1. bookVolume 46 (2019): Issue 46 (December 2019)
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Livelihood adaptation and life satisfaction among land-lost farmers: Critiquing China’s urbanisation-driven land appropriation

Published Online: 20 Dec 2019
Page range: 149 - 161
Received: 20 Sep 2019
Accepted: 25 Oct 2019
Journal Details
License
Format
Journal
First Published
22 Dec 2008
Publication timeframe
4 times per year
Languages
English

Large-scale rural land appropriation and displacement, driven by the unprecedented urban growth currently experienced in China, has created millions of land-lost peasants who live in the city but remain culturally, socially and institutionally rural. The situation has attracted growing attention in the literature because of its negative social impact, but relatively few studies have addressed how land-lost farmers adapt to urban ways of life and what factors influence their life satisfaction. In this paper, we investigate the predictors of livelihood adaptation and life satisfaction of land-lost farmers from a land appropriation case in the city of Changchun, Northeast China. The results show that, five years after the appropriation, livelihood adaptation remained very difficult and life satisfaction was poor among the resettlers. Furthermore, marginalised groups, such as those who were older, less educated and from smaller families, and those with lower pre-displacement income were less likely to have a higher income level after resettlement, resulting in a lower level of life satisfaction. Women also had lower life satisfaction than men. The study highlights an urgent need to improve China’s unjust land appropriation policy with a particular focus on attending to the needs of marginalised groups.

Key words

Introduction

China has experienced an unprecedented rate of urbanisation since opening up in 1978, not only in the growth of urban populations but also in the spatial expansion of built-up areas (Gu et al., 2012, Deng et al., 2015, Liu et al., 2014). This rapid urban expansion is accompanied by the large-scale appropriation of rural land for development purposes, and as a result, millions of peasants have lost their land for agricultural production and living and are displaced involuntarily to purpose-built resettlement neighbourhoods (Xu et al., 2011b; Qian, 2015; Lo and Wang, 2018).

The welfare of land-lost farmers has become an increasing concern among critical geographers. Land not only provides a guarantee of livelihood and direct economic benefits, but also has benefits in the form of inheritance and the potential to be sold or mortgaged off in times when cash is needed (Shen, 2012). Losing land through forced land dispossession therefore poses significant threats to farmers’ well-being. Some land-lost farmers become migrant workers searching for non-farm jobs, or run their own businesses, but many become jobless (He et al., 2009). Others have temporary and part-time jobs with low pay and harsh working environments (Wang and Fan, 2012). The difficulty of livelihood restoration means that most land-lost farmers experience income reduction, which is one of the most identifiable adverse effects of displacement (Ding, 2007; Hui and Bao, 2013). Furthermore, while land-lost farmers may have become urban residents in the household registration system, they do not enjoy the same level of social welfare as urban citizens (Qian, 2017).

This study aims to better understand the challenges and difficulties faced by land-lost farmers in an urbanising China from the perspectives of livelihood adaptation and life satisfaction, which have been demonstrated as insightful concepts that could reveal the impact of development-induced displacement (Colic-Peisker, 2009). In particular, life satisfaction after displacement is an important indicator of individuals’ wellbeing, and factors influencing life satisfaction are important for policy makers to provide better compensation packages and displacement schemes (Fang, 2006). However, one observation we make from a review of studies is that they focus more on the objective aspects of livelihood adaptation, especially employment and income changes, whereas life satisfaction is an understudied facet that deserves further investigation. The remainder of this paper is structured as follows: Section 2 reviews the literature of urbanisation-driven land appropriation and develops the concepts of livelihood adaptation and life satisfaction; Section 3 describes the data collection methods; results are presented in Section 4; and, finally, in Section 5, the key implications of the study are addressed.

Literature review
Background

Since the reform and opening up, China has experienced rapid industrialisation, urbanisation and aggregation of rural-urban migrants in the urban area (Shen et al., 2006). To accommodate the surge in industry production and migration, cities have expanded outward to acquire more space (Chaolin et al., 2012; He et al., 2016; Zhang, 2000), which has led to encroachment on rural land and the removal of villages (Liu et al., 2008). The central and local governments hold contrasting views regarding the massive loss of arable land due to urban expansion (Heilig, 1997). The central government has consistently argued the importance of farmland protection and stipulated many restrictions on converting arable land to construction land (Lin and Ho, 2005). However, various administrative levels of local government serve as the main drivers to land conversion by engaging in legal and illegal land development (Wong and Zhao, 1999).

There are two main factors explaining why local governments are driving rural land appropriation: (1) the binary structure of land ownership and (2) the imbalance of responsibility and fiscal power. First, the land acquisition system is rooted in the binary structure of land ownership in the urban and rural areas of China. According to the Constitution of the People’s Republic of China, the land in urban areas is owned by the state, and the land (including farmland, residential land and private farmed plots of cropland and hilly land) in rural areas is collectively owned (Hui et al., 2013). Rural land can be used for construction purposes only after being transferred from collective to state ownership (Xu et al., 2011b). Land appropriation initiated by the local government is the only legal way to accomplish the transfer. Therefore, the local government has been empowered to acquire rural land and redistribute it to developers to receive land revenue, which has become the most lucrative route to raising revenue under the current fiscal arrangement (Yang and Wang, 2008; Lin and Zhang, 2015). Second, the local responsibilities of urban governance have been re-established through administrative decentralisation (Lin et al., 2015), and local governments are encouraged to upgrade the built-up areas to attract increasingly mobile labour and capital to achieve economic growth and to compete with other cities (Tong et al., 2017). However, at the same time, the financial power of local governments has been constrained by the 1994 tax-sharing system reform (Cao and Zhang, 2018). Consequently, local governments seek to exploit the gap between the land grant fee and the compensation fee to gain income, which does not need to be transferred to the central government (Lin et al., 2015).

Livelihood adaptation

Livelihood can be defined as a means of living, comprised of livelihood capabilities and tangible assets (stores and resources) and intangible assets (claims and access) (Chambers and Conway, 1992). Based on this, Davies and Hossain (1997) proposed livelihood adaptation as a dynamic process of constant change to livelihoods which may enhance existing security and wealth or reduce vulnerability and poverty. The concept of livelihood adaptation has been applied widely to a variety of topics such as climate change adaptation (Chen et al., 2018; King et al., 2018; Wise et al., 2016; Roncoli et al., 2001), ecological resettlement (Dong et al., 2012), refugee resettlement (Mbakem and Collins, 2014), and migration due to hydropower projects (Kura et al., 2017; Wilmsen, 2016a; Tan and Wang, 2003). However, the livelihood adaptation of land-lost peasants going through land appropriation attracts relatively little attention in the research.

While a significant number of farmers in China work as migrant workers in non-agricultural sectors, farmland still serves as the most important form of capital (Cai et al., 2019). After land appropriation, with the loss of their farmland, farmers’ traditional mode of farm-based living has drastically changed (Li et al., 2016). In addition, those who live in the suburbs and urban villages of well-developed cities and whose livelihoods depend on renting out their houses would also be negatively affected (Jiang et al., 2018; Zhao and Webster, 2011). However, it has also been reported that, in eastern coastal China, livelihoods are less affected by land appropriation because land-lost farmers have more opportunities to obtain non-agricultural jobs (Tang et al., 2016; Hao and Tang, 2015). Livelihood adaptation is not a homogeneous process. Tong et al. (2017) have shed light on the livelihood adaptation of different age groups and found that younger farmers with more prior non-agricultural work experience are less disrupted, while middle-aged landless farmers’ livelihoods are the most vulnerable as they are less employable than younger farmers and have greater financial pressure to support their family.

Overall, empirical studies have shown that livelihood adaptation is often difficult for land-lost peasants in China. The studies typically attribute livelihood adaptation difficulties to farmers’ inadequate non-agricultural working skills and lower level of educational attainment (Keliang and Prosterman, 2007, Huang et al., 2017). The lack of post-resettlement support, such as vocational training, is another barrier to livelihood adaptation (Lo et al., 2016). Consequently, unemployment and underemployment tend to be very common among land-lost farmers. One study reports that the unemployment rate among landless farmers is 34.15% of the 14,000 surveyed individuals across China, and 46% of land-appropriated farmers have experienced an income decrease (He et al., 2009). Furthermore, the loss of rental income from renting out their houses has significant negative effects on their livelihood (Jiang et al., 2018; Zhao and Webster, 2011). Coupled with an increase in the cost of living due to the need to pay for food which previously could be supplied by cultivation (Ong, 2014), there is a significant risk of impoverishment among land-lost farmers.

Life satisfaction

Previous studies have shown that there is widespread dissatisfaction with the highly varied, non-transparent, and insufficient distribution of compensation, the income loss caused by underemployment and unemployment, and disrupted social relations and cultural belonging (Tong et al., 2017; He et al., 2009; Chen et al., 2016; Song et al., 2016).

The distribution of compensation is identified as the most dissatisfying factor among landless farmers. Through the land appropriation process, affected farmers would be provided with a compensation package by local government including a land compensation fee, a resettlement fee, compensation for above-ground buildings and other attached objects, and compensation for green crops on the land (Ding, 2007; Guo, 2001). However, compensation rates vary significantly across regions because of their close linking with local economic development levels and government finance (Guo, 2001), and the level of compensation is typically low, leading to lower life satisfaction (He et al., 2009). In addition, the negotiation process for compensation typically takes place between local governments and village committees (cunweihui) without consultation with farmers (Guo, 2001). The village committees almost always agree to the local government’s requests due to political pressure and potential economic profit, leaving the villagers’ interests largely ignored (Tong et al., 2017).

More recently, the compensation criteria have been raised, and many factors which are more beneficial to farmers have been incorporated into the criteria. In 2013, the upper limit of total compensation was removed (Tang et al., 2016), and new national guidelines have urged local authorities to introduce more generous compensation packages, such as provisions for subsidised housing, social insurance and job training programs (Xu et al., 2011a). However, studies have shown that compensation remains unsatisfactory among landless farmers (Ong, 2014; Ding, 2007; Tang et al., 2016; Hu et al., 2014). In fact, a slight improvement in compensation has resulted in more dissatisfaction among farmers and they are more reluctant to engage in land appropriation (Tian and Ma, 2009).

Research by Chen et al. (2016) indicated that their employment and economic situation constitute a serious concern among landless farmers. Many scholars have pointed out that the cutting off of farmers’ main sources of income, both non-agricultural and agricultural, through the dispossession of their farmland and residence, has contributed to the life dissatisfaction of resettled farmers (Qian, 2017; Chen et al., 2016). For instance, as the case of Hongqiao, Shanghai, shows, villagers are dissatisfied with their post-resettlement life because they lose a large share of their rental income through land appropriation (Jiang et al., 2018). This aside, due to their lower level of human capital and poorer non-agricultural experiences, it becomes more difficult for landless farmers to search for jobs in a more competitive job market (Li et al., 2016). This continuous failure to find a job, or working for a low salary, further decreases landless farmers’ self-esteem and negatively impacts their life satisfaction.

Social relations also play an important role in the life satisfaction of resettled villagers. The continuation of existing village organisations and kinship traditions has a strong and positive impact on the life satisfaction of the affected villagers after resettlement (He et al., 2009). In addition, Berry (1997) and Li (2004) have pointed out that resettlers’ adaptation to new communities plays an important role in their life satisfaction. Being involved in both the pre-resettlement and host societies serves to promote the resettlers’ acculturation and identity and contributes to their improved wellbeing after resettlement. Social connection with a host society is also important. Qian (2017) and Dan and Yanfeng (2006) have found that challenging relationships with the host society leads to higher rates of dissatisfaction among resettlers with their post-resettlement life.

Methodology

This paper examines quantitatively the factors influencing Chinese landless farmers’ livelihoods and life satisfaction after land appropriation and displacement. First-hand data were collected from a resettlement community called Xinghuayuan in Changchun, Northeast China. Xinghuayuan is home to landless farmers from the three villages Xinhua, Longxi, and Longbei, which have been demolished to make room for the Changchun High-Tech Development Zone, a national-level development zone (Fig. 1). As in a typical resettlement project in northeast China, the whole village – including farmland (chengbaodi), residential land (zhajidi) and private plots (ziliudi) – was appropriated and demolished by the municipal government between 2009 and 2012. At the time the land was appropriated, the three villages had around 50,000 residents, and all of them were resettled to Xinghuayuan, located within the former administrative boundary of Xinghua village. Xinghuayuan consisted mainly of modern residential buildings.

Fig. 1

The location of Xinghuayuan residential area and the three displaced villages

Fieldwork was conducted in January 2014, which means that the respondents would by then have been living in Xinghuayuan for five years. A face-to-face approach was employed to recruit respondents by visiting them at home. The face-to-face approach was employed to increase the response rate. We visited all households in the community. If the head of the household was not home, the address was skipped, because they were our targeted respondents. Visits took place at varying times of day, including evenings, to prevent the underrepresentation of working people. In total, 503 useful questionnaires were collected, with a response rate of around 50% (10% refusal5 and 40% skipped addresses when the respondents were not home). The fieldwork was conducted independently and there were no government officials accompanying the interviewers. The questionnaire consisted of five parts: (1) individual characteristics, such as age, gender, marital status, education; (2) compensation; (3) livelihood characteristics in terms of employment and income; (4) post-resettlement life satisfaction.

Data analysis was conducted in three steps. First, descriptive analysis was conducted to characterise respondents’ experiences of resettlement. Second, we used regression analysis to examine the factors affecting livelihood adaptation (post-resettlement employment and income level). A multiple regression analysis was used to establish the predictors for post-resettlement income level. The variables of gender, age, education, family size, re-employment difficulty, employment after displacement, and income before displacement were included in the regression based on the analysis of significant correlations, t-tests and analyses of variance. Tests on linearity, multi-collinearity and the distribution of residuals show that conditions for this analysis are met. To examine the factors influencing post-resettlement employment, a logistic regression model was adopted and the dependent variable (post-resettlement employment) was categorised into three groups of outcomes (unemployment, odd jobs, and permanent employment). The model was estimated using the statistical software package SPSS. The bivariate analyses showed that most socio-economic characteristics like gender, age, marital status, education and family size were not related with post-resettlement employment. Potential predictors included compensation, re-employment difficulty and employment before displacement.

Third, path analysis was used to construct a holistic model that seeks to explain the relationship between livelihood adaptation and life satisfaction. To examine the factors influencing post-resettlement income and life satisfaction holistically, as well as to understand the relationship between the two, path analysis was conducted. Path analysis can be used to describe the direct and indirect dependencies among a set of variables and it is a special case of structural equation modelling (SEM) with all measured variables. The model was estimated using the statistical software package LISREL (Jöreskog and Sörbom, 2008). Significant effects of socio-economic characteristics, compensation packages, the post-resettlement employment, changes in employment and income level, and life satisfaction after resettlement, were determined using bivariate analyses. Based on these analyses, the following predictors were selected: gender, age, education, household size, housing compensation, cash compensation, employment, income before relocation, and changes in income level. Next, the links between explanatory and endogenous variables were added. The interrelationships between endogenous variables were also added. All links that were not significant at the 0.05 level were then removed stepwise from the model.

Results
Descriptive analysis

Table 1 shows the respondents’ characteristics in terms of gender, age, marital status, education, household size, and livelihood in terms of employment and income level. Regarding age, the sample was representative of China’s rural populations. Regarding marital status, married respondents were overrepresented, at 88.5%. According to our survey, 7.2% of the respondents were in the 18–30 age group, 43.7% in the 30–50 age group, 25.8% in the 50–60 age group, and 23.3% in the over-60 age group. Also typical for rural populations are lower education levels: 93.2% of the respondents had low education (junior secondary and below) and only 6.8% had college or university degrees. The sample was not completely representative of the population with respect to gender. Whereas 50% of the population in Changchun is male, only 40.8% of respondents in our sample were male. Regarding the household size, 70.2% of respondents were more than three-person families, and only 3.4% of respondents were one-person families.

Socio-economic and livelihood characteristics of the sample

CharacteristicsFrequencyPercent
GenderMale20540.8
Female29859.2
Age18–30367.2
31–5022043.7
51–6013025.8
>6011723.3
Marital statusSingle5811.5
Married44588.5
EducationLow education31863.2
Medium15130
High education346.8
Household size1173.4
213326.4
315330.4
>420039.8
Employment before displacementOnly farming16933.6
Both farming and other jobs33466.4
Household income before displacement<10,000 RMB336.6
10,000–20,000 RMB11422.7
20,000–30,000 RMB15130
>30,000 RMB20540.8
Employment after displacementUnemployment12525.3
Odd jobs31764
Permanent employment5310.7
Household income after displacement<10,000 RMB15130
10,000–20,000 RMB11222.3
20,000–30,000 RMB15731.2
>30,000 RMB8316.5
Changes in household incomeDecreased26953.5
Unchanged17935.6
Increased5510.9
Life satisfactionSatisfied479.3
Neutral13126
Unsatisfied32564.6
Total503100

Our results show that the displacement has a significant negative impact on farmers’ livelihood. Before land appropriation, most households engaged only in farming (33.6%), or a combination of farming and other jobs (66.4%). After the land appropriation and displacement, the majority had jobs (74.7%), but only 10.7% of farmers had a permanent job, while 64% of them took low-paid and insecure odd jobs. There was also a significant level of unemployment (25.3%). There was a significant decrease in income among the landless farmers. Before appropriation, 40.8% of respondents earned over 30,000 RMB annually, 31.2% between 20,000 and 30,000 RMB, 22.3% between 10,000 and 20,000 RMB and 30.0% below 10,000 RMB. After appropriation, 53.5% of respondents suffered a decline in income, especially the group earning over 30,000 RMB, which decreased to 16.5%. Only 10.9% of respondents experienced an increase in income. The results thus show that the landless farmers suffered considerably in the process of land appropriation due to difficulties finding employment.

We also collected information about compensation. The land-lost farmers received two types of compensation: cash payment and apartments. Cash payment consisted of land compensation (50–70 RMB/m2) and crop compensation (2–3.5 RMB/m2). On average, the villagers received cash compensation of 263,400 RMB, which is almost ten years’ worth of agricultural income. The second type of compensation was an apartment similar in size to the original dwelling. The difference in size between the new and original dwellings was offset at 1,350 RMB/m2. In total, 60.2% of respondents obtained one apartment per household, and 35.6% obtained two apartments per household. Only 4.3% of respondents received three apartments as housing compensation.

Our results show that the landless farmers had a very negative outlook on life satisfaction. A total of 64.6% of respondents felt unsatisfied with life after land appropriation and displacement, and only 9.3% of respondents felt satisfied. In total, 26% of respondents were neutral with regard to life satisfaction after land appropriation and displacement.

Regression analysis

The results of the logistic regression model on post-resettlement employment indicate that employment experience and cash compensation are significant predictors of post-resettlement employment, while socio-economic characteristics are not significant in the model (Table 2). Compared with respondents who only engaged in farming before displacement, respondents who had job experience were better at securing job opportunities, including odd jobs and permanent jobs. However, the more cash compensation obtained by individuals, the less likely they were to be employed. This may be because individuals who received a higher level of cash compensation were less pressured to secure a steady income.

Logistic regression model for post-resettlement employment

Post-resettlement employmentBStd. ErrorSig.Exp(B)
Intercept1.2380.8850.162
Hard re-employment=No-0.8520.2880.0030.426
Odd jobsHard re-employment=Yes0b
Employment before=farming-2.0940.2540.0000.123
Employment before=both farming and odd jobs0b
Intercept-0.2381.4790.872
Cash compensation:0-20-0.9930.5090.0510.370
Fixed jobsCash compensation:21-40-1.5790.5230.0030.206
Cash compensation:>410b
Employment before=farming-2.3070.4250.0000.100
Employment before=both farming and odd jobs0b

Notes: The reference category is unemployment

Turning to the linear regression model on post-resettlement income, five explanatory variables were found to have a significant effect on post-resettlement income (Table 3). First, younger resettlers obtained more income. This is a reasonable finding, as younger people are in general better educated and skilled and have more non-agricultural experience, and they tend to be more eager to find urban jobs. Second, education level had a positive effect on post-resettlement income. Third, the household size had a positive effect on post-resettlement income. This could be related to the fact that bigger households have more labour, and that households with children or elderly members are under more pressure to earn income to support the family. Fourth, employment has a direct influence on income level and the result indicates that those who permanent jobs have more income than those who take odd jobs.. Fifth, pre-resettlement income had a positive effect on post-resettlement income. This is likely to be due to high pre-resettlement income indicating that the households had engaged in non-agricultural activities, which aided them in adapting after resettlement.

Linear regression model for post-resettlement income

Post-resettlement incomeβStd. Dev.
Household characteristics:
Gender-0.020.08
Age-0.24**0.03
Education0.50**0.16
Family size0.33**0.03
Re-employment difficulty0.29**0.09
Employment after displacement0.16*0.08
Income before displacement0.48**0.04

Note: **p≤0.001, *p≤0.01

Path analysis

The unstandardised coefficients and t-statistics of direct and total effects of the final model that are significant at the 0.05 significance level are shown in Table 4. The total effects are the direct effects plus indirect effects (via a mediating variable). The direct effects of the explanatory variables are shown in Fig. 2. The goodness of fit statistics of the model are shown at the bottom of Table 4. The overall fit of both models was good. Chi-square divided by the model degrees of freedom has been suggested as a useful measure, and convention suggests that for correct models this measure should be smaller than 2 (Golob, 2003). According to this criterion, the model had a good fit, with a value of 1.21. Another goodness of fit measure, which is based on the chi-square, is the root mean square error of approximation (RMSEA), which measures the discrepancy per degree of freedom. The value should preferably be less than 0.05 (Golob, 2003). The RMSEA of 0.02 also suggests that the model had a good fit with the data.

Fig. 2

Path analysis model

Note: All the links shown in Fig. 2 are significant at the 5% level

Path analysis model estimates (unstandardised effects)

FromTo
Post-resettlement income levelt statisticPost-resettlement life satisfactiont statistic
DirectDirectTotal
Effects between endogenous variables
Post-resettlement income level0.072.03
Effects of explanatory variables
Gender (0=male, 1=female)-0.15-2.68
Age-0.47-11.050.144.28
Education0.382.510.151.26
Household size0.216.760.036.76
Number of housing compensation0.030.64
Cash compensation0.012.550.013.12
Odd jobs-0.23-3.52
Fixed jobs-0.20-1.98
Income before resettlement0.325.160.021.97
Goodness-of-fit of the model
Chi square/degrees of freedom1.21
RESEA0.02
Model AIC148.93
Saturated AIC156
Normed fit index0.88

The model shows that gender, employment, income level and house compensation had direct effects on life satisfaction. We found that females tended to have a lower sense of life satisfaction after resettlement. This result may be due to the fact that most females have lower salaries than males, further influencing their life satisfaction. Housing compensation was found to have a positive effect on life satisfaction. Better housing compensation means more spacious apartments, which has a definite positive impact on life satisfaction. Regarding employment after resettlement, results show that people who had either odd jobs or fixed jobs had lower levels of life satisfaction. This suggests that the landless farmers were deeply unhappy about their livelihood changes. The results also show that the type of jobs they engaged in had no impact on post-resettlement income levels. Finally, higher income levels increase levels of life satisfaction. This is particularly important because, before appropriation, most respondents were largely self-sufficient in producing food. Now, food has to be purchased from markets, resulting in higher costs of living.

Age, education, household size, pre-resettlement income and housing compensation had indirect effects on life satisfaction through the mediator of income level. Regarding age, the younger obtained more income after the displacement, indirectly resulting in a higher level of life satisfaction after resettlement. People with better education were found to have a higher income level, indirectly resulting in better life satisfaction after resettlement. In terms of cash compensation, individuals who were working tended to have higher levels of income if they received more compensation. Regarding pre-resettlement income, people with a higher level of income before resettlement had a higher income level after resettlement, indirectly resulting in a higher level of life satisfaction after resettlement.

Concluding remarks

This study presents new evidence of the deeply problematic state of urbanisation in China from the perspective of land-lost peasants. It presents conclusive and clear evidence that landless farmers, marginalised by unfair and non-participatory land appropriation processes, suffer from livelihood adaptation difficulties, characterised by low-paid and insecure jobs, unemployment, loss of income and poor life satisfaction. Their appropriated land has now been developed into upscale, gated communities, home to wealthy urbanites, which further highlights the injustice of China’s land appropriation system.

This study also widens the literature on the factors influencing livelihood adaptation and life satisfaction among landless farmers. This is important because it allows us to move beyond landless farmers as a homogeneous group. The most concerning implication of the results is that the land appropriation process had a disproportionally negative impact on the most marginalised groups. Younger individuals, the highly educated and those with bigger families and higher pre-displacement income are more likely to have higher income levels after resettlement, resulting in a higher level of life satisfaction after displacement. On the other hand, the older generations experience more difficulties in livelihood adaption and have poorer life satisfaction. Women also have poorer life satisfaction than men, and the reasons for this need to be explored in future studies.

Improving the fairness of land appropriation and displacement in China goes to the heart of social stability in China, and, therefore, it is in the government’s interest to ensure better livelihood adaptation and to improve the life satisfaction of the displaced, especially marginalised groups (Wilmsen, 2016b). Compensation should be increased, and more assistance should be provided to landless farmers to help them secure permanent jobs. The paper has some shortcomings, however. The results are based on a relatively small sample of respondents from a specific case. We should therefore exercise caution in generalising the results. A recommendation for further research would be to repeat this study for other neighbourhoods that have been recently relocated in different suburban areas.

Fig. 1

The location of Xinghuayuan residential area and the three displaced villages
The location of Xinghuayuan residential area and the three displaced villages

Fig. 2

Path analysis modelNote: All the links shown in Fig. 2 are significant at the 5% level
Path analysis modelNote: All the links shown in Fig. 2 are significant at the 5% level

Path analysis model estimates (unstandardised effects)

FromTo
Post-resettlement income levelt statisticPost-resettlement life satisfactiont statistic
DirectDirectTotal
Effects between endogenous variables
Post-resettlement income level0.072.03
Effects of explanatory variables
Gender (0=male, 1=female)-0.15-2.68
Age-0.47-11.050.144.28
Education0.382.510.151.26
Household size0.216.760.036.76
Number of housing compensation0.030.64
Cash compensation0.012.550.013.12
Odd jobs-0.23-3.52
Fixed jobs-0.20-1.98
Income before resettlement0.325.160.021.97
Goodness-of-fit of the model
Chi square/degrees of freedom1.21
RESEA0.02
Model AIC148.93
Saturated AIC156
Normed fit index0.88

Logistic regression model for post-resettlement employment

Post-resettlement employmentBStd. ErrorSig.Exp(B)
Intercept1.2380.8850.162
Hard re-employment=No-0.8520.2880.0030.426
Odd jobsHard re-employment=Yes0b
Employment before=farming-2.0940.2540.0000.123
Employment before=both farming and odd jobs0b
Intercept-0.2381.4790.872
Cash compensation:0-20-0.9930.5090.0510.370
Fixed jobsCash compensation:21-40-1.5790.5230.0030.206
Cash compensation:>410b
Employment before=farming-2.3070.4250.0000.100
Employment before=both farming and odd jobs0b

Linear regression model for post-resettlement income

Post-resettlement incomeβStd. Dev.
Household characteristics:
Gender-0.020.08
Age-0.24**0.03
Education0.50**0.16
Family size0.33**0.03
Re-employment difficulty0.29**0.09
Employment after displacement0.16*0.08
Income before displacement0.48**0.04

Socio-economic and livelihood characteristics of the sample

CharacteristicsFrequencyPercent
GenderMale20540.8
Female29859.2
Age18–30367.2
31–5022043.7
51–6013025.8
>6011723.3
Marital statusSingle5811.5
Married44588.5
EducationLow education31863.2
Medium15130
High education346.8
Household size1173.4
213326.4
315330.4
>420039.8
Employment before displacementOnly farming16933.6
Both farming and other jobs33466.4
Household income before displacement<10,000 RMB336.6
10,000–20,000 RMB11422.7
20,000–30,000 RMB15130
>30,000 RMB20540.8
Employment after displacementUnemployment12525.3
Odd jobs31764
Permanent employment5310.7
Household income after displacement<10,000 RMB15130
10,000–20,000 RMB11222.3
20,000–30,000 RMB15731.2
>30,000 RMB8316.5
Changes in household incomeDecreased26953.5
Unchanged17935.6
Increased5510.9
Life satisfactionSatisfied479.3
Neutral13126
Unsatisfied32564.6
Total503100

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