Mapping Rural Elderly Multidimensional Poverty in China: Intergenerational Support as a Protective Factor

Mapping Rural Elderly Multidimensional Poverty in China: Intergenerational Support as a Protective Factor | 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 Mapping Rural Elderly Multidimensional Poverty in China: Intergenerational Support as a Protective Factor Xiaotong Zhao, Chunhui Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7088539/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 As population aging accelerates, rural older adults in China are increasingly exposed to multidimensional poverty risks, including deprivation in income, health, and living conditions. The weakening of traditional family caregiving functions has further exacerbated this issue. Drawing on nationally representative data from the China Family Panel Studies (CFPS), this study adopts an intergenerational support perspective and employs spatial analysis alongside dynamic monitoring techniques to examine the spatial distribution, temporal evolution, and influencing factors of multidimensional poverty among the rural elderly. The results reveal three key findings. First, from 2016 to 2022, overall multidimensional poverty among rural older adults declined significantly, with heterogeneous trends across dimensions: deprivations in economic conditions and living standards improved markedly, mental health deprivation decreased most substantially, while progress in physical health remained relatively limited. Second, multidimensional poverty exhibited notable regional disparities and spatial clustering. Economic deprivation was more severe in central and western regions, while deprivation in living standards was lower along the eastern coast; certain provinces experienced disproportionately high levels of mental health deprivation. Third, intergenerational support played a critical role in alleviating poverty among the rural elderly. In particular, frequent emotional contact and communication from adult children significantly reduced poverty risks across economic, living, and mental health dimensions, whereas instrumental support such as help with housework had limited impact. This study provides empirical evidence to inform anti-poverty and social policy initiatives under the framework of healthy aging, highlighting the need to complement material assistance with strengthened family-based intergenerational support in order to comprehensively enhance the well-being of older populations. Multidimensional Poverty Rural Elderly Intergenerational Support Regional Disparities China Figures Figure 1 1 Introduction As of late 2024, China's elderly population aged 60 and above reached 310.31 million individuals, constituting 22.0% of the total population. The World Health Organization has identified China as one of the world's most rapidly aging nations (World Health Organization, n.d.). Within this demographic context, rural elderly residents account for 46.0% of China's total elderly population. Against the backdrop of "growing old before getting rich—a phenomenon wherein demographic aging outpaces economic and social preparedness—ensuring the welfare of rural elderly populations in an increasingly aged society has become a central governance priority in China. By the end of 2020, China achieved the comprehensive elimination of absolute poverty in rural areas under current national standards, thereby facilitating a transition toward higher-level poverty alleviation objectives aimed at enabling rural elderly populations to live with prosperity, security, and dignity (Wan et al., 2025 ). In fact, rural elderly populations exhibit pronounced vulnerability relative to other demographic groups. From an economic perspective, rural elderly individuals are characterized by limited and unstable income sources, particularly following the loss of labor capacity, with their financial resources primarily dependent on agricultural income, modest pension benefits, and filial support from adult children (Liu, 2014 ). Furthermore, regarding health and medical security, elderly cohorts demonstrate greater susceptibility to health-related shocks compared to younger age groups (Cho et al., 2023 ). Rural areas, relative to urban centers, are characterized by relatively inadequate provision of public healthcare services, with elderly residents frequently experiencing insufficient coverage in the prevention, treatment, rehabilitation, and follow-up care of common chronic conditions (Liang & Lu, 2014 ). Consequently, the poverty challenges confronting rural elderly populations manifest as a phenomenon of multidimensional deprivation characterized by the mutual reinforcement and compounding of various disadvantages. Within this context, intergenerational support functions as a crucial mechanism for securing the welfare of rural elderly populations in their later years, encompassing multiple dimensions including economic maintenance, daily care assistance, and emotional consolation (Tan et al., 2023 ; Tang et al., 2022). Extensive research demonstrates that intergenerational support, particularly non-monetary forms of assistance, constitutes a critical factor in enhancing elderly quality of life and alleviating multidimensional poverty (Cong & Silverstein, 2008 ; Guo, 2014 ; Wan et al., 2025 ). However, rapid industrialization and urbanization have propelled structural transformations in rural society, posing severe challenges to intergenerational support systems. The nuclearization of family units and increased intergenerational distance have weakened traditional extended family functions, resulting in widespread phenomena of "left-behind" and "empty-nest" elderly in rural areas. While adult children may provide certain economic support through remittances, such financial transfers fail to compensate for the absence of daily care and emotional companionship (Hu et al., 2022 ; Stacey & Ayers, 2012 ). Furthermore, rural elderly populations frequently encounter "information gaps" and "social interaction barriers" when accessing external information, resources, and public services, which further diminishes their subjective well-being and may precipitate deterioration in health status (Golan et al., 2017 ; Huang & Zhang, 2021 ; Lowenstein, 2007 ; Van Gaalen & Dykstra, 2006 ). The declining functionality of family support systems stands in stark contradiction to elderly individuals' continued dependence on familial networks, potentially exacerbating their life vulnerability. Therefore, it becomes critically important to quantitatively assess the current state of multidimensional poverty among rural elderly populations, examine the mechanisms through which intergenerational support mitigates their poverty conditions, and explore transitions in care provision models. This study is guided by the aforementioned research objective. In the following sections, drawing on existing literature and based on four waves of data from the China Family Panel Studies (CFPS) spanning 2016 to 2022, this study first constructs a multidimensional poverty measurement framework for rural elderly, encompassing four key dimensions: economic status, living standards, physical health, and mental health. This study then examines the spatial clustering patterns of deprivation across these dimensions at the national level. Second, the study investigates how various forms of intergenerational support influence deprivation in each individual dimension as well as the overall level of multidimensional poverty among rural elderly. Third, by employing poverty transition matrices and the Kaplan–Meier survival analysis method, this study explores the dynamic trajectories of multidimensional poverty, further assessing the extent to which support from adult children affects the likelihood of exiting or entering poverty. Taken together, this research integrates spatial and temporal analytical perspectives to systematically unravel the spatial heterogeneity of multidimensional poverty and empirically assess the mechanisms through which evolving patterns of intergenerational support, in the context of modernization, shape poverty outcomes among rural elderly. 2 Reviews 2.1 Measurement of Multidimensional Poverty Among the Elderly in Rural Areas In recent years, research on poverty among the elderly has shifted from a singular focus on economic status to a multidimensional perspective that encompasses health, social participation, education levels, and social security. Early scholars primarily utilized monetary indicators centered on income (Kim & Cook, 2011 ) and consumption (Wallace et al., 2013 ) to measure the multidimensional poverty experienced by the elderly population. However, as the elderly demographic tends to have lower incomes, their consumption demand structure differs significantly from that of the general population. Consequently, measures of poverty based solely on income or consumption may overestimate poverty levels among the elderly (Borrowman, 2012 ; Brady et al., 2010 ; Disney & Whitehouse, 2002 ). Therefore, studies on poverty affecting older adults have increasingly incorporated non-economic indicators such as health (Heslop & Gorman, 2002 ), social participation (Rissanen & Ylinen, 2014 ), education (Trani et al., 2024 ), and social security (Amarante & Colacce, 2022 ). These studies are grounded in comprehensive frameworks like the Human Development Index (HDI) and the Multidimensional Poverty Index (MPI), advocating for nuanced adjustments based on the economic, social, and cultural characteristics of specific groups. The research on multidimensional poverty among the elderly in China has aligned with the global trend while making adjustments based on the country's specific contextual characteristics. First, due to the significant income disparities between urban and rural areas, multidimensional poverty studies focusing on rural regions typically incorporate economic dimension indicators into their analytical framework (C. Li et al., 2021 ; G. Li et al., 2019 ). Second, while the educational dimension is widely applied in international multidimensional poverty research, the low overall educational attainment of rural elderly populations has led some scholars to question its relevance (E. Xie, 2015 ). Moreover, researchers have highlighted that the long-standing urban-rural dualistic structure has resulted in relatively underdeveloped infrastructure in rural areas, presenting significant challenges for the elderly in terms of access to safe drinking water, sanitation facilities, and housing warmth. Consequently, such living standard indicators are crucial for comprehensively assessing multidimensional poverty among rural elderly populations (Fan et al., 2018 ; He & Ye, 2014 ). Lastly, research has increasingly tended to further disaggregate the health dimension into physical and mental health subcomponents, thereby expanding the conceptual boundaries of multidimensional poverty research (Chan & Wong, 2025 ; Trani et al., 2022 , 2024 ). Furthermore, early poverty research predominantly relied on static measurements using cross-sectional data. However, recent studies have shifted towards multiple observations across time dimensions, focusing on tracking and identifying poverty processes (Carter & Barrett, 2006 ). This dynamic analytical perspective facilitates understanding the persistence, interactivity, and evolutionary patterns of poverty across different temporal and contextual scenarios (Sen, 1976 ). Some scholars have employed dynamic analysis to differentiate between chronic and transient poverty, exploring the dynamics of entering and exiting poverty states (Q. Wang et al., 2023 ). Nevertheless, existing dynamic poverty research has primarily concentrated on income poverty dynamics, with relatively limited exploration of multidimensional poverty dynamics. In the context of China's successful elimination of absolute rural poverty, research should transition from focusing on absolute poverty to a more nuanced multidimensional approach. This shift necessitates emphasizing dynamic poverty monitoring and the development of sustainable mechanisms to maintain non-poverty status. 2.2 Intergenerational Support and Rural Elderly Multidimensional Poverty In traditional Chinese culture, intergenerational support is regarded as a vital expression of filial piety. Rather than being limited to monetary transfers, such support often takes non-material forms, including daily care, emotional companionship, life guidance, and decision-making assistance provided by adult children to their aging parents (Button & Ncapai, 2019 ; Patrick et al., 2001 ; Sun et al., 2022 ; G. Wang et al., 2022 ). Empirical evidence has demonstrated that intergenerational support significantly enhances the overall well-being of older adults, particularly in rural areas, and plays a crucial role in alleviating multidimensional poverty (Krsteska & Pejoska-Gerazova, 2010 ; Nasser & Overholser, 2005 ; Newsom et al., 2008 ). While financial assistance from children can relieve material deprivation, its effects on mental and physical health poverty remain limited (Tan et al., 2023 ). For instance, scholars have argued that mental poverty may be more pressing than income poverty for rural elders, and the quality of intergenerational relationships is a key determinant of mental well-being (X. Wang et al., 2011 ). Additionally, satisfaction with familial support has been found to be closely associated with daily emotional states among older adults, with such correlations being particularly salient among elderly men (J. Lin et al., 2011 ; Patrick et al., 2001 ). Beyond addressing present poverty conditions, intergenerational support is increasingly recognized as an informal "insurance" mechanism that enables rural older adults to cope with future uncertainties. This function is particularly critical in rural settings where formal social protection systems remain underdeveloped. Studies have shown that households with reliable support networks from children are less likely to fall into poverty when facing income volatility or sudden expenditures such as medical emergencies (Guo, 2014 ; G. Wang et al., 2022 ). Living arrangements also play a significant role in shaping poverty risks among the elderly: those co-residing with their children experience significantly lower poverty rates compared to those living apart (Guo, 2014 ; Waehrer & Crystal, 1995 ), and transitions from living alone to living with children are found to reduce the likelihood of poverty (Rendall & Speare, 1995). While existing literature acknowledges the crucial role of intergenerational support in mitigating multidimensional poverty among older adults, most studies conceptualize it as a singular, undifferentiated construct. Few have examined its varied effects across different dimensions of well-being. Moreover, in the context of rapid modernization and increasing geographic separation of family members, intergenerational support has evolved beyond traditional face-to-face caregiving. Remote forms of support—enabled by telephone, video calls, and other digital communication technologies—are becoming increasingly common, yet their implications for elder well-being remain underexplored. Therefore, it is necessary to adopt a multidimensional analytical lens to distinguish among types of intergenerational support and identify their distinct impact pathways. This is particularly important given the ongoing urban-rural divide and the evolving patterns of intergenerational interaction in contemporary China. 3 Dataset and Methodology 3.1 Dataset The empirical analysis in this study utilizes data from the China Family Panel Studies (CFPS), a nationally representative, comprehensive longitudinal survey conducted by the Institute of Social Science Survey at Peking University. Launched in 2010, the CFPS employs a multi-stage probability sampling methodology to select respondents from 25 provinces, municipalities, and autonomous regions across mainland China (excluding Inner Mongolia, Xinjiang, Tibet, Hainan, Ningxia, and Qinghai), with the surveyed regions representing approximately 95% of China's total population, thereby ensuring strong representativeness. The baseline survey encompassed 14,960 households and 33,600 individuals, collecting comprehensive information at the household, individual, and community levels. Subsequently, follow-up surveys are conducted biennially, continuously tracking the same cohort of respondents to provide longitudinal data. The survey data collection process adheres to stringent quality control standards, ensuring data reliability and comparability (Y. Xie & Hu, 2014 ). This study employs four waves of CFPS data spanning 2016, 2018, 2020, and 2022. The sample is restricted to respondents aged 60 and above with rural hukou. After excluding observations with missing data, the final dataset comprises 10,847 observations across the four waves. To maximize sample retention, we utilize an unbalanced panel structure, with wave-specific sample sizes of 3,344, 3,288, 2,310, and 1,905 observations, respectively. Table 1 presents the main dimensions and indicators employed in this study's multidimensional poverty measurement framework. We utilize "per capita annual household income" as the metric for assessing income poverty, with households falling below the national poverty line classified as income-poor. According to the official standards established by China's National Bureau of Statistics, China's rural poverty threshold is anchored at 2,300-yuan per capita annual household income in constant 2010 prices. Following the calculation methodology employed by Li et al. (2017), we adjust this baseline using the "Consumer Price Index for Poor Rural Households in China 1 ," yielding poverty thresholds of 2,952-yuan, 2,995-yuan, 3,490-yuan, and 3,527-yuan for 2016, 2018, 2020, and 2022, respectively. The living standards dimension encompasses three indicators: drinking water, cooking fuel, and asset ownership. Households are considered deprived in this dimension if they rely on untreated water sources (rivers, lakes, wells, rainwater, cellar water, or pond/spring water), utilize non-clean energy sources for cooking (such as firewood or coal), or lack essential durable goods including automobiles, computers, household appliances, televisions, jewelry, antiques, or high-quality musical instruments. The health dimension simultaneously examines both physical and mental health components. For physical health, individuals reporting "poor" or "very poor" self-rated health status or having chronic diseases are classified as deprived in this indicator. Mental health is assessed through two indicators: life satisfaction and future optimism. Individuals expressing "very unsatisfied" or "unsatisfied" life satisfaction, or indicating "very little confidence" or "no confidence" in the future are considered deprived in the respective mental health indicators. We employ four indicators to measure intergenerational support: "relationship quality with children," "frequency of children's assistance with household tasks," "frequency of face-to-face meetings with children," and "frequency of contact with children." Relationship quality reflects the intensity of intergenerational emotional bonding, capturing the affective mechanisms that drive supportive behaviors (Patrick et al., 2001 ). The frequency of household assistance directly quantifies instrumental support (Tan et al., 2023 ), revealing the actual penetration of daily care provision. Meeting and contact frequencies constitute a spatiotemporal continuum of supportive behaviors (I.-F. Lin et al., 2012 ), where meeting frequency reinforces kinship presence effects, while contact frequency (including telecommunications) compensates for emotional maintenance under geographical separation (Zimmer et al., 2008 ). This indicator design aligns with the dynamic monitoring requirements of multidimensional poverty research and effectively captures the heterogeneity of intergenerational support patterns amid urbanization processes. Additionally, factors such as age, gender, educational attainment, marital status, employment status, and family structure influence the multidimensional deprivation status of rural elderly populations. These indicators serve as control variables in the model. Table 1 Descriptive Statistics Type Variable Mean S.D. Min Max Dependent variables Per capita household income 16629.731 56225.243 0 3794000 Drinking water 2.951 1 7 Cooking fuel 2.823 1 6 Assets 27325.18 121000 0 10000000 Self-rated physical health 2.601 1.28 1 5 Chronic disease status 0.280 0 1 Life satisfaction 4.124 0.979 1 5 Confidence in future 4.021 1.069 1 5 Independent Variables Relationship with children 4.289 0.703 1 5 Frequency of children's household assistance 2.688 2.230 1 7 Frequency of meeting with children 3.891 1.737 1 7 Frequency of contact with children 4.173 1.694 1 7 Control Variables Age 66.703 5.138 60 93 Age squared 44.756 7.091 36 86.49 Gender (1 = female) 0.554 0 1 Years of education 4.410 4.256 0 16 Marital status (1 = married) 0.872 0 1 Employment status (1 = employed) 0.819 0 1 Industry type (1 = non-agricultural) 0.151 0 1 Number of unhealthy family members 0.643 0.755 0 4 Household size 3.115 1.537 1 12 Region 2.320 1 4 3.2 Methodology (1) Multidimensional Poverty Measurement Methods We employ the Alkire-Foster (AF) method to measure poverty status among rural elderly populations (Alkire & Foster, 2011 ). Specifically, we first construct a deprivation matrix for each individual \(\:i\:\) and each poverty indicator \(\:j\) in the sample, based on predetermined deprivation thresholds \(\:{z}_{j}\) . The matrix element \(\:{g}_{ij}\) is defined as: \(\:{g}_{ij}\) = 1 if individual \(\:i\) 's achievement \(\:{x}_{ij}\) in indicator \(\:j\) falls below \(\:{z}_{j}\) ; otherwise \(\:{g}_{ij}\) = 0. Next, we calculate each individual's weighted deprivation score according to the weights \(\:{w}_{j}\) assigned to each indicator: $$\:{c}_{i}={\sum\:}_{j=1}^{d}{w}_{j}{g}_{ij}.$$ Using the dual-cutoff approach, individual \(\:i\:\) is identified as multidimensionally poor when their weighted deprivation score \(\:{c}_{i}\:\) reaches or exceeds a predetermined poverty cutoff \(\:k\) (typically \(\:0<k<1\) ). Based on this definition, the poverty headcount ratio H can be expressed as the proportion of poor individuals in the total sample: $$\:H=\frac{1}{n}{\sum\:}_{i=1}^{n}I\left({c}_{i}\ge\:k\right),$$ where \(\:I\left({c}_{i}\ge\:k\right)\:\) is an indicator function, The poverty intensity A represents the average deprivation score among the poor population: $$\:A=\frac{1}{q}{\sum\:}_{i:{c}_{i}\ge\:k}{c}_{i},$$ where q denotes the number of individuals identified as poor. Finally, the Multidimensional Poverty Index (MPI) is defined as the product of H and A: \(\:MPI=H\times\:A\) (2) Dynamic Poverty Measurement Methods We employ poverty transition matrices to analyze multidimensional poverty dynamics between adjacent survey years (Apablaza & Yalonetzky, 2012 ; Madden, 2022 ). The multidimensional poverty transition patterns between adjacent survey years are classified into four categories: non-poor → non-poor, non-poor → poor, poor → non-poor, and poor → poor. Therefore, the transition matrix for multidimensional poverty from time t to time t + 1 can be expressed as: $$\:{P}_{t,t+1}=\left(\begin{array}{cc}{p}_{11}&\:{p}_{10}\\\:{p}_{01}&\:{p}_{00}\end{array}\right).$$ Since poverty transition analysis based solely on adjacent survey years cannot explore poverty dynamics over the entire survey period, we subsequently employ the Kaplan-Meier method to estimate poverty "survival rates" and "hazard rates" at various time points (Kaplan & Meier, 1958 ). Taking poverty exit as an example, the survival function S(t) represents the probability that an individual remains in poverty after time t, with the formula: \(\:S\left(t\right)=P\left({T}_{i}>t\right)={\prod\:}_{a=0}^{t-1}\frac{{n}_{a}-{d}_{a}}{{n}_{a}},\) where \(\:{T}_{i}\:\) denotes the duration for which individual \(\:i\) remains in multidimensional poverty, \(\:{n}_{a}\) represents the number of individuals still in the risk set at time point \(\:a\) , and \(\:{d}_{a}\:\) represents the number of individuals exiting poverty at time point \(\:a\) . The corresponding hazard function is defined as the probability that an individual escapes multidimensional poverty at time point \(\:t\) . (3) Spatial Analysis Methods We employ hot spot analysis (Getis-Ord \(\:{G}_{i}^{\text{*}}\) ) to investigate hot and cold spots of multidimensional poverty among rural elderly populations in China. The Getis-Ord \(\:{G}_{i}^{\text{*}}\) statistic primarily detects whether geographic features exhibit high-value clustering or low-value clustering patterns in space by calculating the relationship between geographic attributes at a given location and those at neighboring locations. The hot spot analysis formula is as follows: $$\:{G}_{i}^{\text{*}}=\frac{\sum\:_{j=1}^{n}{W}_{i,j}-\stackrel{-}{X}\sum\:_{j=1}^{n}{W}_{i,j}}{S\sqrt{\frac{n\sum\:_{j=1}^{n}{W}_{i,j}-{\left(\sum\:_{j=1}^{n}{W}_{i,j}\right)}^{2}}{n-1}}};\:\stackrel{-}{X}=\frac{\sum\:_{j=1}^{n}{X}_{j}}{n};\text{S}=\sqrt{\frac{\sum\:_{j=1}^{n}{X}_{j}^{2}}{n}-{\left(\stackrel{-}{X}\right)}^{2}}$$ where \(\:{X}_{j}\:\) represents the attribute value of spatial feature j; \(\:{W}_{i,j}\) denotes the spatial weight between features i and j, defined as 1 for adjacent features and 0 for non-adjacent features; n is the total number of spatial features; \(\:\stackrel{-}{X}\) is the mean of spatial features; and S is the standard deviation of spatial features. The \(\:{G}_{i}^{\text{*}}\) statistic is expressed as a z-score, where higher z-scores indicate tighter high-value clustering of spatial features. (4) Ordinary Least Squares (OLS) The model employs Ordinary Least Squares (OLS) for estimation, with the specific formula as follows: $$\:{\text{Y}}_{\text{i}}\text{}\text{=}{\text{β}}_{0}\text{}\text{+}{\text{β}}_{1}{X}_{i}\text{}\text{+}{\text{ε}}_{i}$$ where \(\:{\text{Y}}_{i}\) represents the multidimensional deprivation level of individual \(\:i\) , \(\:{X}_{i}\:\) denotes the independent variables including intergenerational support and control variables, and \(\:{\text{ε}}_{i}\) ​ is the error term. 3.3 Weight Determination In this study, we employ a frequency weight-based method to determine the relative importance of each indicator, using the 2016 survey data as the baseline period. First, we calculate the incidence rate \(\:{h}_{j,base}\) for each deprivation indicator within the baseline period. Subsequently, following the AF methodological framework, we construct frequency weights for each indicator. This method is a data-driven frequency weighting approach, with the core principle being: indicators with lower deprivation proportions are assigned higher weights, reflecting the severity represented by the "scarcity" of such deprivation in the overall population. The underlying principle is as follows: $$\:{w}_{j,base}=\frac{\text{ln}\left(1/{h}_{j,base}\right)}{{\sum\:}_{k=1}^{J}\text{ln}\left(1/{h}_{j,base}\right)}=\frac{\text{ln}{h}_{j,base}}{{\sum\:}_{k=1}^{J}\text{ln}{h}_{j,base}}$$ where \(\:{h}_{j,base}\) represents the poverty incidence rate (i.e., the uncorrected proportion of poor population) for the j indicator in the base period (2016). The denominator aggregates the logarithm of inverse deprivation rates for all J indicators. Additionally, to enhance the interpretability within each sub-dimension (economic level, physical health, mental health, living standards), we further normalize the weights within each sub-dimension. Finally, we conduct equal-weight averaging of the scores from these four major dimensions at the cross-dimensional level. 4. Spatial Distribution Characteristics of Multidimensional Deprivation We further employ hot spot analysis to identify hot and cold spots of multidimensional deprivation among rural elderly populations, namely high-value clustering areas and low-value clustering areas. As shown in Fig. 1 , the clustering characteristics of deprivation in the economic dimension are most pronounced, with areas exhibiting higher deprivation levels relatively concentrated in central and western regions, while areas with lower deprivation levels are concentrated in the Jiangsu-Zhejiang-Shanghai region and Beijing-Tianjin area. From the living standards dimension, Jilin Province and Gansu Province form high-high clustering patterns with their surrounding regions, while Shanghai and neighboring provinces constitute low-value clustering areas. The clustering pattern in the physical health dimension is less distinct. In the mental health dimension, Fujian Province and Guangdong Province exhibit high-value clustering, while Beijing and Tianjin represent "depressions" compared to their surrounding areas, indicating that rural elderly populations in Beijing and Tianjin experience lower levels of mental health deprivation. 5. Multidimensional Poverty Measurement Outcomes and Determinants Table 2 reveals that from 2016 to 2022, the degree of deprivation across all dimensions generally exhibited a declining trend, though different dimensions displayed varying trajectories of change. Specifically, deprivation in the economic dimension decreased gradually from 0.164 in 2016 to 0.106 in 2022, indicating that the income and production conditions of rural elderly populations have been significantly improved under the impetus of poverty alleviation policies. Deprivation in the living standards dimension continuously declined from 0.330 in 2016 to 0.223 in 2022, demonstrating that infrastructure development and improvements in living conditions have substantially alleviated the burden of material poverty. From the health perspective, the physical health dimension exhibited fluctuations during 2016–2022: the deprivation value decreased slightly from 0.347 to 0.321 in 2020, then rose to 0.332 in 2022, suggesting that despite improvements in the medical security system, issues such as uneven distribution of rural healthcare resources have not been completely resolved. The mental health dimension demonstrates the positive role that improvements in social security and public service systems have played in alleviating psychological stress among rural residents: mental health deprivation decreased rapidly from 0.113 in 2016 to 0.056 in 2018, subsequently maintaining relatively low deprivation values. Considering comprehensive multidimensional poverty deprivation, the value progressively declined from 0.239 in 2016 to 0.176 in 2022, indicating remarkable achievements in multidimensional poverty alleviation. Table 2 Degrees of Deprivation across Different Dimensions (2016–2022) Year Economic conditions Living standard Physical health Mental health MP 2016 0.164 0.330 0.347 0.113 0.239 2018 0.113 0.289 0.339 0.056 0.199 2020 0.115 0.240 0.321 0.041 0.179 2022 0.106 0.223 0.332 0.044 0.176 The regression results in Table 3 reveal the influencing factors of poverty deprivation across different dimensions and multidimensional poverty. The regression outcomes indicate that intergenerational support exerts significantly differentiated impacts on multidimensional poverty among rural elderly populations: intergenerational relationship quality demonstrates cross-dimensional protective effects. For every unit increase in relationship score, the probability of multidimensional poverty decreases by 1.5% (β=-0.015, p < 0.01), with particularly pronounced effects in the mental health domain (β=-0.030, p < 0.01). Notably, the frequency of domestic assistance does not significantly affect multidimensional poverty, reflecting that instrumental support may exhibit adverse selection—children are more inclined to assist highly dependent elderly who have already fallen into poverty. Furthermore, face-to-face contact exhibits selective alleviating effects. Meeting frequency significantly improves living standards (β=-0.012, p < 0.05) and economic levels (β=-0.008, p < 0.05), but shows no statistical significance for health dimensions. This confirms the particular value of "physical presence" for domestic assistance (such as heavy physical labor like fuel collection and water fetching), though it cannot substitute for daily health management. The frequency of intergenerational contact demonstrates robust negative effects across multiple dimensions of multidimensional poverty. For every unit increase in contact frequency, the probability of economic poverty decreases significantly by 1.0% (β=-0.010, p < 0.01), living standard deprivation declines by 1.2%, mental poverty risk reduces by 0.6% (β=-0.006, p < 0.01), and the comprehensive suppressive effect on multidimensional poverty reaches 0.4% (β=-0.004, p < 0.01). Contact frequency with children shows a significant positive correlation with physical health, consistent with the reality that children maintain more frequent contact when elderly parents are in poor health. These results indicate that while communication technology cannot fully compensate for physical separation, high-frequency contact remains a core channel for maintaining emotional support. With the wave of rural young and middle-aged populations migrating for work accompanying urbanization, labor migration induces poverty accumulation among rural elderly through the dual mechanisms of disrupting both "spatial accessibility" and "emotional responsiveness" of intergenerational support. Compared to instrumental support (domestic assistance), emotional support (contact frequency, relationship quality) demonstrates broader anti-poverty effects, providing empirical evidence for expanding targeted poverty alleviation policies from material assistance to spiritual care. Additionally, individual characteristics and family structure variables significantly influence elderly multidimensional poverty levels. Regarding demographic characteristics, elderly women exhibit significantly lower multidimensional poverty levels (β=-0.016, p < 0.01), possibly related to women's stronger family resource management capabilities and social network maintenance abilities. Extended educational attainment produces significant negative effects (β=-0.003, p < 0.01), with each additional year of education reducing multidimensional poverty by 0.3%, confirming the sustained protective role of human capital accumulation for elderly livelihoods. Notably, employment status presents contradictory effects: while employment significantly reduces multidimensional poverty (β=-0.017, p < 0.01), its improvement effects on economic dimensions and living standards are not obvious, suggesting that rural elderly employment may concentrate in low-quality positions, with poverty alleviation effects primarily achieved through psychological and social functions rather than economic improvement. Family structure variables demonstrate stronger explanatory power. Family health shocks exhibit the most significant poverty-inducing effects (β = 0.076, p < 0.01), with each additional unhealthy member leading to a 7.6% increase in multidimensional poverty, far exceeding the impact magnitude of other variables. This verifies the pivotal role of health as a core capability deficit in rural elderly poverty. Family size expansion produces significant protective effects (β=-0.019, p < 0.01), consistent with theoretical expectations: large-scale families enhance resistance to multidimensional deprivation through risk-sharing mechanisms. Regional difference analysis reveals significant advantages in eastern regions (β=-0.028, p < 0.05), while northeastern regions show anomalous positive effects in living standard dimensions (β = 0.099, p < 0.01), possibly reflecting institutional differences in elderly welfare under regional industrial transformation contexts. Notably, the impacts of age and marital status are statistically insignificant, suggesting that life cycle effects and traditional family protection mechanisms have weakened among rural elderly samples. Table 3 Influencing Factors of Deprivation Levels across Different Dimensions Model 1 Model 2 Model 3 Model 4 Model 5 Variables Economic conditions Living standard Physical health Mental health MP Relationship with children -0.006 -0.002 -0.022*** -0.030*** -0.015*** (-0.983) (-0.276) (-5.229) (-9.543) (-4.297) Frequency of children's household assistance 0.002 -0.001 0.003 -0.001 0.001 (1.058) (-0.242) (1.405) (-1.195) (0.823) Frequency of meeting with children -0.008** -0.012** -0.005 -0.002** -0.007*** (-2.625) (-2.588) (-1.504) (-2.180) (-3.452) Frequency of contact with children -0.010*** -0.012*** 0.010*** -0.006*** -0.004*** (-5.050) (-4.360) (3.938) (-4.580) (-4.212) Age 0.007 -0.016 0.016 -0.008 -0.000 (0.563) (-1.161) (0.962) (-0.986) (-0.052) Age squared -0.001 0.014 -0.012 0.004 0.001 (-0.161) (1.381) (-0.984) (0.764) (0.278) Gender(1 = female) -0.004 0.005 -0.067*** 0.002 -0.016*** (-0.630) (0.751) (-7.555) (0.298) (-5.745) Education years -0.004** -0.006*** -0.001 -0.001** -0.003*** (-2.567) (-4.923) (-0.880) (-2.456) (-5.930) Marital status (1 = married) 0.038*** 0.028** -0.037** -0.030*** -0.000 (2.967) (2.507) (-2.701) (-4.028) (-0.051) Employment status (1 = employed) -0.011 0.030*** -0.071*** -0.017** -0.017*** (-1.122) (4.420) (-6.480) (-2.747) (-4.982) Number of unhealthy family members 0.033*** 0.041*** 0.193*** 0.037*** 0.076*** (6.489) (6.433) (30.021) (9.866) (24.507) Household size -0.030*** -0.025*** -0.018*** -0.004* -0.019*** (-5.536) (-5.289) (-8.281) (-1.749) (-8.340) Region (east) -0.043** -0.028 -0.046*** 0.004 -0.028** (-2.162) (-0.764) (-3.351) (0.388) (-2.077) Region (west) 0.020 0.022 -0.019 0.007 0.007 (1.248) (0.709) (-0.971) (0.689) (0.610) Region (north east) -0.040* 0.099*** -0.067*** 0.013 0.001 (-1.942) (4.003) (-4.557) (1.504) (0.121) Time FE Control Control Control Control Control Constant -0.055 0.891* -0.026 0.622** 0.358 (-0.130) (1.947) (-0.046) (2.278) (1.693) N 10,847 10,847 10,847 10,847 10,847 \(\:{\text{R}}^{2}\) 0.050 0.100 0.176 0.061 0.200 Robust t-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 6. Results of Multidimensional Dynamic Poverty Employing the internationally prevalent threshold of k = 0.33 as the benchmark for determining multidimensional poverty status, the dynamic changes in multidimensional poverty between adjacent survey periods are presented in Table 4 . The results reveal that the persistent non-poor category accounts for the largest proportion at 87.08%, indicating that most rural elderly populations maintained a relatively stable non-poverty status throughout the observation period. Concurrently, the proportion of persistent poor individuals reached 41.49%, approaching nearly half of the sample. The relatively high proportions within these respective states underscore the persistent and path-dependent characteristics of poverty. However, the proportion of individuals transitioning "from poverty to non-poverty" exceeded that of the "persistent poor," demonstrating the dynamic nature of poverty status and the feasibility of poverty alleviation. Table 4 Poverty Transition Categories and Proportions Transition Category Frequency Proportion persistent non-poor 3288 87.08% Non-poor→ poor 488 12.92% Poor→ non-poor 631 58.81% persistent poor 442 41.19% Table 5 reveals the differentiated mechanisms through which children's support factors operate in elderly poverty dynamics. Analysis of core independent variables demonstrates that interaction frequency between children and elderly parents, rather than emotional relationships per se, exerts significant influence on poverty transitions. Meeting frequency exhibits a significant negative effect at the 1% level in the poverty entry model (7) (β = -0.010, p < 0.01), indicating that each unit increase in meeting frequency reduces poverty entry risk by 1 percentage point. This effect may stem from direct assistance provided through children's physical visits, including economic support and essential living supplies. Contact frequency also demonstrates marginal significance at the 10% level in the poverty entry model (β = -0.004, p < 0.1), suggesting that remote care through telephone/communication serves as an important buffering mechanism. Notably, children's assistance with household chores and emotional relationship quality show no significant impact on either poverty exit or entry, implying that rural elderly support systems rely more heavily on substantive behaviors rather than emotional bonds. Regarding control variables, the poverty exit model reveals that gender variables indicate women are more likely to escape poverty. Additionally, household size significantly increases poverty exit probability (β = 0.058), potentially reflecting advantages that larger households possess in resource sharing and income diversification. Conversely, a greater number of unhealthy family members significantly reduces poverty exit likelihood (coefficient = -0.086). In the poverty entry model, the coefficient for years of education is negative and significant (β = -0.003), indicating that rural elderly individuals with higher educational attainment face significantly lower risks of falling into poverty, highlighting the protective role of human capital in poverty transition dynamics. Larger household size is associated with reduced poverty entry risk, demonstrating the risk-sharing effect of family size. Conversely, the number of unhealthy family members significantly increases poverty entry risk (β = 0.047), suggesting that health shocks constitute a major risk factor for rural household poverty recurrence. Collectively, these analytical findings reveal the differentiated mechanisms through which intergenerational support and household characteristics operate in poverty dynamics, emphasizing the crucial role of health status, education level, and family structure in rural poverty exit and re-entry processes. The research findings provide important insights for policy formulation: poverty prevention strategies should strengthen intergenerational support networks while enhancing rural households' health security systems and educational investment, thereby effectively reducing poverty vulnerability and consolidating poverty alleviation achievements. Table 5 Characteristics Analysis of Populations Prone to Poverty Exit and Entry Model 6 Model 7 Variables Poverty Exit Poverty Entry Relationship with children -0.015 -0.011 (-0.698) (-1.273) Frequency of children's household assistance -0.008 0.002 (-0.848) (0.820) Frequency of meeting with children 0.013 -0.010*** (0.959) (-3.279) Frequency of contact with children -0.004 -0.004* (-0.301) (-1.852) Age 0.036 -0.043 (0.574) (-1.248) Age squared -0.034 0.034 (-0.741) (1.338) Gender(1 = female) 0.067* -0.011 (2.008) (-0.815) Education years 0.003 -0.003* (0.761) (-1.947) Marital status (1 = married) 0.052 0.014 (0.790) (0.966) Employment status (1 = employed) 0.026 -0.009 (0.800) (-0.570) Number of unhealthy family members -0.086*** 0.047*** (-6.013) (6.416) Household size 0.058*** -0.027*** (5.275) (-4.451) Region (east) -0.016 -0.038** (-0.381) (-2.142) Region (west) 0.003 0.013 (0.102) (0.639) Region (north east) -0.049 -0.002 (-1.021) (-0.114) Time FE Control Control Constant -0.427 1.691 (-0.205) (1.443) N 1,073 3,776 \(\:{\text{R}}^{2}\) 0.065 0.037 Robust t-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The survival analysis results presented in Table 6 demonstrate that with increasing survey waves, poverty numbers exhibit a continuous declining trend, with survival function values showing a significant downward trajectory, reflecting the dynamic poverty reduction process among the impoverished population. Specifically, the survival function for multidimensional poverty drops sharply to 0.449 in the second wave, indicating that over half of the multidimensionally poor population has achieved poverty exit. However, a considerable proportion of samples remain persistently impoverished across multiple waves of the panel survey, revealing the structural characteristics and path dependency inherent in multidimensional poverty. Table 6 Kaplan-Meier Estimation Results for Multidimensional Poverty (k = 0.33) Wave Count of Poor Individuals Count of Poverty Exits Count of Censoring Survival Function Standard Error 95% CI 1 1675 0 708 1 . . 2 967 533 176 0.449 0.016 (0.417, 0.480) 3 258 122 63 0.237 0.016 (0.205, 0.269) 4 73 32 41 0.133 0.017 (0.103, 0.167) 7. Conclusions and Discussion In recent years, the level of multidimensional poverty among the rural elderly population has experienced a continuous decline, exhibiting heterogeneous trends across various poverty dimensions. Between 2016 and 2022, the multidimensional poverty index for rural elderly individuals significantly decreased, reflecting the effectiveness of poverty alleviation efforts in China. Nonetheless, the persistence of poverty remains a significant concern, with nearly half of the older adults classified as impoverished continuing to experience poverty throughout the observation period. The alleviation of poverty in economic and living standards dimensions has been markedly influenced by targeted poverty alleviation policies and improvements in infrastructure, while the mental health dimension has shown significant enhancement due to advancements in social security and public services. In contrast, the reduction of poverty in the physical health dimension has been relatively modest, with some evidence of a rebound in later stages. Despite rapid advances in medical resources that have extended average life expectancy, ensuring healthspan remains a critical focus for future medical endeavors. Additionally, multidimensional poverty is characterized by significant regional disparities and spatial clustering: the central and western regions exhibit higher levels of economic deprivation, whereas southeastern coastal areas experience lower levels of living deprivation, with certain provinces showing notable deficiencies in the mental health dimension. Intergenerational support plays a pivotal role in alleviating and transforming multidimensional poverty among the elderly. Empirical analysis reveals that the quality of relationships and frequent communication between children and their elderly parents significantly reduces the risk of poverty across economic, living standards, and mental health dimensions, serving as a vital protective factor against multidimensional poverty. In contrast, instrumental support from children, such as assistance with household chores, has negligible effects on reducing overall poverty. This may be attributed to the fact that children often intervene only when their elderly parents are already entrenched in poverty, thus limiting the potential to fundamentally alter the poverty situation. Furthermore, regular face-to-face visits positively impact the economic status, living standards, and mental health of older individuals; however, their effect on physical health is less pronounced, potentially restricted by reverse causality concerns. Overall, emotional support demonstrates a broader and more enduring anti-poverty effect in comparison to instrumental support. With industrialization and labor migration contributing to increased intergenerational distance within families, the traditional role of “raising children to provide for old age” is progressively diminishing. High-frequency emotional support and remote connections remain essential pathways for ensuring the well-being of the elderly. Additionally, demographic and familial characteristics significantly influence the incidence of multidimensional poverty among older adults. The occurrence of poverty is lower among elderly women and those with higher educational attainment, indicating that the accumulation of human capital is instrumental in reducing elderly poverty. Larger family sizes can lower the probability of poverty through risk-sharing mechanisms, whereas poor health among family members significantly increases the risk of elderly individuals falling into poverty. Traditional marital status and age factors exhibit minimal influence, suggesting that the direct effect of lifecycle stages and marital status on elderly poverty has diminished with societal progress. This study highlights the complexity of multidimensional poverty among rural elderly and underscores the critical role of intergenerational support. First, moving beyond traditional conceptualizations that emphasize co-residential or face-to-face care, this research introduces the dimension of “remote support” enabled by modern communication technologies. By framing intergenerational support through the lens of spatial separation and emotional connectedness, the study demonstrates the positive impact of non-cohabiting children’s support on elderly well-being, thereby extending the scope and meaning of intergenerational support theory within the context of societal modernization. Second, this study goes beyond static measurements of poverty by incorporating a dynamic analytical perspective into multidimensional poverty research. It systematically examines transitions in poverty status among rural elderly and reveals the heterogeneous effects of intergenerational support in both mitigating the risk of falling back into poverty and facilitating poverty alleviation. Moreover, this study elucidates the complexity of multidimensional poverty among rural elderly populations while underscoring the critical role of intergenerational support. It provides empirical evidence to inform the enhancement of poverty alleviation and social policies in the context of healthy aging. On one hand, poverty alleviation efforts in the post-poverty alleviation era should broaden their focus beyond mere income improvement to encompass health, eldercare, and mental well-being. This necessitates the establishment of robust monitoring and long-term support mechanisms for multidimensional poverty, with a sustained focus on relative poverty and potential re-poverty risks among the elderly population. On the other hand, it is essential to prioritize the role of intergenerational support by refining eldercare policies to encourage and facilitate adult children in fulfilling their filial duties (for example, by exploring the establishment of "filial piety leave"). Active promotion of intergenerational contact and visitation is crucial in mitigating the adverse impacts of children’s labor migration on the well-being of the elderly, thereby enhancing the overall welfare of them. Declarations Funding The research leading to these results received funding from the National Natural Science Foundation of China (Grant No. 42271245). Competing interests The authors declare no competing interests. Ethics approval This study used the publicly available China Family Panel Studies (CFPS) database. The CFPS project is a human subject research project that regularly submits ethical review or continuous review applications to the "Peking University Biomedical Ethics Committee." The ethical review approval number is uniformly: IRB00001052-14010. The data source is available at: https://www.isss.pku.edu.cn/cfps/ Consent Informed consent was obtained from all participants involved in the study Data This study used the publicly available China Family Panel Studies (CFPS) data. Materials and/or Code availability The code and materials used in this study are available upon request. Author Contribution Xiaotong ZHAO primarily drafted the initial manuscript and made significant contributions to the acquisition, analysis, and interpretation of data. Additionally, she played a key role in the conception and design of the work.Chunhui LIU revised the manuscription critically for important intellectual content. Also, he played a key role in the theoretical design of the work. Data Availability This study used the publicly available China Family Panel Studies (CFPS) database. 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Ageing and health—China. https://www.who.int/china/health-topics/ageing Xie, E. (2015). Public transfers and multidimensional poverty of older people. China Industrial Economics , 11 , 32–46. Xie, Y., & Hu, J. (2014). An introduction to the China family panel studies (CFPS). Chinese Sociological Review , 47 (1), 3–29. Zimmer, Z., Korinek, K., Knodel, J., & Chayovan, N. (2008). Migrant Interactions With Elderly Parents in Rural Cambodia and Thailand. Journal of Marriage and Family , 70 (3), 585–598. https://doi.org/10.1111/j.1741-3737.2008.00507.x Footnotes Consumer Price Index for Poor Rural Households in China = Rural CPI × 0.4 + Rural Food CPI × 0.6 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. 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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-7088539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495761418,"identity":"f264f1d1-955f-4d67-b63f-c1eb00be7d61","order_by":0,"name":"Xiaotong Zhao","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Zhao","suffix":""},{"id":495761420,"identity":"af26c901-2091-4990-b09a-1de5ea30294f","order_by":1,"name":"Chunhui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACPgYexgcMPGC2AXFa2Bh4mA1I1sImAWUTq4X97LHqAhm7xAb25m0SDDV3iNDCk5d2ewZPcmIDz7EyCYZjz4jQIsFjdpuHhzmxQSLHTIKx4TBxWop5eOoTG+TfkKCFmYfnMNAWHmK18OQYS/PwHDdu40krtkg4RoQWfvYzhp95e6pl+9kPb7zxoYYILWDA2AOKICBIIFIDEPwgXukoGAWjYBSMQAAAa+kr65CCc0IAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Chunhui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-10 03:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7088539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7088539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88643060,"identity":"5b1a3f84-df7e-4f9d-aef6-53f67963e436","added_by":"auto","created_at":"2025-08-08 16:14:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHot Spot Analysis Results of Multidimensional Deprivation among Rural Elderly Populations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7088539/v1/bf953f02d69f277f0799c2c2.jpg"},{"id":95972799,"identity":"dbab34b1-1ac5-42d1-bca5-255670c0d206","added_by":"auto","created_at":"2025-11-15 07:54:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298261,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7088539/v1/f6a09098-ce1c-416b-ba60-7857dd4fdab6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Rural Elderly Multidimensional Poverty in China: Intergenerational Support as a Protective Factor","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs of late 2024, China's elderly population aged 60 and above reached 310.31\u0026nbsp;million individuals, constituting 22.0% of the total population. The World Health Organization has identified China as one of the world's most rapidly aging nations (World Health Organization, n.d.). Within this demographic context, rural elderly residents account for 46.0% of China's total elderly population. Against the backdrop of \"growing old before getting rich\u0026mdash;a phenomenon wherein demographic aging outpaces economic and social preparedness\u0026mdash;ensuring the welfare of rural elderly populations in an increasingly aged society has become a central governance priority in China. By the end of 2020, China achieved the comprehensive elimination of absolute poverty in rural areas under current national standards, thereby facilitating a transition toward higher-level poverty alleviation objectives aimed at enabling rural elderly populations to live with prosperity, security, and dignity (Wan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn fact, rural elderly populations exhibit pronounced vulnerability relative to other demographic groups. From an economic perspective, rural elderly individuals are characterized by limited and unstable income sources, particularly following the loss of labor capacity, with their financial resources primarily dependent on agricultural income, modest pension benefits, and filial support from adult children (Liu, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, regarding health and medical security, elderly cohorts demonstrate greater susceptibility to health-related shocks compared to younger age groups (Cho et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rural areas, relative to urban centers, are characterized by relatively inadequate provision of public healthcare services, with elderly residents frequently experiencing insufficient coverage in the prevention, treatment, rehabilitation, and follow-up care of common chronic conditions (Liang \u0026amp; Lu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, the poverty challenges confronting rural elderly populations manifest as a phenomenon of multidimensional deprivation characterized by the mutual reinforcement and compounding of various disadvantages. Within this context, intergenerational support functions as a crucial mechanism for securing the welfare of rural elderly populations in their later years, encompassing multiple dimensions including economic maintenance, daily care assistance, and emotional consolation (Tan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tang et al., 2022). Extensive research demonstrates that intergenerational support, particularly non-monetary forms of assistance, constitutes a critical factor in enhancing elderly quality of life and alleviating multidimensional poverty (Cong \u0026amp; Silverstein, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Guo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, rapid industrialization and urbanization have propelled structural transformations in rural society, posing severe challenges to intergenerational support systems. The nuclearization of family units and increased intergenerational distance have weakened traditional extended family functions, resulting in widespread phenomena of \"left-behind\" and \"empty-nest\" elderly in rural areas. While adult children may provide certain economic support through remittances, such financial transfers fail to compensate for the absence of daily care and emotional companionship (Hu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stacey \u0026amp; Ayers, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, rural elderly populations frequently encounter \"information gaps\" and \"social interaction barriers\" when accessing external information, resources, and public services, which further diminishes their subjective well-being and may precipitate deterioration in health status (Golan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang \u0026amp; Zhang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lowenstein, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Van Gaalen \u0026amp; Dykstra, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The declining functionality of family support systems stands in stark contradiction to elderly individuals' continued dependence on familial networks, potentially exacerbating their life vulnerability. Therefore, it becomes critically important to quantitatively assess the current state of multidimensional poverty among rural elderly populations, examine the mechanisms through which intergenerational support mitigates their poverty conditions, and explore transitions in care provision models.\u003c/p\u003e\u003cp\u003eThis study is guided by the aforementioned research objective. In the following sections, drawing on existing literature and based on four waves of data from the China Family Panel Studies (CFPS) spanning 2016 to 2022, this study first constructs a multidimensional poverty measurement framework for rural elderly, encompassing four key dimensions: economic status, living standards, physical health, and mental health. This study then examines the spatial clustering patterns of deprivation across these dimensions at the national level. Second, the study investigates how various forms of intergenerational support influence deprivation in each individual dimension as well as the overall level of multidimensional poverty among rural elderly. Third, by employing poverty transition matrices and the Kaplan\u0026ndash;Meier survival analysis method, this study explores the dynamic trajectories of multidimensional poverty, further assessing the extent to which support from adult children affects the likelihood of exiting or entering poverty. Taken together, this research integrates spatial and temporal analytical perspectives to systematically unravel the spatial heterogeneity of multidimensional poverty and empirically assess the mechanisms through which evolving patterns of intergenerational support, in the context of modernization, shape poverty outcomes among rural elderly.\u003c/p\u003e"},{"header":"2 Reviews","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Measurement of Multidimensional Poverty Among the Elderly in Rural Areas\u003c/h2\u003e\u003cp\u003eIn recent years, research on poverty among the elderly has shifted from a singular focus on economic status to a multidimensional perspective that encompasses health, social participation, education levels, and social security. Early scholars primarily utilized monetary indicators centered on income (Kim \u0026amp; Cook, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and consumption (Wallace et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to measure the multidimensional poverty experienced by the elderly population. However, as the elderly demographic tends to have lower incomes, their consumption demand structure differs significantly from that of the general population. Consequently, measures of poverty based solely on income or consumption may overestimate poverty levels among the elderly (Borrowman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brady et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Disney \u0026amp; Whitehouse, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Therefore, studies on poverty affecting older adults have increasingly incorporated non-economic indicators such as health (Heslop \u0026amp; Gorman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), social participation (Rissanen \u0026amp; Ylinen, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), education (Trani et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and social security (Amarante \u0026amp; Colacce, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies are grounded in comprehensive frameworks like the Human Development Index (HDI) and the Multidimensional Poverty Index (MPI), advocating for nuanced adjustments based on the economic, social, and cultural characteristics of specific groups.\u003c/p\u003e\u003cp\u003eThe research on multidimensional poverty among the elderly in China has aligned with the global trend while making adjustments based on the country's specific contextual characteristics. First, due to the significant income disparities between urban and rural areas, multidimensional poverty studies focusing on rural regions typically incorporate economic dimension indicators into their analytical framework (C. Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G. Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, while the educational dimension is widely applied in international multidimensional poverty research, the low overall educational attainment of rural elderly populations has led some scholars to question its relevance (E. Xie, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, researchers have highlighted that the long-standing urban-rural dualistic structure has resulted in relatively underdeveloped infrastructure in rural areas, presenting significant challenges for the elderly in terms of access to safe drinking water, sanitation facilities, and housing warmth. Consequently, such living standard indicators are crucial for comprehensively assessing multidimensional poverty among rural elderly populations (Fan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He \u0026amp; Ye, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Lastly, research has increasingly tended to further disaggregate the health dimension into physical and mental health subcomponents, thereby expanding the conceptual boundaries of multidimensional poverty research (Chan \u0026amp; Wong, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Trani et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, early poverty research predominantly relied on static measurements using cross-sectional data. However, recent studies have shifted towards multiple observations across time dimensions, focusing on tracking and identifying poverty processes (Carter \u0026amp; Barrett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This dynamic analytical perspective facilitates understanding the persistence, interactivity, and evolutionary patterns of poverty across different temporal and contextual scenarios (Sen, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Some scholars have employed dynamic analysis to differentiate between chronic and transient poverty, exploring the dynamics of entering and exiting poverty states (Q. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, existing dynamic poverty research has primarily concentrated on income poverty dynamics, with relatively limited exploration of multidimensional poverty dynamics. In the context of China's successful elimination of absolute rural poverty, research should transition from focusing on absolute poverty to a more nuanced multidimensional approach. This shift necessitates emphasizing dynamic poverty monitoring and the development of sustainable mechanisms to maintain non-poverty status.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Intergenerational Support and Rural Elderly Multidimensional Poverty\u003c/h2\u003e\u003cp\u003eIn traditional Chinese culture, intergenerational support is regarded as a vital expression of filial piety. Rather than being limited to monetary transfers, such support often takes non-material forms, including daily care, emotional companionship, life guidance, and decision-making assistance provided by adult children to their aging parents (Button \u0026amp; Ncapai, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Patrick et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; G. Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Empirical evidence has demonstrated that intergenerational support significantly enhances the overall well-being of older adults, particularly in rural areas, and plays a crucial role in alleviating multidimensional poverty (Krsteska \u0026amp; Pejoska-Gerazova, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nasser \u0026amp; Overholser, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Newsom et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). While financial assistance from children can relieve material deprivation, its effects on mental and physical health poverty remain limited (Tan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, scholars have argued that mental poverty may be more pressing than income poverty for rural elders, and the quality of intergenerational relationships is a key determinant of mental well-being (X. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, satisfaction with familial support has been found to be closely associated with daily emotional states among older adults, with such correlations being particularly salient among elderly men (J. Lin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Patrick et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond addressing present poverty conditions, intergenerational support is increasingly recognized as an informal \"insurance\" mechanism that enables rural older adults to cope with future uncertainties. This function is particularly critical in rural settings where formal social protection systems remain underdeveloped. Studies have shown that households with reliable support networks from children are less likely to fall into poverty when facing income volatility or sudden expenditures such as medical emergencies (Guo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; G. Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Living arrangements also play a significant role in shaping poverty risks among the elderly: those co-residing with their children experience significantly lower poverty rates compared to those living apart (Guo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Waehrer \u0026amp; Crystal, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), and transitions from living alone to living with children are found to reduce the likelihood of poverty (Rendall \u0026amp; Speare, 1995).\u003c/p\u003e\u003cp\u003eWhile existing literature acknowledges the crucial role of intergenerational support in mitigating multidimensional poverty among older adults, most studies conceptualize it as a singular, undifferentiated construct. Few have examined its varied effects across different dimensions of well-being. Moreover, in the context of rapid modernization and increasing geographic separation of family members, intergenerational support has evolved beyond traditional face-to-face caregiving. Remote forms of support\u0026mdash;enabled by telephone, video calls, and other digital communication technologies\u0026mdash;are becoming increasingly common, yet their implications for elder well-being remain underexplored. Therefore, it is necessary to adopt a multidimensional analytical lens to distinguish among types of intergenerational support and identify their distinct impact pathways. This is particularly important given the ongoing urban-rural divide and the evolving patterns of intergenerational interaction in contemporary China.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Dataset and Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Dataset\u003c/h2\u003e\u003cp\u003eThe empirical analysis in this study utilizes data from the China Family Panel Studies (CFPS), a nationally representative, comprehensive longitudinal survey conducted by the Institute of Social Science Survey at Peking University. Launched in 2010, the CFPS employs a multi-stage probability sampling methodology to select respondents from 25 provinces, municipalities, and autonomous regions across mainland China (excluding Inner Mongolia, Xinjiang, Tibet, Hainan, Ningxia, and Qinghai), with the surveyed regions representing approximately 95% of China's total population, thereby ensuring strong representativeness. The baseline survey encompassed 14,960 households and 33,600 individuals, collecting comprehensive information at the household, individual, and community levels. Subsequently, follow-up surveys are conducted biennially, continuously tracking the same cohort of respondents to provide longitudinal data. The survey data collection process adheres to stringent quality control standards, ensuring data reliability and comparability (Y. Xie \u0026amp; Hu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This study employs four waves of CFPS data spanning 2016, 2018, 2020, and 2022. The sample is restricted to respondents aged 60 and above with rural hukou. After excluding observations with missing data, the final dataset comprises 10,847 observations across the four waves. To maximize sample retention, we utilize an unbalanced panel structure, with wave-specific sample sizes of 3,344, 3,288, 2,310, and 1,905 observations, respectively.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the main dimensions and indicators employed in this study's multidimensional poverty measurement framework. We utilize \"per capita annual household income\" as the metric for assessing income poverty, with households falling below the national poverty line classified as income-poor. According to the official standards established by China's National Bureau of Statistics, China's rural poverty threshold is anchored at 2,300-yuan per capita annual household income in constant 2010 prices. Following the calculation methodology employed by Li et al. (2017), we adjust this baseline using the \"Consumer Price Index for Poor Rural Households in China\u003csup\u003e1\u003c/sup\u003e,\" yielding poverty thresholds of 2,952-yuan, 2,995-yuan, 3,490-yuan, and 3,527-yuan for 2016, 2018, 2020, and 2022, respectively. The living standards dimension encompasses three indicators: drinking water, cooking fuel, and asset ownership. Households are considered deprived in this dimension if they rely on untreated water sources (rivers, lakes, wells, rainwater, cellar water, or pond/spring water), utilize non-clean energy sources for cooking (such as firewood or coal), or lack essential durable goods including automobiles, computers, household appliances, televisions, jewelry, antiques, or high-quality musical instruments. The health dimension simultaneously examines both physical and mental health components. For physical health, individuals reporting \"poor\" or \"very poor\" self-rated health status or having chronic diseases are classified as deprived in this indicator. Mental health is assessed through two indicators: life satisfaction and future optimism. Individuals expressing \"very unsatisfied\" or \"unsatisfied\" life satisfaction, or indicating \"very little confidence\" or \"no confidence\" in the future are considered deprived in the respective mental health indicators.\u003c/p\u003e\u003cp\u003eWe employ four indicators to measure intergenerational support: \"relationship quality with children,\" \"frequency of children's assistance with household tasks,\" \"frequency of face-to-face meetings with children,\" and \"frequency of contact with children.\" Relationship quality reflects the intensity of intergenerational emotional bonding, capturing the affective mechanisms that drive supportive behaviors (Patrick et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The frequency of household assistance directly quantifies instrumental support (Tan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), revealing the actual penetration of daily care provision. Meeting and contact frequencies constitute a spatiotemporal continuum of supportive behaviors (I.-F. Lin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where meeting frequency reinforces kinship presence effects, while contact frequency (including telecommunications) compensates for emotional maintenance under geographical separation (Zimmer et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This indicator design aligns with the dynamic monitoring requirements of multidimensional poverty research and effectively captures the heterogeneity of intergenerational support patterns amid urbanization processes. Additionally, factors such as age, gender, educational attainment, marital status, employment status, and family structure influence the multidimensional deprivation status of rural elderly populations. These indicators serve as control variables in the model.\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\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\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\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer capita household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16629.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56225.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3794000\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\u003eDrinking water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\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\u003eCooking fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\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\u003eAssets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27325.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10000000\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\u003eSelf-rated physical health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\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\u003eChronic disease status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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 satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\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\u003eConfidence in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelationship with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\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\u003eFrequency of children's household assistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\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\u003eFrequency of meeting with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\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\u003eFrequency of contact with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93\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\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.49\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 (1\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16\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\u003eMarital status (1\u0026thinsp;=\u0026thinsp;married)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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\u003eEmployment status (1\u0026thinsp;=\u0026thinsp;employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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\u003eIndustry type (1\u0026thinsp;=\u0026thinsp;non-agricultural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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\u003eNumber of unhealthy family members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\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\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\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\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Methodology\u003c/h2\u003e\u003cp\u003e(1) Multidimensional Poverty Measurement Methods\u003c/p\u003e\u003cp\u003eWe employ the Alkire-Foster (AF) method to measure poverty status among rural elderly populations (Alkire \u0026amp; Foster, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Specifically, we first construct a deprivation matrix for each individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003eand each poverty indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e in the sample, based on predetermined deprivation thresholds \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{j}\\)\u003c/span\u003e\u003c/span\u003e. The matrix element \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is defined as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{ij}\\)\u003c/span\u003e\u003c/span\u003e= 1 if individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e's achievement \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{ij}\\)\u003c/span\u003e\u003c/span\u003e in indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e falls below \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{j}\\)\u003c/span\u003e\u003c/span\u003e; otherwise \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{ij}\\)\u003c/span\u003e\u003c/span\u003e = 0. Next, we calculate each individual's weighted deprivation score according to the weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{j}\\)\u003c/span\u003e\u003c/span\u003e assigned to each indicator:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{c}_{i}={\\sum\\:}_{j=1}^{d}{w}_{j}{g}_{ij}.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing the dual-cutoff approach, individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003eis identified as multidimensionally poor when their weighted deprivation score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003ereaches or exceeds a predetermined poverty cutoff \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e (typically \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\u0026lt;k\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e). Based on this definition, the poverty headcount ratio H can be expressed as the proportion of poor individuals in the total sample:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:H=\\frac{1}{n}{\\sum\\:}_{i=1}^{n}I\\left({c}_{i}\\ge\\:k\\right),$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\left({c}_{i}\\ge\\:k\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eis an indicator function, The poverty intensity A represents the average deprivation score among the poor population:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:A=\\frac{1}{q}{\\sum\\:}_{i:{c}_{i}\\ge\\:k}{c}_{i},$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere q denotes the number of individuals identified as poor. Finally, the Multidimensional Poverty Index (MPI) is defined as the product of H and A:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MPI=H\\times\\:A\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e(2) Dynamic Poverty Measurement Methods\u003c/p\u003e\u003cp\u003eWe employ poverty transition matrices to analyze multidimensional poverty dynamics between adjacent survey years (Apablaza \u0026amp; Yalonetzky, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Madden, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The multidimensional poverty transition patterns between adjacent survey years are classified into four categories: non-poor \u0026rarr; non-poor, non-poor \u0026rarr; poor, poor \u0026rarr; non-poor, and poor \u0026rarr; poor. Therefore, the transition matrix for multidimensional poverty from time t to time t\u0026thinsp;+\u0026thinsp;1 can be expressed as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{P}_{t,t+1}=\\left(\\begin{array}{cc}{p}_{11}\u0026amp;\\:{p}_{10}\\\\\\:{p}_{01}\u0026amp;\\:{p}_{00}\\end{array}\\right).$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSince poverty transition analysis based solely on adjacent survey years cannot explore poverty dynamics over the entire survey period, we subsequently employ the Kaplan-Meier method to estimate poverty \"survival rates\" and \"hazard rates\" at various time points (Kaplan \u0026amp; Meier, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Taking poverty exit as an example, the survival function S(t) represents the probability that an individual remains in poverty after time t, with the formula:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\left(t\\right)=P\\left({T}_{i}\u0026gt;t\\right)={\\prod\\:}_{a=0}^{t-1}\\frac{{n}_{a}-{d}_{a}}{{n}_{a}},\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes the duration for which individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e remains in multidimensional poverty, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{a}\\)\u003c/span\u003e\u003c/span\u003e represents the number of individuals still in the risk set at time point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{a}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the number of individuals exiting poverty at time point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003e. The corresponding hazard function is defined as the probability that an individual escapes multidimensional poverty at time point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(3) Spatial Analysis Methods\u003c/p\u003e\u003cp\u003eWe employ hot spot analysis (Getis-Ord \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e) to investigate hot and cold spots of multidimensional poverty among rural elderly populations in China. The Getis-Ord \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e statistic primarily detects whether geographic features exhibit high-value clustering or low-value clustering patterns in space by calculating the relationship between geographic attributes at a given location and those at neighboring locations. The hot spot analysis formula is as follows:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{G}_{i}^{\\text{*}}=\\frac{\\sum\\:_{j=1}^{n}{W}_{i,j}-\\stackrel{-}{X}\\sum\\:_{j=1}^{n}{W}_{i,j}}{S\\sqrt{\\frac{n\\sum\\:_{j=1}^{n}{W}_{i,j}-{\\left(\\sum\\:_{j=1}^{n}{W}_{i,j}\\right)}^{2}}{n-1}}};\\:\\stackrel{-}{X}=\\frac{\\sum\\:_{j=1}^{n}{X}_{j}}{n};\\text{S}=\\sqrt{\\frac{\\sum\\:_{j=1}^{n}{X}_{j}^{2}}{n}-{\\left(\\stackrel{-}{X}\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{j}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the attribute value of spatial feature j; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e denotes the spatial weight between features i and j, defined as 1 for adjacent features and 0 for non-adjacent features; n is the total number of spatial features; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e is the mean of spatial features; and S is the standard deviation of spatial features. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e statistic is expressed as a z-score, where higher z-scores indicate tighter high-value clustering of spatial features.\u003c/p\u003e\u003cp\u003e(4) Ordinary Least Squares (OLS)\u003c/p\u003e\u003cp\u003eThe model employs Ordinary Least Squares (OLS) for estimation, with the specific formula as follows:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Y}}_{\\text{i}}\\text{}\\text{=}{\\text{\u0026beta;}}_{0}\\text{}\\text{+}{\\text{\u0026beta;}}_{1}{X}_{i}\\text{}\\text{+}{\\text{\u0026epsilon;}}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the multidimensional deprivation level of individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes the independent variables including intergenerational support and control variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026epsilon;}}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ is the error term.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Weight Determination\u003c/h2\u003e\u003cp\u003eIn this study, we employ a frequency weight-based method to determine the relative importance of each indicator, using the 2016 survey data as the baseline period. First, we calculate the incidence rate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{j,base}\\)\u003c/span\u003e\u003c/span\u003e for each deprivation indicator within the baseline period. Subsequently, following the AF methodological framework, we construct frequency weights for each indicator. This method is a data-driven frequency weighting approach, with the core principle being: indicators with lower deprivation proportions are assigned higher weights, reflecting the severity represented by the \"scarcity\" of such deprivation in the overall population. The underlying principle is as follows:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:{w}_{j,base}=\\frac{\\text{ln}\\left(1/{h}_{j,base}\\right)}{{\\sum\\:}_{k=1}^{J}\\text{ln}\\left(1/{h}_{j,base}\\right)}=\\frac{\\text{ln}{h}_{j,base}}{{\\sum\\:}_{k=1}^{J}\\text{ln}{h}_{j,base}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{j,base}\\)\u003c/span\u003e\u003c/span\u003e represents the poverty incidence rate (i.e., the uncorrected proportion of poor population) for the j indicator in the base period (2016). The denominator aggregates the logarithm of inverse deprivation rates for all J indicators. Additionally, to enhance the interpretability within each sub-dimension (economic level, physical health, mental health, living standards), we further normalize the weights within each sub-dimension. Finally, we conduct equal-weight averaging of the scores from these four major dimensions at the cross-dimensional level.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Spatial Distribution Characteristics of Multidimensional Deprivation","content":"\u003cp\u003eWe further employ hot spot analysis to identify hot and cold spots of multidimensional deprivation among rural elderly populations, namely high-value clustering areas and low-value clustering areas. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the clustering characteristics of deprivation in the economic dimension are most pronounced, with areas exhibiting higher deprivation levels relatively concentrated in central and western regions, while areas with lower deprivation levels are concentrated in the Jiangsu-Zhejiang-Shanghai region and Beijing-Tianjin area. From the living standards dimension, Jilin Province and Gansu Province form high-high clustering patterns with their surrounding regions, while Shanghai and neighboring provinces constitute low-value clustering areas. The clustering pattern in the physical health dimension is less distinct. In the mental health dimension, Fujian Province and Guangdong Province exhibit high-value clustering, while Beijing and Tianjin represent \"depressions\" compared to their surrounding areas, indicating that rural elderly populations in Beijing and Tianjin experience lower levels of mental health deprivation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. Multidimensional Poverty Measurement Outcomes and Determinants","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that from 2016 to 2022, the degree of deprivation across all dimensions generally exhibited a declining trend, though different dimensions displayed varying trajectories of change. Specifically, deprivation in the economic dimension decreased gradually from 0.164 in 2016 to 0.106 in 2022, indicating that the income and production conditions of rural elderly populations have been significantly improved under the impetus of poverty alleviation policies. Deprivation in the living standards dimension continuously declined from 0.330 in 2016 to 0.223 in 2022, demonstrating that infrastructure development and improvements in living conditions have substantially alleviated the burden of material poverty. From the health perspective, the physical health dimension exhibited fluctuations during 2016\u0026ndash;2022: the deprivation value decreased slightly from 0.347 to 0.321 in 2020, then rose to 0.332 in 2022, suggesting that despite improvements in the medical security system, issues such as uneven distribution of rural healthcare resources have not been completely resolved. The mental health dimension demonstrates the positive role that improvements in social security and public service systems have played in alleviating psychological stress among rural residents: mental health deprivation decreased rapidly from 0.113 in 2016 to 0.056 in 2018, subsequently maintaining relatively low deprivation values. Considering comprehensive multidimensional poverty deprivation, the value progressively declined from 0.239 in 2016 to 0.176 in 2022, indicating remarkable achievements in multidimensional poverty alleviation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDegrees of Deprivation across Different Dimensions (2016\u0026ndash;2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic conditions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiving standard\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical health\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMental health\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe regression results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal the influencing factors of poverty deprivation across different dimensions and multidimensional poverty. The regression outcomes indicate that intergenerational support exerts significantly differentiated impacts on multidimensional poverty among rural elderly populations: intergenerational relationship quality demonstrates cross-dimensional protective effects. For every unit increase in relationship score, the probability of multidimensional poverty decreases by 1.5% (β=-0.015, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with particularly pronounced effects in the mental health domain (β=-0.030, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, the frequency of domestic assistance does not significantly affect multidimensional poverty, reflecting that instrumental support may exhibit adverse selection\u0026mdash;children are more inclined to assist highly dependent elderly who have already fallen into poverty.\u003c/p\u003e\u003cp\u003eFurthermore, face-to-face contact exhibits selective alleviating effects. Meeting frequency significantly improves living standards (β=-0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and economic levels (β=-0.008, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but shows no statistical significance for health dimensions. This confirms the particular value of \"physical presence\" for domestic assistance (such as heavy physical labor like fuel collection and water fetching), though it cannot substitute for daily health management.\u003c/p\u003e\u003cp\u003eThe frequency of intergenerational contact demonstrates robust negative effects across multiple dimensions of multidimensional poverty. For every unit increase in contact frequency, the probability of economic poverty decreases significantly by 1.0% (β=-0.010, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), living standard deprivation declines by 1.2%, mental poverty risk reduces by 0.6% (β=-0.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the comprehensive suppressive effect on multidimensional poverty reaches 0.4% (β=-0.004, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Contact frequency with children shows a significant positive correlation with physical health, consistent with the reality that children maintain more frequent contact when elderly parents are in poor health.\u003c/p\u003e\u003cp\u003eThese results indicate that while communication technology cannot fully compensate for physical separation, high-frequency contact remains a core channel for maintaining emotional support. With the wave of rural young and middle-aged populations migrating for work accompanying urbanization, labor migration induces poverty accumulation among rural elderly through the dual mechanisms of disrupting both \"spatial accessibility\" and \"emotional responsiveness\" of intergenerational support. Compared to instrumental support (domestic assistance), emotional support (contact frequency, relationship quality) demonstrates broader anti-poverty effects, providing empirical evidence for expanding targeted poverty alleviation policies from material assistance to spiritual care.\u003c/p\u003e\u003cp\u003eAdditionally, individual characteristics and family structure variables significantly influence elderly multidimensional poverty levels. Regarding demographic characteristics, elderly women exhibit significantly lower multidimensional poverty levels (β=-0.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), possibly related to women's stronger family resource management capabilities and social network maintenance abilities. Extended educational attainment produces significant negative effects (β=-0.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with each additional year of education reducing multidimensional poverty by 0.3%, confirming the sustained protective role of human capital accumulation for elderly livelihoods. Notably, employment status presents contradictory effects: while employment significantly reduces multidimensional poverty (β=-0.017, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), its improvement effects on economic dimensions and living standards are not obvious, suggesting that rural elderly employment may concentrate in low-quality positions, with poverty alleviation effects primarily achieved through psychological and social functions rather than economic improvement.\u003c/p\u003e\u003cp\u003eFamily structure variables demonstrate stronger explanatory power. Family health shocks exhibit the most significant poverty-inducing effects (β\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with each additional unhealthy member leading to a 7.6% increase in multidimensional poverty, far exceeding the impact magnitude of other variables. This verifies the pivotal role of health as a core capability deficit in rural elderly poverty. Family size expansion produces significant protective effects (β=-0.019, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), consistent with theoretical expectations: large-scale families enhance resistance to multidimensional deprivation through risk-sharing mechanisms. Regional difference analysis reveals significant advantages in eastern regions (β=-0.028, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while northeastern regions show anomalous positive effects in living standard dimensions (β\u0026thinsp;=\u0026thinsp;0.099, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), possibly reflecting institutional differences in elderly welfare under regional industrial transformation contexts. Notably, the impacts of age and marital status are statistically insignificant, suggesting that life cycle effects and traditional family protection mechanisms have weakened among rural elderly samples.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInfluencing Factors of Deprivation Levels across Different Dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiving standard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMental health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.022***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.015***\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\u003e(-0.983)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-5.229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-9.543)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-4.297)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of children's household assistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\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\u003e(1.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.242)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-1.195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.823)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of meeting with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.012**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.007***\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\u003e(-2.625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.588)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.504)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-2.180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-3.452)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of contact with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.010***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.012***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.006***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.004***\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\u003e(-5.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.360)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.938)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-4.580)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-4.212)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000\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\u003e(0.563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-0.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.052)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\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\u003e(-0.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.984)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.764)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.278)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(1\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.016***\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\u003e(-0.630)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.751)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-7.555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-5.745)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.006***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.003***\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\u003e(-2.567)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.880)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-2.456)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-5.930)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (1\u0026thinsp;=\u0026thinsp;married)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.037**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000\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\u003e(2.967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-4.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.051)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status (1\u0026thinsp;=\u0026thinsp;employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.017**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.017***\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\u003e(-1.122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-6.480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-2.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-4.982)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of unhealthy family members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.193***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.076***\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\u003e(6.489)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.433)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(30.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(9.866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(24.507)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.025***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.018***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.019***\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\u003e(-5.536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-5.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-8.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-1.749)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-8.340)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (east)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.043**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.046***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.028**\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\u003e(-2.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.764)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-3.351)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.388)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-2.077)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (west)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\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\u003e(1.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.709)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.689)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.610)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (north east)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.040*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.099***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\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\u003e(-1.942)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.504)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.891*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.622**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.358\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\u003e(-0.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.693)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10,847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eRobust t-statistics in parentheses\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"6. Results of Multidimensional Dynamic Poverty","content":"\u003cp\u003eEmploying the internationally prevalent threshold of k\u0026thinsp;=\u0026thinsp;0.33 as the benchmark for determining multidimensional poverty status, the dynamic changes in multidimensional poverty between adjacent survey periods are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results reveal that the persistent non-poor category accounts for the largest proportion at 87.08%, indicating that most rural elderly populations maintained a relatively stable non-poverty status throughout the observation period. Concurrently, the proportion of persistent poor individuals reached 41.49%, approaching nearly half of the sample. The relatively high proportions within these respective states underscore the persistent and path-dependent characteristics of poverty. However, the proportion of individuals transitioning \"from poverty to non-poverty\" exceeded that of the \"persistent poor,\" demonstrating the dynamic nature of poverty status and the feasibility of poverty alleviation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePoverty Transition Categories and Proportions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransition Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epersistent non-poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-poor\u0026rarr; poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u0026rarr; non-poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.81%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epersistent poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.19%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals the differentiated mechanisms through which children's support factors operate in elderly poverty dynamics. Analysis of core independent variables demonstrates that interaction frequency between children and elderly parents, rather than emotional relationships per se, exerts significant influence on poverty transitions. Meeting frequency exhibits a significant negative effect at the 1% level in the poverty entry model (7) (β = -0.010, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that each unit increase in meeting frequency reduces poverty entry risk by 1 percentage point. This effect may stem from direct assistance provided through children's physical visits, including economic support and essential living supplies. Contact frequency also demonstrates marginal significance at the 10% level in the poverty entry model (β = -0.004, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), suggesting that remote care through telephone/communication serves as an important buffering mechanism. Notably, children's assistance with household chores and emotional relationship quality show no significant impact on either poverty exit or entry, implying that rural elderly support systems rely more heavily on substantive behaviors rather than emotional bonds.\u003c/p\u003e\u003cp\u003eRegarding control variables, the poverty exit model reveals that gender variables indicate women are more likely to escape poverty. Additionally, household size significantly increases poverty exit probability (β\u0026thinsp;=\u0026thinsp;0.058), potentially reflecting advantages that larger households possess in resource sharing and income diversification. Conversely, a greater number of unhealthy family members significantly reduces poverty exit likelihood (coefficient = -0.086).\u003c/p\u003e\u003cp\u003eIn the poverty entry model, the coefficient for years of education is negative and significant (β = -0.003), indicating that rural elderly individuals with higher educational attainment face significantly lower risks of falling into poverty, highlighting the protective role of human capital in poverty transition dynamics. Larger household size is associated with reduced poverty entry risk, demonstrating the risk-sharing effect of family size. Conversely, the number of unhealthy family members significantly increases poverty entry risk (β\u0026thinsp;=\u0026thinsp;0.047), suggesting that health shocks constitute a major risk factor for rural household poverty recurrence.\u003c/p\u003e\u003cp\u003eCollectively, these analytical findings reveal the differentiated mechanisms through which intergenerational support and household characteristics operate in poverty dynamics, emphasizing the crucial role of health status, education level, and family structure in rural poverty exit and re-entry processes. The research findings provide important insights for policy formulation: poverty prevention strategies should strengthen intergenerational support networks while enhancing rural households' health security systems and educational investment, thereby effectively reducing poverty vulnerability and consolidating poverty alleviation achievements.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics Analysis of Populations Prone to Poverty Exit and Entry\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoverty Exit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoverty Entry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.011\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\u003e(-0.698)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.273)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of children's household assistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\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\u003e(-0.848)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.820)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of meeting with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.010***\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\u003e(0.959)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-3.279)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of contact with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.004*\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\u003e(-0.301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.852)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.043\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\u003e(0.574)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.248)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034\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\u003e(-0.741)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.338)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(1\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.067*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.011\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\u003e(2.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.815)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.003*\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\u003e(0.761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.947)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (1\u0026thinsp;=\u0026thinsp;married)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\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\u003e(0.790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.966)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status (1\u0026thinsp;=\u0026thinsp;employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.009\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\u003e(0.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.570)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of unhealthy family members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.086***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.047***\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\u003e(-6.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.416)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.058***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027***\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\u003e(5.275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.451)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (east)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.038**\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\u003e(-0.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.142)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (west)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\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\u003e(0.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.639)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (north east)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\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\u003e(-1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.114)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.691\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\u003e(-0.205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.443)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eRobust t-statistics in parentheses\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe survival analysis results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate that with increasing survey waves, poverty numbers exhibit a continuous declining trend, with survival function values showing a significant downward trajectory, reflecting the dynamic poverty reduction process among the impoverished population. Specifically, the survival function for multidimensional poverty drops sharply to 0.449 in the second wave, indicating that over half of the multidimensionally poor population has achieved poverty exit. However, a considerable proportion of samples remain persistently impoverished across multiple waves of the panel survey, revealing the structural characteristics and path dependency inherent in multidimensional poverty.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKaplan-Meier Estimation Results for Multidimensional Poverty (k\u0026thinsp;=\u0026thinsp;0.33)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWave\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount of Poor Individuals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount of Poverty Exits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCount of Censoring\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSurvival Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.417, 0.480)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.205, 0.269)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.103, 0.167)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"7. Conclusions and Discussion","content":"\u003cp\u003eIn recent years, the level of multidimensional poverty among the rural elderly population has experienced a continuous decline, exhibiting heterogeneous trends across various poverty dimensions. Between 2016 and 2022, the multidimensional poverty index for rural elderly individuals significantly decreased, reflecting the effectiveness of poverty alleviation efforts in China. Nonetheless, the persistence of poverty remains a significant concern, with nearly half of the older adults classified as impoverished continuing to experience poverty throughout the observation period. The alleviation of poverty in economic and living standards dimensions has been markedly influenced by targeted poverty alleviation policies and improvements in infrastructure, while the mental health dimension has shown significant enhancement due to advancements in social security and public services. In contrast, the reduction of poverty in the physical health dimension has been relatively modest, with some evidence of a rebound in later stages. Despite rapid advances in medical resources that have extended average life expectancy, ensuring healthspan remains a critical focus for future medical endeavors. Additionally, multidimensional poverty is characterized by significant regional disparities and spatial clustering: the central and western regions exhibit higher levels of economic deprivation, whereas southeastern coastal areas experience lower levels of living deprivation, with certain provinces showing notable deficiencies in the mental health dimension.\u003c/p\u003e\u003cp\u003eIntergenerational support plays a pivotal role in alleviating and transforming multidimensional poverty among the elderly. Empirical analysis reveals that the quality of relationships and frequent communication between children and their elderly parents significantly reduces the risk of poverty across economic, living standards, and mental health dimensions, serving as a vital protective factor against multidimensional poverty. In contrast, instrumental support from children, such as assistance with household chores, has negligible effects on reducing overall poverty. This may be attributed to the fact that children often intervene only when their elderly parents are already entrenched in poverty, thus limiting the potential to fundamentally alter the poverty situation. Furthermore, regular face-to-face visits positively impact the economic status, living standards, and mental health of older individuals; however, their effect on physical health is less pronounced, potentially restricted by reverse causality concerns. Overall, emotional support demonstrates a broader and more enduring anti-poverty effect in comparison to instrumental support. With industrialization and labor migration contributing to increased intergenerational distance within families, the traditional role of \u0026ldquo;raising children to provide for old age\u0026rdquo; is progressively diminishing. High-frequency emotional support and remote connections remain essential pathways for ensuring the well-being of the elderly.\u003c/p\u003e\u003cp\u003eAdditionally, demographic and familial characteristics significantly influence the incidence of multidimensional poverty among older adults. The occurrence of poverty is lower among elderly women and those with higher educational attainment, indicating that the accumulation of human capital is instrumental in reducing elderly poverty. Larger family sizes can lower the probability of poverty through risk-sharing mechanisms, whereas poor health among family members significantly increases the risk of elderly individuals falling into poverty. Traditional marital status and age factors exhibit minimal influence, suggesting that the direct effect of lifecycle stages and marital status on elderly poverty has diminished with societal progress.\u003c/p\u003e\u003cp\u003eThis study highlights the complexity of multidimensional poverty among rural elderly and underscores the critical role of intergenerational support. First, moving beyond traditional conceptualizations that emphasize co-residential or face-to-face care, this research introduces the dimension of \u0026ldquo;remote support\u0026rdquo; enabled by modern communication technologies. By framing intergenerational support through the lens of spatial separation and emotional connectedness, the study demonstrates the positive impact of non-cohabiting children\u0026rsquo;s support on elderly well-being, thereby extending the scope and meaning of intergenerational support theory within the context of societal modernization. Second, this study goes beyond static measurements of poverty by incorporating a dynamic analytical perspective into multidimensional poverty research. It systematically examines transitions in poverty status among rural elderly and reveals the heterogeneous effects of intergenerational support in both mitigating the risk of falling back into poverty and facilitating poverty alleviation.\u003c/p\u003e\u003cp\u003eMoreover, this study elucidates the complexity of multidimensional poverty among rural elderly populations while underscoring the critical role of intergenerational support. It provides empirical evidence to inform the enhancement of poverty alleviation and social policies in the context of healthy aging. On one hand, poverty alleviation efforts in the post-poverty alleviation era should broaden their focus beyond mere income improvement to encompass health, eldercare, and mental well-being. This necessitates the establishment of robust monitoring and long-term support mechanisms for multidimensional poverty, with a sustained focus on relative poverty and potential re-poverty risks among the elderly population. On the other hand, it is essential to prioritize the role of intergenerational support by refining eldercare policies to encourage and facilitate adult children in fulfilling their filial duties (for example, by exploring the establishment of \"filial piety leave\"). Active promotion of intergenerational contact and visitation is crucial in mitigating the adverse impacts of children\u0026rsquo;s labor migration on the well-being of the elderly, thereby enhancing the overall welfare of them.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe research leading to these results received funding from the National Natural Science Foundation of China (Grant No. 42271245).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThis study used the publicly available China Family Panel Studies (CFPS) database. The CFPS project is a human subject research project that regularly submits ethical review or continuous review applications to the \u0026quot;Peking University Biomedical Ethics Committee.\u0026quot; The ethical review approval number is uniformly: IRB00001052-14010.\u003c/p\u003e\n\u003cp\u003eThe data source is available at: https://www.isss.pku.edu.cn/cfps/\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Consent\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all participants involved in the study\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Data\u003c/h2\u003e\n\u003cp\u003eThis study used the publicly available China Family Panel Studies (CFPS) data.\u003c/p\u003e\n\u003ch2\u003eMaterials and/or Code availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe code and materials used in this study are available upon request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiaotong ZHAO primarily drafted the initial manuscript and made significant contributions to the acquisition, analysis, and interpretation of data. Additionally, she played a key role in the conception and design of the work.Chunhui LIU revised the manuscription critically for important intellectual content. Also, he played a key role in the theoretical design of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study used the publicly available China Family Panel Studies (CFPS) database. The CFPS project is a human subject research project that regularly submits ethical review or continuous review applications to the \"Peking University Biomedical Ethics Committee.\" The ethical review approval number is uniformly: IRB00001052-14010.The data source is available at: https://www.isss.pku.edu.cn/cfps/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlkire, S., \u0026amp; Foster, J. (2011). 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Migrant Interactions With Elderly Parents in Rural Cambodia and Thailand. \u003cem\u003eJournal of Marriage and Family\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(3), 585\u0026ndash;598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1741-3737.2008.00507.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1741-3737.2008.00507.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Consumer Price Index for Poor Rural Households in China\u0026thinsp;=\u0026thinsp;Rural CPI \u0026times; 0.4\u0026thinsp;+\u0026thinsp;Rural Food CPI \u0026times; 0.6\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":"Multidimensional Poverty, Rural Elderly, Intergenerational Support, Regional Disparities, China","lastPublishedDoi":"10.21203/rs.3.rs-7088539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7088539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs population aging accelerates, rural older adults in China are increasingly exposed to multidimensional poverty risks, including deprivation in income, health, and living conditions. The weakening of traditional family caregiving functions has further exacerbated this issue. Drawing on nationally representative data from the China Family Panel Studies (CFPS), this study adopts an intergenerational support perspective and employs spatial analysis alongside dynamic monitoring techniques to examine the spatial distribution, temporal evolution, and influencing factors of multidimensional poverty among the rural elderly. The results reveal three key findings. First, from 2016 to 2022, overall multidimensional poverty among rural older adults declined significantly, with heterogeneous trends across dimensions: deprivations in economic conditions and living standards improved markedly, mental health deprivation decreased most substantially, while progress in physical health remained relatively limited. Second, multidimensional poverty exhibited notable regional disparities and spatial clustering. Economic deprivation was more severe in central and western regions, while deprivation in living standards was lower along the eastern coast; certain provinces experienced disproportionately high levels of mental health deprivation. Third, intergenerational support played a critical role in alleviating poverty among the rural elderly. In particular, frequent emotional contact and communication from adult children significantly reduced poverty risks across economic, living, and mental health dimensions, whereas instrumental support such as help with housework had limited impact. This study provides empirical evidence to inform anti-poverty and social policy initiatives under the framework of healthy aging, highlighting the need to complement material assistance with strengthened family-based intergenerational support in order to comprehensively enhance the well-being of older populations.\u003c/p\u003e","manuscriptTitle":"Mapping Rural Elderly Multidimensional Poverty in China: Intergenerational Support as a Protective Factor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 16:14:04","doi":"10.21203/rs.3.rs-7088539/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":"e25ecf9a-ec75-4e52-8d56-6a9e5457226a","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-15T07:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 16:14:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7088539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7088539","identity":"rs-7088539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}