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Liang, F.;  Wang, Z.;  Lin, S. Withdrawal from Rural Homesteads in Jinjiang. Encyclopedia. Available online: https://encyclopedia.pub/entry/24964 (accessed on 07 October 2024).
Liang F,  Wang Z,  Lin S. Withdrawal from Rural Homesteads in Jinjiang. Encyclopedia. Available at: https://encyclopedia.pub/entry/24964. Accessed October 07, 2024.
Liang, Fachao, Zehua Wang, Sheng-Hau Lin. "Withdrawal from Rural Homesteads in Jinjiang" Encyclopedia, https://encyclopedia.pub/entry/24964 (accessed October 07, 2024).
Liang, F.,  Wang, Z., & Lin, S. (2022, July 09). Withdrawal from Rural Homesteads in Jinjiang. In Encyclopedia. https://encyclopedia.pub/entry/24964
Liang, Fachao, et al. "Withdrawal from Rural Homesteads in Jinjiang." Encyclopedia. Web. 09 July, 2022.
Withdrawal from Rural Homesteads in Jinjiang
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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
(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
(0.151) (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
(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 ***
(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 ***
(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
(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 *
(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
(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
(0.178) (0.179) (0.189) (0.191) (0.179) (0.179) (0.202)
Income   0.188 *         0.226 *
(0.107) (0.120)
Social     1.231 ***       0.960 ***
(0.142) (0.158)
Environment       1.410 ***     1.229 ***
(0.156) (0.173)
Culture         −0.0465   −0.0549
(0.0987) (0.109)
Policy           −0.0986 −0.104
(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
(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
(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
(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 ***
(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 ***
(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 ***
(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 ***
(0.134) (0.143) (0.144) (0.136) (0.135) (1.286)
Economic*Mode1 0.162 *         0.343 **
(0.107) (0.15)
Economic*Mode2 0.253 **         0.0490
(0.118) (0.233)
Economic*Mode3 0.197 *         0.0312
(0.111) (0.303)
Social*Mode1   1.195 ***       0.817 ***
(0.141) (0.179)
Social*Mode2   1.247 ***       1.312 ***
(0.143) (0.350)
Social*Mode3   1.218 ***       1.494 ***
(0.143) (0.389)
Environment*Mode1     1.355 ***     1.288 ***
(0.153) (0.205)
Environment*Mode2     1.525 ***     1.338 ***
(0.168) (0.342)
Environment*Mode3     1.351 ***     0.949 ***
(0.153) (0.334)
Cultural*Mode1       −0.0751   −0.0990
(0.100) (0.129)
Cultural*Mode2       0.00914   −0.0657
(0.100) (0.243)
Cultural*Mode3       −0.0236   0.106
(0.107) (0.266)
Policy*Mode1         −0.142 * 0.0191
(0.0841) (0.131)
Policy*Mode2         −0.111 −0.222
(0.109) (0.165)
Policy*Mode3         −0.0984 −0.216
(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 ***
(0.186) (0.185)
Social 0.808 *** 0.985 ***
(0.177) (0.168)
Economic 0.510 *** 0.266 **
(0.123) (0.132)
Satisfaction
(Instrumental variable)
0.494 ***  
(0.0522)
Cultural   −0.0166
(0.124)
Policy   −0.109
(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.

References

  1. Zhou, T.; Jiang, G.; Li, G.; Zhoum, D.; Qu, Y. Neglected idle rural residential land IRRL in metropolitan suburbs: Spatial differentiation and influencing factors. J. Rural Stud. 2020, 78, 163–175.
  2. Tian, Y.; Liu, Y.; Liu, X.; Kong, X.; Liu, G. Restructuring rural settlements based on subjective well-being SWB: A case study in Hubei province, central China. Land Use Policy 2017, 63, 255–265.
  3. Algan, Y.; Murtin, F.; Beasley, E.; Higa, K.; Senik, C. Well-being through the lens of the internet. PLoS ONE 2019, 14, e0209562.
  4. Sørensen, J.F.L. Rural-urban differences in life satisfaction: Evidence from the European Union. Reg. Stud. 2014, 48, 1451–1466.
  5. Nanor, M.A.; Poku-Boansi, M.; Adarkwa, K.K. Determinants of subjective wellbeing in rural communities: Evidence from the Juaben Municipality, Ghana. Cities 2021, 113, 103–140.
  6. Li, L.; Zhang, Z.; Fu, C. The subjective well-being effect of public goods provided by village collectives: Evidence from China. PLoS ONE 2020, 15, e0230065.
  7. Tsurumi, T.; Yamaguchi, R.; Kagohashi, K.; Managi, S. Material and relational consumption to improve subjective well-being: Evidence from rural and urban Vietnam. J. Clean. Prod. 2021, 310, 127499.
  8. He, S.; Liu, Y.; Webster, C.; Wu, F. Property rights redistribution, entitlement failure and the impoverishment of landless farmers in China. Urban Stud. 2009, 46, 1925–1949.
  9. Liang, Y.; Zhu, D. Subjective Well-Being of Chinese Landless Peasants in Relatively Developed Regions: Measurement Using PANAS and SWLS. Soc. Indic. Res. 2015, 123, 817–835.
  10. Liu, W.; Li, J.; Ren, L.; Xu, J.; Li, C.; Li, S. Exploring Livelihood Resilience and Its Impact on Livelihood Strategy in Rural China. Int. J. Soc. Indic. Res. 2020, 150, 977–998.
  11. Kumar, P.; Kumar, P.; Garg, R.K. A study on farmers’ satisfaction and happiness after the land sale for urban expansion in India. Land Use Policy 2021, 109, 105603.
  12. Zhao, X.; Jin, L.; Sun, B. Gone with the land: Effects of land expropriation on health and subjective well-being in rural China. Health Place 2021, 73, 102614.
  13. Chen, H.; Zhao, L.; Zhao, Z. Influencing factors of farmers’ willingness to withdraw from rural homesteads: A survey in Zhejiang, China. Land Use Policy. 2017, 68, 524–530.
  14. Tang, P.; Chen, J.; Gao, J.; Li, M.; Wang, J. What role s do village committees play in the withdrawal from rural homesteads? Evidence from Sichuan Province in Western China. Land 2020, 9, 477.
  15. Long, H.; Tu, S.; Ge, D.; Li, T.; Liu, Y. The allocation and management of critical resources in rural China under restructuring: Problems and prospects. J. Rural. Stud. 2016, 47, 392–412.
  16. Sun, X.; Zhang, Z.; Zhang, Y. Factors influencing farmer’s decision-making behavior on rural construction land transformation. Sustainability 2018, 10, 4288.
  17. Shan, Z.; Feng, C. The redundancy of residential land in rural China: The evolution process, current status and policy implications. Land Use Policy 2018, 74, 179–186.
  18. Fan, W.; Zhang, L. Does cognition matter? Applying the push-pull-mooring model to Chinese farmers’ willingness to withdraw from rural homesteads. Pap. Reg. Sci. 2019, 98, 2355–2369.
  19. Cao, Q.; Sarker, M.N.I.; Sun, J. Model of the influencing factors of the withdrawal from rural homesteads in China: Application of grounded theory method. Land Use Policy. 2019, 85, 285–289.
  20. Zhang, S.; Qian, Z. Villagers’ acculturation in China’s land expropriation-induced resettlement neighborhood: A Shanghai case. Pap Reg Sci. 2020, 74, 174–188.
  21. Liu, L.; Wang, T.; Xie, L.; Zhan, X. Influencing Factors Analysis on Land-Lost Farmers’ Happiness Based on the Rough DEMATEL Method. Discrete Dyn. Nat. Soc. 2020, 2020, 6439476.
  22. Liang, F.; Lin, C.; Lin, S.H. Farmers’ livelihood, risk expectations and homestead withdrawal policy: Evidence on Jinjiang Pilot of China. Int. J. Strateg. Prop. Manag. 2022, 26, 56–71.
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