The Impact of Land Transfer on the Way Of Old-age care of Elderly Farmers in China：an empirical analysis based on the endogenous conversion probit model

The Impact of Land Transfer on the Way Of Old-age care of Elderly Farmers in China：an empirical analysis based on the endogenous conversion probit model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Land Transfer on the Way Of Old-age care of Elderly Farmers in China：an empirical analysis based on the endogenous conversion probit model Shiwei Li, junlong Ma, aijun jiang, wenlu xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7018481/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Based on data from the 2018 China Health and Aging Survey (CHARLS), this paper employs an endogenous switching probit model to analyze the impact of land transfer on the way of Chinese elderly farmers care. The study finds that, first, after land transfer, Chinese elderly farmers prefer to choose family retirement, and the above conclusion still holds after a series of robustness tests. Second, the mechanism analysis shows that land transfer pushes elderly farmers to choose family old-age care through dual paths, namely, the effect of children living with them and non-farm employment, respectively. Third, further analysis shows that elderly farmers in eastern China and the main grain-producing areas tend to choose family old-age pension after land transfer, while elderly farmers in western China and the main non-grain-producing areas tend to choose individual old-age pension. Finally, age, gender, pension insurance ownership, the need to take care of grandchildren, children's satisfaction, and daily life ability all have a significant impact on the aging styles of elderly farmers.Based on the above conclusions, this paper proposes recommendations such as strengthening the family-based elderly care support system, enhancing re-employment support and safety net protection for elderly farmers, and improving the rural social security system. In view of the special characteristics of the western region of China, It was proposed that the central government should increase its transfer payments to western regions, specifically for the construction of elderly care facilities; encourage social participation in elderly care to fill the gap in family-based care; and strengthen support for family-based care by improving family income through industrial poverty alleviation. Land transfer Way of old-age care Population aging Endogenous transformation probit model 1 Introduction By the end of 2023, China's population aged 60 and above reached 296.79 million, accounting for 21.1% of the total population, with those aged 65 and above numbering 216.76 million, representing 15.4% of the total population. This indicates that China has already entered a moderately aged society ahead of schedule. The results of China's seventh national census show significant urban-rural disparities in ageing levels. Specifically, the proportions of people aged 60 and 65 and above in rural areas are as high as 23.81% and 17.72%, respectively, far exceeding the 7.99% and 6.61% in urban areas. This indicates that China's ageing problem is becoming increasingly severe, and the phenomenon of "urban-rural inversion" in ageing is also intensifying. Traditionally, China's primary method of elderly care has been family-based, reflecting the traditional notion of "raising children as a form of old-age insurance," characterised by an intergenerational exchange-based "feedback model" (Fei, 1983 ). However, with the continuous large-scale migration of rural labour and changes in population structure, families have become smaller and more nuclear, leading to a gradual weakening of the family's elderly care functions (He, 2022 ; Chen et al., 2015). Additionally, after moving to urban areas, children face diverse costs such as housing and education, gradually forming a resource allocation rule of "raising children but not caring for the elderly" (He, 2021 ). In this context, families have increasingly shifted their focus to end-of-life care, assuming the responsibility of providing a safety net for elderly care. Meanwhile, the government-led elderly care security system has only been in place for a short time. Although basic social pension insurance has achieved universal coverage, the overall level of protection remains low, particularly in rural areas, where pension insurance levels are even far below the rural minimum living allowance standard. According to relevant data, in 2022, the average pension insurance received by rural residents in China was 204.7 yuan per month, while the average rural minimum living allowance standard in China in 2022 was 582.1 yuan per person per month, meaning that rural pension insurance was only 35.2% of the average rural minimum living allowance standard. In addition, the level of elderly care services in rural China is also low, and the barriers to access are high. Constrained by economic conditions, remote geographical locations, and sparse populations, many rural areas lack necessary village-level or regional day care centres. Even where such facilities have been established, they often face low utilisation rates due to poor maintenance and shortages of human resources. As a result, the practice of "land-based elderly care" where elderly farmers rely on their land for support, has become the primary means of addressing ageing in these areas (Xia, 2018 ). For farmers, land is not only a means of production but also a guarantee of stable employment, while also serving important economic and security functions (Wang, 2005 ). These dual functions of land are also key to the formation of farmers' self-reliant livelihood systems. However, as the scale of land transfers in China continues to expand, the impact on the two major functions that rural land serves has become increasingly evident, and the way of elderly farmers care will also undergo corresponding changes. What impact will land transfers in China have on the way of elderly farmers care? Does this impact vary by region? In particular, in western rural areas of China, where social security levels are relatively lower, elderly farmers face more prominent pension-related challenges (Chen et al., 2024; Li, 2019 ). Unlike the "work-based pension" model in developed rural areas, the "land-based pension" phenomenon is more pronounced in western rural areas. Against this backdrop, the unique characteristics and potential changes in the way of elderly farmers care in western China under the impact of land transfers have become another key focus of this study. Existing research related to this study primarily focuses on two aspects: first, the impact of land transfers on rural pension models. Early studies primarily drew on traditional family-based elderly care theories to explore the dual functions of land as both a means of production and a form of social security in supporting farmers' elderly care (Cui et al., 2015; Li et al., 2015 ). As rural land system reforms deepened, research shifted its focus to the impact and restructuring of land transfers on the elderly care choices of rural seniors (Xu et al., 2018 ). In recent years, scholars have incorporated life cycle theory, intergenerational support theory, and livelihood capital theory into their analytical frameworks, arguing that land transfers reshape pension models by altering family resource allocation, intergenerational relationships, and economic structures (Zhu et al., 2021; Li et al., 2020 ). Second, the relationship between social security and the transformation of pension models. Early studies primarily focused on the substitution effect of the New Rural Social Pension Insurance on family-based elderly care (Zhang et al., 2014; Cheng, 2013 ). As research has deepened, some studies have begun to explore the impact of innovative models such as "land-for-social-security" and "homestead withdrawal" on the elderly care choices of rural households (Li et al., 2024 ; Gao et al., 2024; Zhang et al., 2024 ), offering new perspectives on understanding institutional reforms and pension transitions. Overall, existing research provides some references for exploring the relationship between land transfers and elderly farmers' pension arrangements, but there are still areas that require further exploration. First, existing literature has insufficiently addressed the mechanisms and effects through which land transfers influence elderly farmers' pension arrangements. Additionally, in terms of measuring the way of old-age care, existing research categorises them into three types: individual pension, family pension, and social pension. However, with the comprehensive coverage of China's new rural social pension insurance, following the perspective of Yang ( 2021 ), elderly farmers who rely on social pensions should not be excluded from the category of individual pensions, as social pension insurance has become part of their pension resources and can provide some support for their self-funded pensions. Therefore, this paper categorises the way of old-age care into two types: individual pension and family pension. Furthermore, existing research on the relationship between land transfer and the way of old-age care generally lacks adequate treatment of endogeneity issues and overlooks regional differences. To address this, this paper employs an endogenous conversion probit model based on the 2018 China Household Income Project (CHARLS) data to systematically investigate the impact of land transfer on the way of elderly farmers care in China. 2 Theoretical analysis and research hypothesis 2.1 The Direct Impact of Land Transfer on the Elderly Farmers’way of old-age care in China Land serves as the core pension capital for elderly farmers in rural China, providing not only economic income but also fulfilling important social security functions (Deininger, 2003 ). In rural China, the land-based pension model has long dominated, with elderly farmers achieving economic self-sufficiency and social value through land management (Ma, 2023 ). This "land-based pension" model is widely prevalent in rural China (Zhang et al., 2021). However, after land transfers, the livelihood patterns of elderly farmers have been disrupted. While land transfers can generate some rental income, the limited rental income and growth mechanisms result in a limited compensatory effect of "land for security," forcing elderly farmers to combine transfer income with family support (Izuhara, 2010 ). Meanwhile, China's rural social security system remains underdeveloped, with the basic pension benefits under the New Rural Pension Scheme being relatively low (Zhang et al., 2014), further reinforcing elderly farmers' reliance on family economic support. In summary, this paper proposes Hypothesis 1: H1 After land transfer, elderly farmers in China choose family pension as their primary elderly care method. 2.2 Land transfers affect the cohabitation of elderly farmers and their children in China, thereby influencing their way of old-age care. Land transfers have not only altered the economic foundation of elderly farmers' retirement in China but have also profoundly impacted intergenerational family relationships. Land serves as the material carrier of intergenerational bonds, maintaining family retirement agreements through inheritance relationships (Zhu et al., 2021). Once land is transferred, this material carrier disappears, necessitating new ways to maintain intergenerational relationships. Research indicates that elderly farmers in China generally provide emotional support, such as intergenerational care, in exchange for their children's commitment to their old-age care (Cong & Silverstein, 2012). Additionally, from the perspective of living arrangements, land transfer can lead to changes in intergenerational cohabitation patterns. On the one hand, elderly farmers who have lost their land are more likely to live with their children to obtain daily care; on the other hand, children, who no longer need to inherit land, have weakened economic ties with their parents. This contradictory situation has given rise to a new form of intergenerational contract: elderly farmers maintain intergenerational reciprocity by making non-economic contributions such as performing household chores and caring for grandchildren (Liu et al., 2020). In summary, this paper proposes Hypothesis 2: H2 After land transfer, elderly farmers in China tend to choose to live with their children and thus select family-based elderly care as their primary elderly care. 2.3 Land transfers have made it difficult for elderly farmers in China to engage in non-agricultural employment, thereby affecting their way of old-age care. Following land transfers, elderly farmers in China face multiple obstacles in securing non-agricultural employment. First, insufficient human capital is the core factor constraining non-agricultural employment for elderly farmers in China. In 2021, 84.3% of the elderly rural population in China had an educational attainment of junior high school or below, indicating that the overall educational level of the elderly rural population in China is currently relatively low. This low educational level limits their ability to acquire new technologies and information, making it difficult for them to meet the skill requirements of non-agricultural employment positions. Second, declining physical functions also pose significant obstacles. As they age, elderly farmers experience an irreversible decline in physical functions. At the same time, deficiencies in financial literacy further constrain the elderly farmers' ability to achieve capital appreciation through land transfer funds. These capability deficits create cumulative disadvantages, trapping elderly farmers in a "capability poverty" dilemma after land transfer (Sen, 1999 ). As Qu ( 2017 ) pointed out, when human capital, health capital, and financial capital are all lacking, rural elderly are forced to rely on family support networks. In summary, this paper proposes Hypothesis 3: H3 After land transfers, Chinese elderly farmers are unable to engage in non-agricultural employment due to capability deficits and are forced to choose family-based elderly care as their primary means of support. 3 Data Sources, Model Setting and Variable Selection 3.1 Data sources This paper uses data collected from the fourth round of the China Health and Retirement Survey (CHARLS) organized by Peking University in 2018 as the basis for analysis. This round of the survey had a wide coverage across China, covering 28 provinces (autonomous regions and municipalities), involving 150 counties and districts, and collecting data from 450 villages (residents' committees). The data thus formed fully reflects national characteristics and has strong representativeness. Given that this study aims to explore the impact of land transfers on the pension arrangements of elderly farmers, and since land ownership is a prerequisite for land transfers, the original sample data underwent a series of necessary adjustments. The specific steps are as follows: first, the study focused on farmers who own land, thus excluding individual samples without land. Second, considering that the research subjects are primarily elderly farmers, to make the sample more targeted, only samples of farmers aged 60 and above were retained. Finally, to ensure data quality and the reliability of research results, samples with missing variables or outliers were excluded. Ultimately, a total of 3,712 elderly farmer samples meeting the research requirements were obtained. 3.2 Modeling At present, the aging problem of Farmers aged 60 and above is becoming more and more prominent. Their way of eldly care has has a significant "self-selection" characteristic. which is intertwined with land transfer, giving rise to complex endogenous problems, posing a serious challenge to the accuracy of the results of the study, and thus needs to be taken into account when choosing the econometric model. In view of this, in order to accurately analyze the impact of land transfer on the way of rural Farmers aged 60 and above, this study draws on the endogenous switching probit model proposed by Lokshin et al. (2009). This model can effectively deal with the endogeneity problem mentioned above, correct the "self-selection" bias through a rigorous method, so as to reveal the real relationship between land transfer and the Farmers aged 60 and above' elderly care more accurately, and provide a more reliable basis for the relevant research and policy making. This model is divided into two stages. In the first stage, the probit model is used to estimate the probability of land transfer for farmers. Drawing on the analysis of Li et al.,(2020) assuming that farmers are risk neutral, the decision on land transfer mainly depends on the utility brought by land transfer. Assuming that the utility that farmers can obtain from transferring land is \(\:{Z}_{1i}^{\ast\:}\) , when not transferring \(\:{Z}_{0i}^{\ast\:}\) , if \(\:{Z}_{i}^{\ast\:}{=Z}_{1i}^{\ast\:}\) - \(\:{Z}_{0i}^{\ast\:}\) >0, farmers will choose to transfer land, otherwise, not transfer land. Since it \(\:{Z}_{i}^{\ast\:}\) is unobservable, it is represented by the following formula: $$\:{Z}_{i}^{\ast\:}=\alpha\:{P}_{i}+{\mu\:}_{i}，{Z}_{i}=\left\{\begin{array}{c}1，if{\:\text{Z}}_{\text{i}}^{\ast\:}>0\\\:0，if{\:\text{Z}}_{\text{i}}^{\ast\:}\le\:0\end{array}\right.$$ 1 (1) The formula is a selection equation, where \(\:{Z}_{i}\) =1 indicates that Farmers aged 60 and above have transferred their land, and vice versa, they have not transferred their land. Among them, it \(\:{P}_{i}\) represents the relevant variables that affect the land transfer of Farmers aged 60 and above; \(\:\alpha\:\) For the parameters to be estimated; \(\:{\mu\:}_{i}\) It is a random perturbation term. The second stage of the ESP model is to estimate the impact of land transfer on the elderly care strategies of rural households. The resulting equation is constructed as follows: \(\:{Z}_{i}\) When = 1 $$\:{Y}_{1i}={\beta\:}_{1}{X}_{1i}+{\gamma\:}_{1i}{\sigma\:}_{1u}+{\epsilon\:}_{1i}$$ 2 \(\:{\text{Z}}_{\text{i}}\) When = 0 $$\:{Y}_{0i}={\beta\:}_{0}{X}_{0i}+{\gamma\:}_{0i}{\sigma\:}_{0u}+{\epsilon\:}_{0i}$$ 3 Among them, \(\:{Y}_{1i}\) and \(\:{Y}_{0i}\) respectively represent the probabilities of elderly care strategy choices for the land transfer group and the land non transfer group; \(\:{X}_{1i}\) And \(\:{X}_{0i}\) respectively represent the influencing factors of elderly care strategies chosen by the land transfer group and the land non transfer group; \(\:{\beta\:}_{1i}\) And \(\:{\beta\:}_{0i}\) for the parameters to be estimated; \(\:{\epsilon\:}_{1i}\) And \(\:{\epsilon\:}_{0i}\) is a random error term. \(\:{\gamma\:}_{1i}\) And \(\:{\gamma\:}_{0i}\) is the inverse Mills ratio \(\:{\sigma\:}_{1u}\) = Cov( \(\:{u}_{i},{\epsilon\:}_{1i}\) ) and \(\:{\sigma\:}_{0u}\) =cov( \(\:{u}_{i},{\epsilon\:}_{0i}\) ) are covariances. If there are unobservable variables simultaneously affecting \(\:\mu\:i\) and \(\:{\epsilon\:}_{1i}({\epsilon\:}_{0i}\) , resulting in at least one significant non-zero covariance between the two, it indicates that the model has selective bias. Finally, in real situations, the expected values for Farmers aged 60 and above who choose land transfer and those who do not can be expressed as: $$\:E\left[{Y}_{1i}|{Z}_{i}=1\right]={\beta\:}_{1}{X}_{1i}+{\gamma\:}_{1i}{\sigma\:}_{1u}$$ 4 $$\:E\left[{Y}_{0i}|{Z}_{i}=0\right]={\beta\:}_{0}{X}_{0i}+{\gamma\:}_{0i}{\sigma\:}_{0u}$$ 5 Under the counterfactual situation, the expected choice of elderly care strategy for Farmers aged 60 and above who choose land transfer is: $$\:E\left[{Y}_{0i}|{Z}_{i}=1\right]={\beta\:}_{0}{X}_{1i}+{\gamma\:}_{1i}{\sigma\:}_{0u}$$ 6 The expected choice of elderly care strategy for Farmers aged 60 and above who have not chosen land transfer is: $$\:E\left[{Y}_{1i}|{Z}_{i}=0\right]={\beta\:}_{1}{X}_{0i}+{\gamma\:}_{0i}{\sigma\:}_{1u}$$ 7 Based on this, the average treatment effect (ATT) for selecting the land transfer group is obtained; $$\:ATT=({\beta\:}_{1}-{\beta\:}_{0}){X}_{1i}+{\gamma\:}_{1i}({\sigma\:}_{1u}-{\sigma\:}_{0u})$$ 8 The average treatment effect (ATU) obtained for the unselected land transfer group is: $$\:ATU=({\beta\:}_{1}-{\beta\:}_{0}){X}_{0i}+{\gamma\:}_{0i}({\sigma\:}_{1u}-{\sigma\:}_{0u})$$ 9 3.3 Variable Selection 3.3.1 Explained Variables For most elderly people in China, wealth or financial arrangements still play a very important role in retirement planning, as has been noted in previous studies (Huang et al., 2005). Therefore, this study defines way of old-age care as the economic safeguards that elderly people put in place to ensure their livelihood in old age. Building on the research framework proposed by Zhang et al. ( 2018 ), this study measures economic sources by comparing the proportions of personal labor income, family economic support, and social economic support in the total income of the elderly. Specifically, those whose primary economic sources are personal labor income and social economic support are defined as individual pension, while those whose primary economic source is family economic support are defined as family pension. Personal labor income includes non-agricultural income and family agricultural operating income. The questionnaire item "In the past year, how much total wages did you receive? This includes bonuses and various subsidies, but excludes retirement wages" is defined as non-agricultural income. The question "In the past year, what was the total value of the agricultural, forestry, livestock, and aquatic products your family sold?" was used to indicate household agricultural income. Additionally, the question "In the past year, when your children were not living with you, how much economic support did you or your spouse receive from your children?" was defined as family economic support. The question in the questionnaire, "In the past year, did you receive any of the following transfer payments? Including retirement pensions or old-age insurance, unemployment benefits, old-age allowance cards/vouchers, old-age allowances for the elderly, work-related injury insurance benefits including compensation for lost wages and disability allowances, allowances for the elderly with only one child, medical assistance, other government subsidies to individuals, and other social transfer payments to individuals," is defined as social economic support. 3.3.2 Core Explanatory Variables In the field of land transfer research, a common classification method is to divide it into two main forms: land inflow and land outflow. For the elderly population in rural China, due to their declining labor capacity, they are less likely to participate in land inflow. Additionally, sample data shows that land inflow accounts for only 7.44% of the total sample. Therefore, this study focuses on the impact of land outflow on the pension arrangements of elderly farmers in China. This study uses the question "Have you rented out farmland, forest land, pasture land, or ponds to others in the past year?" from the questionnaire as the basis for determining whether elderly farmers have engaged in land transfer. If elderly farmers have rented out such land in the past year, it is deemed that they have undergone land outflow, and the corresponding variable is assigned a value of 1 in the research variable settings; otherwise, it is deemed as other circumstances, with the variable assigned a value of 0. 3.3.3 Instrumental variables In order to ensure that the ESP model is recognizable and meets the requirements of relevance and exogeneity, referring to the research idea of (Ma et al., 2022), the proportion of land transfer in the village and whether or not the village has carried out land rights in the past five years are selected as instrumental variables. In the practice of rural land transfer, the decision to transfer land does not exist in isolation, and is often influenced by the "peer effect". Specifically, the land transfer situation of other people in the same community will have a significant impact on the respondents' own land transfer. When the proportion of land transfer in the community is at a high level, it means that the land transfer market in the community is more active. In such an environment, the respondents can observe the people around them and feel the atmosphere brought by the active market, which will psychologically enhance their willingness to participate in land transfer. In addition, land empowerment gives a clearer definition of land property rights and allows respondents to have a clearer understanding of their own rights and interests in the land. When interviewees are clear about the ownership of the land and their own rights and interests, they will be able to make more rational choices based on more adequate information when facing the decision of land transfer, which will help to promote the smooth implementation of land transfer. In summary, the two instrumental variables selected in this paper meet the requirements of endogeneity and exogeneity. 3.3.4 Control variables Based on previous research, this paper comprehensively selected demographic characteristics and family characteristics as control variables and added virtual variables at the provincial level. Specific descriptions are provided in the descriptive statistics below. Table 1 Variable Definition and Descriptive Statistics Variable type variable describe total Leased Out Not Leased Out Level of significant difference Dependent variable Way of old-age care Way of old-age care 0 is individual pension, 1 is family pension 0.432 (0.500) 0.490 (0.500) 0.415 (0.493) 0.0001*** Explanatory variables landout Whether to transfer land 0 No 1 is 0.239 (0.427) Mechanism variables Together Do you live with your children 0 No 1 is 0.311 (0.463) 0.291 (0.454) 0.318 (0.466) 0.1348 No_agriculture Whether engaged in non-agricultural work 0 No 1 is 0.147 (0.354) 0.158 (0.365) 0.143 (0.350) 0.2762 Control variable Age1 Age of respondents 68.456 (6.200) 68.847 (6.150) 68.333 (6.211) 0.0312** gender Gender 0 female 1 male 0.444 (0.497) 0.422 (0.494) 0.451 (0.498) 0.1252 education Years of education 2.583 (3,753) 2.828 (3.906) 2.506 (3.701) 0.0261** health Self assessed health 1 = very poor, 2 = poor, 3 = average, 4 = good, 5 = very good 2.722 (0.971) 2.787 (1.007) 2.701 (0.959) 0.0219** marriage marital status 0 = Other 1 = Married 0.809 (0.393) 0.781 (0.414) 0.817 (0.386) 0.0172** pension Do you have pension insurance 0 = No 1 = Yes 0.026 (0.160) 0.036 (0.187) 0.023 (0.151) 0.0394** grandchilden Do you take care of your grandchildren 0 = No 1 = Yes 0.384 (0.486) 0.366 (0.482) 0.389 (0.488) 0.2269 children_satisfication Child satisfaction 0 = dissatisfied 1 = satisfied 0.929 (0.257) 0.928 (0.259) 0.930 (0.256) 0.8625 adl Daily life ability assessment 0 = No problem 1 = There is a problem 0.323 (0.468) 0.313 (0.464) 0.326 (0.469) 0.4719 life_care Is there any life care available 0 = No 1 = Yes 0.157 (0.364) 0.145 (0.353) 0.160 (0.367) 0.2865 farmer Whether engaged in agricultural activities 0 = No 1 = Yes 0.624 (0.484) 0.487 (0.500) 0.667 (0.471) 0.0000*** lnexp Total household expenditure in the past year (Take logarithm) 8.00 (2.141) 8.076 (2.175) 7.976 (2.130) 0.2260 Instrumental variable Iv_qq Has land ownership confirmation been carried out in the past five years 0.320 (0.467) 0.375 (0.485) 0.303 (0.459) 0.0000*** Iv_percentage The proportion of land transfer in this village 11.098 (15.473) 16.073 (18.443) 9.536 (14.060) 0.0000*** Obsevations 3712 887 2825 Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 4 Empirical Tests 4.1 Benchmark regression results This paper tests the validity of instrumental variables. First, we examine the issue of weak instrumental variables. The Cragg-Donald Wald F statistic value is 43.156, which is significantly greater than the critical value of 19.93 at the 10% level in the Stock-Yogo test. This significantly rejects the null hypothesis of weak instrumental variables, indicating that the model does not suffer from weak instrumental variables, the instrumental variables exhibit strong correlation with the endogenous variables. Next, in terms of tool variable testing, the Anderson-Canon-Corr-LM test rejects the null hypothesis at the 1% level, indicating that there is no under-identification issue with the tool variables, meaning that the selected tool variables are correlated with the endogenous explanatory variables. Finally, an exogeneity test for the tool variables is conducted, specifically an over-identification test. The p-values of the Sargan statistic are all greater than 0.1, accepting the null hypothesis that the instrumental variables are exogenous, thereby ensuring the exogeneity of the selected instrumental variables. In summary, the instrumental variables used in this study meet the requirements, and the results obtained using these instrumental variables are valid. Table 2 The impact of land transfer on the elderly care methods of elderly farmers (1)OLS (2)2SLS Variable Name Way of old-age care Way of old-age care landout 0.0915*** 0.339*** (0.0192) (0.128) age1 0.00700*** 0.00679*** (0.00146) (0.00147) gender -0.0738*** -0.0671*** (0.0170) (0.0177) education 0.00288 0.00132 (0.00231) (0.00248) health -0.0144* -0.0183** (0.00867) (0.00901) marriage 0.0143 0.0231 (0.0210) (0.0221) pension -0.268*** -0.278*** (0.0394) (0.0502) grandchilden 0.0572*** 0.0582*** (0.0170) (0.0173) children_satisfication 0.150*** 0.155*** (0.0288) (0.0313) adl 0.0353* 0.0384** (0.0182) (0.0182) life_care -0.0648*** -0.0521** (0.0235) (0.0242) farmer -0.0548*** -0.0216 (0.0176) (0.0246) lnexp -0.00115 (0.00393) -0.00173 (0.00382) Province dummy YES YES Constant -0.180 -0.245* (0.120) (0.127) Observations 3,712 3,712 R-squared 0.038 Andersoncanon.corr.LM statistic 85.226 (0.0000) Cragg-DonaldWaldF statistic 43.156 (19.93) Sargan statistic 0.709 (0.3999) Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. First, regardless of whether OLS or 2SLS is used, the impact of land transfer on the way of old-age care of elderly farmers is significantly positive, meaning that after transferring their land, elderly farmers tend to choose family-based old-age care as their primary way. This basically verifies Hypothesis 1 proposed in this paper. In addition, other control variables are also basically in line with expectations. Age has a significant positive impact on the way of old-age care of elderly farmers, indicating that older elderly farmers are more inclined to choose family-based old-age care. Gender has a significant negative impact on the way of old-age care of elderly farmers, meaning that male elderly farmers are less likely to choose family-based old-age care than females and more inclined to choose individual old-age care. Furthermore, self-assessed health has a significant negative impact on the way of old-age care of elderly farmers, implying that those in better health are more prone to choosing individual old-age care. Elderly farmers with pension insurance also tend to choose individual old-age care. Whether they take care of grandchildren also has a significant positive impact on the way of old-age care of elderly farmers. Meanwhile, elderly farmers with poorer daily care abilities are more inclined to choose family-based old-age care. Finally, elderly farmers who regularly receive daily care from their children are more prone to choosing individual old-age care. However, it should be noted that the OLS model often overlooks the "self-selection" problem of elderly farmers. Therefore, the endogenous switching probit (ESP) model is adopted to further analyze the impact of land transfer on the way of old-age care of elderly farmers. 4.2 Endogenous switching probit (ESP) model estimation results Table 3 reports the ESP model estimation results of the impact of land transfer on the elderly farmers' retirement methods. The Wald test value for the independence of the equation is 375.20, which rejects the hypothesis that the choice equation and the result equation are independent at the 1% level. The rho1 value is significant at the 5% level. This indicates that there are unobservable factors that simultaneously affect whether elderly farmers choose land transfer and their retirement methods, suggesting that there is indeed a selection bias in the equation. Therefore, it is reasonable to use the ESP model for analysis. Column (1) of Table 3 reports the estimation results of the factors influencing land transfer. Gender has a significant negative impact on the land transfer choice of elderly farmers, meaning that male elderly farmers tend to retain their land. Additionally, the higher the education level of elderly farmers, the greater the possibility of their participation in land transfer. Elderly farmers with good self-assessed health conditions also tend to participate in land transfer. However, marital status has a negative impact on land transfer for elderly farmers, meaning that married elderly farmers tend to retain their land. Similarly, regular care from children significantly reduces the willingness of elderly farmers to transfer their land. In terms of agricultural activities, elderly farmers engaged in agricultural activities mostly tend to retain their land. Regarding the two instrumental variables, elderly farmers in areas with a higher proportion of land transfer and land confirmation are more willing to transfer their land. Columns (2) and (3) of Table 3 report the estimation results of the factors influencing the way of old-age care of elderly farmers. Age has a positive impact on the way of old-age care of both elderly farmers who have transferred land and those who have not at the 5% and 1% significance levels, respectively. This means that regardless of whether they participate in land transfer or not, elderly farmers tend to choose family-based old-age care as they age. Gender has a negative impact on the way of old-age care of both types of elderly farmers at the 1% significance level, indicating that male elderly farmers are more likely to choose individual old-age care compared to female elderly farmers. Self-assessed health has a negative impact on the way of old-age care of elderly farmers who have transferred land at the 5% significance level, meaning that elderly farmers who have transferred land and have good self-assessed health are more likely to choose individual old-age care. Marital status has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 10% significance level, suggesting that married elderly farmers who have not transferred land tend to choose family-based old-age care. Pension insurance has a negative impact on the way of old-age care of both types of elderly farmers at the 1% significance level, meaning that those with insurance are more likely to choose individual old-age care. Whether or not to take care of grandchildren has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 1% significance level. This implies that even if they have not transferred land, elderly farmers tend to choose family-based old-age care when they need to take care of their grandchildren. The satisfaction of children has a positive impact on the way of old-age care of both types of elderly farmers at the 1% significance level, indicating that when the intergenerational relationship within the family is good, both types of elderly farmers tend to choose family-based old-age care after land transfer. Daily living ability has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 1% significance level, meaning that elderly farmers who have not transferred land and have poor daily living ability are more likely to choose family-based old-age care. Regular care from children has a negative impact on the way of old-age care of elderly farmers who have transferred land at the 1% significance level, indicating that elderly farmers who have transferred land and receive regular care from their children tend to choose individual old-age care. Table 3 Estimation results of ESP model on the impact of land transfer on the elderly care mode of elderly farmers Select equation Result equation Choose to transfer land Land transfer not selected (1) (2) (3) Variables landout The way of old-age care_1 The way of old-age care_0 age1 0.00275 0.0167** 0.0192*** (0.00438) (0.00798) (0.00466) gender -0.0894* -0.303*** -0.147*** (0.0523) (0.102) (0.0558) education 0.0220*** -0.00432 0.00546 (0.00694) (0.0125) (0.00781) health 0.0535** -0.0906** -0.0414 (0.0262) (0.0441) (0.0278) marriage -0.139** -0.0219 0.124* (0.0635) (0.111) (0.0691) pension 0.0804 -1.121*** -0.707*** (0.144) (0.318) (0.187) grandchilden -0.0242 0.0472 0.183*** (0.0523) (0.0922) (0.0531) children_satisfication -0.0582 0.592*** 0.368*** (0.0930) (0.177) (0.100) adl -0.0439 -0.0484 0.144*** (0.0552) (0.0973) (0.0556) life_care -0.161** -0.406*** -0.0649 (0.0708) (0.147) (0.0733) farmer -0.440*** 0.150 -0.119 (0.0520) (0.117) (0.0736) lnexp 0.00954 0.0188 -0.00866 Province dummy (0.0115) YES (0.0201) YES (0.0117) YES iv_percentage 0.0136*** (0.00164) iv_qq 0.159*** (0.0571) Constant -0.833** -0.767 -2.083*** (0.366) (0.739) (0.380) rho1 rho0 -0.535** (0.216) -0.349 (0.235) Wald test value 375.20*** LR test value 6.03** Log Likelihood -4130.407 Observations 3,712 3,712 3,712 Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 4.3 Mean treatment effects of the impact of land transfer on the elderly farmer way of old-age care in china Table 4 reports the average treatment effect of land transfer on the elderly farmers' choice of the way of old-age care. ATT and ATU respectively represent the average treatment effect of elderly farmers who participated in land transfer and those who did not. As shown in Table 4 , first, choosing to transfer land leads elderly farmers to tend to choose family-based old-age care. Specifically, the ATT value of the average treatment effect of land transfer on the elderly farmers' choice of the way of old-age care is 0.949, which has passed the significance test at the 1% level. This indicates that if elderly farmers who chose to transfer land had chosen to retain their land, the probability of choosing family-based old-age care would have decreased by 0.949 units, that is, they would be more inclined to choose individual old-age care. The value of ATU is 0.855, which has passed the significance test at the 1% level. This indicates that if elderly farmers who did not choose to transfer land had chosen to transfer land, the probability of choosing family-based old-age care would have increased by 0.855 units. Therefore, it can be concluded that Hypothesis H1 is reasonable. Table 4 The average treatment effect of land transfer on the elderly care methods of elderly farmers Participate in land transfer Not involved in land transfer ATT ATU Participate in land transfer 0.250 (0.021) -0.699 (0.025) 0.949*** (0.024) Not involved in land transfer 0.262 (0.020) -0.593 (0.009) 0.855*** (0.019) Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 4.4 Robustness test In order to verify the reliability of the ESP model estimation results, this paper conducts a robustness test by propensity matching score matching (PSM) and instrumental variable regression (IV-probit). 4.4.1 Propensity to Match Score (PSM) The common support domain test and balance test were conducted for matching, based on the results passed. Propensity score matching was used for estimation. Table 5 demonstrates the results of treatment effects under four different matching methods. It can be found that the treatment group means are higher than the control group means regardless of the matching used, and the corresponding ATT values are all significantly positive at the 1% level. Therefore, it can be inferred that the benchmark results are robust. Of course, there are differences that the ATT values obtained with the PSM model are much smaller than those calculated by the ESP model, which is because the PSM model does not take into account the effects of unobservable factors and the estimates obtained are biased. The ESP model, on the other hand, fully considers the selective bias caused by observable and unobservable factors, and automatically adds the bias term obtained in the first stage to the second stage to estimate the impact of land transfer on farmers' entrepreneurship, and the estimation results obtained are more scientific (Li et al., 2020 ). Table 5 Robustness Test 1 Matching method Processing group mean Control group mean ATT Standard error T value K = 1 nearest neighbor matching 0.491 0.405 0.086*** 0.028 3.02 K = 4 nearest neighbor matching 0.491 0.405 0.086*** 0.023 3.68 Radius matching 0.491 0.409 0.082*** 0.021 3.80 Nuclear matching 0.491 0.411 0.080*** 0.022 3.73 Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 4.4.2 IV-probit Table 6 reports the endogeneity-corrected iv-probit model estimation results, with columns (1) and (2) representing the first and second stage regression results of the IV model estimation, respectively. From the above, it can be seen that the two selected instrumental variables are consistent with endogeneity and exogeneity, so the IV-probit model estimation results are reasonable. As can be seen from Table 6 , the effect of land transfer on Farmers aged 60 and above' way of old-age care is positive and significant at 1% level. That is, after the land transfer, the Farmers aged 60 and above will be more inclined to choose family pension. Therefore, it can be seen that the benchmark results are robust. Table 6 Robustness Test 2 (1) (2) Variable Name landout Way of old-age care landout 0.883*** (0.312) age1 0.000593 0.0181*** (0.00123) (0.00405) gender -0.0239* -0.181*** (0.0145) (0.0490) education 0.00596*** 0.00350 (0.00196) (0.00669) health 0.0156** -0.0481** (0.00735) (0.0239) marriage -0.0376** 0.0678 (0.0181) (0.0590) pension 0.0307 -0.873*** (0.0418) (0.160) grandchilden -0.00386 0.154*** (0.0145) (0.0464) children_satisfication -0.0144 0.425*** (0.0262) (0.0871) adl -0.0112 0.103** (0.0152) (0.0484) life_care -0.0466** -0.134** (0.0195) (0.0660) farmer -0.130*** -0.0623 (0.0148) (0.0676) lnexp 0.00292 -0.00440 (0.00319) (0.0101) Province dummy YES YES iv_qq 0.0485*** (0.0163) iv_percentage 0.00423*** (0.000492) Constant 0.220** -1.989*** (0.103) (0.331) Observations 3,712 3,712 Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 4.5 Mechanism analysis Theoretical analysis suggests that the mechanism by which land transfer affects the elderly care methods of elderly farmers lies in non-agricultural employment and whether they live with their children. This paper draws on the research of Jiang ( 2022 ) and sets up the following mediating effect model for testing. $$\:M={\alpha\:}_{1}+{\lambda\:Landout}_{i}+{\beta\:}_{2}Z+{\epsilon\:}_{2}$$ 10 Among them, \(\:M\) is the mechanism variable, representing respectively whether engaged in non-agricultural work \(\:(No\_agriculture)\) and whether living with children \(\:\left(Togetℎer\right)\) . In terms of non-agricultural employment, it mainly involves two aspects: The first is agricultural employment. Select the question from the questionnaire: "In the past year, have you worked on the farm for other farmers or employers to earn money for at least 10 days?" To define; The second is non-agricultural employment. By asking, "Excluding jobs related to farming, did you work for at least one hour last week?" To define. If at least one of the above conditions is met, it is determined to participate in non-agricultural work and is assigned a value of 1; otherwise, it is assigned a value of 0. Select "At least one child has lived with you and your spouse in the past year" to indicate whether you live with your children. If it meets the requirement, it is recorded as 1; otherwise, it is recorded as 0. In column (1) of Table 7 , the regression coefficient of land transfer is 0.0391 and has passed the significance test, indicating that land transfer can promote elderly farmers to live with their children and then choose family-based elderly care as their main elderly care method. Hypothesis H2 is verified. In column (2) of Table 7 , the regression coefficient of land transfer is not significant. That is to say, after land transfer, elderly farmers find it difficult to engage in non-agricultural employment and are thus forced to choose family-based elderly care as their main way of elderly care. Therefore, hypothesis H3 is verified. Table 7 Mechanism Analysis variable (1) (2) Together No_agriculture Landout -0.0391** (0.0176) 0.0069 (0.0136) Constant -0.214* (0.1170) 0.531*** (0.0903) control variable YES YES Province Dummy YES YES sample size 3712 3712 Adjusted \(\:{R}^{2}\) 0.120 0.101 Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 5 Further analysis The impact of land transfers in different regions of China on the way of old-age care for elderly farmers may exhibit regional heterogeneity. Building on the previous analysis, this study further examines the effects of land transfers in China's grain-producing and non-grain-producing regions, as well as in the eastern, central, and western regions, on the way of old-age care for elderly farmers. As shown in Table 8 , elderly farmers in the western region are more likely to opt for individual old-age care after transferring their land. Specifically, the average treatment effect (ATT) value for the impact of land transfers on elderly farmers' the way of old-age care in the western region is -0.302, which is statistically significant at the 1% level. This means that elderly farmers who choose to transfer their land would have a 0.302-unit lower probability of choosing individual old-age care if they retained their land. The ATU value is -0.324, which is also statistically significant at the 1% level. This indicates that elderly farmers who did not transfer their land would have a 0.324-unit increase in the probability of choosing individual old-age care if they had transferred their land. In contrast, the average treatment effects of land transfers on elderly farmers' the way of old-age care in central and eastern regions are positive and both pass the 1% significance test, indicating that elderly farmers in these two regions tend to choose family-based old-age care after land transfers. Additionally, we found that the average treatment effect in the central region is significantly greater than that in the eastern region. This may be due to the following reasons: first, the eastern region has a more developed social security system and elderly care facilities. After land transfer, elderly farmers can utilize these social old-age care resources to some extent, reducing their reliance on family-based old-age care. Second, elderly farmers in the eastern region have relatively higher income levels and more substantial savings, enabling them to address old-age care issues through personal savings. Therefore, even if elderly farmers in the eastern region choose family-based old-age care after land transfer, their reliance on family-based old-age care is relatively lower compared to elderly farmers in the central region. This difference is reflected in the average treatment effect, with the eastern region's average treatment effect value being lower than that of the central region. Finally, the average treatment effect (ATT) value of land transfers in grain-producing regions on the way of old-age care for elderly farmers is 1.432, which is significant at the 1% level. This indicates that if elderly farmers who have transferred their land choose to retain it, the probability of them choosing family-based old-age care will decrease by 1.432 units. The ATU value is 1.438, which also passes the significance test at the 1% level, meaning that if elderly farmers who have not transferred their land choose to transfer it, the probability of choosing family-based old-age care will increase by 1.438 units. However, the average treatment effects in non-grain-producing regions are all negative. The ATT value is -0.173 and is statistically significant at the 1% level, indicating that if elderly farmers in non-grain-producing regions choose to retain their land, the probability of them opting for individual-based old-age care decreases by 0.173 units. The ATU value is -0.178 and is also statistically significant at the 1% level, This indicates that if elderly farmers in non-grain-producing regions choose to transfer their land, the probability of them choosing individual old-age care will increase by 0.178 units. Table 8 Further Analysis sample size ATT ATU Western Region 1320 -0.302*** (0.018) -0.324*** (0.009) Central region 1232 1.414*** (0.057) 1.352*** (0.029) Eastern region 1160 0.259*** 0.301*** (0.017) (0.011) Main grain producing areas 1547 1.432*** 1.438*** (0.015) (0.010) Non grain producing areas 2165 -0.173*** (0.046) -0.178*** (0.022) Note: ①The last column is the result of t-test of mean. p < 0.01 is shown as "***", p < 0.05 is shown as "**", p < 0.1 is shown as "*" ;. ②Standard errors are in parentheses. 6 Conclusions and Recommendations The main conclusions are as follows: First, after land transfer, elderly farmers in China tend to choose family-based elderly care as their primary method of elderly care, and this conclusion remains valid under a series of robustness tests. Second, mechanism analysis indicates that after land transfer, elderly farmers in China, constrained by their own capabilities, find it difficult to engage in non-agricultural employment and are forced to rely on family economic support, thereby choosing family-based elderly care. Additionally, land transfer disrupts the material foundation of traditional intergenerational bonds, prompting elderly farmers to choose to live with their children to access elderly care resources, such as caring for grandchildren in exchange for support, and thus opt for family-based elderly care. Third, further analysis indicates that the impact of land transfers on the pension arrangements of elderly farmers in China exhibits significant regional differences. Specifically, elderly farmers in central and eastern China and grain-producing regions tend to choose family-based pension arrangements after land transfers, while elderly farmers in western regions and non-grain-producing areas are more inclined to choose individual-based pension arrangements. Finally, other factors also influence elderly farmers' pension arrangements, such as age, gender, pension insurance coverage, self-assessed health, the need to care for grandchildren, children's satisfaction, daily living abilities, children's care provision, and whether they engage in agricultural activities. Based on the empirical research findings, this paper offers the following recommendations: First, strengthen the family-based pension support system. Given that elderly farmers choose family-based elderly care after land transfers, the Chinese government should provide targeted subsidies based on household economic conditions and the burden of elderly care, implementing a tiered strategy to incentivize families to assume elderly care responsibilities. Second, strengthen support for re-employment and safety net protections for elderly farmers. Addressing the current challenges of non-agricultural employment for elderly farmers, the Chinese government could conduct age-appropriate vocational skills training, such as short-term courses in domestic services or handicrafts, to help them achieve nearby employment and income generation. For elderly farmers with poor health and no ability to work, the Chinese government should strengthen policy safety nets. On one hand, it should establish a "rural special subsidy for impoverished elderly" to directly increase their transfer income; on the other hand, it should provide special subsidies to children who support their elderly parents to enhance the family's economic motivation for support. Third, improve the rural social security system. The Chinese government should continuously optimize the pension insurance system, establish a pension adjustment mechanism, and strengthen the rural healthcare service system to reduce the medical burden on elderly farmers. Fourth, optimize the land transfer income mechanism to enhance the economic security of elderly farmers. Explore a "base rent plus profit-sharing" land rent model to ensure long-term stable income growth for elderly farmers and alleviate the burden of family-based elderly care. At the same time, actively explore a "land transfer income reinvestment in elderly care" mechanism, allocating a certain proportion of land transfer income to build village-level elderly care facilities. Fifth, establish a "family-society" collaborative elderly care model. Relying on village or community organizations, establish mutual aid elderly care stations. Take villages or communities as units to integrate idle school buildings, village collective housing, and other resources to build standardized mutual aid elderly care stations. Given the special characteristics of China's western regions, this paper proposes the following targeted recommendations: First, increase central government fiscal transfers to western regions, specifically allocated for strengthening rural elderly care facilities, such as day care centers and senior activity centers. Additionally, encourage social organizations and volunteers to participate in elderly care services to supplement family-based care. Finally, enhance the economic income levels of rural households in western China through industrial poverty alleviation and employment poverty alleviation, promote the orderly transfer of industries from eastern to western regions, foster inter-regional industrial synergy, attract the return of young and middle-aged individuals, and alleviate family-based elderly care pressures. Declarations Funding: No Funding Ethical Approval : Not applicable. Author Contribution L mainly Wrote the main manuscript text. and X and J maily Drew the chart. M mainly Engaging in the submission and editing of papers. Data Availability This article uses data from the fourth round of the China Health and Retirement Longitudinal Study (CHARLS) organised by Peking University in 2018, which is publicly accessible data. The DOI is: https://charls.charlsdata.com/pages/Data/2013-charls-wave2/zh-cn.html. References Chen, D., & Zhang, Y. (2015). 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Interaction mechanism between social pension and land pension from heterogeneity of family pension function. Resources Science, *43*(10), 2003–2012. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7018481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481694309,"identity":"305e5678-0523-476b-a5e5-8b452b018048","order_by":0,"name":"Shiwei Li","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shiwei","middleName":"","lastName":"Li","suffix":""},{"id":481694310,"identity":"4d801725-b0a8-425d-80e8-1caeb405efe2","order_by":1,"name":"junlong Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFCCww0HPhjYyLGxNx8gVsvBxoczKtKM+XiOJRCrhbHZmOfM4cR5EjkKxGnQbTzYJsHblpbexpDDwPCjYhthLWYHgFok22xy2xjOHmDsOXObSC2GbWm5bYx9CcyMbcRqSWw7nM7GzGNAtJZmgwNnDiewsZGgpfFhQ0WaYRsPW8JB4vxy4/CBw38MbOTl5z8++OBHBRFaGCQOINgHcClCBfwNxKkbBaNgFIyCEQwAnbJE6gU4NogAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"junlong","middleName":"","lastName":"Ma","suffix":""},{"id":481694311,"identity":"9d8e615f-9cf0-43b4-8e51-2067b79a6c7c","order_by":2,"name":"aijun jiang","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"aijun","middleName":"","lastName":"jiang","suffix":""},{"id":481694312,"identity":"ea925014-9ae2-42ba-a0b0-dbb8a40a679f","order_by":3,"name":"wenlu xue","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"wenlu","middleName":"","lastName":"xue","suffix":""}],"badges":[],"createdAt":"2025-07-01 09:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7018481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7018481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93413271,"identity":"330e8a6d-ce47-493b-8158-f88b52284bd5","added_by":"auto","created_at":"2025-10-13 14:53:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7018481/v1/fff7aacc-03b1-4c3c-b56f-5b2a9ceeb3ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Land Transfer on the Way Of Old-age care of Elderly Farmers in China：an empirical analysis based on the endogenous conversion probit model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBy the end of 2023, China's population aged 60 and above reached 296.79\u0026nbsp;million, accounting for 21.1% of the total population, with those aged 65 and above numbering 216.76\u0026nbsp;million, representing 15.4% of the total population. This indicates that China has already entered a moderately aged society ahead of schedule. The results of China's seventh national census show significant urban-rural disparities in ageing levels. Specifically, the proportions of people aged 60 and 65 and above in rural areas are as high as 23.81% and 17.72%, respectively, far exceeding the 7.99% and 6.61% in urban areas. This indicates that China's ageing problem is becoming increasingly severe, and the phenomenon of \"urban-rural inversion\" in ageing is also intensifying.\u003c/p\u003e\u003cp\u003eTraditionally, China's primary method of elderly care has been family-based, reflecting the traditional notion of \"raising children as a form of old-age insurance,\" characterised by an intergenerational exchange-based \"feedback model\" (Fei, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). However, with the continuous large-scale migration of rural labour and changes in population structure, families have become smaller and more nuclear, leading to a gradual weakening of the family's elderly care functions (He, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., 2015). Additionally, after moving to urban areas, children face diverse costs such as housing and education, gradually forming a resource allocation rule of \"raising children but not caring for the elderly\" (He, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, families have increasingly shifted their focus to end-of-life care, assuming the responsibility of providing a safety net for elderly care. Meanwhile, the government-led elderly care security system has only been in place for a short time. Although basic social pension insurance has achieved universal coverage, the overall level of protection remains low, particularly in rural areas, where pension insurance levels are even far below the rural minimum living allowance standard. According to relevant data, in 2022, the average pension insurance received by rural residents in China was 204.7 yuan per month, while the average rural minimum living allowance standard in China in 2022 was 582.1 yuan per person per month, meaning that rural pension insurance was only 35.2% of the average rural minimum living allowance standard. In addition, the level of elderly care services in rural China is also low, and the barriers to access are high. Constrained by economic conditions, remote geographical locations, and sparse populations, many rural areas lack necessary village-level or regional day care centres. Even where such facilities have been established, they often face low utilisation rates due to poor maintenance and shortages of human resources. As a result, the practice of \"land-based elderly care\" where elderly farmers rely on their land for support, has become the primary means of addressing ageing in these areas (Xia, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For farmers, land is not only a means of production but also a guarantee of stable employment, while also serving important economic and security functions (Wang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These dual functions of land are also key to the formation of farmers' self-reliant livelihood systems. However, as the scale of land transfers in China continues to expand, the impact on the two major functions that rural land serves has become increasingly evident, and the way of elderly farmers care will also undergo corresponding changes. What impact will land transfers in China have on the way of elderly farmers care? Does this impact vary by region? In particular, in western rural areas of China, where social security levels are relatively lower, elderly farmers face more prominent pension-related challenges (Chen et al., 2024; Li, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unlike the \"work-based pension\" model in developed rural areas, the \"land-based pension\" phenomenon is more pronounced in western rural areas. Against this backdrop, the unique characteristics and potential changes in the way of elderly farmers care in western China under the impact of land transfers have become another key focus of this study.\u003c/p\u003e\u003cp\u003eExisting research related to this study primarily focuses on two aspects: first, the impact of land transfers on rural pension models. Early studies primarily drew on traditional family-based elderly care theories to explore the dual functions of land as both a means of production and a form of social security in supporting farmers' elderly care (Cui et al., 2015; Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As rural land system reforms deepened, research shifted its focus to the impact and restructuring of land transfers on the elderly care choices of rural seniors (Xu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In recent years, scholars have incorporated life cycle theory, intergenerational support theory, and livelihood capital theory into their analytical frameworks, arguing that land transfers reshape pension models by altering family resource allocation, intergenerational relationships, and economic structures (Zhu et al., 2021; Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, the relationship between social security and the transformation of pension models. Early studies primarily focused on the substitution effect of the New Rural Social Pension Insurance on family-based elderly care (Zhang et al., 2014; Cheng, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As research has deepened, some studies have begun to explore the impact of innovative models such as \"land-for-social-security\" and \"homestead withdrawal\" on the elderly care choices of rural households (Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gao et al., 2024; Zhang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), offering new perspectives on understanding institutional reforms and pension transitions. Overall, existing research provides some references for exploring the relationship between land transfers and elderly farmers' pension arrangements, but there are still areas that require further exploration. First, existing literature has insufficiently addressed the mechanisms and effects through which land transfers influence elderly farmers' pension arrangements. Additionally, in terms of measuring the way of old-age care, existing research categorises them into three types: individual pension, family pension, and social pension. However, with the comprehensive coverage of China's new rural social pension insurance, following the perspective of Yang (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), elderly farmers who rely on social pensions should not be excluded from the category of individual pensions, as social pension insurance has become part of their pension resources and can provide some support for their self-funded pensions. Therefore, this paper categorises the way of old-age care into two types: individual pension and family pension. Furthermore, existing research on the relationship between land transfer and the way of old-age care generally lacks adequate treatment of endogeneity issues and overlooks regional differences. To address this, this paper employs an endogenous conversion probit model based on the 2018 China Household Income Project (CHARLS) data to systematically investigate the impact of land transfer on the way of elderly farmers care in China.\u003c/p\u003e"},{"header":"2 Theoretical analysis and research hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 The Direct Impact of Land Transfer on the Elderly Farmers\u0026rsquo;way of old-age care in China\u003c/h2\u003e\u003cp\u003eLand serves as the core pension capital for elderly farmers in rural China, providing not only economic income but also fulfilling important social security functions (Deininger, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In rural China, the land-based pension model has long dominated, with elderly farmers achieving economic self-sufficiency and social value through land management (Ma, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This \"land-based pension\" model is widely prevalent in rural China (Zhang et al., 2021). However, after land transfers, the livelihood patterns of elderly farmers have been disrupted. While land transfers can generate some rental income, the limited rental income and growth mechanisms result in a limited compensatory effect of \"land for security,\" forcing elderly farmers to combine transfer income with family support (Izuhara, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Meanwhile, China's rural social security system remains underdeveloped, with the basic pension benefits under the New Rural Pension Scheme being relatively low (Zhang et al., 2014), further reinforcing elderly farmers' reliance on family economic support. In summary, this paper proposes Hypothesis 1:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eAfter land transfer, elderly farmers in China choose family pension as their primary elderly care method.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e2.2 Land transfers affect the cohabitation of elderly farmers and their children in China, thereby influencing their way of old-age care.\u003c/p\u003e\u003cp\u003eLand transfers have not only altered the economic foundation of elderly farmers' retirement in China but have also profoundly impacted intergenerational family relationships. Land serves as the material carrier of intergenerational bonds, maintaining family retirement agreements through inheritance relationships (Zhu et al., 2021). Once land is transferred, this material carrier disappears, necessitating new ways to maintain intergenerational relationships. Research indicates that elderly farmers in China generally provide emotional support, such as intergenerational care, in exchange for their children's commitment to their old-age care (Cong \u0026amp; Silverstein, 2012). Additionally, from the perspective of living arrangements, land transfer can lead to changes in intergenerational cohabitation patterns. On the one hand, elderly farmers who have lost their land are more likely to live with their children to obtain daily care; on the other hand, children, who no longer need to inherit land, have weakened economic ties with their parents. This contradictory situation has given rise to a new form of intergenerational contract: elderly farmers maintain intergenerational reciprocity by making non-economic contributions such as performing household chores and caring for grandchildren (Liu et al., 2020). In summary, this paper proposes Hypothesis 2:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eAfter land transfer, elderly farmers in China tend to choose to live with their children and thus select family-based elderly care as their primary elderly care.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e2.3 Land transfers have made it difficult for elderly farmers in China to engage in non-agricultural employment, thereby affecting their way of old-age care.\u003c/p\u003e\u003cp\u003eFollowing land transfers, elderly farmers in China face multiple obstacles in securing non-agricultural employment. First, insufficient human capital is the core factor constraining non-agricultural employment for elderly farmers in China. In 2021, 84.3% of the elderly rural population in China had an educational attainment of junior high school or below, indicating that the overall educational level of the elderly rural population in China is currently relatively low. This low educational level limits their ability to acquire new technologies and information, making it difficult for them to meet the skill requirements of non-agricultural employment positions. Second, declining physical functions also pose significant obstacles. As they age, elderly farmers experience an irreversible decline in physical functions. At the same time, deficiencies in financial literacy further constrain the elderly farmers' ability to achieve capital appreciation through land transfer funds. These capability deficits create cumulative disadvantages, trapping elderly farmers in a \"capability poverty\" dilemma after land transfer (Sen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). As Qu (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) pointed out, when human capital, health capital, and financial capital are all lacking, rural elderly are forced to rely on family support networks. In summary, this paper proposes Hypothesis 3:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eAfter land transfers, Chinese elderly farmers are unable to engage in non-agricultural employment due to capability deficits and are forced to choose family-based elderly care as their primary means of support.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Data Sources, Model Setting and Variable Selection","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data sources\u003c/h2\u003e\u003cp\u003eThis paper uses data collected from the fourth round of the China Health and Retirement Survey (CHARLS) organized by Peking University in 2018 as the basis for analysis. This round of the survey had a wide coverage across China, covering 28 provinces (autonomous regions and municipalities), involving 150 counties and districts, and collecting data from 450 villages (residents' committees). The data thus formed fully reflects national characteristics and has strong representativeness. Given that this study aims to explore the impact of land transfers on the pension arrangements of elderly farmers, and since land ownership is a prerequisite for land transfers, the original sample data underwent a series of necessary adjustments. The specific steps are as follows: first, the study focused on farmers who own land, thus excluding individual samples without land. Second, considering that the research subjects are primarily elderly farmers, to make the sample more targeted, only samples of farmers aged 60 and above were retained. Finally, to ensure data quality and the reliability of research results, samples with missing variables or outliers were excluded. Ultimately, a total of 3,712 elderly farmer samples meeting the research requirements were obtained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Modeling\u003c/h2\u003e\u003cp\u003eAt present, the aging problem of Farmers aged 60 and above is becoming more\u003c/p\u003e\u003cp\u003eand more prominent. Their way of eldly care has has a significant \"self-selection\" characteristic. which is intertwined with land transfer, giving rise to complex endogenous problems, posing a serious challenge to the accuracy of the results of the study, and thus needs to be taken into account when choosing the econometric model. In view of this, in order to accurately analyze the impact of land transfer on the way of rural Farmers aged 60 and above, this study draws on the endogenous switching probit model proposed by Lokshin et al. (2009). This model can effectively deal with the endogeneity problem mentioned above, correct the \"self-selection\" bias through a rigorous method, so as to reveal the real relationship between land transfer and the Farmers aged 60 and above' elderly care more accurately, and provide a more reliable basis for the relevant research and policy making.\u003c/p\u003e\u003cp\u003eThis model is divided into two stages. In the first stage, the probit model is used to estimate the probability of land transfer for farmers. Drawing on the analysis of Li et al.,(2020) assuming that farmers are risk neutral, the decision on land transfer mainly depends on the utility brought by land transfer. Assuming that the utility that farmers can obtain from transferring land is\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{1i}^{\\ast\\:}\\)\u003c/span\u003e\u003c/span\u003e, when not transferring\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{0i}^{\\ast\\:}\\)\u003c/span\u003e\u003c/span\u003e, if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}^{\\ast\\:}{=Z}_{1i}^{\\ast\\:}\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{0i}^{\\ast\\:}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt;0, farmers will choose to transfer land, otherwise, not transfer land. Since it \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}^{\\ast\\:}\\)\u003c/span\u003e\u003c/span\u003eis unobservable, it is represented by the following formula:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Z}_{i}^{\\ast\\:}=\\alpha\\:{P}_{i}+{\\mu\\:}_{i}，{Z}_{i}=\\left\\{\\begin{array}{c}1，if{\\:\\text{Z}}_{\\text{i}}^{\\ast\\:}\u0026gt;0\\\\\\:0，if{\\:\\text{Z}}_{\\text{i}}^{\\ast\\:}\\le\\:0\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(1) The formula is a selection equation, where\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}\\)\u003c/span\u003e\u003c/span\u003e=1 indicates that Farmers aged 60 and above have transferred their land, and vice versa, they have not transferred their land. Among them, it \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e\u003c/span\u003erepresents the relevant variables that affect the land transfer of Farmers aged 60 and above; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003eFor the parameters to be estimated; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003eIt is a random perturbation term.\u003c/p\u003e\u003cp\u003eThe second stage of the ESP model is to estimate the impact of land transfer on the elderly care strategies of rural households. The resulting equation is constructed as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}\\)\u003c/span\u003e\u003c/span\u003e When =\u0026thinsp;1\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{1i}={\\beta\\:}_{1}{X}_{1i}+{\\gamma\\:}_{1i}{\\sigma\\:}_{1u}+{\\epsilon\\:}_{1i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e When =\u0026thinsp;0\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{0i}={\\beta\\:}_{0}{X}_{0i}+{\\gamma\\:}_{0i}{\\sigma\\:}_{0u}+{\\epsilon\\:}_{0i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{1i}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{0i}\\)\u003c/span\u003e\u003c/span\u003erespectively represent the probabilities of elderly care strategy choices for the land transfer group and the land non transfer group; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1i}\\)\u003c/span\u003e\u003c/span\u003eAnd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{0i}\\)\u003c/span\u003e\u003c/span\u003erespectively represent the influencing factors of elderly care strategies chosen by the land transfer group and the land non transfer group; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003eAnd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003efor the parameters to be estimated; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003eAnd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003eis a random error term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003eAnd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003eis the inverse Mills ratio\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{1u}\\)\u003c/span\u003e\u003c/span\u003e= Cov(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i},{\\epsilon\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003e) and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{0u}\\)\u003c/span\u003e\u003c/span\u003e=cov(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i},{\\epsilon\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003e) are covariances. If there are unobservable variables simultaneously affecting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:i\\)\u003c/span\u003e\u003c/span\u003eand\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{1i}({\\epsilon\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003e, resulting in at least one significant non-zero covariance between the two, it indicates that the model has selective bias.\u003c/p\u003e\u003cp\u003eFinally, in real situations, the expected values for Farmers aged 60 and above who choose land transfer and those who do not can be expressed as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:E\\left[{Y}_{1i}|{Z}_{i}=1\\right]={\\beta\\:}_{1}{X}_{1i}+{\\gamma\\:}_{1i}{\\sigma\\:}_{1u}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:E\\left[{Y}_{0i}|{Z}_{i}=0\\right]={\\beta\\:}_{0}{X}_{0i}+{\\gamma\\:}_{0i}{\\sigma\\:}_{0u}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUnder the counterfactual situation, the expected choice of elderly care strategy for Farmers aged 60 and above who choose land transfer is:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:E\\left[{Y}_{0i}|{Z}_{i}=1\\right]={\\beta\\:}_{0}{X}_{1i}+{\\gamma\\:}_{1i}{\\sigma\\:}_{0u}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe expected choice of elderly care strategy for Farmers aged 60 and above who have not chosen land transfer is:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:E\\left[{Y}_{1i}|{Z}_{i}=0\\right]={\\beta\\:}_{1}{X}_{0i}+{\\gamma\\:}_{0i}{\\sigma\\:}_{1u}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on this, the average treatment effect (ATT) for selecting the land transfer group is obtained;\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:ATT=({\\beta\\:}_{1}-{\\beta\\:}_{0}){X}_{1i}+{\\gamma\\:}_{1i}({\\sigma\\:}_{1u}-{\\sigma\\:}_{0u})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe average treatment effect (ATU) obtained for the unselected land transfer group is:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:ATU=({\\beta\\:}_{1}-{\\beta\\:}_{0}){X}_{0i}+{\\gamma\\:}_{0i}({\\sigma\\:}_{1u}-{\\sigma\\:}_{0u})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Variable Selection\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Explained Variables\u003c/h2\u003e\u003cp\u003eFor most elderly people in China, wealth or financial arrangements still play a very important role in retirement planning, as has been noted in previous studies (Huang et al., 2005). Therefore, this study defines way of old-age care as the economic safeguards that elderly people put in place to ensure their livelihood in old age. Building on the research framework proposed by Zhang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this study measures economic sources by comparing the proportions of personal labor income, family economic support, and social economic support in the total income of the elderly. Specifically, those whose primary economic sources are personal labor income and social economic support are defined as individual pension, while those whose primary economic source is family economic support are defined as family pension. Personal labor income includes non-agricultural income and family agricultural operating income. The questionnaire item \"In the past year, how much total wages did you receive? This includes bonuses and various subsidies, but excludes retirement wages\" is defined as non-agricultural income. The question \"In the past year, what was the total value of the agricultural, forestry, livestock, and aquatic products your family sold?\" was used to indicate household agricultural income. Additionally, the question \"In the past year, when your children were not living with you, how much economic support did you or your spouse receive from your children?\" was defined as family economic support. The question in the questionnaire, \"In the past year, did you receive any of the following transfer payments? Including retirement pensions or old-age insurance, unemployment benefits, old-age allowance cards/vouchers, old-age allowances for the elderly, work-related injury insurance benefits including compensation for lost wages and disability allowances, allowances for the elderly with only one child, medical assistance, other government subsidies to individuals, and other social transfer payments to individuals,\" is defined as social economic support.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Core Explanatory Variables\u003c/h2\u003e\u003cp\u003eIn the field of land transfer research, a common classification method is to divide it into two main forms: land inflow and land outflow. For the elderly population in rural China, due to their declining labor capacity, they are less likely to participate in land inflow. Additionally, sample data shows that land inflow accounts for only 7.44% of the total sample. Therefore, this study focuses on the impact of land outflow on the pension arrangements of elderly farmers in China. This study uses the question \"Have you rented out farmland, forest land, pasture land, or ponds to others in the past year?\" from the questionnaire as the basis for determining whether elderly farmers have engaged in land transfer. If elderly farmers have rented out such land in the past year, it is deemed that they have undergone land outflow, and the corresponding variable is assigned a value of 1 in the research variable settings; otherwise, it is deemed as other circumstances, with the variable assigned a value of 0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Instrumental variables\u003c/h2\u003e\u003cp\u003eIn order to ensure that the ESP model is recognizable and meets the requirements of relevance and exogeneity, referring to the research idea of (Ma et al., 2022), the proportion of land transfer in the village and whether or not the village has carried out land rights in the past five years are selected as instrumental variables. In the practice of rural land transfer, the decision to transfer land does not exist in isolation, and is often influenced by the \"peer effect\". Specifically, the land transfer situation of other people in the same community will have a significant impact on the respondents' own land transfer. When the proportion of land transfer in the community is at a high level, it means that the land transfer market in the community is more active. In such an environment, the respondents can observe the people around them and feel the atmosphere brought by the active market, which will psychologically enhance their willingness to participate in land transfer. In addition, land empowerment gives a clearer definition of land property rights and allows respondents to have a clearer understanding of their own rights and interests in the land. When interviewees are clear about the ownership of the land and their own rights and interests, they will be able to make more rational choices based on more adequate information when facing the decision of land transfer, which will help to promote the smooth implementation of land transfer. In summary, the two instrumental variables selected in this paper meet the requirements of endogeneity and exogeneity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 Control variables\u003c/h2\u003e\u003cp\u003eBased on previous research, this paper comprehensively selected demographic characteristics and family characteristics as control variables and added virtual variables at the provincial level. Specific descriptions are provided in the descriptive statistics below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definition and Descriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edescribe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeased Out\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot Leased Out\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLevel of significant difference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWay of old-age care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWay of old-age care\u003c/p\u003e\u003cp\u003e0 is individual pension,\u003c/p\u003e\u003cp\u003e1 is family pension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003cp\u003e(0.500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003cp\u003e(0.500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003cp\u003e(0.493)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elandout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether to transfer land\u003c/p\u003e\u003cp\u003e0 No\u003c/p\u003e\u003cp\u003e1 is\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003cp\u003e(0.427)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanism variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTogether\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDo you live with your children\u003c/p\u003e\u003cp\u003e0 No\u003c/p\u003e\u003cp\u003e1 is\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003cp\u003e(0.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003cp\u003e(0.454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003cp\u003e(0.466)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo_agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether engaged in non-agricultural work\u003c/p\u003e\u003cp\u003e0 No\u003c/p\u003e\u003cp\u003e1 is\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003cp\u003e(0.354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003cp\u003e(0.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003cp\u003e(0.350)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge of respondents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.456\u003c/p\u003e\u003cp\u003e(6.200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.847\u003c/p\u003e\u003cp\u003e(6.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.333\u003c/p\u003e\u003cp\u003e(6.211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0312**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003cp\u003e0 female\u003c/p\u003e\u003cp\u003e1 male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003cp\u003e(0.497)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003cp\u003e(0.494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003cp\u003e(0.498)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.583\u003c/p\u003e\u003cp\u003e(3,753)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.828\u003c/p\u003e\u003cp\u003e(3.906)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.506\u003c/p\u003e\u003cp\u003e(3.701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0261**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf assessed health\u003c/p\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;very poor, 2\u0026thinsp;=\u0026thinsp;poor, 3\u0026thinsp;=\u0026thinsp;average, 4\u0026thinsp;=\u0026thinsp;good, 5\u0026thinsp;=\u0026thinsp;very good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.722\u003c/p\u003e\u003cp\u003e(0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.787\u003c/p\u003e\u003cp\u003e(1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.701\u003c/p\u003e\u003cp\u003e(0.959)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0219**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emarriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emarital status\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;Other 1\u0026thinsp;=\u0026thinsp;Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003cp\u003e(0.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003cp\u003e(0.414)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003cp\u003e(0.386)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0172**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDo you have pension insurance\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003cp\u003e(0.160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003cp\u003e(0.187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003cp\u003e(0.151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0394**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egrandchilden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDo you take care of your grandchildren\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003cp\u003e(0.486)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003cp\u003e(0.482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003cp\u003e(0.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echildren_satisfication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChild satisfaction\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;dissatisfied 1\u0026thinsp;=\u0026thinsp;satisfied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003cp\u003e(0.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003cp\u003e(0.259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003cp\u003e(0.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eadl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDaily life ability assessment 0\u0026thinsp;=\u0026thinsp;No problem\u003c/p\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;There is a problem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003cp\u003e(0.468)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003cp\u003e(0.464)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003cp\u003e(0.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.4719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elife_care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIs there any life care available\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003cp\u003e(0.364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003cp\u003e(0.353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003cp\u003e(0.367)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether engaged in agricultural activities\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;No 1\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003cp\u003e(0.484)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003cp\u003e(0.500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003cp\u003e(0.471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elnexp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal household expenditure in the past year\u003c/p\u003e\u003cp\u003e(Take logarithm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003cp\u003e(2.141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.076\u003c/p\u003e\u003cp\u003e(2.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.976\u003c/p\u003e\u003cp\u003e(2.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstrumental variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIv_qq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHas land ownership confirmation been carried out in the past five years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003cp\u003e(0.467)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003cp\u003e(0.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003cp\u003e(0.459)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIv_percentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe proportion of land transfer in this village\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.098\u003c/p\u003e\u003cp\u003e(15.473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.073\u003c/p\u003e\u003cp\u003e(18.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.536\u003c/p\u003e\u003cp\u003e(14.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObsevations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \"***\", p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \"**\", p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \"*\" ;. ②Standard errors are in parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Empirical Tests","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Benchmark regression results\u003c/h2\u003e\n \u003cp\u003eThis paper tests the validity of instrumental variables. First, we examine the issue of weak instrumental variables. The Cragg-Donald Wald F statistic value is 43.156, which is significantly greater than the critical value of 19.93 at the 10% level in the Stock-Yogo test. This significantly rejects the null hypothesis of weak instrumental variables, indicating that the model does not suffer from weak instrumental variables, the instrumental variables exhibit strong correlation with the endogenous variables. Next, in terms of tool variable testing, the Anderson-Canon-Corr-LM test rejects the null hypothesis at the 1% level, indicating that there is no under-identification issue with the tool variables, meaning that the selected tool variables are correlated with the endogenous explanatory variables. Finally, an exogeneity test for the tool variables is conducted, specifically an over-identification test. The p-values of the Sargan statistic are all greater than 0.1, accepting the null hypothesis that the instrumental variables are exogenous, thereby ensuring the exogeneity of the selected instrumental variables. In summary, the instrumental variables used in this study meet the requirements, and the results obtained using these instrumental variables are valid.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe impact of land transfer on the elderly care methods of elderly farmers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)OLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWay of old-age care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWay of old-age care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elandout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0915***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00700***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00679***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00147)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0738***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0671***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0177)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0144*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0183**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00901)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0221)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.268***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.278***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0502)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrandchilden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0572***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0582***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0173)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echildren_satisfication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0313)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0353*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0384**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elife_care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0648***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0521**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0242)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0548***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0246)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnexp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00115\u003c/p\u003e\n \u003cp\u003e(0.00393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00173\u003c/p\u003e\n \u003cp\u003e(0.00382)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince dummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.245*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndersoncanon.corr.LM statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.226\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCragg-DonaldWaldF statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.156\u003c/p\u003e\n \u003cp\u003e(19.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargan statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003cp\u003e(0.3999)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFirst, regardless of whether OLS or 2SLS is used, the impact of land transfer on the way of old-age care of elderly farmers is significantly positive, meaning that after transferring their land, elderly farmers tend to choose family-based old-age care as their primary way. This basically verifies Hypothesis 1 proposed in this paper. In addition, other control variables are also basically in line with expectations. Age has a significant positive impact on the way of old-age care of elderly farmers, indicating that older elderly farmers are more inclined to choose family-based old-age care. Gender has a significant negative impact on the way of old-age care of elderly farmers, meaning that male elderly farmers are less likely to choose family-based old-age care than females and more inclined to choose individual old-age care. Furthermore, self-assessed health has a significant negative impact on the way of old-age care of elderly farmers, implying that those in better health are more prone to choosing individual old-age care. Elderly farmers with pension insurance also tend to choose individual old-age care. Whether they take care of grandchildren also has a significant positive impact on the way of old-age care of elderly farmers. Meanwhile, elderly farmers with poorer daily care abilities are more inclined to choose family-based old-age care. Finally, elderly farmers who regularly receive daily care from their children are more prone to choosing individual old-age care. However, it should be noted that the OLS model often overlooks the \u0026quot;self-selection\u0026quot; problem of elderly farmers. Therefore, the endogenous switching probit (ESP) model is adopted to further analyze the impact of land transfer on the way of old-age care of elderly farmers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Endogenous switching probit (ESP) model estimation results\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports the ESP model estimation results of the impact of land transfer on the elderly farmers\u0026apos; retirement methods. The Wald test value for the independence of the equation is 375.20, which rejects the hypothesis that the choice equation and the result equation are independent at the 1% level. The rho1 value is significant at the 5% level. This indicates that there are unobservable factors that simultaneously affect whether elderly farmers choose land transfer and their retirement methods, suggesting that there is indeed a selection bias in the equation. Therefore, it is reasonable to use the ESP model for analysis.\u003c/p\u003e\n \u003cp\u003eColumn (1) of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports the estimation results of the factors influencing land transfer. Gender has a significant negative impact on the land transfer choice of elderly farmers, meaning that male elderly farmers tend to retain their land. Additionally, the higher the education level of elderly farmers, the greater the possibility of their participation in land transfer. Elderly farmers with good self-assessed health conditions also tend to participate in land transfer. However, marital status has a negative impact on land transfer for elderly farmers, meaning that married elderly farmers tend to retain their land. Similarly, regular care from children significantly reduces the willingness of elderly farmers to transfer their land. In terms of agricultural activities, elderly farmers engaged in agricultural activities mostly tend to retain their land. Regarding the two instrumental variables, elderly farmers in areas with a higher proportion of land transfer and land confirmation are more willing to transfer their land.\u003c/p\u003e\n \u003cp\u003eColumns (2) and (3) of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e report the estimation results of the factors influencing the way of old-age care of elderly farmers. Age has a positive impact on the way of old-age care of both elderly farmers who have transferred land and those who have not at the 5% and 1% significance levels, respectively. This means that regardless of whether they participate in land transfer or not, elderly farmers tend to choose family-based old-age care as they age. Gender has a negative impact on the way of old-age care of both types of elderly farmers at the 1% significance level, indicating that male elderly farmers are more likely to choose individual old-age care compared to female elderly farmers. Self-assessed health has a negative impact on the way of old-age care of elderly farmers who have transferred land at the 5% significance level, meaning that elderly farmers who have transferred land and have good self-assessed health are more likely to choose individual old-age care. Marital status has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 10% significance level, suggesting that married elderly farmers who have not transferred land tend to choose family-based old-age care. Pension insurance has a negative impact on the way of old-age care of both types of elderly farmers at the 1% significance level, meaning that those with insurance are more likely to choose individual old-age care. Whether or not to take care of grandchildren has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 1% significance level. This implies that even if they have not transferred land, elderly farmers tend to choose family-based old-age care when they need to take care of their grandchildren. The satisfaction of children has a positive impact on the way of old-age care of both types of elderly farmers at the 1% significance level, indicating that when the intergenerational relationship within the family is good, both types of elderly farmers tend to choose family-based old-age care after land transfer. Daily living ability has a positive impact on the way of old-age care of elderly farmers who have not transferred land at the 1% significance level, meaning that elderly farmers who have not transferred land and have poor daily living ability are more likely to choose family-based old-age care. Regular care from children has a negative impact on the way of old-age care of elderly farmers who have transferred land at the 1% significance level, indicating that elderly farmers who have transferred land and receive regular care from their children tend to choose individual old-age care.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results of ESP model on the impact of land transfer on the elderly care mode of elderly farmers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSelect equation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResult equation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChoose to transfer land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand transfer not selected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elandout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe way of old-age care_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe way of old-age care_0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0167**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0192***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00466)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0894*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.303***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0558)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0220***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00781)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0535**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0906**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0441)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0278)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.139**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0691)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.121***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.707***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrandchilden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0531)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echildren_satisfication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0556)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elife_care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.406***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0733)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.440***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0736)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnexp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince dummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0115)\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0201)\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0117)\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiv_percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0136***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiv_qq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.833**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.083***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.366)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erho1\u003c/p\u003e\n \u003cp\u003erho0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.535**\u003c/p\u003e\n \u003cp\u003e(0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.349\u003c/p\u003e\n \u003cp\u003e(0.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWald test value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR test value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4130.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e4.3 Mean treatment effects of the impact of land transfer on the elderly farmer way of old-age care in china\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reports the average treatment effect of land transfer on the elderly farmers\u0026apos; choice of the way of old-age care. ATT and ATU respectively represent the average treatment effect of elderly farmers who participated in land transfer and those who did not. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, first, choosing to transfer land leads elderly farmers to tend to choose family-based old-age care. Specifically, the ATT value of the average treatment effect of land transfer on the elderly farmers\u0026apos; choice of the way of old-age care is 0.949, which has passed the significance test at the 1% level. This indicates that if elderly farmers who chose to transfer land had chosen to retain their land, the probability of choosing family-based old-age care would have decreased by 0.949 units, that is, they would be more inclined to choose individual old-age care. The value of ATU is 0.855, which has passed the significance test at the 1% level. This indicates that if elderly farmers who did not choose to transfer land had chosen to transfer land, the probability of choosing family-based old-age care would have increased by 0.855 units. Therefore, it can be concluded that Hypothesis H1 is reasonable.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe average treatment effect of land transfer on the elderly care methods of elderly farmers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipate in land transfer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNot involved in land transfer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipate in land transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.699\u003c/p\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.949***\u003c/p\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot involved in land transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.593\u003c/p\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855***\u003c/p\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Robustness test\u003c/h2\u003e\n \u003cp\u003eIn order to verify the reliability of the ESP model estimation results, this paper conducts a robustness test by propensity matching score matching (PSM) and instrumental variable regression (IV-probit).\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.1 Propensity to Match Score (PSM)\u003c/h2\u003e\n \u003cp\u003eThe common support domain test and balance test were conducted for matching, based on the results passed. Propensity score matching was used for estimation. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrates the results of treatment effects under four different matching methods. It can be found that the treatment group means are higher than the control group means regardless of the matching used, and the corresponding ATT values are all significantly positive at the 1% level. Therefore, it can be inferred that the benchmark results are robust. Of course, there are differences that the ATT values obtained with the PSM model are much smaller than those calculated by the ESP model, which is because the PSM model does not take into account the effects of unobservable factors and the estimates obtained are biased. The ESP model, on the other hand, fully considers the selective bias caused by observable and unobservable factors, and automatically adds the bias term obtained in the first stage to the second stage to estimate the impact of land transfer on farmers\u0026apos; entrepreneurship, and the estimation results obtained are more scientific (Li et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness Test 1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMatching method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProcessing group mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u0026thinsp;=\u0026thinsp;1 nearest neighbor matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u0026thinsp;=\u0026thinsp;4 nearest neighbor matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadius matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNuclear matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.2 IV-probit\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e reports the endogeneity-corrected iv-probit model estimation results, with columns (1) and (2) representing the first and second stage regression results of the IV model estimation, respectively. From the above, it can be seen that the two selected instrumental variables are consistent with endogeneity and exogeneity, so the IV-probit model estimation results are reasonable. As can be seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the effect of land transfer on Farmers aged 60 and above\u0026apos; way of old-age care is positive and significant at 1% level. That is, after the land transfer, the Farmers aged 60 and above will be more inclined to choose family pension. Therefore, it can be seen that the benchmark results are robust.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness Test 2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elandout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWay of old-age care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elandout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0181***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00405)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0239*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0490)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00596***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00669)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0156**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0481**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0239)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0376**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0590)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.873***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0418)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrandchilden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0464)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echildren_satisfication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0871)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0484)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elife_care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0466**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0660)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0676)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnexp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince dummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiv_qq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e0.0485***\u003c/p\u003e\n \u003cp\u003e(0.0163)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiv_percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e0.00423***\u003c/p\u003e\n \u003cp\u003e(0.000492)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.989***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Mechanism analysis\u003c/h2\u003e\n \u003cp\u003eTheoretical analysis suggests that the mechanism by which land transfer affects the elderly care methods of elderly farmers lies in non-agricultural employment and whether they live with their children. This paper draws on the research of Jiang (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and sets up the following mediating effect model for testing.\u003c/p\u003e\n \u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ10\" class=\"mathdisplay\"\u003e$$\\:M={\\alpha\\:}_{1}+{\\lambda\\:Landout}_{i}+{\\beta\\:}_{2}Z+{\\epsilon\\:}_{2}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAmong them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e is the mechanism variable, representing respectively whether engaged in non-agricultural work\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(No\\_agriculture)\\)\u003c/span\u003e\u003c/span\u003eand whether living with children\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(Togetℎer\\right)\\)\u003c/span\u003e\u003c/span\u003e. In terms of non-agricultural employment, it mainly involves two aspects: The first is agricultural employment. Select the question from the questionnaire: \u0026quot;In the past year, have you worked on the farm for other farmers or employers to earn money for at least 10 days?\u0026quot; To define; The second is non-agricultural employment. By asking, \u0026quot;Excluding jobs related to farming, did you work for at least one hour last week?\u0026quot; To define. If at least one of the above conditions is met, it is determined to participate in non-agricultural work and is assigned a value of 1; otherwise, it is assigned a value of 0. Select \u0026quot;At least one child has lived with you and your spouse in the past year\u0026quot; to indicate whether you live with your children. If it meets the requirement, it is recorded as 1; otherwise, it is recorded as 0.\u003c/p\u003e\n \u003cp\u003eIn column (1) of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the regression coefficient of land transfer is 0.0391 and has passed the significance test, indicating that land transfer can promote elderly farmers to live with their children and then choose family-based elderly care as their main elderly care method. Hypothesis H2 is verified. In column (2) of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the regression coefficient of land transfer is not significant. That is to say, after land transfer, elderly farmers find it difficult to engage in non-agricultural employment and are thus forced to choose family-based elderly care as their main way of elderly care. Therefore, hypothesis H3 is verified.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMechanism Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTogether\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo_agriculture\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e-0.0391**\u003c/p\u003e\n \u003cp\u003e(0.0176)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e0.0069\u003c/p\u003e\n \u003cp\u003e(0.0136)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.214*\u003c/p\u003e\n \u003cp\u003e(0.1170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531***\u003c/p\u003e\n \u003cp\u003e(0.0903)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003econtrol variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince Dummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \u0026quot;***\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \u0026quot;**\u0026quot;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \u0026quot;*\u0026quot; ;. ②Standard errors are in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5 Further analysis","content":"\u003cp\u003eThe impact of land transfers in different regions of China on the way of old-age care for elderly farmers may exhibit regional heterogeneity. Building on the previous analysis, this study further examines the effects of land transfers in China's grain-producing and non-grain-producing regions, as well as in the eastern, central, and western regions, on the way of old-age care for elderly farmers. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, elderly farmers in the western region are more likely to opt for individual old-age care after transferring their land. Specifically, the average treatment effect (ATT) value for the impact of land transfers on elderly farmers' the way of old-age care in the western region is -0.302, which is statistically significant at the 1% level. This means that elderly farmers who choose to transfer their land would have a 0.302-unit lower probability of choosing individual old-age care if they retained their land. The ATU value is -0.324, which is also statistically significant at the 1% level. This indicates that elderly farmers who did not transfer their land would have a 0.324-unit increase in the probability of choosing individual old-age care if they had transferred their land. In contrast, the average treatment effects of land transfers on elderly farmers' the way of old-age care in central and eastern regions are positive and both pass the 1% significance test, indicating that elderly farmers in these two regions tend to choose family-based old-age care after land transfers. Additionally, we found that the average treatment effect in the central region is significantly greater than that in the eastern region. This may be due to the following reasons: first, the eastern region has a more developed social security system and elderly care facilities. After land transfer, elderly farmers can utilize these social old-age care resources to some extent, reducing their reliance on family-based old-age care. Second, elderly farmers in the eastern region have relatively higher income levels and more substantial savings, enabling them to address old-age care issues through personal savings. Therefore, even if elderly farmers in the eastern region choose family-based old-age care after land transfer, their reliance on family-based old-age care is relatively lower compared to elderly farmers in the central region. This difference is reflected in the average treatment effect, with the eastern region's average treatment effect value being lower than that of the central region. Finally, the average treatment effect (ATT) value of land transfers in grain-producing regions on the way of old-age care for elderly farmers is 1.432, which is significant at the 1% level. This indicates that if elderly farmers who have transferred their land choose to retain it, the probability of them choosing family-based old-age care will decrease by 1.432 units. The ATU value is 1.438, which also passes the significance test at the 1% level, meaning that if elderly farmers who have not transferred their land choose to transfer it, the probability of choosing family-based old-age care will increase by 1.438 units. However, the average treatment effects in non-grain-producing regions are all negative. The ATT value is -0.173 and is statistically significant at the 1% level, indicating that if elderly farmers in non-grain-producing regions choose to retain their land, the probability of them opting for individual-based old-age care decreases by 0.173 units. The ATU value is -0.178 and is also statistically significant at the 1% level, This indicates that if elderly farmers in non-grain-producing regions choose to transfer their land, the probability of them choosing individual old-age care will increase by 0.178 units.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFurther Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esample size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eATU\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.302***\u003c/p\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.324***\u003c/p\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.414***\u003c/p\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.352***\u003c/p\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.259***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.301***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain grain producing areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.432***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.438***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon grain producing areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.173***\u003c/p\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.178***\u003c/p\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ①The last column is the result of t-test of mean. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 is shown as \"***\", p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is shown as \"**\", p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is shown as \"*\" ;. ②Standard errors are in parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"6 Conclusions and Recommendations","content":"\u003cp\u003eThe main conclusions are as follows: First, after land transfer, elderly farmers in China tend to choose family-based elderly care as their primary method of elderly care, and this conclusion remains valid under a series of robustness tests. Second, mechanism analysis indicates that after land transfer, elderly farmers in China, constrained by their own capabilities, find it difficult to engage in non-agricultural employment and are forced to rely on family economic support, thereby choosing family-based elderly care. Additionally, land transfer disrupts the material foundation of traditional intergenerational bonds, prompting elderly farmers to choose to live with their children to access elderly care resources, such as caring for grandchildren in exchange for support, and thus opt for family-based elderly care. Third, further analysis indicates that the impact of land transfers on the pension arrangements of elderly farmers in China exhibits significant regional differences. Specifically, elderly farmers in central and eastern China and grain-producing regions tend to choose family-based pension arrangements after land transfers, while elderly farmers in western regions and non-grain-producing areas are more inclined to choose individual-based pension arrangements. Finally, other factors also influence elderly farmers' pension arrangements, such as age, gender, pension insurance coverage, self-assessed health, the need to care for grandchildren, children's satisfaction, daily living abilities, children's care provision, and whether they engage in agricultural activities.\u003c/p\u003e\u003cp\u003eBased on the empirical research findings, this paper offers the following recommendations: First, strengthen the family-based pension support system. Given that elderly farmers choose family-based elderly care after land transfers, the Chinese government should provide targeted subsidies based on household economic conditions and the burden of elderly care, implementing a tiered strategy to incentivize families to assume elderly care responsibilities. Second, strengthen support for re-employment and safety net protections for elderly farmers. Addressing the current challenges of non-agricultural employment for elderly farmers, the Chinese government could conduct age-appropriate vocational skills training, such as short-term courses in domestic services or handicrafts, to help them achieve nearby employment and income generation. For elderly farmers with poor health and no ability to work, the Chinese government should strengthen policy safety nets. On one hand, it should establish a \"rural special subsidy for impoverished elderly\" to directly increase their transfer income; on the other hand, it should provide special subsidies to children who support their elderly parents to enhance the family's economic motivation for support. Third, improve the rural social security system. The Chinese government should continuously optimize the pension insurance system, establish a pension adjustment mechanism, and strengthen the rural healthcare service system to reduce the medical burden on elderly farmers. Fourth, optimize the land transfer income mechanism to enhance the economic security of elderly farmers. Explore a \"base rent plus profit-sharing\" land rent model to ensure long-term stable income growth for elderly farmers and alleviate the burden of family-based elderly care. At the same time, actively explore a \"land transfer income reinvestment in elderly care\" mechanism, allocating a certain proportion of land transfer income to build village-level elderly care facilities. Fifth, establish a \"family-society\" collaborative elderly care model. Relying on village or community organizations, establish mutual aid elderly care stations. Take villages or communities as units to integrate idle school buildings, village collective housing, and other resources to build standardized mutual aid elderly care stations.\u003c/p\u003e\u003cp\u003eGiven the special characteristics of China's western regions, this paper proposes the following targeted recommendations: First, increase central government fiscal transfers to western regions, specifically allocated for strengthening rural elderly care facilities, such as day care centers and senior activity centers. Additionally, encourage social organizations and volunteers to participate in elderly care services to supplement family-based care. Finally, enhance the economic income levels of rural households in western China through industrial poverty alleviation and employment poverty alleviation, promote the orderly transfer of industries from eastern to western regions, foster inter-regional industrial synergy, attract the return of young and middle-aged individuals, and alleviate family-based elderly care pressures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eFunding: No Funding\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eEthical Approval\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL mainly Wrote the main manuscript text. and X and J maily Drew the chart. M mainly Engaging in the submission and editing of papers.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis article uses data from the fourth round of the China Health and Retirement Longitudinal Study (CHARLS) organised by Peking University in 2018, which is publicly accessible data. The DOI is: https://charls.charlsdata.com/pages/Data/2013-charls-wave2/zh-cn.html.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, D., \u0026amp; Zhang, Y. (2015). Different pension models' impact on rural elderly happiness in China: Empirical test based on CHARLS baseline data. \u003cem\u003eJournal of Agrotechnical Economics\u003c/em\u003e, (04), 78\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, L., \u0026amp; Gui, H. (2024). How is the self-supporting order of rural elderly possible? Field survey based on the perspective of aging in place. \u003cem\u003eChina Rural Survey\u003c/em\u003e, (02), 131\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng, L., \u0026amp; Zhang, Y. (2013). Can the New Rural Social Pension Insurance change the pension model of Chinese rural residents? Economic Research Journal, *48*(08), 42\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCong, Z., \u0026amp; Silverstein, M. (2008). Intergenerational support and depression among elders in rural China: Do daughters-in‐law matter? \u003cem\u003eJournal of Marriage and Family\u003c/em\u003e, *70*(3), 599\u0026ndash;612.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui, B., \u0026amp; Xie, Y. (2015). \u003cem\u003eImpact of land expropriation on elderly farmers' labor supply: Social security function of rural land\u003c/em\u003e (pp. 154\u0026ndash;165). 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Resources Science, *43*(10), 2003\u0026ndash;2012.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land transfer, Way of old-age care, Population aging, Endogenous transformation probit model","lastPublishedDoi":"10.21203/rs.3.rs-7018481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7018481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on data from the 2018 China Health and Aging Survey (CHARLS), this paper employs an endogenous switching probit model to analyze the impact of land transfer on the way of Chinese elderly farmers care. The study finds that, first, after land transfer, Chinese elderly farmers prefer to choose family retirement, and the above conclusion still holds after a series of robustness tests. Second, the mechanism analysis shows that land transfer pushes elderly farmers to choose family old-age care through dual paths, namely, the effect of children living with them and non-farm employment, respectively. Third, further analysis shows that elderly farmers in eastern China and the main grain-producing areas tend to choose family old-age pension after land transfer, while elderly farmers in western China and the main non-grain-producing areas tend to choose individual old-age pension. Finally, age, gender, pension insurance ownership, the need to take care of grandchildren, children's satisfaction, and daily life ability all have a significant impact on the aging styles of elderly farmers.Based on the above conclusions, this paper proposes recommendations such as strengthening the family-based elderly care support system, enhancing re-employment support and safety net protection for elderly farmers, and improving the rural social security system. In view of the special characteristics of the western region of China, It was proposed that the central government should increase its transfer payments to western regions, specifically for the construction of elderly care facilities; encourage social participation in elderly care to fill the gap in family-based care; and strengthen support for family-based care by improving family income through industrial poverty alleviation.\u003c/p\u003e","manuscriptTitle":"The Impact of Land Transfer on the Way Of Old-age care of Elderly Farmers in China：an empirical analysis based on the endogenous conversion probit model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 07:19:14","doi":"10.21203/rs.3.rs-7018481/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4f16587-a207-4f3b-a0d6-d815dccb07ba","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T14:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 07:19:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7018481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7018481","identity":"rs-7018481","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}