. 4, 135–160. Cai, G.L., Xu, X.F., 2012. The carding and evaluation of the typical rural homestead withdrawal model. Land Resour. Inf. 7, 37–41 (in Chinese). Campbell, J.Y., Cocco, J.F., 2007. How do house prices affect consumption? Evidence from micro data. J. Monet. Econ. 54 (3), 591–621. Chen, M.Q., He, W.J., Liu, T.J., Wang, Y.P., Wu, Q.S., 2009. The empirical analysis on the willingness of the farmers about intensive use of rural residential land.-Take Jiangxi Province for example. Chin. Rural Econ. 8, 63–69 (in Chinese). Fu, C., 2014. The new focus of rural land system reform: give famers withdrawal rights. (in Chinese). http://news.xinhuanet.com/house/cz/2014-07-07/c_1111481599.htm. GLP, 2005. Global Land Project: Science Plan and Implementation Strategy. IGBP, Stockholm, pp. 64. Goodman, D.S.G., 2008. China’s Regional Development, fifth ed. Royal Institute of International Affairs, London.

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Land Use Policy 68 (2017) 524–530

H. Chen et al.

willingness and its influencing factors based on the research of Wangkou county and Duliu county. Rural Econ. 1, 33–37 (in Chinese). Wang, C.F., 2013. Social communication of expropriated farmers and cultural adaptation to city. Issues Agric. Econ. 1, 68–72 (in Chinese). Wu, Y.M., 2010. Service boundary and responsibility position of the government in rural public services. Reform 6, 74–79 (in Chinese). Xu, H.Z., Guo, Y.Y., 2012. The analysis of the rural homestead compensation and influence factors–Take Linqing in Shandong Province for example. China Land Sci. 10, 75–81 (in Chinese). Xu, X.F., Hu, Y.G., He, A.Q., Ni, N., 2012. The analysis based on the rural homestead withdrawal mode of the Logistic. J. Anhui Agric. Sci. 31, 15542–15545 (in Chinese). Zhejiang Bureau of Statistics of China (ZJBSC), 2012. The statistical bulletin of national economy & social development in Zhejiang Province in 2012. (in Chinese). http:// zjrb.zjol.com.cn/html/2015-02/27/content_2854000.htm. Zhejiang Bureau of Statistics of China (ZJBSC), 2017. Relevant statistical data bulletin of Zhejiang people’s livelihood in 2016. (in Chinese). http://www.zj.stats.gov.cn/tjgb/ jmszgb/201702/t20170207_191229.html. Zhejiang Bureau of Statistics of China (ZJBSC), 2013. The land-use change of Zhejiang Province in 2013. (in Chinese). http://www.zjdlr.gov.cn/art/2014/12/31/art_78_ 135160.html. Zandbergen, P.A., Ignizio, D.A., 2010. Comparison of dasymetric mapping techniques for small-area population estimates. Cartogr. Geogr. Inf. Sci. 37 (3), 199–214. Zhang, L.R., Li, J.C., Fan, H.L., 2011. A study on the demand preferences and satisfaction degree of public services in rural areas from the angle of income-based differences. Chin. Public Admin. 10, 118–122 (in Chinese). Zhang, F.L., 2013. 2013. The auction for the rural homestead will be pushed during the year. China Real Estate Business 2013-04-15(A02) (in Chinese). Zhou, Q.R., 2013. City and Countryside in China. China CITIC Press, Beijing, pp. 182 (in Chinese).

Long, H.L., Heilig, G.K., Li, X.B., Zhang, M., 2007. Socio-economic development and landuse change: analysis of rural housing land transition in the Transect of the Yangtse River, China. Land Use Policy 24, 141–153. Long, H.L., Li, Y.R., Liu, Y.S., Woods, M., Zou, J., 2012. Accelerated restructuring in rural China fueled by increasing vs. decreasing balance land-use policy for dealing with hollowed villages. Land Use Policy 29 (1), 11–22. Ministry of Land and Resources, 2011. The notice about the strict standards on the increase and decrease connection of urban and rural construction land use. (in Chinese). http://www.mlr.gov.cn/zwgk/zytz/201112/t20111228_1051488.htm. 2011-12-28. Mukherjee, A., Zhang, X., 2007. Rural industrialization in China and India: role of policies and institutions. World Dev. 35, 1621–1634. National Bureau of Statistics of China (NBSC), 2017. Statistical Communique on National Economic and Social Development of The People's Republic of China in 2016. (in Chinese) http://www.stats.gov.cn/tjsj/zxfb/201702/t20170228_1467424.html. Ouyang, A.J., Cai, F.M., Chen, L.D., 2009. The discussion of the rural homestead withdrawal system. China Land Sci. 10, 26–30 (in Chinese). Peng, C.S., Fan, Z.Y., 2012. The analysis of the rural homestead withdrawal willingness and its influencing factors based on the empirical research of 1413 famers from 6 counties in Anhui Province. Comp. Econ. Soc. Syst. 2, 154–162 (in Chinese). Seto, K.C., Reenberg, A., Boone, C.G., Fragkias, M., Haase, D., Langanke, T., Marcotullio, P., Munroe, D.K., Olah, B., Simon, D., 2012. Urban land teleconnections and sustainability. Proc. Natl. Acad. Sci. U. S. A. 109 (20), 7687–7692. Tang, Y., Mason, R.J., Sun, P., 2012. Interest distribution in the process of coordination of urban and rural construction land in China. Habitat Int. 36, 388–395. Tilt, B., 2008. Smallholders and the household responsibility system: adapting to institutional change in Chinese agriculture. Hum. Ecol. 36, 189–199. Unger, J., 2002. The Transformation of Rural China. Armonk, ME Sharpe. Wang, J., Yu, Z.P., Li, H., 2015. The analysis of the rural homestead withdrawal

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