Economic and Spatial Integration of Land-Lost Households in Inland China: Evidence from a Survey in Nanchang City

Abstract

Many farmers lost their land and resettled in urban areas because of the rapid urban expansion of China. Although many studies have investigated the livelihood conditions of farmers after their resettlement, very little is known about their long-term outcomes. Based on a questionnaire survey, this study investigated the economic and spatial integration of land-lost farmers living in urban areas for more than ten years. Further, this study also examined the association between their economic and spatial integration. Our survey results indicate that the economic and spatial integration of the offspring from land-lost households were low. Although the educational level of the offspring was higher compared to the previous generation, there were still considerable disparities between them and other urbanites. Additionally, although their employment rate was improved, the quality of their occupations was not significantly enhanced, with unskilled or semi-skilled jobs being the most common. Furthermore, nearly half of the offspring reported a relatively low level of spatial integration (still living in the collective resettlement neighborhoods), which is influenced by demographic characteristics, family characteristics, and economic integration. To achieve sustainable urbanization, efforts should be made to improve the employment qualities and the residential mobility of land-lost households.

1. Introduction

Since the 1990s, China has witnessed rapid urban expansion. The urban built-up area, which was 12,856 km² in 1990, has quintupled over the past 30 years to 60,312 km² in 2019 [1]. This widespread urban expansion resulted in the expropriation of massive agricultural land [2,3], causing large numbers of farmers to lose their land and houses. Although there are no statistics on the number of land-lost farmers, that number has been estimated to be around 100 million as of the end of 2020 [4]. Many land-lost farmers were resettled to collective resettlement housing located in urban areas, thus passively becoming new urbanites. As the result of long-standing unequal economic development and the provision of education between rural and urban China, many rural residents have limited education compared to urban residents [5,6]. Consequently, it is difficult for those uncompetitive former rural residents to find a suitable non-agricultural job after losing the land they lived on. Many land-lost farmers reported experiencing unemployment and income reduction, while some even fell into poverty [7,8,9].

Additionally, the resettlement pattern of land-lost farmers (i.e., collective resettlement housing) may also have resulted in residential segregation from other urbanites. There are considerable disparities in socioeconomic status between land-lost households living in resettlement housing and other urbanites. Land-lost farmers also reported being prejudiced against by other urbanites [10]. Furthermore, the residential environment of collective resettlement housing is often not as favorable as other residential communities. Yet, even if land-lost farmers are dissatisfied with living conditions, they may not have the ability to move out from the resettlement housing because of financial reasons. A recent study suggested that this socio-spatial segregation may be exacerbated because of the stigmatization effects of resettlement housing [11]. Thus, understanding the economic and spatial integration mechanisms of land-lost farmers is essential for sustainable urbanization and social justice in China. Although the changes in the socioeconomic status of land-lost farmers after land expropriation have been examined by prior studies, little is known about their long-term outcomes, including the integration of their offspring.

Since the residential environment is an important determinant of integration [12], land-lost farmers may accomplish upward social mobility as a result of relocation to urban areas. Living in urban areas may provide land-lost farmers the opportunity to meet more diverse types of people and thus establish broader social ties, which could provide them the social capital they need to get ahead in life [13,14]. Furthermore, unlike their parents who grew up in rural areas, the offspring of land-lost households who grow up in urban areas might have better access to educational resources, which may improve their educational attainment and their odds of successful integration in the long run. The spatial assimilation theory argues that, over the course of time, new arrivals will move from segregated neighborhoods into mainstream neighborhoods after achieving socioeconomic integration [15,16,17]. From this perspective, land-lost farmers and their offspring may move out from the collective resettlement neighborhoods after improving their socioeconomic status.

However, according to the segmented assimilation theory, not all new arrivals can achieve upward socioeconomic mobility. Some may trend on a downward trajectory and even fall into permanent poverty [18]. In the case of the offspring of land-lost farmers, although their education environment is improved compared to their parents, they still face many disadvantages (e.g., limited parental social and human capital, segmented communities, and discrimination from other urbanites) in the process of integration. Can the offspring of land-lost farmers improve their socioeconomic status despite the obstacle of rural-urban inequality? If so, will their improvement in socioeconomic status lead to residential mobility (moving out from collective resettlement housing)? This study aims to answer these questions by investigating the long-term economic and spatial integration of land-lost households and their association with each other based on a survey conducted in Nanchang City of Jiangxi Province in January 2022.

2. Literature Review

2.1. Theories on Integration

Integration or assimilation is a complex concept referring to the successful settlement of migrants who have the knowledge and capacity to build a successful, fulfilling life in the host society [19,20]. The framework for integration studies originated from the study of immigrants integrating into Western countries. In the early 2010s, this concept was introduced to China to explain the experiences of new urbanites such as rural-to-urban migrants [21,22,23,24,25]. Integration has multiple aspects, including social, psychological, economic, and cultural dimensions [20,24]. This study will focus on the economic dimension of integration, which is usually measured by indicators such as education, occupation, and income [26,27,28].

According to classic social assimilation theory, over the course of time, immigrants will eventually integrate into mainstream society [29]. However, this theory was found to be empirically and ideologically limited when various outcomes for immigrants were confirmed [30]. Building off the theory of social assimilation, the segmented assimilation theory emerged, which argues that in addition to integration into the majority middle-class, some may trend downward into the lower class and fall into permanent poverty [18,31]. Additionally, the segmented assimilation theory also indicates that parental human capital, modes of incorporation (the way the host society receives them), and family structure jointly play significant roles in determining the integration path of second-generation immigrants, especially when they come across external obstacles (e.g., discrimination, bifurcated labor markets, and negative environmental effects) [12,32].

The outcomes of integration are associated with immigrants’ residential patterns, which is also expressed in the spatial assimilation theory [15,33]. After immigrants arrive in the host country, they will usually settle in neighborhoods populated by people who share similar backgrounds with themselves (e.g., enclaves in downtown areas). As they improve their socioeconomic status over time or across generations, they move out from such communities into better neighborhoods in the suburbs that are dominated by the local residents. At this point, it can be said that they have achieved spatial assimilation. In other words, spatial integration follows naturally from economic integration.

The literature on the integration of new urbanites in China has mainly focused on the rural-to-urban migrants who demonstrated poor integration, especially economic integration [34,35]. Further, migrants living in enclaves are found to have lower integration levels than those living in other types of neighborhoods; they are stuck there because they cannot integrate from these enclaves into the city [25]. Like many international immigrants and rural-to-urban migrants in China, land-lost farmers are also lacking in human capital and often struggle to adapt to life in the new environment. However, unlike international immigrants and rural-to-urban migrants, land-lost farmers are involuntarily displaced into collective resettlement neighborhoods together with their former fellow villagers. Meanwhile, land-lost farmers also have certain advantages over migrant workers because they are provided with urban registration and social security during their displacement; however, lacking urban registration and social security is considered to be the structural and institutional constraint in the integration process of rural-to-urban migrants. Furthermore, their remaining personal networks may have positive effects on their economic integration [36]. Therefore, it remains to be seen whether pre-existing theories and experiences are also applicable in the case of land-lost farmers.

2.2. Land-Lost Farmers in China

The 21st century has seen rapid industrialization and urbanization in many Asian developing countries, such as Vietnam, India, and China [3,37,38], which has coincided with many development and construction projects resulting in massive land acquisition. These land use changes have had a dramatic impact on the livelihoods of rural households, despite their social and cultural differences. In addition to the concept of integration, other approaches such as sustainable livelihood framework [38,39] and livelihood vulnerability analytical framework [40] have been applied to examine rural households’ living conditions.

The term land-lost farmers (Shidi nongmin) appeared in Chinese academic literature in the early 2000s as a result of the social problems that arose because of unregulated land acquisition. Since the 1990s, a large number of local government-led urban development projects (e.g., new towns and economic development zones) have been launched in China, which resulted in the acquisition of massive amounts of agricultural land [3]. One of the institutional drivers of rapid urban development has been the dual land ownership system [41,42]. In China, there are two kinds of ownership of land—state ownership (e.g., the land in the urban areas of cities) and peasant collective ownership (e.g., the land in rural and suburban areas). Ownership of the land is usually transferred to the state through land expropriation to implement land development on the collectively owned land. The Land Administration Law of the People’s Republic of China stipulates that the state may lawfully expropriate land in the interest of the public. This process enables the local government to gain a significant amount of revenue [43] because it could supply the land use rights of the new state-owned land to private sectors (e.g., industrial enterprises and real estate developers) at a high price but only needs to pay the village collective (i.e., the previous owner of the land) the compensation calculated based on the original use of the land [4].

Since land acquisition compensation standards were not adjusted in time to accommodate China’s transition to a market economy, many farmers lost their land without adequate compensation and fell into poverty, an issue that remains a source of intense conflict [44]. To mitigate the social issues induced by land acquisition, land acquisition transparency, compensation criteria, and resettlement approaches have been gradually improved since the early 2000s; however, land-lost farmers are still reporting difficulties in adapting to their new urban life, economically, socially, and psychologically [4,7,9,10].

Although the resettlement of land-lost farmers varies by time, region, and reason for land acquisition, the provision of resettlement housing, which is usually built by the local government, is a very common form of compensation [45]. Most land-lost farmers are resettled in concentrated resettlement communities and are thus largely segregated from other urban residents [7,9,10,45]. Despite improvements in their physical surroundings, some consider their resettlement slots to be new villages in the city that are surrounded by commercial developments and up-market communities [7,45].

However, land-lost farmers have also reported diverse integration outcomes. Recently, more detailed research has been conducted on the in-group and between-group differentiation of the livelihoods of land-lost farmers. The differences in their compensation, response capabilities, socioeconomic status, and relocation outcomes varies based on their demographic characteristics, policies, resource structures, and former status [40,46,47,48,49,50]. Although the integration situation of land-lost farmers in the short term has been extensively studied, little is known about their long-term outcomes, albeit a single longitudinal study reported that land-lost households are even more vulnerable a decade after the land acquisition, suffering from deteriorating financial security, high unemployment, and isolation [39].

Furthermore, land-lost farmers from different regions in China have also reported distinct outcomes [8,51]. As the drivers of urban development varied between inland and coastal regions, land-lost farmers from developed coastal regions (e.g., Guangdong Province) were found to be less vulnerable during the urban expansion [8,51,52,53]. After benefiting from preferential policies promoting development and advantaged location, cities in the coastal regions attracted significant foreign investment and achieved rapid economic growth, which contributed significantly to urban development. Local governments in developed coastal regions can share some benefits with farmers as they could enjoy handsome revenue from land expropriation compared to local governments in less developed inland regions [51]. Owing to well-established village organizations and the ambiguity of property rights, many villagers from coastal regions maintain their collective ownership of the land and carry out development projects by themselves rather than having their land expropriated by the government [8,42,54]. Meanwhile, economic development in coastal areas has also attracted millions of migrant workers and factories, and some land-lost farmers made a great fortune by renting housing and factory space [8,54]. Furthermore, the abundant employment opportunities in developed coastal areas can better help land-lost farmers transition to employment in the non-agricultural sector [55].

However, unlike coastal regions, urban development in vast inland regions is mainly promoted by the local government [52]. Without advantages in location and economic development, urban development projects in inland regions rely on revenue from land expropriation [52,56,57]. As a result, local governments may act as revenue maximizers, and the compensation offered to land-lost farmers could be inadequate [8,51]. Therefore, we should pay more attention to the integration outcomes of land-lost farmers in inland China, whose situation could be worse.

Compared with other new urbanites, land-lost farmers seem to enjoy more advantages in their new urban lives as their welcome-to-the-city package usually contains an urban registration, a resettlement flat, monetary compensation, and social security. However, as addressed in many studies, this package is not satisfactory nor sufficient [7,9,58]. Unlike rural-to-urban migrants who moved to cities voluntarily to pursue a better life, support their families back home, or save money for their future [58], land-lost farmers became new urbanites passively [59]. Regardless of whether they can adapt or not, their identity as an urbanite is not a choice, nor is it reversible. Therefore, in this sense, the outcome of integration is more vital for them.

3. Data and Methods

3.1. The Study Area

Nanchang is the capital city of Jiangxi Province in central China (Figure 1), and it was chosen as the research case because it is a typical inland city that has experienced rapid urban expansion since the 1990s. In 2000, the urban built-up area of Nanchang was 85 km², which increased to 208 km² in 2010, and reached 297 km² in 2020. The driver of its urban expansion has been the dual land ownership system [57], which has resulted in many land-lost households. Over the past 20 years, several urban development projects have taken place in Nanchang, such as Honggutan New District and High-Tech Industrial Development Zone. Although there were slight differences in the standard of monetary compensation for land expropriation across time and areas, the compensation contents were quite similar. This study focused on land-lost farmers in Honggutan New District, who may have a better integration outcome compared to those in other areas. This is because their land was expropriated for the construction of a new city center; thus, their resettlement sites are relatively close to the new downtown area, where there are more employment opportunities; moreover, their compensation rates were among the highest areas in Nanchang.

In 2000, Nanchang started to develop the Honggutan New District with hopes of it becoming the new city center in the future. The area of the new district was adjusted three times: from 4.28 km² to 50 km² in 2002, 78 km² in 2007, and eventually to 175 km² in 2012. The number of residents in the new district was 555,755 in 2020 [60]. As the development progressed, the new district evolved into the new city center and officially became one of the six administrative districts of Nanchang in 2019.

The government expropriated a large amount of collective-owned agricultural land to develop the new district. The very first round of land expropriation and house demolition occurred in 1999 during the preparation phase of the development. The compensation was paid out in two parts: once for the expropriated land and once for the demolished housing. Additionally, land-lost farmers were provided with basic pension insurance, which is paid by the government, village collectives, and individuals. As the land was owned collectively, monetary compensation was paid to the villagers committee (cunmin weiyuanhui). The villagers committee distributed part of the compensation to the villagers and kept the rest to establish and develop a collective business (e.g., real estate investment). Thus, the compensation that the villagers actually received was not a large sum.

Regarding the compensation for demolished housing, villagers could choose either monetary compensation or resettlement housing. Monetary compensation was calculated based on the area of their former farmhouse and its construction materials, which ranged from RMB 720 to 1550 (approximately 101 to 217 USD as of December 2022) per m². As the housing prices in the new district soared after the announcement of the development plan, it was difficult to purchase a house there with monetary compensation. Therefore, almost all the villagers chose resettlement flats. The area for the resettlement housing was calculated based on the number of people in the household, with 50 m² per person and up to 350 m² per household. Some households with many family members acquired multiple flats. However, villagers did not have ownership of the resettlement flats, which meant they could not sell the houses—they could only rent them out if they did not want to live there anymore or if they had any extra units. Thus, there are many tenants in the resettlement housing complex. The first resettlement housing complex was completed in 2003. Most of the resettlement housing complexes are close to where the villages used to be. Therefore, although the physical distance between villagers’ former homes and the resettlement housing complex is not large, the living environments are completely different. Figure 2 shows satellite images of part of the new district taken in 2002 and 2020. In 2002, most of the land in the Fenghuangzhou area was farmland; the farmhouses were near the main road. In 2020, most of the area had been developed and was full of high-rise buildings. The land-lost farmers in this area moved to the resettlement neighborhood located in relatively remote areas.

Given that the development is still ongoing, this study will focus on land-lost households living in areas that were planned in 2007 (Figure 3). The study areas include Fenghuang, Central, Hongjiaozhou, and Jiulonghu areas. The central area where the city government is located was developed earliest, followed by the Fenghuang and Hongjiaozhou areas. The Jiulonghu area, where the high-speed railway station was built, was developed relatively recently.

3.2. Data Collection

Since the offspring of land-lost households who have achieved a high level of integration may have moved to various locations in the city or even abroad, it is almost impossible to survey them directly. To solve this problem, we directly surveyed the land-lost farmers who were still living in the resettlement housing neighborhoods and asked about the socioeconomic status and residence situation of their children to capture the long-term change of land-lost households. Before starting the survey, we visited several villagers committees whose land had been expropriated. We were told that most of their villagers (i.e., land-lost farmers) are still living in the collective resettlement housing neighborhoods.

A questionnaire survey titled “Employment, residence, and integration of residents in Honggutan District” was conducted in January 2022. Although it was conducted during the COVID-19 pandemic, Nanchang had no cases of COVID-19 infection during the survey period, and thus the survey was conducted smoothly. Since there is no accessible sampling frame for land-lost households, the survey was a face-to-face street survey. The survey was conducted among 14 neighborhoods in the study area, including eight resettlement housing neighborhoods (Figure 3). We recruited and trained investigators from undergraduate students at the College of Economic and Management of Jiangxi Agricultural University. Those investigators visited these neighborhoods with the author at various times during weekdays and weekends. The author collected the questionnaire sheets from the investigators and performed quality checks daily.

The targets were residents of these neighborhoods who were over 18 years old. The survey aimed to thoroughly understand the integration situation of land-lost households. Questions included demographic characteristics, socioeconomic status, residential situation, social and economic integration, land expropriation experience, etc. For those respondents with children, we also asked them about their children’s working status, educational attainment, and living arrangements. A total of 548 valid responses were obtained, of which 402 were from residents of resettlement neighborhoods, and 245 respondents were identified as the target of this study—from households who had their land expropriated because of the development of the new district (Figure 4).

3.3. Measures

Economic integration refers to improved socioeconomic status, which is measured by educational and occupational attainment in this study. Spatial integration was presented by residential mobility—moving from the collective resettlement housing neighborhoods to other types of neighborhoods that are dominated by locals. We asked where the respondents’ non-student children live. The possible answers were “Together with me”, “In the same housing complex”, “In the new district”, “Other areas in Nanchang”, “Other cities in Jiangxi”, “Other provinces”, and “Overseas”. Those who were living with their parents (i.e., the respondents) or in the same housing complex were thus defined as having achieved a lower level of spatial integration. Conversely, those who lived outside the collective resettlement housing neighborhoods were defined as having achieved a higher-level spatial integration.

3.4. Analysis

The analysis is mainly based on the data of the respondents’ children. Descriptive statistics were first calculated for all variables by age groups and spatial integration status. As we did not ask respondents about their children’s age, the age of the respondents was used to estimate the age group of their children. Children of respondents under the age of 60 were classified as the younger generation, while the rest were classified as the older generation. It is necessary to note that some children of respondents, especially among the older generation, are themselves first-generation land-lost farmers who grew up in rural areas. All variables were compared by generation and spatial integration status using chi-square tests for categorical variables and t-tests for continuous variables.

Then, logistic regressions were applied to examine the effect of economic integration on spatial integration. Control variables include sex, generation, location of parents’ residence, number of siblings, and number of owned houses. Following this, stratified analyses based on generations were performed. Statistical analyses were performed using Stata/SE 17.0. The significance level in this study was set at p < 0.05.

4. Results

4.1. Sample Description

This study only included respondents from land-lost households (n = 245). The mean age of participants was 49.4 years, of whom 55.5% were female. The mean number of children per respondent was 2.5 (n = 215). The sociodemographic characteristics of the respondents are described in

Table 1. Sociodemographic characteristics of the respondents (n = 245).

	n	%
Sex
Male	108	44.1
Female	136	55.5
No answer	1	0.4
Age (years)
18–29	26	10.6
30–39	52	21.2
40–49	39	15.9
50–59	62	25.3
60 or more	66	26.9
Marital status
Single	23	9.4
Married	209	85.3
Divorced/widowed	13	5.3
Location of resettlement housing
Fenghuang area	71	29.0
Central area	59	24.1
Hongjiaozhou area	53	21.6
Jiulonghu area	62	25.3
Number of children
None	20	8.2
1	35	14.3
2	87	35.5
3	57	23.3
4	18	7.3
5 or more	18	7.3
No answer	10	4.1

.

The total number of children was 547, among whom 190 were students or not yet in school, and the rest were adult children who had already graduated from school (n = 357). Thirty children were excluded from this study because of incomplete information. Detailed data on these adult children were collected, among whom 57.2% were male (

Table 2. Sociodemographic characteristics of respondents’ non-student children (n = 327).

	Total (n = 327)		Generation of Offspring			Spatial Integration
			Younger (n = 136)	Older (n = 191)	p	Lower (n = 151)	Higher (n = 157)	p
	n/Mean	%/SD	%/Mean	%/Mean		%/Mean	%/Mean
Sex					n.s.			<0.001
Male	187	57.2	61.8	53.9		76.2	39.5
Female	140	42.8	38.2	46.1		23.8	60.5
Generation								0.005
Younger generation offspring	136	41.6	-	-		51.7	35.7
Older generation offspring	191	58.4	-	-		48.3	64.3
Location of parents’ residence					n.s.			<0.001
Fenghuang area	90	27.5	25.0	29.3		36.4	18.5
Central area	97	29.7	32.4	27.8		30.5	30.6
Hongjiaozhou area	62	19.0	23.5	15.7		12.6	25.5
Jiulonghu area	78	23.9	19.1	27.2		20.5	25.5
Educational attainment					<0.001			n.s.
Middle school or below	167	51.1	39.7	59.2		48.3	53.5
High school	80	24.5	26.5	23.0		27.8	21.0
Higher education	80	24.5	33.8	17.8		23.8	25.5
Occupational attainment					n.s.			0.013
Agricultural and manual workers	124	37.9	33.1	41.4		43.1	33.8
Service and sales workers	51	15.6	14.7	16.2		19.2	12.1
Professionals/office clerks/managers	63	19.3	24.3	15.7		14.6	24.2
Small business owners	47	14.4	12.5	15.7		15.2	13.4
Not working/don’t know/no answer	42	12.8	15.4	11.0		8.0	16.6
Living arrangements					<0.001
Living with parents	72	22.0	41.2	8.4		-	-
Living in the same complex as parents	79	24.2	16.2	29.8		-	-
Living in other areas in the new district	52	15.9	11.8	18.9		-	-
Living in other areas in Nanchang	85	26.0	23.5	27.8		-	-
Living outside Nanchang	20	6.1	5.9	6.3		-	-
No answer	19	5.8	1.5	8.9		-	-
Number of siblings	3.3	1.3	2.7	3.8	<0.001	2.9	3.7	<0.001
Number of houses owned	2.4	1.5	2.4	2.3	n.s.	2.4	2.4	n.s.

p values are for chi-square tests for categorical variables or t-tests for continuous variables. n.s. not significant.

). Further, 41.6% of them were classified as the younger generation, with the rest as the older generation (58.4%) based on their parents’ age.

4.2. Descriptive Results

As shown in Table 2, more than half of the respondents’ children only had compulsory education or below (51.1%). In contrast, the percentage of those who received higher education was only 24.5%. Regarding occupational status, 37.9% of them were agricultural or manual workers, followed by skilled workers (i.e., professionals/office clerks/managers; 19.3%), service and sales workers (15.6%), and small business owners (14.4%).

Regarding spatial integration, we found that nearly half the children from land-lost households were still living in resettlement neighborhoods, of which 22% were still living with their parents and 24.2% in other flats within the same complex. The percentage of those who were considered as having a higher level of spatial integration (i.e., not living in the resettlement neighborhoods) was 48%. Furthermore, between groups with different spatial integration levels, significant statistical differences were observed in terms of sex, generation, location of parents’ residence, occupational attainment, and the number of siblings through chi-square tests or t-tests. Females, the older generation, those whose parents were living in the Hongjiaozhou area, skilled workers, and those who had more siblings were more likely to live outside of resettlement neighborhoods. No significant differences were observed in educational attainment and the number of houses owned between lower- and higher-level spatial integration groups.

Additionally, as shown in Table 2, statistically significant differences in educational attainment, living arrangements, and number of siblings between generations were observed. Compared to older generation offspring from land-lost households, the younger generation was more likely to receive higher education. However, as many of the younger generation (41.2%) were still living with their parents in the resettlement flats, the degree of their spatial integration was relatively low. No significant differences were found in sex, location of parents’ residence, occupational attainment, and number of houses owned between generations.

Trends of educational attainment, occupational attainment, and living arrangements of respondents’ children by parental age were displayed in Figure 5. The younger the land-lost farmers are, the higher the proportion of them with higher education (Figure 5a). Figure 5b shows that the proportion of the younger generation working in unskilled occupations was significantly lower compared to previous generations. The ratio of small business owners has not changed considerably in recent generations. However, the proportion of skilled occupations was U-shaped. Figure 5c shows that the percentage of individuals from the younger generation living with parents or in other flats of the same complex was higher.

4.3. Multivariable Logistic Regression

Table 3. Logistic regression models of spatial integration (full sample).

	Model 1				Model 2
	OR		95 CIs		OR		95 CIs
Sex
Male	1.00 (reference)				1.00 (reference)
Female	4.86	***	2.73	8.63	4.99	***	2.71	9.18
Generation
Younger generation offspring	1.00 (reference)				1.00 (reference)
Older generation offspring	1.88	*	1.03	3.44	2.07	*	1.09	3.91
Location of parents’ residence
Fenghuang area	0.38	**	0.19	0.78	0.30	**	0.14	0.66
Central area	1.00 (reference)				1.00 (reference)
Hongjiaozhou area	1.54		0.68	3.48	1.35		0.57	3.18
Jiulonghu area	1.00		0.47	2.13	0.87		0.39	1.91
Number of siblings	1.41	*	1.08	1.83	1.51	**	1.14	2.00
Number of houses owned	0.86		0.72	1.03	0.80	*	0.66	0.97
Educational attainment
Middle school or below	0.36	**	0.18	0.73	0.67		0.30	1.47
High school	0.44	*	0.20	0.96	0.64		0.27	1.50
Higher education	1.00 (reference)				1.00 (reference)
Occupational attainment
Agricultural and manual workers					0.17	***	0.07	0.45
Service and sales workers					0.17	**	0.06	0.46
Professionals/office clerks/managers					1.00 (reference)
Small business owners					0.25	**	0.09	0.70
Not working/don’t know/no answer					0.51		0.17	1.53
Constant	0.09	***	0.03	0.25	0.20	**	0.06	0.61
n	296				296
McFadden’s R2	0.192				0.239

OR, odds ratio; CI, confidence interval. * p < 0.05, ** p < 0.01, *** p < 0.001.

presents the results of multivariable logistic regression analyses examining the association between economic and spatial integration. Independent variables in Model 1 included educational attainment and control variables, while occupational attainment was added in Model 2. A significant relationship between educational level and spatial integration was confirmed even after adjusting for background variables such as sex and age groups (Model 1). Those with lower educational attainment were more likely to live in collective resettlement housing. However, the effects of educational attainment on spatial integration were no longer significant upon occupational attainment in Model 2. Occupation attainment was found to be significantly related to spatial integration. Compared to skilled workers, workers in other jobs were less likely to achieve spatial integration.

Additionally, sex, age group, parental residence location, number of siblings, and number of houses owned were significantly associated with spatial integration. Being female, middle-aged, and growing up in a family with more children were positively associated with a higher level of spatial integration. In contrast, those with parents who lived in the Fenghuang area, or whose families owned more houses were less likely to integrate spatially.

Results of stratified analyses based on generation are shown in

Table 4. Results of stratified analyses by generation.

	Younger Generation Offspring
	Model 3				Model 4
	OR		95 CIs		OR		95 CIs
Sex
Male	1.00 (reference)				1.00 (reference)
Female	9.51	***	3.74	24.16	8.32	***	3.07	22.52
Location of parents’ residence
Fenghuang area	0.85		0.26	2.78	0.76		0.22	2.66
Central area	1.00 (reference)				1.00 (reference)
Hongjiaozhou area	0.63		0.17	2.38	0.68		0.17	2.72
Jiulonghu area	2.64		0.77	9.02	2.19		0.56	8.52
Number of siblings	1.46		0.82	2.60	1.56		0.82	2.97
Number of houses owned	0.78		0.60	1.03	0.81		0.61	1.09
Educational attainment
Middle school or below	0.88		0.31	2.51	2.42		0.63	9.22
High school	1.04		0.34	3.18	1.85		0.52	6.67
Higher education	1.00 (reference)				1.00 (reference)
Occupational attainment
Agricultural and manual workers					0.15	*	0.03	0.69
Service and sales workers					0.18	*	0.04	0.90
Professionals/office clerks/managers					1.00 (reference)
Small business owners					0.14	*	0.02	0.81
Not working/don’t know/no answer					1.11		0.22	5.71
Constant	0.02	***	0.00	0.11	0.03	***	0.00	0.21
n	130				130
McFadden’s R2	0.229				0.299
	Older generation offspring
	Model 5				Model 6
	OR		95 CIs		OR		95 CIs
Sex
Male	1.00 (reference)				1.00 (reference)
Female	4.38	***	1.94	9.91	5.63	***	2.30	13.80
Location of parents’ residence
Fenghuang area	0.21	**	0.08	0.58	0.17	**	0.06	0.52
Central area	1.00 (reference)				1.00 (reference)
Hongjiaozhou area	3.74		0.86	16.20	2.82		0.60	13.35
Jiulonghu area	0.48		0.16	1.39	0.44		0.14	1.38
Number of siblings	1.57	**	1.12	2.19	1.58	*	1.10	2.25
Number of houses owned	0.77		0.58	1.03	0.67	*	0.49	0.93
Educational attainment
Middle school or below	0.14	**	0.04	0.46	0.19	*	0.05	0.69
High school	0.12	**	0.03	0.45	0.14	**	0.03	0.60
Higher education	1.00 (reference)				1.00 (reference)
Occupational attainment
Agricultural and manual workers					0.13	*	0.03	0.68
Service and sales workers					0.09	**	0.02	0.49
Professionals/office clerks/managers					1.00 (reference)
Small business owners					0.22		0.04	1.26
Not working/don’t know/no answer					0.21		0.03	1.37
Constant	0.54		0.10	2.77	2.59		0.33	20.38
n	166				166
McFadden’s R2	0.250				0.293

Logistic regression. The dependent variable is spatial integration. OR, odds ratio; CI, confidence interval. * p < 0.05, ** p < 0.01, *** p < 0.001.

. We found that the effects of educational attainment on spatial integration were only evident among older generation offspring, even after controlling for occupation status. The effects of occupational attainment were confirmed across generations. As for background variables, only sex was significantly related to spatial integration among both generations, while the effects of parental residence location, number of siblings, and number of houses were only evident among older generation offspring.

5. Discussion

5.1. Economic Integration of Land-Lost Households

Although many studies have confirmed the poor livelihoods of land-lost farmers after their land expropriation, very little is known about their long-term changes. Based on a questionnaire survey, this study investigated the economic and spatial integration of land-lost farmers who have been relocated to urban areas for more than ten years. Further, this study also examined whether economic integration would lead to spatial integration among land-lost households.

The survey results show that the educational attainment of land-lost farmers, as well as that of their offspring, did not improve significantly. This may be because improving the socioeconomic status of one population may require a long time. Noteworthily, the proportion of individuals from the younger generation of land-lost farmers with higher education (33.8%) was much higher than that of the previous generation (17.8%). However, as the overall education level of the Chinese population has been improving rapidly in recent years (including those who live in rural areas), it is difficult to conclude whether the improvement of educational attainment among land-lost offspring is because of the overall educational structure changes or their resettlement.

As a reference, according to the 2020 census in China, the proportions of residents who were highly educated in Nanchang and the new district were 26.8% and 54.3%, respectively [61]. It is difficult to compare these figures with that of the younger generation of land-lost farmers directly, as the statistics data included the older residents and residents in rural areas of the city that are usually less educated. The education level of the young generation of land-lost farmers was only slightly better than the average level of all residents of the city albeit it was still far below the average level of new district residents around them. Since the new city center comes equipped with good public facilities and livable residential environments, it has attracted many affluent families despite the soaring housing prices. Most of the parents in these families are not only well-educated themselves, but they are also able to invest a lot of resources in their children’s education. Thus, the disparities in education level between land-lost households and new district residents are larger than compared to the disparities with other residents of the city and are difficult to narrow. Predictably, the younger generation from land-lost households is at a significant disadvantage in terms of human capital when it comes to finding a desired job in their place of residence.

With regard to occupational attainment, most land-lost farmers in our survey were working, which is a result different from that of a previous study reporting a lack of jobs for displaced villagers in a development project on the outskirts of Shanghai [7], and a study reporting dramatically increased unemployment among land-lost farmers in Shandong Province [39]. This may be because the land-lost households in this study were resettled near the new city center, where many new public facilities, shopping malls, and commercial housing projects were built, creating many new job opportunities. However, the quality of their employment was not improved significantly. Although the proportion of those in unskilled or semi-skilled jobs has declined (Figure 5b), it was still at a high level—49.3% among the younger generation of land-lost households. Many of them, including the young generation, are still working in low-skilled jobs. Nonetheless, a rise in the proportion of skilled jobs among land-lost farmers was observed. Additionally, the proportion of skilled labor among land-lost farmers presented a U-shape, indicating that the proportions of those in skilled jobs among the youngest and oldest land-lost farmers’ offspring were both relatively high. The older generation may have become skilled workers through the accumulation of work experience, whereas the youngest generation may have entered the labor market very recently in professional jobs because of an improvement in their educational attainment.

5.2. Spatial Integration of Land-Lost Farmers

Nearly half of the offspring of land-lost households reported a relatively low level of spatial integration. The spatial integration among offspring of land-lost farmers was associated with their demographic characteristics (i.e., sex and age group), family characteristics (i.e., number of siblings, number of houses owned, and location of parents’ residence), and their economic integration (i.e., educational and occupational attainment). Some effects differed according to generation.

Female offspring were more likely to have higher residential mobility regardless of the generation. This is because when female offspring marry, they are more likely to move in with their husbands who live outside the resettlement neighborhoods. Moreover, if they had already married before the land expropriation, they were not considered family members when calculating the area of resettlement flats. Therefore, the land-lost households usually would not give a resettlement flat to their married female offspring if they have male offspring. In other words, female offspring were more likely to achieve spatial integration passively.

The proportion of those who moved out from the resettlement housing was lowest among the youngest generation of land-lost households (Figure 5c). This may be because, during the time of the survey, these young offspring of land-lost farmers had not yet begun to live independently, as many of them were living with their parents in the resettlement housing. Another reason is that, as the number of children declined by generation, the probability of them living with their parents might rise accordingly, which subsequently decreases their probability of moving out from the resettlement neighborhoods. As shown in Model 2 of Table 3, the probability of moving out from the resettlement neighborhood increases by 1.5 times [OR = 1.51, 95 CI 1.14–2.00] for each additional sibling in the land-lost households. The positive association between the number of siblings and spatial integration was also confirmed among younger [1.56, 0.82–2.97] and older generation offspring [1.58, 1.10–2.25] in stratified analyses, but it was not statistically significant among younger generation offspring (Table 4).

Besides the number of siblings, other family characteristics, such as the number of houses owned, were negatively related to the residential mobility of their offspring. This is because most houses that land-lost farmers owned were resettlement flats located in the same neighborhood; their offspring will still be in the resettlement neighborhood when they move into these extra resettlement flats. Therefore, the more houses they owned, the lower the residential mobility of their offspring. Although land-lost households could choose either monetary compensation or resettlement housing as compensation for their expropriated farmhouse, most chose the latter. Owing to the large household size, many of them obtained multiple resettlement flats. Since resettlement houses cannot be traded, people cannot sell them and add some money to buy a house elsewhere. Therefore, the threshold for them to move elsewhere could be high, and they would have to either rent or buy a brand-new house. In other words, the current compensation policy is one of the factors hindering the spatial integration of land-lost households; increasing the rate of monetary compensation and allowing transactions of resettlement housing may alleviate such obstacles.

With regard to economic integration, educational attainment was associated with residential mobility before controlling for occupational status, indicating that occupation status has stronger effects on spatial integration. Those who were skilled workers were more likely to achieve spatial integration regardless of generation, which may be because of their higher wages and increased ability to purchase their own flat. Furthermore, as in the case of rural-to-urban migrants, working in labor-intensive jobs may be a barrier for those new urbanites to form a border social network [25], which may prevent them from accumulating resources for upward social mobility and residential mobility.

Moreover, the effects of educational attainment varied in different generations. Among younger generation offspring, educational attainment was not associated with spatial integration, even before controlling for occupational status. However, educational attainment was significantly associated with spatial integration among older generation offspring, even after controlling for occupational status. This may be because, even though higher education may lead to better jobs and higher wages, it takes time to accumulate enough wealth to purchase a flat. In addition, with the rapid expansion of higher education in China, highly educated youth are very common nowadays, which may lead to overqualification or overeducation [62]. Therefore, the value of a college degree may not be as great as it used to be; those young offspring of land-lost households who received higher education do not necessarily enjoy an extra advantage in their goal of achieving spatial integration if they cannot find a suitable job in labor markets. Moreover, other factors, such as the reputation of the institution they graduated from or their majors, may be more important for higher education returns. However, students with low socioeconomic status students, such as offspring of land-lost households, are disadvantaged when it comes to accessing elite Chinese universities [63,64]. As a result, it will be more challenging for offspring from land-lost households to improve their occupational attainment and, subsequently, spatial integration.

Although this study captured the long-term changes in land-lost households in inland China, there are some limitations that need to be mentioned. First, the survey used in this study did not apply a probability sampling method, as there were no accessible sampling frames for land-lost households. Although we were told that most first-generation land-lost farmers were still living in the resettlement housing, there were still households that had moved out of these neighborhoods and, therefore, were not surveyed. Moreover, people who have more free time, such as those who are unemployed or retired, may be over-represented. Second, as our survey was cross-sectional, causality between economic and spatial integration could not be demonstrated. That is, spatial integration may be a cause rather than a consequence of economic integration because those who moved out of the resettlement neighborhoods may build a more diverse social network, which facilitates their economic integration. Third, this study focuses on the land-lost households of only one development project in the selected city. As demonstrated in a previous study, the economic integration of land-lost farmers may vary across regions [50]. Nanchang is an inland provincial capital city that is less developed than cities in the coastal area, but still probably more developed than many other non-capital inland cities. The land-lost households of the study area were relocated relatively close to the new city center. Therefore, the integration outcomes of land-lost households in this study may be better than those from other areas, particularly less developed inland cities. Nevertheless, the economic and spatial integration of the land-lost households in this study was not high.

6. Conclusions

Although land-lost households have lived in urban areas for more than ten years, their economic integration and spatial integration of their offspring remain low. The educational level of younger offspring was higher than the previous generation; however, there are still considerable disparities between them and other urbanites. Although their employment rates have increased, improvements in the quality of their employment have been limited. Low levels of economic integration may hinder their residential mobility and thus exacerbate the residential segregation of collective resettlement neighborhoods. There is no denying that the construction of the new district has had significant positive effects on the city in terms of economic development and the improvement of the residential environment. However, to achieve sustainable urbanization, governments should pay attention to the employment qualities and residential mobility of land-lost households in the long term. It is worth noting that an important reason for the low level of economic integration among land-lost farmers is institutional inequality—the long-standing inequality in economic development and educational provision between rural and urban China. Thus, improving the economic integration of land-lost households, including their offspring, is an important issue from a social justice perspective. As urbanization and urban development continue, more new land-lost farmers are certain to emerge in the future; narrowing the gap between rural and urban China is the foundation for sustainable urbanization.