Withdrawal from Rural Homesteads in Jinjiang

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This entry is adapted from the peer-reviewed paper 10.3390/ijerph19127414

Urbanization and aging populations are threatening the sustainability of rural development around the world. Improving the happiness of rural residents is closely related not only to rural development but also to the harmony and stability of a country. Sustainable development has become an important strategy for China’s rural areas.

withdrawal from rural homestead (WRH) happiness subjective well-being (SWB)

1. Introduction

Urbanization and aging populations worldwide have increased the occurrence of rural hollowing, which refers to the neglect and vacancy of rural dwellings ^[1]. Withdrawal from rural homesteads (WRH) is a land policy which attempts to organize idle and abandoned homesteads to optimize the use of rural areas to provide residents with living and production spaces to meet basic needs ^[2], including the happiness of local residents. Algan ^[3] pointed out that happiness reflects an individual’s satisfaction with life as well as the quality of the social system in which they live. Research shows that rural residents are happier than urban residents ^[4], likely due to high levels of urbanization in urban areas and contemporary social networking in rural areas ^[5]. Improving the happiness of rural residents is closely related to rural development; however, it is also related to the harmony, stability, and sustainable development of a country ^[6]. Exploring this issue is crucial to achieving the third goal of the 17 Sustainable Development Goals (SDGs)—ensuring healthy living and promoting life well-being ^[7].

In the context of rapid urbanization in developing countries such as China or India, the livelihood of rural residents, which depends on access to productive land, is under threat. The impact of the transfer of agricultural land is an important index measuring whether transfer behavior brings positive benefits. Land-lost rural residents have become a significant focus of studies on happiness ^[8]. More and more studies in developing countries have been focusing on the happiness of rural residents and its determinants after farmland expropriation or lease ^[9]^[10]^[11]^[12]. However, rarely has research focused on the relationship between WRH and the happiness of farmers in rural China. The term ‘homestead’ refers to land allocated by Chinese townships or village collectives to residents for the construction of housing. WRH offers money or new houses as compensation to residents for giving up their right to rural homesteads ^[13]. Homesteads play a decisive role in residents’ livelihoods and rural society. The WRH policy is a key strategy to achieve “rural revitalization” based on the premise of perfecting integrated rural development and enhancing rural sustainability ^[14].

The close connection between WRH policy and sustainable rural development lies in activating existing land resources of farmers who have withdrawn and enhancing the life cycle of farmers’ houses by redeveloping or renovating existing houses which are not being used efficiently. Systematic re-planning of the countryside has been adopted as the model that will ensure the sustainable development of China through improving living environments and maximizing idle land resources. Rural residents face both favorable and unfavorable impacts after WRH. These impacts involve land resources, human resources, and personal wealth ^[15]. Farmers benefit from WRH policy through the improvement of idle housing and rural living environments. This results in higher satisfaction with the policy, which is conducive to the expansion and implementation of the policy. However, the implementation of WRH policy may encourage rural residents to relocate to more urbanized areas to experience more convenient living conditions and better access to facilities that promote their health and well-being. Local governments can re-arrange rural land after WRH to provide public services needed to optimize land use ^[16]. Investigating farmers’ perceptions following the implementation of WRH enables researchers to examine the policy effects of WRH. Residents may suffer from psychological, cultural, and social exclusion due to a lack of social and economic security, conflicts with surrounding communities related to employment opportunities, and an inability to integrate into urban life. While many studies have examined the determinants of WRH from a decision-making perspective ^[17]^[18]^[19]^[20], the subjective well-being (SWB) of rural residents following WRH has rarely been investigated.

Are rural residents happy after WRH? What aspects of SWB do they experience? As different WRH pilots are implemented in various parts of rural China, different WRH models have different practical effects, such as monetary compensation or asset allocation. Are there differences in SWB under different WRH models? These are the questions asked paper. The results contribute to the literature on WRH and the life quality of rural residents, specifically in helping to understand the perception of residents of WRH policy. Rural residents represent a large group in China, and with current trends of urbanization, this group has an important role to play in realizing the integrated development of urban and rural areas ^[21]. The Chinese experience in WRH policy could serve to help other developing countries in the design of rural land policies.

2. Determinants of SWB after WRH

Researchers observed the impact of satisfaction with various aspects on SWB after WRH. In all models, household variables such as education level, HLAPC, income, health, and social capital all positively impacted SWB, with the exception of gender, which exerted an insignificant negative impact (see Table 1). The pooled model (model 7) indicated that rural residents with higher HLAPC, income, or stronger social capital were happier after WRH. Observing the impact of different dimensions of satisfaction on SWB revealed that economic, social, and environmental aspects significantly enhanced SWB, with the strongest influence exerted by the environmental aspect (coefficient value of 1.229). Satisfaction with cultural preservation and compensation policy inhibited SWB; however, this was not significant. Similarly, the dummy variable representing the WRH model did not reach statistical significance. In short, the above results partially support Hypothesis 1.

Table 1. Pooled results (dependent variables: SWB).

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Age	−0.0513	−0.0460	−0.188 *	−0.0594	−0.0507	−0.0553	−0.160
Age	(0.0982)	(0.0984)	(0.104)	(0.104)	(0.0982)	(0.0983)	(0.109)
Gender	0.247	0.247	0.136	0.212	0.247	0.250 *	0.141
	0.247	(0.151)	(0.159)	(0.159)	(0.151)	(0.151)	(0.166)
	(0.151)
Education	0.0634	0.0580	0.0629	0.0224	0.0641	0.0655	0.0252
Education	(0.0498)	(0.0501)	(0.0521)	(0.0526)	(0.0499)	(0.0499)	(0.0550)
HLAPC	0.0303 ***	0.0293 ***	0.0266 ***	0.0305 ***	0.0301 ***	0.0297 ***	0.0267 ***
HLAPC	(0.00433)	(0.00437)	(0.00464)	(0.00463)	(0.00435)	(0.00436)	(0.00498)
Log Revenue	0.491 ***	0.432 ***	0.359 ***	0.615 ***	0.489 ***	0.498 ***	0.448 ***
Log Revenue	(0.0760)	(0.0833)	(0.0811)	(0.0830)	(0.0761)	(0.0764)	(0.0949)
Health	0.126	0.116	0.0233	0.136	0.128	0.109	0.0252
Health	(0.112)	(0.112)	(0.118)	(0.119)	(0.112)	(0.113)	(0.126)
Social Capital	−0.0749	−0.0839	0.132	0.163	−0.0733	−0.0707	0.289 *
Social Capital	(0.134)	(0.135)	(0.144)	(0.145)	(0.135)	(0.135)	(0.153)
Mode2	0.289	0.327 *	0.0504	0.531 ***	0.299	0.208	0.291
Mode2	(0.181)	(0.183)	(0.193)	(0.195)	(0.182)	(0.196)	(0.226)
Mode3	0.112	0.0975	0.0333	−0.0267	0.110	0.128	−0.0787
Mode3	(0.178)	(0.179)	(0.189)	(0.191)	(0.179)	(0.179)	(0.202)
Income		0.188 *					0.226 *
Income		(0.107)					(0.120)
Social			1.231 ***				0.960 ***
Social			(0.142)				(0.158)
Environment				1.410 ***			1.229 ***
Environment				(0.156)			(0.173)
Culture					−0.0465		−0.0549
Culture					(0.0987)		(0.109)
Policy						−0.0986	−0.104
Policy						(0.0905)	(0.101)
Obs.	315	315	315	315	315	315	315

Notes: *, and *** represent significance at the 10% and 1% levels, respectively. Robust standard errors are shown in parentheses. All of the variables were counted and calculated by the authors.

3. Determinants of SWB after Different WRH

Researchers further examined whether different WRH models promote the satisfaction of farmers and thus promote their SWB (see Table 2). As above, most of the results regarding household variables were consistent with established theory. Among them, HLAPC, income, social capital, and health exerted statistically significant impacts on SWB. The interaction terms verified Hypothesis 2; that is, satisfaction under different WRH models has differing effects on SWB. In the pooled model (Model 6), satisfaction with the economic dimension only exerted a significant effect under the index replacement model (coefficient value of 0.343). Satisfaction with economic dimension of the other two models did not reach statistical significance. Satisfaction with the social dimension significantly promoted SWB. This was confirmed in all three models. Especially in the monetary compensation mode and asset replacement mode, the positive effect of satisfaction with the social dimension on SWB was strong (coefficient values of 1.32 and 1.494, respectively). The positive effect of satisfaction with the environment on SWB was statistically significant under all models; it was strongest under the index replacement model and the monetary compensation model, and slightly weaker under asset replacement. However, satisfaction with culture and policy did not exert a statistically significant effect on SWB.

Table 2. Index system for variable cross-items.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
Age	−0.0357	−0.190 *	−0.0640	−0.0332	−0.0491	−0.136
Age	(0.0972)	(0.103)	(0.103)	(0.0973)	(0.0977)	(0.112)
Gender	0.251 *	0.133	0.204	0.242	0.250 *	0.0884
Gender	(0.150)	(0.158)	(0.159)	(0.150)	(0.15)	(0.169)
Education	0.0666	0.0584	0.0191	0.0741	0.0718	0.0320
Education	(0.0493)	(0.0513)	(0.0519)	(0.0491)	(0.0491)	(0.0555)
HLAPC	0.0289 ***	0.0270 ***	0.0301 ***	0.0300 ***	0.0293 ***	0.0263 ***
HLAPC	(0.00434)	(0.00462)	(0.00460)	(0.00434)	(0.00438)	(0.00519)
Log Revenue	0.437 ***	0.374 ***	0.631 ***	0.493 ***	0.494 ***	0.434 ***
Log Revenue	(0.0834)	(0.0804)	(0.0821)	(0.0750)	(0.0755)	(0.0996)
Health	0.121	0.0238	0.130	0.131	0.107	6.961 ***
Health	(0.112)	(0.118)	(0.119)	(0.112)	(0.114)	(1.25)
Social Capital	−0.0823	0.119	0.160	−0.0887	−0.0602	8.614 ***
Social Capital	(0.134)	(0.143)	(0.144)	(0.136)	(0.135)	(1.286)
Economic*Mode1	0.162 *					0.343 **
Economic*Mode1	(0.107)					(0.15)
Economic*Mode2	0.253 **					0.0490
Economic*Mode2	(0.118)					(0.233)
Economic*Mode3	0.197 *					0.0312
Economic*Mode3	(0.111)					(0.303)
Social*Mode1		1.195 ***				0.817 ***
Social*Mode1		(0.141)				(0.179)
Social*Mode2		1.247 ***				1.312 ***
Social*Mode2		(0.143)				(0.350)
Social*Mode3		1.218 ***				1.494 ***
Social*Mode3		(0.143)				(0.389)
Environment*Mode1			1.355 ***			1.288 ***
Environment*Mode1			(0.153)			(0.205)
Environment*Mode2			1.525 ***			1.338 ***
Environment*Mode2			(0.168)			(0.342)
Environment*Mode3			1.351 ***			0.949 ***
Environment*Mode3			(0.153)			(0.334)
Cultural*Mode1				−0.0751		−0.0990
Cultural*Mode1				(0.100)		(0.129)
Cultural*Mode2				0.00914		−0.0657
Cultural*Mode2				(0.100)		(0.243)
Cultural*Mode3				−0.0236		0.106
Cultural*Mode3				(0.107)		(0.266)
Policy*Mode1					−0.142 *	0.0191
Policy*Mode1					(0.0841)	(0.131)
Policy*Mode2					−0.111	−0.222
Policy*Mode2					(0.109)	(0.165)
Policy*Mode3					−0.0984	−0.216
Policy*Mode3					(0.0896)	(0.334)
Observation	315	315	315	315	315	315

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors are shown in parentheses. All of the variables were counted and calculated by the authors.

4. Robustness

In order to check the robustness of the results, resesarchers introduced instrumental variables through a simple OLS model. The instrumental variable replaces the original policy and cultural variables with “overall satisfaction with implementation of WRH (1 = very satisfied; 5 = very satisfied)”. This variable better reflects the farmers’ perception of overall village progress after WRH, meeting the requirements of “correlation” and “exogenousness” of instrumental variables. The first column of Table 3 shows that the direction of the coefficients for economic, social, and environmental factors remains significantly positive, which supports the pooled result in Table 1. Researchers also drew on the work of Liang et al. ^[22] and reduced the total number of samples from 315 to 285 through bilateral shrinking of the 5% quantile. The second column of Table 2 shows that coefficients of all variables remained unchanged and reached significance.

Table 3. Result of robustness check.

Variables	(1)	(2)
Environment	0.975 ***	0.904 ***
Environment	(0.186)	(0.185)
Social	0.808 ***	0.985 ***
Social	(0.177)	(0.168)
Economic	0.510 ***	0.266 **
Economic	(0.123)	(0.132)
Satisfaction (Instrumental variable)	0.494 ***
Satisfaction (Instrumental variable)	(0.0522)
Cultural		−0.0166
Cultural		(0.124)
Policy		−0.109
Policy		(0.120)
Control variables	Yes	Yes
Observations	315	285
Type of method	OLS	Probit

Notes: **, and *** represent significance at the 5%, and 1% levels, respectively. Robust standard errors are shown in parentheses. All of the variables were counted and calculated by the authors.

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Fachao Liang

Zehua Wang

Sheng-Hau Lin

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Entry Collection: Environmental Sciences

Update Date: 11 Jul 2022

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