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Which Factors Affect Farmers’ Willingness for rural community remediation? A tale of three rural villages in China
Land Use Policy 74 (2018) 195–203
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Which Factors Affect Farmers’ Willingness for rural community remediation? A tale of three rural villages in China
T
Xiaoling Zhanga,b,c, Lu Hand,⁎ a
Department of Public Policy, City University of Hong Kong, Hong Kong, China City University of Hong Kong, Shenzhen Research Institute, Shenzhen, People's Republic of China c Tongji University Sustainable Development and New-Type Urbanization Think Tank, China d Institute of Land and Urban-Rural Development, Zhejiang University of Finance & Economics, Hangzhou, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Rural residential concentration Farmers’ willingness Mutual influence Mutual influence mechanism Logistic regression
With the rapid development of industrialization and urbanization in China, rural China is entering into a social and economic transformation period. As the national policy has shifted towards ensuring economic development while retaining considerable arable land, China has strictly controlled the conversion of agricultural land to construction land, with the amount of unused land diminishing. In this context, the search for new construction land has become an overwhelmingly urgent task. As a result, remediation of the rural community has gradually become an important choice for Chinese government. In this paper, we have investigated the characteristics of rural residential concentrations and factors affecting the willingness for rural community remediation in different regions of China by using the logistic regression method. A mutual influence model is built to provide a scientific basis for the reclamation and improvement of rural land. The results show that rural farmers in regions with different economic development levels have different preferences in large-scale operations and compensation method. 1) In line with the willingness to remediate (WtR), farmers in the western and central regions have significantly more WtR than those in the eastern region (eastern region < central region < western region) – being affected by largescale operations rather than themselves and subcontracting/leasehold and, in terms of land mode, the influence of age, family income and compensation mode rather than contract land. 2) In terms of the mutual influence between different regions and large-scale operations, farmers from the eastern region have less WtR than those from western and central regions. 3) In terms of the mutual influence between different regions and the compensation level, farmers from the eastern region also has less WtR than those from western and central regions. The main reason for these differences is likely to be due to the eastern region being much more developed than the other two regions. While the more scattered central and western communities have little impact on living and production, farmers in the eastern region have more entrepreneur activities associated with their land and therefore generate more income, increasing their desire for more land and houses which made governments’ subsidies less attractive. The results could therefore provide a scientific basis and policy guidance for investigation of cooperative development of comprehensive rural community remediation in other regions with similar contexts.
1. Introduction China’s urbanization drive post-1978, especially since the designation of real estate and auto production as growth anchors by Premier Zhu Rongji in 1998 have brought about a round of ‘great leap forward’ of capital switch from manufacturing and civic consumption toward urban built environment. In this context, rural areas are entering into an economic and social development transition period (Chen et al., 2016; Yu et al., 2015; Long et al., 2010; Goodman, 2008; Unger, 2002; Paik and Lee, 2012) with an increasingly prominent imbalance in construction land supply and demand. With the policy of ensuring ⁎
Corresponding author. E-mail addresses: [email protected] (X. Zhang), [email protected] (L. Han).
http://dx.doi.org/10.1016/j.landusepol.2017.08.014 Received 9 February 2017; Received in revised form 15 June 2017; Accepted 11 August 2017 0264-8377/ © 2017 Elsevier Ltd. All rights reserved.
economic development while retaining sufficient (considerable) arable land, China strictly controls the conversion of agricultural rural land to construction land, with the amount of unused land diminishing and the search for new construction land intensifying. The search for new construction land has therefore become an overwhelmingly urgent task. As a result, remediation of the rural community has gradually become the focus of the government (Lia et al., 2014; Long et al., 2010). The remediation of the rural community may be an integrated approach to coordinate the numeric change of rural population and residential land, protect farmland, add quota of construction land and balance urban–rural development (Liu et al., 2010; Liu and Liu, 2010; Long et al.,
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development and other related concepts, many regions have had to pay more attention to the influence of the people's behavior and wishes concerning the distribution, morphology and structure of rural residential areas (Grath, 1998; Carmen and Elena, 2004; Erickson et al., 2011; Kupidura et al., 2014). However, the ensuing policies have produced mixed results (Abrams and Gosnell, 2012; van Assche and Djanibekov, 2012; Berke et al., 2013; Cabanillas et al., 2013; Pasakarnis et al., 2013; Li et al., 2014), with a continuing tension between the pressure to provide land for economic growth and the imperative to preserve agricultural land. The resulting conflicts continue to be played out in all tiers of government in many developed countries around the world (Skinner et al., 2001; Tang et al., 2012). Rural residential land consolidation (RRLC) is an effective way of reducing the conflicts between economic development and arable land protection. Moreover, RRLC should be able to reduce rural land waste, develop rural infrastructure and promote new rural construction (Liu and Hao, 2011; Tang et al., 2012). In the process of RRLC, farmers are critical stakeholders, so their willingness to pay (WTP) needs to be respected in order to guard against any RRLC conflicts in interest (Li and Wang, 2009; Zhang, 2011; Tang et al., 2012). Rural residential land consolidation (RRLC) and its policies have significant impacts on the social and economic development in China. It promotes the transformation of rural farmers’ production and lifestyles. This is due to the fact that the RRLC activities are a social process (Zuo and Zhao, 2014). As a result, a large number of rural farmers have participated in the RRLC process. Thus, it is important to investigate the rural farmers’ willingness to pay (WTP) associated with RRLC. In this regard, scholars are becoming more aware of the importance of farmers’ willingness to pay in rural residential land consolidation (RRLC). Some scholars pay attention to the different perspectives of willingness to pay, such as the socio-political willingness to pay, community willingness to pay, economic willingness to pay (Yuan et al., 2015). Other scholars pay attention to different stakeholders’ willingness to pay. For instance, Lu et al. (2015) suggested that stakeholders could be identified as public or private clients, designers, consultants, main contractors, sub-contractors, regulators, environmentalists, and the general public. There are also different factors identified that may affect the willingness to pay for rural residential land consolidation (RRLC). On one hand, previous studies have tried to link the willingness to pay with the individual characteristics, such as gender, age, education, and income. Also, scholars concerned about the impact of psychological and behavioral factors, such as the degree of awareness and social trust. On the other hand, scholars concerned about the impact of social environment, policy environment, and economic conditions (An et al., 2017; Bullock and O'Shea, 2016; Sakaguchi et al., 2015; Yuan et al., 2015, 2011). For instance, Yuan et al. (2015) found that better understanding of the social, economic and environmental benefits would contribute to higher level of willingness to pay. Stigka et al. (2014) suggested that the initial cost is one of the primary factors that affect the level of willingness to pay. However, according to previous studies, we note that stakeholders’ willingness to pay varied from region to region (Lu et al., 2015). Therefore, this paper intends to investigate the rural farmers in regions with different economic development levels with different characteristics and willingness to pay. It is therefore necessary and valuable to understand stakeholders’ attitudes upon rural residential land consolidation (RRLC). Many rural areas in North America, Western Europe, Australia and other developed regions have a long tradition and significant practical experience in the farmers’ WTP of RRLC (Smailes, 2000; Valbuena et al., 2010; Erickson et al., 2011; Kupidura et al., 2014; Lisec et al., 2014). It is generally believed that the farmers’ WTP has a close relationship with occupation, age, place-identity, sense of belonging and primary social contact patterns (Hu, 1997; Smailes, 2000; Hamin and Marcucci, 2008; Long et al., 2012; McManus et al., 2012; Wang et al., 2014).
2010; Gao et al., 2011; Wang et al., 2012a,b; Li et al., 2014). In November 2013, the Third Plenary Session of the Eighteenth Central Committee of the Communist Party of China (CPC) put forward policies for speeding up the construction of a new agricultural operation system, giving farmers more property rights, promoting the equal exchange of urban and rural elements and the balanced allocation of public resources, and improving the system and method of urban healthy development. In March 2014, the government report delivered by Premier Li Keqiang promoting a new people-oriented urbanization was one of the highlights of 2014. Although rural community remediation provides an effective means of exploring the potential of land resources, it also a major problem in the social and economic development of the country (Liu et al., 2010). It also has significant meaning in the protection of arable land resources and promoting the intensive use of land. Many local and external studies of rural community remediation have been made, with some fruitful results (Liu et al., 2013; Long et al., 2012; Wang et al., 2014; Li et al., 2014; Garcia and Ayuga, 2007; Tang et al., 2012; Long et al., 2011). An important issues is the farmers’ willingness to remediate (WtR), which has been found to be affected by many factors, including economic, social and cultural, system and management and environmental factors, with the motivation for construction work mainly coming from population growth, family reorganization, traditional customs and the defects of old houses (Liu et al., 2010; Chen et al., 2010; Long et al., 2012; Wang et al., 2012a,b). This has led many farmers to build houses (Long et al., 2012, 2010; Zaslavskaya et al., 1984). Some studies suggest that there is a close relationship between WtR and income, and that young famers are more concerned with medical, education and employment factors (Wang et al., 2014). At present, however, little is known of their quantitative impact on WtR. In China, natural, social and economic conditions and the speed of agricultural development differs between regions with different levels of economic development. Therefore, with the rapid development of industrialization and urbanization, rural community remediation is smoother in the eastern region, which has rapid economic development, superior natural conditions and convenient transportation. Hence, there has been a tendency for remediation to spread from the southeast to northwest regions, resulting in different levels of remediation in different regions (Long et al., 2009; Liu et al., 2008; Long et al., 2007a,b). In this paper, typical areas in Zhejiang, Henan and Gansu are selected as study areas to investigate the relationship of the interactive impact of rural community remediation from the view of the farmers. A quantitative analysis is conducted using logistic regression to provide a scientific basis for the systematic and cooperative development of comprehensive rural community remediation. 2. Literature review 2.1. Rural residential land consolidation and willingness to pay Many areas in the North Americas, Europe, Australia, Japan and other developed regions have experienced urban renewal and socioeconomic transition during a process of accelerated industrialization and urbanization after World War II. This has caused a rapid decline in population in many rural areas (Clout, 1972; Cloke, 1979; Walser and Anderlik, 2004; Bjorna and Aarsaether, 2009; Stead, 2011; McGreevy, 2012; Li et al., 2014), slow infrastructure development and inefficient use of rural residential land (MacDonald et al., 2000; Bjorna and Aarsaether, 2009; Long and Woods, 2011; McGreevy, 2012). In response to this and associated rural socio-economic issues, many regions have introduced corresponding policies and explored the formation, development, types, functions, planning, etc., in rural residential areas (Hoskins, 1955; Roberts, 1979; Pacione, 1984; Grath, 1998; Vesterby and Krupa, 2002; Erickson et al., 2002; Bjorna and Aarsaether, 2009; Natsuda et al., 2012; Li et al., 2014). In particular, under the influence of the “measurement revolution”, “behavioral revolution”, sustainable 196
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information, living status, farmland circulation, WtR and influencing factors.
2.2. Overview of the study areas Jiaxing in Zhejiang province is located on the southeast coast of China and is one of the 15 cities in the Yangtze River Delta. It has a shortage of land, especially arable land and reserved arable land. The private economy in the area is well developed. Due to the reorganization and reconstructing of enterprises, circulation of the collective construction land usage rights is very common (Jiaxing Municipal People's Government, 2012). In 2008, to implement the policy of “planning ahead, developing in advance” approved by the State Council, Jiaxing adopted a rural community remediation mode of “two separates and two exchanges” (homestead separated from contracted land, relocation separated from land transfer; and exchange for shares and rent by the contractual management right of land, and exchange for money, house and place by the homestead). This can be summarized as the strategic goal of the integrated development of urban and rural areas with consensus, mechanism and policy being the guarantee, “two changes” (change in the mode of production and way of life) being the focus and “two separates and two exchanges” being the breakthrough point. Reform would be realized in the change of rural land use system and employment, social security, household registration, resident management, agriculture related system, rural construction, rural finance, public service and overall planning. A “two new” (new town and new community) project with “1 + X” (“1” refers to the construction of a new town in each town, “X” refers to the construction of village centers or new rural communities in addition to the new town) as the basic spatial distribution model was further proposed (Fig. 1). Xinxiang, Nanyang and Pingdingshan in Henan province are located in China's central plains with a dense population and long history of land development. For historic reasons, rural settlements are scattered, with a large amount of idle land and abandoned houses. The farmers' lives have been greatly improved in recent years, with the rapid development of the rural economy. In 2008, as a major grain-producing province, Henan proposed a “unified planning, centralized layout, size sorting and step-by-step implementation” form of land remediation. Xinxiang and Nanyang constructed land remediation projects of the south-north water diversion canal head and the lower reaches of the Xiaolangdi Dam, remediating more than 13 300 ha of arable land. This not only improved grain production capacity, but also strengthened the core status of Henan province in grain production. With the exception of the Huicheng basin, Longnan in the Gansu province has very rough land. With a complex terrain and mountains, especially steeply sloped land, arable land is scarce and scattered. The town is located in a remote area with poor traffic conditions and is one of the most economically backward areas in Gansu province. With the slow development, farmers continue to rely on traditional and backward agriculture methods to maintain what is a meagre standard of living (Agricultural Bureau of Longnan City, 2006). In 2013, the Gansu provincial government issued its Land Remediation Plan of Gansu Province (2011–2015), which proposed a policy of differentiated land remediation control. According to the plan, it was anticipated that a total area of 4 839 500 ha of land could be remediated and an increase of 695 500 ha of arable land.
3.2. Variable selection Of the various influencing factors involved, WtR was selected as an explanatory variable, with 18 indicators, the main ones being regional type, farmland circulation mode and intention, basic demographics, present living conditions and compensation method as independent variables as follows. (1) Regional type. In order to compare different regions, Zhejiang, Henan and Gansu were selected to represent the eastern, central and western areas respectively. (2) Farmland circulation mode and intention. Farmland circulation modes mainly include household plots, subcontract/rental, household plots+ subcontract/rental andlarge-scale operations large-scale farming operations. With the different levels of economic development in different regions, farmland circulation may affect land remediation. Selecting this variable enables the interaction between rural community remediation and farmland circulation to be analyzed. (3) Basic demographics. This variable was selected to reflect the influence of the basic situation such gender, age, education, occupation, employment, contracted land management mode, consumption patterns and annual family income. (4) Present living conditions. The homestead numbers and area were selected to reflect the influence of the current living conditions. (5) Compensation methods. This variable was selected to reflect the impact of different compensation methods on rural community remediation. There are three such methods, to provide: the first compensation methods is enough money to buy a new apartment in the town plus benefits including social security and employment training; the second compensation methods is a small amount of money plus a new homestead for building a new house; and the third compensation methods is a small amount of money plus a new homestead plus buying an apartment built by the government at a lower price than the market price. In response to if the farmers were WtR, 329 (71.37%) of households answered “yes”. The results for the continuous and categorical variables are summarized in Tables 1 and 2 respectively. 3.3. Research method Logistic regression analysis is an applicable form of regression analysis when the dependent variable is dichotomous and is the ideal model to analyze individual decision-making behavior. In this study, the farmers had only two cognitive situations: yes or no. Therefore, binary logistic regression is applied in the analysis (Freedman, 2009). The logistic model here is
P=
Exp (β0 + β1 χ1 + β2 χ2 +⋯+βm χm ) 1 + Exp (β0 + β1 χ1 + β2 χ2 +⋯+βm χm )
(1)
3. Data source, variable selection and model construction where, P represents the dependent variable, WtR, with only two discrete values, 0 and 1; Xi represents the WtR and its influence; β0 is constant and independent of Xi, and is the natural logarithm of the ratio of WtR and reluctance when the independent variables are all 0. β1, β2, …, βm, are the partial regression coefficients, showing the contribution the various factors make to P.
3.1. Data source The study data are from the site investigation of farmers in the three rural areas – mainly by questionnaire survey and interviews with different types of farmers. A total of 515 questionnaires were distributed. This involved 300 households in 11 villages in Zhejiang, with 280 issued and 259 (97.5%) valid responses returned; 150 households in four villages In Henan, with 140 issued and 120 (85.7%) returned; and 120 households in four villages in Gansu, with 95 issued and 82 (86.3%) returned. The questionnaire solicited basic personal and family
4. Results and analysis Backward selection at the 5% significance level is used to determine the independent variables in the final regression model. Two steps are involved: 197
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Fig. 1. Locale of the study areas.
Table 1 Descriptive statistics for the dependent variable and continuous variables (unit: %). Variable
Number of samples
Mean
Standard deviation
Min
max
Median
Village renovation wishes Family income Quantity homestead Homestead area
461 461 461 461
0.38 4.88 1.15 0.50
0.49 3.88 0.39 0.44
0.00 0.20 1.00 0.10
1.00 40.00 4.00 3.00
0.00 4.00 1.00 0.36
4.1. Analysis of the influence factors of the WtR in regions with different levels of economic development
(1) Logistic regression analysis of a single factor. This involves seven independent variables of region, household plot + subcontract/ rental, scale management, age, contracted land management mode, annual family income and compensation method. (2) Logistic regression analysis of multiple factors. With this, the seven variables are changed into dummy variables according to their attributes. The continuous and dummy variables are then used as covariates and classified covariates respectively. The results of the relationship analysis are shown in Table 3. (3) Based on the results of (2), the interactions between region and other variables are analyzed to establish the influence of each variable in different regions. Here, variables such as large-scale operations and the third compensation method were selected to compare their degree of influence and difference between the three regions. Figs. 2 and 3 illustrate the interaction results and Tables 4 and 5 give the significance levels.
From the results of the overall regression analysis (Table 3), there are differences in the direction, impact and significance level of rural community remediation in different regions.
4.1.1. Comparison of WtR in different regions In the eastern region, with significance at the 1% level, the regression coefficient is −1.941, a negative correlation with general WtR. The influence is not significant in the central region, but with a negative regression coefficient of −0.036. This suggests that ceteris paribus, compared with people in the western region, those in the eastern region have the lowest WtR, followed by those in the central region.
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Table 2 Statistics for categorical variables (section). Independent variable
Village renovation wishes No (%)
Yes (%)
Total (%)
Region
Eastern Region Central Region Western Region
200 (69.90) 61 (21.30) 25 (8.70)
59 (33.70) 59 (33.70) 57 (32.60)
259 (56.20) 120 (26.00) 82 (17.80)
Household plots + subcontract/rental
Very reluctant Reluctance Neutral attitude Willing Very willing
16 76 80 86 28
10 22 52 80 11
(5.70) (12.60) (29.70) (45.70) (6.30)
26 (5.60) 98 (21.30) 132 (28.60) 166 (36.00) 39 (8.50)
Large-scale operations
Very reluctant Reluctance Neutral attitude Willing Very willing
13 (4.50) 62 (21.70) 58 (20.30) 103 (36.00) 50 (17.50)
7 (4.00) 12 (6.90) 29 (16.60) 86 (49.10) 41 (23.40)
20 (4.30) 74 (16.10) 87 (18.90) 189 (41.00) 91 (19.70)
Age
25–29 30–39 40–49 50–59 ≥60
24 32 90 67 73
40 24 55 39 17
(22.90) (13.70) (31.40) (22.30) (9.70)
64 (13.90) 56 (12.10) 145 (31.50) 106 (23.00) 90 (19.50)
The contracted land management mode
Has been subcontracted to others to operate Operating lease to others Grow your own Shares in cooperatives Other
145 (50.70) 8 (2.80) 114 (39.90) 4 (1.40) 15 (5.20)
44 (25.10) 20 (11.40) 99 (56.60) 5 (2.90) 7 (4.00)
189 (41.00) 28 (6.10) 213 (46.20) 9 (2.00) 22 (4.80)
The third compensation method
Very reluctant Reluctance Neutral attitude Willing Very willing
4 (1.40) 48 (16.80) 98 (34.30) 108 (37.80) 28 (9.80)
5 (2.90) 17 (9.70) 35 (20.00) 83 (47.40) 35 (20.00)
9 (2.00) 65 (14.10) 133 (28.90) 191 (41.40) 63 (13.70)
(5.60) (26.60) (28.00) (30.10) (9.80)
(8.40) (11.20) (31.50) (23.40) (25.50)
government at lower than the market price. Its neutral attitude towards the compensation has a significant negative regression coefficient of −0.945, indicating that, ceteris paribus, the influence of the third compensation method is quite great.
4.1.2. Comparison of influence factors in different regions (1) In the farmland circulation mode, large-scale operations have significant influence while household plot + subcontract/rental had less influence. In large-scale operations, the negative regression coefficient of reluctance of −1.833 passes the significance test at the 1% level. This indicates that, ceteris paribus, compared with those who support large-scale operations, people most reluctant to carry out scale management have the lowest WtR. (2) Age has a significant impact. People in age groups of 25–29, 30–39 and 50–59 pass the significance test at 1%, 10% and 5% level respectively, with positive regression coefficients of 1.518, 0.841 and 0.817. This indicates that, ceteris paribus, younger farmers have greater WtR. This is because they are more open and have strong acceptance and cognitive ability. On the contrary, older farmers are more conservative, with less ability to adapt to new environments and the less willing to change their living environment. (3) The contracted land management mode has less influence. Due to the increasing diversity in the contracted land operator and mode, coupled with the accelerated rate of urbanization, the influence of contracted land management patterns is not significant. (4) The level of family income has a negative regression coefficient of −0.066, indicating that, ceteris paribus, higher income families have less WtR. This is contrary to expectations. Many higher income farmers are in the eastern region, where they are engaged in secondary or tertiary industries. Therefore, they prefer moving to cities with more comfortable living conditions instead of remediation. In addition, many farmers use their homestead to engage in secondary or tertiary industries, so their WtR is not strong. This is consistent with the actual situation. (5) The third compensation method is to give a small amount of money plus a new homestead plus buying an apartment built by the
4.2. Interaction analysis of WtR in regions with different levels of economic development 4.2.1. Large-scale operations As Fig. 2 illustrates, there are clear overlaps of large-scale operations in different regions. Table 4 indicates the significance of these by the interaction regression model between region and large-scale operations, with:
• a significant (at the 10% level) negative regression coefficient of −1.184 of the eastern region; • a significant (at the 1% level) negative regression coefficient of −2.773 of those very reluctant to large-scale operations; • a significant (at the 10% level) positive regression coefficient of 1.273 of acceptance to large-scale operations; a • significant (at the 1% level) positive interaction coefficient of • • • 199
3.620 between the eastern region and those very reluctant to largescale operations; a significant (at the 5% level) negative interaction coefficient of −1.747 between the eastern region and those supporting large-scale operations; a significant (at the 10% level) negative interaction coefficient of −2.541 between the central region and those very reluctant to large-scale operations; and a significant (at the 10% level) negative interaction coefficient of −1.888 between the central region and those neutral attitude towards large-scale operations.
Land Use Policy 74 (2018) 195–203
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Table 3 Overall impact of different regions. Variable
Region Region (1) Region (2) Household plots + subcontract/rental Household plots + subcontract/rental (1) Household plots + subcontract/rental (2) Household plots + subcontract/rental (3) Household plots + subcontract/rental (4) Large-scale operations Large-scale operations (1) Large-scale operations (2) Large-scale operations (3) Large-scale operations (4) Age Age (1) Age (2) Age (3) Age (4) The contracted land management mode The contracted land management mode (1) The contracted land management mode (2) The contracted land management mode (3) The contracted land management mode (4) Family income The third compensation method The third compensation method(1) The third compensation method(2) The third compensation method(3) The third compensation method (4) Constant
Regression coefficients (B)
Standard deviation (S.E.)
−1.941*** −0.036
0.463 0.383
0.292 −0.581 0.348 0.344
0.623 0.516 0.460 0.457
−0.689 −1.833*** −0.500 −0.052
0.630 0.463 0.388 0.314
1.518*** 0.841* 0.539 0.817**
0.441 0.451 0.370 0.390
−0.235 0.949 −0.815 1.434 −0.066**
0.535 0.820 0.634 0.899 0.029
0.358 −0.497 −0.945** −0.281 1.277
0.795 0.464 0.405 0.366 0.948
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
22.50 17.55 0.01 7.79 0.22 1.27 0.57 0.57 18.80 1.20 15.70 1.66 0.03 12.95 11.83 3.48 2.12 4.40 11.70 0.19 1.34 1.65 2.55 5.11 8.25 0.20 1.15 5.45 0.59 1.81
2 1 1 4 1 1 1 1 4 1 1 1 1 4 1 1 1 1 4 1 1 1 1 1 4 1 1 1 1 1
0.000 0.000 0.926 0.100 0.639 0.261 0.449 0.452 0.001 0.274 0.000 0.197 0.870 0.012 0.001 0.062 0.145 0.036 0.020 0.660 0.247 0.199 0.111 0.024 0.083 0.652 0.284 0.020 0.443 0.178
OR values (Exp (B))
0.144 0.965 1.340 0.559 1.416 1.410 0.502 0.160 0.607 0.950 4.563 2.318 1.715 2.264 0.791 2.583 0.443 4.195 0.936 1.431 0.608 0.389 0.755 3.587
Note: *, **, *** represent 10%, 5% and 1% levels of significance respectively.
Fig. 3. Interactions between region and compensation methods. Fig. 2. Interactions between region and scale management.
• farmers in the eastern regions under large-scale operations, have no WtR; and • most people in the central and western regions under large-scale
Other items have less influence. This indicates that ceteris paribus:
• people in the eastern region have less WtR than those in the western regions; • people who are very reluctant to large-scale operations have the lowest WtR, and those supporting large-scale operations have the highest WtR; • compared with the interaction between people in the western re-
operations, are WtR, which is consistent with the above analysis.
4.2.2. Third compensation method Fig. 3 suggests there are overlaps between the third compensation method in different regions. Table 5 indicates the significance of these by the interaction regression model between region and the third compensation method, with:
gions and who support large-scale operations, people in the overlap area between the eastern region and those reluctant to large-scale operations have the highest WtR, and people in the overlap area between the central region and those reluctant to large-scale operations have the lowest WtR.
• a significant (at the 1% level) negative regression coefficient of −3.430 of the eastern region;
200
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Table 4 Different regions and large-scale operations interactions. Variable
Region Region (1) Region (2) Large-scale operations Large-scale operations (1) Large-scale operations (2) Large-scale operations (3) Large-scale operations (4) Region * Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (2) by Large-scale operations Region (2) by Large-scale operations Region (2) by Large-scale operations Constant
(1) (2) (3) (4) (2) (3) (4)
Regression coefficients (B)
Standard deviation (S.E.)
−1.184* 1.338
0.650 0.859
−2.773*** −0.742 −0.074 1.273*
0.917 1.085 0.721 0.717
3.620*** −0.490 −0.465 −1.747** −2.541* −1.888* −1.443 0.336
1.191 1.277 0.851 0.804 1.320 1.099 1.055 0.586
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
14.800 3.319 2.422 16.895 9.148 0.468 0.011 3.151 23.088 9.230 0.147 0.298 4.724 3.705 2.951 1.869 0.330
2 1 1 4 1 1 1 1 7 1 1 1 1 1 1 1 1
0.001 0.068 0.120 0.002 0.002 0.494 0.918 0.076 0.002 0.002 0.701 0.585 0.030 0.054 0.086 0.172 0.566
OR values (Exp (B))
0.306 3.810 0.063 0.476 0.929 3.571 37.333 0.613 0.628 0.174 0.079 0.151 0.236 1.400
Note: *, **, *** represent 10%, 5% and 1% level of significance respectively.
• a significant (at the 5% level) negative regression coefficient of −2.228 of people accepting the third compensation method; and • a significant (at the 5% level) positive interaction coefficient of
perspectives of a single factor, multi factors and interactions of various levels, the influencing factors and their interactions. In addition to finding that younger people and lower income families have more WtR, the main conclusions are that people in regions with different economic development levels have different preferences in large-scale operations and compensation method. That is, WtR: 1) in the order of low to high, is eastern region < central region < western region – being affected by large-scale operations rather than themselves and subcontracting/leasehold and, in terms of land mode, the influence of age, family income and compensation mode rather than contract land; 2) eastern region < western region < central region with the mutual influence between different regions and large-scale operations; and 3) eastern region < central region < western region with the mutual influence between different regions and their level of compensation. In general, therefore, people in the western and central regions have significantly more WtR than those in the eastern region. Although those supporting large-scale operations generally have more WtR, the effects vary between regions, with people in the eastern region who support large-scale operations having less WtR. In terms of compensation method, people that accept the method also have generally less WtR, although this is again reversed in the eastern region. The main reason for these differences is likely to be due to the eastern region being much
2.732 between people accepting the third compensation method and in the eastern region; This indicates that, ceteris paribus:
• compared with people in the western region, those in the eastern region have the lowest WtR; • people accepting the third compensation method have the lowest WtR; and • people in the eastern region that accept the third compensation method have the highest WtR.
5. Conclusions and suggestions In this study, an interactive logistic regression model is constructed and analyzed according to the characteristics of rural community remediation in regions with different economic development levels, to provide a scientific basis for collaborative development in the comprehensive improvement of rural land in China. This involves the Table 5 Different regions and ways to the third compensation method interaction. Variable
Region Region (1) Region (2) The third compensation method The third compensation method (1) The third compensation method (2) The third compensation method (3) The third compensation method (4) Region * The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (2) by The third compensation method Region (2) by The third compensation method Region (2) by The third compensation method Constant
(1) (2) (3) (4) (2) (3) (4)
Regression coefficients (B)
Standard deviation (S.E.)
−3.430*** −0.927
0.938 0.881
−0.251 19.331 −2.228** −0.881
1.303 2.842E + 04 0.905 0.826
1.809 −19.136 2.732** 1.096 −20.499 −0.198 0.080 1.872
1.633 2.842E + 04 1.087 1.027 2.842E + 04 1.124 0.976 0.760
Note: *, **, *** represent 10%, 5% and 1% level of significance respectively.
201
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
17.782 13.376 1.109 7.802 0.037 0.000 6.058 1.138 16.621 1.227 0.000 6.322 1.138 0.000 0.031 0.007 6.073
2 1 1 4 1 1 1 1 7 1 1 1 1 1 1 1 1
0.000 0.000 0.292 0.099 0.847 0.999 0.014 0.286 0.020 0.268 0.999 0.012 0.286 0.999 0.860 0.935 0.014
OR values (Exp (B))
0.032 0.396 0.778 2.485E + 08 0.108 0.414 6.107 0.000 15.371 2.992 0.000 0.821 1.083 6.500
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References
more developed, with convenient transportation, than the other two regions. While the more scattered central and western communities have little impact on living and production, people in the eastern region have more business activities associated with their land and therefore generate more income, increasing their desire for more land and houses and making governments’ subsidies less attractive. There are perhaps two main contributions from this paper. Firstly, this paper has collected a new and rich survey data for the study, which could provide a solid evidence for the investigation of rural residential concentration. Secondly, the large-scale survey practice can help provide Chinese government to make ‘tailor-made’ policy incentive schemes as well as policies to enhance the satisfaction of rural community remediation. In this way, social exclusion and social sustainability could therefore be maintained to a certain extent. The results suggest several possibilities to help alleviate the conflict of rural community remediation, realize urban and rural co-development and promote a more people-oriented new urbanization:
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• Consideration of the farmers’ psychological needs and enhancement
•
•
•
of their breadth and depth of participation. Farmers, as the main body and beneficiary of rural community remediation and new urbanization, are the premise and guarantee of smooth remediation and social stability. Therefore, full attention needs to be paid to the farmers’ wishes, to let them take part in the whole process of planning and remediation, and provide legal guarantees in organization, and scale and form of participation, etc. Consideration of regional differences and different regulation strategies. Various factors need to be considered in the remediation regions according to their local situation and economic and social development to select appropriate remediation and compensation methods. For the eastern region with rapid economic development, optimization of the urban and industrial structure could further improve the quality and level of remediation involved while, for the less economically advanced central and western regions, to explore their urban development potential and strengthen security policies. Consideration of the farmers’ interests and establishment of an open benefit-sharing mechanism. In the remediation process, not only do peoples’ land property rights need protecting, but also to include them and other social organizations in sharing the benefits of regional development to inspire their participation in regional urbanization and help safeguard social stability. In this paper, the variables of large-scale operations and the third compensation method were used to compare the influence and difference between the three regions with different economic development levels in China. Future research needs to be focused on increasing the index of interaction and analyzing the long-term effect of rural residential land consolidation in different economic development levels in China. It can more fully and scientifically reflect the wishes of farmers and their differences of rural residential land consolidation in rural areas.
Acknowledgements The research reported in this paper is supported by the National Natural Science Foundation of China (71673232; 71704152); Science Foundation of Zhejiang Province (LQ17G030005); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (16YJC630030); General soft science research project of Zhejiang provincial science and Technology Department (2016C35002) and City University of Hong Kong’s Internal Funds for PRC Grants(MFPRC) (9680195); the work described in this paper was partial supported by a grant from the Research Grant Council of the Hong Kong Special Administrative region, China (Project No: CityU 11271716 and CityU 21209715).
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Which Factors Affect Farmers’ Willingness for rural community remediation? A tale of three rural villages in China
T
Xiaoling Zhanga,b,c, Lu Hand,⁎ a
Department of Public Policy, City University of Hong Kong, Hong Kong, China City University of Hong Kong, Shenzhen Research Institute, Shenzhen, People's Republic of China c Tongji University Sustainable Development and New-Type Urbanization Think Tank, China d Institute of Land and Urban-Rural Development, Zhejiang University of Finance & Economics, Hangzhou, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Rural residential concentration Farmers’ willingness Mutual influence Mutual influence mechanism Logistic regression
With the rapid development of industrialization and urbanization in China, rural China is entering into a social and economic transformation period. As the national policy has shifted towards ensuring economic development while retaining considerable arable land, China has strictly controlled the conversion of agricultural land to construction land, with the amount of unused land diminishing. In this context, the search for new construction land has become an overwhelmingly urgent task. As a result, remediation of the rural community has gradually become an important choice for Chinese government. In this paper, we have investigated the characteristics of rural residential concentrations and factors affecting the willingness for rural community remediation in different regions of China by using the logistic regression method. A mutual influence model is built to provide a scientific basis for the reclamation and improvement of rural land. The results show that rural farmers in regions with different economic development levels have different preferences in large-scale operations and compensation method. 1) In line with the willingness to remediate (WtR), farmers in the western and central regions have significantly more WtR than those in the eastern region (eastern region < central region < western region) – being affected by largescale operations rather than themselves and subcontracting/leasehold and, in terms of land mode, the influence of age, family income and compensation mode rather than contract land. 2) In terms of the mutual influence between different regions and large-scale operations, farmers from the eastern region have less WtR than those from western and central regions. 3) In terms of the mutual influence between different regions and the compensation level, farmers from the eastern region also has less WtR than those from western and central regions. The main reason for these differences is likely to be due to the eastern region being much more developed than the other two regions. While the more scattered central and western communities have little impact on living and production, farmers in the eastern region have more entrepreneur activities associated with their land and therefore generate more income, increasing their desire for more land and houses which made governments’ subsidies less attractive. The results could therefore provide a scientific basis and policy guidance for investigation of cooperative development of comprehensive rural community remediation in other regions with similar contexts.
1. Introduction China’s urbanization drive post-1978, especially since the designation of real estate and auto production as growth anchors by Premier Zhu Rongji in 1998 have brought about a round of ‘great leap forward’ of capital switch from manufacturing and civic consumption toward urban built environment. In this context, rural areas are entering into an economic and social development transition period (Chen et al., 2016; Yu et al., 2015; Long et al., 2010; Goodman, 2008; Unger, 2002; Paik and Lee, 2012) with an increasingly prominent imbalance in construction land supply and demand. With the policy of ensuring ⁎
Corresponding author. E-mail addresses: [email protected] (X. Zhang), [email protected] (L. Han).
http://dx.doi.org/10.1016/j.landusepol.2017.08.014 Received 9 February 2017; Received in revised form 15 June 2017; Accepted 11 August 2017 0264-8377/ © 2017 Elsevier Ltd. All rights reserved.
economic development while retaining sufficient (considerable) arable land, China strictly controls the conversion of agricultural rural land to construction land, with the amount of unused land diminishing and the search for new construction land intensifying. The search for new construction land has therefore become an overwhelmingly urgent task. As a result, remediation of the rural community has gradually become the focus of the government (Lia et al., 2014; Long et al., 2010). The remediation of the rural community may be an integrated approach to coordinate the numeric change of rural population and residential land, protect farmland, add quota of construction land and balance urban–rural development (Liu et al., 2010; Liu and Liu, 2010; Long et al.,
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development and other related concepts, many regions have had to pay more attention to the influence of the people's behavior and wishes concerning the distribution, morphology and structure of rural residential areas (Grath, 1998; Carmen and Elena, 2004; Erickson et al., 2011; Kupidura et al., 2014). However, the ensuing policies have produced mixed results (Abrams and Gosnell, 2012; van Assche and Djanibekov, 2012; Berke et al., 2013; Cabanillas et al., 2013; Pasakarnis et al., 2013; Li et al., 2014), with a continuing tension between the pressure to provide land for economic growth and the imperative to preserve agricultural land. The resulting conflicts continue to be played out in all tiers of government in many developed countries around the world (Skinner et al., 2001; Tang et al., 2012). Rural residential land consolidation (RRLC) is an effective way of reducing the conflicts between economic development and arable land protection. Moreover, RRLC should be able to reduce rural land waste, develop rural infrastructure and promote new rural construction (Liu and Hao, 2011; Tang et al., 2012). In the process of RRLC, farmers are critical stakeholders, so their willingness to pay (WTP) needs to be respected in order to guard against any RRLC conflicts in interest (Li and Wang, 2009; Zhang, 2011; Tang et al., 2012). Rural residential land consolidation (RRLC) and its policies have significant impacts on the social and economic development in China. It promotes the transformation of rural farmers’ production and lifestyles. This is due to the fact that the RRLC activities are a social process (Zuo and Zhao, 2014). As a result, a large number of rural farmers have participated in the RRLC process. Thus, it is important to investigate the rural farmers’ willingness to pay (WTP) associated with RRLC. In this regard, scholars are becoming more aware of the importance of farmers’ willingness to pay in rural residential land consolidation (RRLC). Some scholars pay attention to the different perspectives of willingness to pay, such as the socio-political willingness to pay, community willingness to pay, economic willingness to pay (Yuan et al., 2015). Other scholars pay attention to different stakeholders’ willingness to pay. For instance, Lu et al. (2015) suggested that stakeholders could be identified as public or private clients, designers, consultants, main contractors, sub-contractors, regulators, environmentalists, and the general public. There are also different factors identified that may affect the willingness to pay for rural residential land consolidation (RRLC). On one hand, previous studies have tried to link the willingness to pay with the individual characteristics, such as gender, age, education, and income. Also, scholars concerned about the impact of psychological and behavioral factors, such as the degree of awareness and social trust. On the other hand, scholars concerned about the impact of social environment, policy environment, and economic conditions (An et al., 2017; Bullock and O'Shea, 2016; Sakaguchi et al., 2015; Yuan et al., 2015, 2011). For instance, Yuan et al. (2015) found that better understanding of the social, economic and environmental benefits would contribute to higher level of willingness to pay. Stigka et al. (2014) suggested that the initial cost is one of the primary factors that affect the level of willingness to pay. However, according to previous studies, we note that stakeholders’ willingness to pay varied from region to region (Lu et al., 2015). Therefore, this paper intends to investigate the rural farmers in regions with different economic development levels with different characteristics and willingness to pay. It is therefore necessary and valuable to understand stakeholders’ attitudes upon rural residential land consolidation (RRLC). Many rural areas in North America, Western Europe, Australia and other developed regions have a long tradition and significant practical experience in the farmers’ WTP of RRLC (Smailes, 2000; Valbuena et al., 2010; Erickson et al., 2011; Kupidura et al., 2014; Lisec et al., 2014). It is generally believed that the farmers’ WTP has a close relationship with occupation, age, place-identity, sense of belonging and primary social contact patterns (Hu, 1997; Smailes, 2000; Hamin and Marcucci, 2008; Long et al., 2012; McManus et al., 2012; Wang et al., 2014).
2010; Gao et al., 2011; Wang et al., 2012a,b; Li et al., 2014). In November 2013, the Third Plenary Session of the Eighteenth Central Committee of the Communist Party of China (CPC) put forward policies for speeding up the construction of a new agricultural operation system, giving farmers more property rights, promoting the equal exchange of urban and rural elements and the balanced allocation of public resources, and improving the system and method of urban healthy development. In March 2014, the government report delivered by Premier Li Keqiang promoting a new people-oriented urbanization was one of the highlights of 2014. Although rural community remediation provides an effective means of exploring the potential of land resources, it also a major problem in the social and economic development of the country (Liu et al., 2010). It also has significant meaning in the protection of arable land resources and promoting the intensive use of land. Many local and external studies of rural community remediation have been made, with some fruitful results (Liu et al., 2013; Long et al., 2012; Wang et al., 2014; Li et al., 2014; Garcia and Ayuga, 2007; Tang et al., 2012; Long et al., 2011). An important issues is the farmers’ willingness to remediate (WtR), which has been found to be affected by many factors, including economic, social and cultural, system and management and environmental factors, with the motivation for construction work mainly coming from population growth, family reorganization, traditional customs and the defects of old houses (Liu et al., 2010; Chen et al., 2010; Long et al., 2012; Wang et al., 2012a,b). This has led many farmers to build houses (Long et al., 2012, 2010; Zaslavskaya et al., 1984). Some studies suggest that there is a close relationship between WtR and income, and that young famers are more concerned with medical, education and employment factors (Wang et al., 2014). At present, however, little is known of their quantitative impact on WtR. In China, natural, social and economic conditions and the speed of agricultural development differs between regions with different levels of economic development. Therefore, with the rapid development of industrialization and urbanization, rural community remediation is smoother in the eastern region, which has rapid economic development, superior natural conditions and convenient transportation. Hence, there has been a tendency for remediation to spread from the southeast to northwest regions, resulting in different levels of remediation in different regions (Long et al., 2009; Liu et al., 2008; Long et al., 2007a,b). In this paper, typical areas in Zhejiang, Henan and Gansu are selected as study areas to investigate the relationship of the interactive impact of rural community remediation from the view of the farmers. A quantitative analysis is conducted using logistic regression to provide a scientific basis for the systematic and cooperative development of comprehensive rural community remediation. 2. Literature review 2.1. Rural residential land consolidation and willingness to pay Many areas in the North Americas, Europe, Australia, Japan and other developed regions have experienced urban renewal and socioeconomic transition during a process of accelerated industrialization and urbanization after World War II. This has caused a rapid decline in population in many rural areas (Clout, 1972; Cloke, 1979; Walser and Anderlik, 2004; Bjorna and Aarsaether, 2009; Stead, 2011; McGreevy, 2012; Li et al., 2014), slow infrastructure development and inefficient use of rural residential land (MacDonald et al., 2000; Bjorna and Aarsaether, 2009; Long and Woods, 2011; McGreevy, 2012). In response to this and associated rural socio-economic issues, many regions have introduced corresponding policies and explored the formation, development, types, functions, planning, etc., in rural residential areas (Hoskins, 1955; Roberts, 1979; Pacione, 1984; Grath, 1998; Vesterby and Krupa, 2002; Erickson et al., 2002; Bjorna and Aarsaether, 2009; Natsuda et al., 2012; Li et al., 2014). In particular, under the influence of the “measurement revolution”, “behavioral revolution”, sustainable 196
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information, living status, farmland circulation, WtR and influencing factors.
2.2. Overview of the study areas Jiaxing in Zhejiang province is located on the southeast coast of China and is one of the 15 cities in the Yangtze River Delta. It has a shortage of land, especially arable land and reserved arable land. The private economy in the area is well developed. Due to the reorganization and reconstructing of enterprises, circulation of the collective construction land usage rights is very common (Jiaxing Municipal People's Government, 2012). In 2008, to implement the policy of “planning ahead, developing in advance” approved by the State Council, Jiaxing adopted a rural community remediation mode of “two separates and two exchanges” (homestead separated from contracted land, relocation separated from land transfer; and exchange for shares and rent by the contractual management right of land, and exchange for money, house and place by the homestead). This can be summarized as the strategic goal of the integrated development of urban and rural areas with consensus, mechanism and policy being the guarantee, “two changes” (change in the mode of production and way of life) being the focus and “two separates and two exchanges” being the breakthrough point. Reform would be realized in the change of rural land use system and employment, social security, household registration, resident management, agriculture related system, rural construction, rural finance, public service and overall planning. A “two new” (new town and new community) project with “1 + X” (“1” refers to the construction of a new town in each town, “X” refers to the construction of village centers or new rural communities in addition to the new town) as the basic spatial distribution model was further proposed (Fig. 1). Xinxiang, Nanyang and Pingdingshan in Henan province are located in China's central plains with a dense population and long history of land development. For historic reasons, rural settlements are scattered, with a large amount of idle land and abandoned houses. The farmers' lives have been greatly improved in recent years, with the rapid development of the rural economy. In 2008, as a major grain-producing province, Henan proposed a “unified planning, centralized layout, size sorting and step-by-step implementation” form of land remediation. Xinxiang and Nanyang constructed land remediation projects of the south-north water diversion canal head and the lower reaches of the Xiaolangdi Dam, remediating more than 13 300 ha of arable land. This not only improved grain production capacity, but also strengthened the core status of Henan province in grain production. With the exception of the Huicheng basin, Longnan in the Gansu province has very rough land. With a complex terrain and mountains, especially steeply sloped land, arable land is scarce and scattered. The town is located in a remote area with poor traffic conditions and is one of the most economically backward areas in Gansu province. With the slow development, farmers continue to rely on traditional and backward agriculture methods to maintain what is a meagre standard of living (Agricultural Bureau of Longnan City, 2006). In 2013, the Gansu provincial government issued its Land Remediation Plan of Gansu Province (2011–2015), which proposed a policy of differentiated land remediation control. According to the plan, it was anticipated that a total area of 4 839 500 ha of land could be remediated and an increase of 695 500 ha of arable land.
3.2. Variable selection Of the various influencing factors involved, WtR was selected as an explanatory variable, with 18 indicators, the main ones being regional type, farmland circulation mode and intention, basic demographics, present living conditions and compensation method as independent variables as follows. (1) Regional type. In order to compare different regions, Zhejiang, Henan and Gansu were selected to represent the eastern, central and western areas respectively. (2) Farmland circulation mode and intention. Farmland circulation modes mainly include household plots, subcontract/rental, household plots+ subcontract/rental andlarge-scale operations large-scale farming operations. With the different levels of economic development in different regions, farmland circulation may affect land remediation. Selecting this variable enables the interaction between rural community remediation and farmland circulation to be analyzed. (3) Basic demographics. This variable was selected to reflect the influence of the basic situation such gender, age, education, occupation, employment, contracted land management mode, consumption patterns and annual family income. (4) Present living conditions. The homestead numbers and area were selected to reflect the influence of the current living conditions. (5) Compensation methods. This variable was selected to reflect the impact of different compensation methods on rural community remediation. There are three such methods, to provide: the first compensation methods is enough money to buy a new apartment in the town plus benefits including social security and employment training; the second compensation methods is a small amount of money plus a new homestead for building a new house; and the third compensation methods is a small amount of money plus a new homestead plus buying an apartment built by the government at a lower price than the market price. In response to if the farmers were WtR, 329 (71.37%) of households answered “yes”. The results for the continuous and categorical variables are summarized in Tables 1 and 2 respectively. 3.3. Research method Logistic regression analysis is an applicable form of regression analysis when the dependent variable is dichotomous and is the ideal model to analyze individual decision-making behavior. In this study, the farmers had only two cognitive situations: yes or no. Therefore, binary logistic regression is applied in the analysis (Freedman, 2009). The logistic model here is
P=
Exp (β0 + β1 χ1 + β2 χ2 +⋯+βm χm ) 1 + Exp (β0 + β1 χ1 + β2 χ2 +⋯+βm χm )
(1)
3. Data source, variable selection and model construction where, P represents the dependent variable, WtR, with only two discrete values, 0 and 1; Xi represents the WtR and its influence; β0 is constant and independent of Xi, and is the natural logarithm of the ratio of WtR and reluctance when the independent variables are all 0. β1, β2, …, βm, are the partial regression coefficients, showing the contribution the various factors make to P.
3.1. Data source The study data are from the site investigation of farmers in the three rural areas – mainly by questionnaire survey and interviews with different types of farmers. A total of 515 questionnaires were distributed. This involved 300 households in 11 villages in Zhejiang, with 280 issued and 259 (97.5%) valid responses returned; 150 households in four villages In Henan, with 140 issued and 120 (85.7%) returned; and 120 households in four villages in Gansu, with 95 issued and 82 (86.3%) returned. The questionnaire solicited basic personal and family
4. Results and analysis Backward selection at the 5% significance level is used to determine the independent variables in the final regression model. Two steps are involved: 197
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Fig. 1. Locale of the study areas.
Table 1 Descriptive statistics for the dependent variable and continuous variables (unit: %). Variable
Number of samples
Mean
Standard deviation
Min
max
Median
Village renovation wishes Family income Quantity homestead Homestead area
461 461 461 461
0.38 4.88 1.15 0.50
0.49 3.88 0.39 0.44
0.00 0.20 1.00 0.10
1.00 40.00 4.00 3.00
0.00 4.00 1.00 0.36
4.1. Analysis of the influence factors of the WtR in regions with different levels of economic development
(1) Logistic regression analysis of a single factor. This involves seven independent variables of region, household plot + subcontract/ rental, scale management, age, contracted land management mode, annual family income and compensation method. (2) Logistic regression analysis of multiple factors. With this, the seven variables are changed into dummy variables according to their attributes. The continuous and dummy variables are then used as covariates and classified covariates respectively. The results of the relationship analysis are shown in Table 3. (3) Based on the results of (2), the interactions between region and other variables are analyzed to establish the influence of each variable in different regions. Here, variables such as large-scale operations and the third compensation method were selected to compare their degree of influence and difference between the three regions. Figs. 2 and 3 illustrate the interaction results and Tables 4 and 5 give the significance levels.
From the results of the overall regression analysis (Table 3), there are differences in the direction, impact and significance level of rural community remediation in different regions.
4.1.1. Comparison of WtR in different regions In the eastern region, with significance at the 1% level, the regression coefficient is −1.941, a negative correlation with general WtR. The influence is not significant in the central region, but with a negative regression coefficient of −0.036. This suggests that ceteris paribus, compared with people in the western region, those in the eastern region have the lowest WtR, followed by those in the central region.
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Table 2 Statistics for categorical variables (section). Independent variable
Village renovation wishes No (%)
Yes (%)
Total (%)
Region
Eastern Region Central Region Western Region
200 (69.90) 61 (21.30) 25 (8.70)
59 (33.70) 59 (33.70) 57 (32.60)
259 (56.20) 120 (26.00) 82 (17.80)
Household plots + subcontract/rental
Very reluctant Reluctance Neutral attitude Willing Very willing
16 76 80 86 28
10 22 52 80 11
(5.70) (12.60) (29.70) (45.70) (6.30)
26 (5.60) 98 (21.30) 132 (28.60) 166 (36.00) 39 (8.50)
Large-scale operations
Very reluctant Reluctance Neutral attitude Willing Very willing
13 (4.50) 62 (21.70) 58 (20.30) 103 (36.00) 50 (17.50)
7 (4.00) 12 (6.90) 29 (16.60) 86 (49.10) 41 (23.40)
20 (4.30) 74 (16.10) 87 (18.90) 189 (41.00) 91 (19.70)
Age
25–29 30–39 40–49 50–59 ≥60
24 32 90 67 73
40 24 55 39 17
(22.90) (13.70) (31.40) (22.30) (9.70)
64 (13.90) 56 (12.10) 145 (31.50) 106 (23.00) 90 (19.50)
The contracted land management mode
Has been subcontracted to others to operate Operating lease to others Grow your own Shares in cooperatives Other
145 (50.70) 8 (2.80) 114 (39.90) 4 (1.40) 15 (5.20)
44 (25.10) 20 (11.40) 99 (56.60) 5 (2.90) 7 (4.00)
189 (41.00) 28 (6.10) 213 (46.20) 9 (2.00) 22 (4.80)
The third compensation method
Very reluctant Reluctance Neutral attitude Willing Very willing
4 (1.40) 48 (16.80) 98 (34.30) 108 (37.80) 28 (9.80)
5 (2.90) 17 (9.70) 35 (20.00) 83 (47.40) 35 (20.00)
9 (2.00) 65 (14.10) 133 (28.90) 191 (41.40) 63 (13.70)
(5.60) (26.60) (28.00) (30.10) (9.80)
(8.40) (11.20) (31.50) (23.40) (25.50)
government at lower than the market price. Its neutral attitude towards the compensation has a significant negative regression coefficient of −0.945, indicating that, ceteris paribus, the influence of the third compensation method is quite great.
4.1.2. Comparison of influence factors in different regions (1) In the farmland circulation mode, large-scale operations have significant influence while household plot + subcontract/rental had less influence. In large-scale operations, the negative regression coefficient of reluctance of −1.833 passes the significance test at the 1% level. This indicates that, ceteris paribus, compared with those who support large-scale operations, people most reluctant to carry out scale management have the lowest WtR. (2) Age has a significant impact. People in age groups of 25–29, 30–39 and 50–59 pass the significance test at 1%, 10% and 5% level respectively, with positive regression coefficients of 1.518, 0.841 and 0.817. This indicates that, ceteris paribus, younger farmers have greater WtR. This is because they are more open and have strong acceptance and cognitive ability. On the contrary, older farmers are more conservative, with less ability to adapt to new environments and the less willing to change their living environment. (3) The contracted land management mode has less influence. Due to the increasing diversity in the contracted land operator and mode, coupled with the accelerated rate of urbanization, the influence of contracted land management patterns is not significant. (4) The level of family income has a negative regression coefficient of −0.066, indicating that, ceteris paribus, higher income families have less WtR. This is contrary to expectations. Many higher income farmers are in the eastern region, where they are engaged in secondary or tertiary industries. Therefore, they prefer moving to cities with more comfortable living conditions instead of remediation. In addition, many farmers use their homestead to engage in secondary or tertiary industries, so their WtR is not strong. This is consistent with the actual situation. (5) The third compensation method is to give a small amount of money plus a new homestead plus buying an apartment built by the
4.2. Interaction analysis of WtR in regions with different levels of economic development 4.2.1. Large-scale operations As Fig. 2 illustrates, there are clear overlaps of large-scale operations in different regions. Table 4 indicates the significance of these by the interaction regression model between region and large-scale operations, with:
• a significant (at the 10% level) negative regression coefficient of −1.184 of the eastern region; • a significant (at the 1% level) negative regression coefficient of −2.773 of those very reluctant to large-scale operations; • a significant (at the 10% level) positive regression coefficient of 1.273 of acceptance to large-scale operations; a • significant (at the 1% level) positive interaction coefficient of • • • 199
3.620 between the eastern region and those very reluctant to largescale operations; a significant (at the 5% level) negative interaction coefficient of −1.747 between the eastern region and those supporting large-scale operations; a significant (at the 10% level) negative interaction coefficient of −2.541 between the central region and those very reluctant to large-scale operations; and a significant (at the 10% level) negative interaction coefficient of −1.888 between the central region and those neutral attitude towards large-scale operations.
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Table 3 Overall impact of different regions. Variable
Region Region (1) Region (2) Household plots + subcontract/rental Household plots + subcontract/rental (1) Household plots + subcontract/rental (2) Household plots + subcontract/rental (3) Household plots + subcontract/rental (4) Large-scale operations Large-scale operations (1) Large-scale operations (2) Large-scale operations (3) Large-scale operations (4) Age Age (1) Age (2) Age (3) Age (4) The contracted land management mode The contracted land management mode (1) The contracted land management mode (2) The contracted land management mode (3) The contracted land management mode (4) Family income The third compensation method The third compensation method(1) The third compensation method(2) The third compensation method(3) The third compensation method (4) Constant
Regression coefficients (B)
Standard deviation (S.E.)
−1.941*** −0.036
0.463 0.383
0.292 −0.581 0.348 0.344
0.623 0.516 0.460 0.457
−0.689 −1.833*** −0.500 −0.052
0.630 0.463 0.388 0.314
1.518*** 0.841* 0.539 0.817**
0.441 0.451 0.370 0.390
−0.235 0.949 −0.815 1.434 −0.066**
0.535 0.820 0.634 0.899 0.029
0.358 −0.497 −0.945** −0.281 1.277
0.795 0.464 0.405 0.366 0.948
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
22.50 17.55 0.01 7.79 0.22 1.27 0.57 0.57 18.80 1.20 15.70 1.66 0.03 12.95 11.83 3.48 2.12 4.40 11.70 0.19 1.34 1.65 2.55 5.11 8.25 0.20 1.15 5.45 0.59 1.81
2 1 1 4 1 1 1 1 4 1 1 1 1 4 1 1 1 1 4 1 1 1 1 1 4 1 1 1 1 1
0.000 0.000 0.926 0.100 0.639 0.261 0.449 0.452 0.001 0.274 0.000 0.197 0.870 0.012 0.001 0.062 0.145 0.036 0.020 0.660 0.247 0.199 0.111 0.024 0.083 0.652 0.284 0.020 0.443 0.178
OR values (Exp (B))
0.144 0.965 1.340 0.559 1.416 1.410 0.502 0.160 0.607 0.950 4.563 2.318 1.715 2.264 0.791 2.583 0.443 4.195 0.936 1.431 0.608 0.389 0.755 3.587
Note: *, **, *** represent 10%, 5% and 1% levels of significance respectively.
Fig. 3. Interactions between region and compensation methods. Fig. 2. Interactions between region and scale management.
• farmers in the eastern regions under large-scale operations, have no WtR; and • most people in the central and western regions under large-scale
Other items have less influence. This indicates that ceteris paribus:
• people in the eastern region have less WtR than those in the western regions; • people who are very reluctant to large-scale operations have the lowest WtR, and those supporting large-scale operations have the highest WtR; • compared with the interaction between people in the western re-
operations, are WtR, which is consistent with the above analysis.
4.2.2. Third compensation method Fig. 3 suggests there are overlaps between the third compensation method in different regions. Table 5 indicates the significance of these by the interaction regression model between region and the third compensation method, with:
gions and who support large-scale operations, people in the overlap area between the eastern region and those reluctant to large-scale operations have the highest WtR, and people in the overlap area between the central region and those reluctant to large-scale operations have the lowest WtR.
• a significant (at the 1% level) negative regression coefficient of −3.430 of the eastern region;
200
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Table 4 Different regions and large-scale operations interactions. Variable
Region Region (1) Region (2) Large-scale operations Large-scale operations (1) Large-scale operations (2) Large-scale operations (3) Large-scale operations (4) Region * Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (1) by Large-scale operations Region (2) by Large-scale operations Region (2) by Large-scale operations Region (2) by Large-scale operations Constant
(1) (2) (3) (4) (2) (3) (4)
Regression coefficients (B)
Standard deviation (S.E.)
−1.184* 1.338
0.650 0.859
−2.773*** −0.742 −0.074 1.273*
0.917 1.085 0.721 0.717
3.620*** −0.490 −0.465 −1.747** −2.541* −1.888* −1.443 0.336
1.191 1.277 0.851 0.804 1.320 1.099 1.055 0.586
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
14.800 3.319 2.422 16.895 9.148 0.468 0.011 3.151 23.088 9.230 0.147 0.298 4.724 3.705 2.951 1.869 0.330
2 1 1 4 1 1 1 1 7 1 1 1 1 1 1 1 1
0.001 0.068 0.120 0.002 0.002 0.494 0.918 0.076 0.002 0.002 0.701 0.585 0.030 0.054 0.086 0.172 0.566
OR values (Exp (B))
0.306 3.810 0.063 0.476 0.929 3.571 37.333 0.613 0.628 0.174 0.079 0.151 0.236 1.400
Note: *, **, *** represent 10%, 5% and 1% level of significance respectively.
• a significant (at the 5% level) negative regression coefficient of −2.228 of people accepting the third compensation method; and • a significant (at the 5% level) positive interaction coefficient of
perspectives of a single factor, multi factors and interactions of various levels, the influencing factors and their interactions. In addition to finding that younger people and lower income families have more WtR, the main conclusions are that people in regions with different economic development levels have different preferences in large-scale operations and compensation method. That is, WtR: 1) in the order of low to high, is eastern region < central region < western region – being affected by large-scale operations rather than themselves and subcontracting/leasehold and, in terms of land mode, the influence of age, family income and compensation mode rather than contract land; 2) eastern region < western region < central region with the mutual influence between different regions and large-scale operations; and 3) eastern region < central region < western region with the mutual influence between different regions and their level of compensation. In general, therefore, people in the western and central regions have significantly more WtR than those in the eastern region. Although those supporting large-scale operations generally have more WtR, the effects vary between regions, with people in the eastern region who support large-scale operations having less WtR. In terms of compensation method, people that accept the method also have generally less WtR, although this is again reversed in the eastern region. The main reason for these differences is likely to be due to the eastern region being much
2.732 between people accepting the third compensation method and in the eastern region; This indicates that, ceteris paribus:
• compared with people in the western region, those in the eastern region have the lowest WtR; • people accepting the third compensation method have the lowest WtR; and • people in the eastern region that accept the third compensation method have the highest WtR.
5. Conclusions and suggestions In this study, an interactive logistic regression model is constructed and analyzed according to the characteristics of rural community remediation in regions with different economic development levels, to provide a scientific basis for collaborative development in the comprehensive improvement of rural land in China. This involves the Table 5 Different regions and ways to the third compensation method interaction. Variable
Region Region (1) Region (2) The third compensation method The third compensation method (1) The third compensation method (2) The third compensation method (3) The third compensation method (4) Region * The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (1) by The third compensation method Region (2) by The third compensation method Region (2) by The third compensation method Region (2) by The third compensation method Constant
(1) (2) (3) (4) (2) (3) (4)
Regression coefficients (B)
Standard deviation (S.E.)
−3.430*** −0.927
0.938 0.881
−0.251 19.331 −2.228** −0.881
1.303 2.842E + 04 0.905 0.826
1.809 −19.136 2.732** 1.096 −20.499 −0.198 0.080 1.872
1.633 2.842E + 04 1.087 1.027 2.842E + 04 1.124 0.976 0.760
Note: *, **, *** represent 10%, 5% and 1% level of significance respectively.
201
Statistics (Wald)
Degrees of freedom (df)
P values (Sig.)
17.782 13.376 1.109 7.802 0.037 0.000 6.058 1.138 16.621 1.227 0.000 6.322 1.138 0.000 0.031 0.007 6.073
2 1 1 4 1 1 1 1 7 1 1 1 1 1 1 1 1
0.000 0.000 0.292 0.099 0.847 0.999 0.014 0.286 0.020 0.268 0.999 0.012 0.286 0.999 0.860 0.935 0.014
OR values (Exp (B))
0.032 0.396 0.778 2.485E + 08 0.108 0.414 6.107 0.000 15.371 2.992 0.000 0.821 1.083 6.500
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References
more developed, with convenient transportation, than the other two regions. While the more scattered central and western communities have little impact on living and production, people in the eastern region have more business activities associated with their land and therefore generate more income, increasing their desire for more land and houses and making governments’ subsidies less attractive. There are perhaps two main contributions from this paper. Firstly, this paper has collected a new and rich survey data for the study, which could provide a solid evidence for the investigation of rural residential concentration. Secondly, the large-scale survey practice can help provide Chinese government to make ‘tailor-made’ policy incentive schemes as well as policies to enhance the satisfaction of rural community remediation. In this way, social exclusion and social sustainability could therefore be maintained to a certain extent. The results suggest several possibilities to help alleviate the conflict of rural community remediation, realize urban and rural co-development and promote a more people-oriented new urbanization:
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• Consideration of the farmers’ psychological needs and enhancement
•
•
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of their breadth and depth of participation. Farmers, as the main body and beneficiary of rural community remediation and new urbanization, are the premise and guarantee of smooth remediation and social stability. Therefore, full attention needs to be paid to the farmers’ wishes, to let them take part in the whole process of planning and remediation, and provide legal guarantees in organization, and scale and form of participation, etc. Consideration of regional differences and different regulation strategies. Various factors need to be considered in the remediation regions according to their local situation and economic and social development to select appropriate remediation and compensation methods. For the eastern region with rapid economic development, optimization of the urban and industrial structure could further improve the quality and level of remediation involved while, for the less economically advanced central and western regions, to explore their urban development potential and strengthen security policies. Consideration of the farmers’ interests and establishment of an open benefit-sharing mechanism. In the remediation process, not only do peoples’ land property rights need protecting, but also to include them and other social organizations in sharing the benefits of regional development to inspire their participation in regional urbanization and help safeguard social stability. In this paper, the variables of large-scale operations and the third compensation method were used to compare the influence and difference between the three regions with different economic development levels in China. Future research needs to be focused on increasing the index of interaction and analyzing the long-term effect of rural residential land consolidation in different economic development levels in China. It can more fully and scientifically reflect the wishes of farmers and their differences of rural residential land consolidation in rural areas.
Acknowledgements The research reported in this paper is supported by the National Natural Science Foundation of China (71673232; 71704152); Science Foundation of Zhejiang Province (LQ17G030005); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (16YJC630030); General soft science research project of Zhejiang provincial science and Technology Department (2016C35002) and City University of Hong Kong’s Internal Funds for PRC Grants(MFPRC) (9680195); the work described in this paper was partial supported by a grant from the Research Grant Council of the Hong Kong Special Administrative region, China (Project No: CityU 11271716 and CityU 21209715).
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