Search Results (186)

In the context of global climate change, understanding how extreme heat affects energy poverty is critical for both policy and household welfare. This study investigates the association between heat shocks and energy poverty among rural households in China, using data from the China Family Panel Studies (CFPS) and NASA climate records. We find that heat shocks are positively and significantly associated with greater energy poverty, with northern households and those farther from provincial capitals being most vulnerable. Income heterogeneity is also important: low-income households exhibit weaker increases in energy expenditures under heat shocks, indicating that affordability constraints can mask their energy deprivation. This reveals the presence of hidden energy poverty, which conventional objective measures may underestimate. Moreover, the dynamic inter-wave cumulative estimates indicate persistent associations between heat shocks and household energy poverty, suggesting limited adaptive capacity among some rural households. Our results are robust across multiple heat-shock indicators. These findings provide household-level evidence on the pathways through which climate shocks shape energy poverty and underscore the need for targeted interventions to enhance adaptive capacity and protect vulnerable populations in rural China. Full article

This study examines how trust is associated with household commercial-insurance participation in China and separates two sources of variation that pooled estimates combine. Within-person change asks whether changes in the same respondent’s trust across survey waves are accompanied by changes in that respondent’s household insurance participation. Between-person differences compare respondents who maintain different average trust levels over time. Using six waves of the China Family Panel Studies (CFPS) from 2012 to 2022, we analyze 47,006 respondent-wave observations from 15,176 respondents. Pooled linear probability and Probit models show a positive association between the aggregate trust index and insurance participation. In contrast, respondent fixed-effects, conditional fixed-effects Logit, and city-by-wave fixed-effects models provide little evidence of a systematic within-person association. Correlated random-effects estimates locate most of the pooled relationship in persistent differences between respondents. The trust dimensions also differ: persistent trust in strangers is positively associated with participation, whereas trust in local government cadres is negatively associated. Expenditure models lead to a similar conclusion. The results show why generalized interpersonal trust and institutional trust should not be treated as interchangeable and why pooled trust–insurance associations need not describe changes within the same person over time. Full article

The global shift toward clean and low-carbon household energy is central to achieving the Sustainable Development Goals and improving welfare in developing countries, yet it also raises distributional concerns about who benefits and who is left behind. Using five rounds of data from the China Family Panel Studies (CFPS) spanning 2014–2022, this study employs a two-way fixed-effects model with province and year dummies and the Bootstrap method to examine the association between cooking energy transition and rural household development resilience. The results show that the share of traditional solid fuels (fuelwood and coal) declined from 56.29% in 2014 to 36.56% in 2022, while the share of fossil gas rose from 22.26% to 39.91%, indicating a transitional stage in rural cooking energy use. Cooking energy transition is positively associated with rural household development resilience. Household education and farmers’ digital literacy are positively associated with both cooking energy transition and household development resilience, suggesting they may be relevant channels. The association is more pronounced among households with a low elderly dependency ratio, poor infrastructure accessibility, and adequate energy affordability, underscoring that the benefits of energy transition are not evenly distributed. These findings highlight the importance of inclusive and differentiated energy policies for ensuring that the gains from clean cooking reach the most vulnerable rural households. Full article

Green innovation is an important means of mitigating environmental pollution, improving production efficiency, and promoting high-quality economic development. Using prefecture-level city data and China Family Panel Studies (CFPS) data from 2010 to 2022, this study applies the Super-SBM model to measure urban green innovation efficiency and examines its effects on residents’ consumption levels and household consumption structures. The results show that (1) green innovation efficiency significantly increases consumption among urban residents and urban households, whereas its effects on rural residents and rural households are not statistically significant; (2) green innovation efficiency significantly reduces the share of subsistence-oriented consumption while increasing the shares of housing, development-oriented, and tourism consumption, thereby promoting a shift in household expenditure from basic needs toward development- and enjoyment-oriented consumption; (3) wage income and housing debt constitute two distinct transmission channels. Higher wage income facilitates development- and enjoyment-oriented consumption, whereas housing debt increases the share of housing consumption and exerts a crowding-out effect on development-oriented and tourism consumption; and (4) green innovation efficiency has a more pronounced effect on development-oriented and tourism consumption among young and high-income households. For middle-aged and older households, as well as low-income households, the effects of green innovation are reflected more strongly in an increase in the share of housing consumption. The findings provide important empirical evidence that green innovation contributes to improving residents’ quality of life. Full article

The natural disasters caused by climate change are increasingly threatening the livelihood sustainability of rural households. How to enhance the adaptability of farmers has become a major issue that urgently needs to be addressed in rural development. An increasing amount of research suggests that the digital transformation in rural areas may provide solutions to this problem. This study selected large sample data from the China Family Panel Studies (CFPS) from 2014 to 2020 to empirically test the role of digital economy in mitigating the adverse impact of natural disasters on livelihood resilience of rural households in China. The empirical results indicate that although natural disasters have a significant negative impact on the livelihood resilience of rural households, the embedding of digital technology can alleviate this negative impact to some extent by expanding non-agricultural employment opportunities for rural households, enhancing households’ access to credit, and improving agricultural production strategies. During this process, the buffering capacity, self-organization ability, and learning ability of rural families have been significantly improved. Moreover, the mitigating effect of the digital economy exhibits heterogeneity. It is more pronounced in the central and western regions than in the eastern regions, and also more evident among high-income families relative to low-income families. The key to improving the benefits of digital technology may lie in tailored policy interventions and digital skills training for farmers. Finally, this study provides empirical evidence from rural China on the role of the digital economy in strengthening farmers’ adaptive capacity against disaster risks. While these findings are context-specific, they may still provide valuable insights for other agriculture-dependent developing countries facing persistent climate risks. Full article

Enhancing the risk-coping capacity and economic resilience of rural households is of great significance for consolidating the foundation of household development and improving people’s well-being. As a core form of human capital in the digital era, digital literacy opens up a new pathway for addressing the economic challenges faced by rural households. Using data from the 2022 China Family Panel Studies (CFPS), this paper empirically examines the association between digital literacy and rural household economic vulnerability, as well as its underlying mechanisms. The findings reveal that: (1) Digital literacy is significantly negatively associated with rural household economic vulnerability, and this conclusion is consistently validated across different model specifications and measurement approaches, supporting Hypothesis 1; (2) Mediation analysis shows that employment quality and social trust mediate the negative association between digital literacy and rural household economic vulnerability, supporting Hypotheses 2 and 3; (3) Moderation analysis shows that education level moderates the association, and the negative association between digital literacy and rural household economic vulnerability is stronger among less-educated households, supporting Hypothesis 4b, while Hypothesis 4a is not supported; (4) Heterogeneity analysis indicates that the negative association between digital literacy and rural household economic vulnerability varies significantly across different characteristics of household heads, household endowments, and regions. It should be noted that, based on cross-sectional data, the above findings reflect correlations rather than causal relationships. Accordingly, this paper proposes policy recommendations including accelerating the construction of rural digital infrastructure, prioritizing digital literacy training for less-educated rural households, and adopting targeted policies tailored to different conditions, so as to comprehensively enhance rural households’ digital literacy and inject sustainable endogenous momentum into the modernization of agriculture and rural areas. Full article

As China advances price reform and modernizes its governance system, the optimization of residential increasing-block tariffs (IBT) has received increasing policy attention. Using data from the China Family Panel Studies (CFPS), this paper employs a multi-period difference-in-differences (DID) model to examine the impact of changing the implementation cycle of increasing-block tariffs from monthly to annual on residential electricity consumption. The results show that extending the billing cycle significantly increases residential electricity consumption. Mechanism analysis indicates that annual implementation smooths monthly consumption fluctuations, reduces the probability of entering higher tariff tiers, and lowers the average effective electricity price, thereby weakening households’ sensitivity to marginal electricity prices and increasing electricity use. Heterogeneity analysis further shows that this effect is mainly concentrated among low-income and less-educated households. These findings suggest adopting a hybrid policy combining monthly implementation with seasonal tier design to improve price signal clarity and enhance both efficiency and equity. Full article

Background: This study aimed to investigate the association between educational attainment and depressive symptoms among young-old adults aged 60–74 years, and to examine the mediating effect of self-reported memory function between them, so as to provide a reference for the early screening and intervention of depressive symptom severity in this population. Methods: This was a cross-sectional study, and data were derived from the 2022 China Family Panel Studies (CFPS). After data cleaning, 3666 valid samples were included. Depressive symptom severity was assessed using the 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) (Cronbach’s α = 0.771, Guttman Split-Half Coefficient = 0.742), with a total score ≥ 9 defined as probable depression. Self-reported memory function was measured using a single item (scored 0–4), with higher scores indicating better perceived memory, and was treated as a continuous variable for analysis. Descriptive statistics and Pearson correlation analysis were performed using R software (version 4.2.3). The mediation effect was tested using the product-of-coefficients method combined with the bootstrap method (5000 resamples), supplemented by a quasi-Bayesian model and multiple sensitivity analyses (including outcome variable substitution and model diagnostic corrections) for robustness testing. Results: Among the study participants, the prevalence of probable depression was 25.18%. Correlation analyses showed that educational attainment was significantly negatively correlated with depressive symptom severity (r = −0.177, p < 0.001) and significantly positively correlated with self-reported memory function (r = 0.241, p < 0.001). Self-reported memory function was significantly negatively correlated with depressive symptom severity (r = −0.223, p < 0.001). After adjusting for age, gender, place of residence, subjective income, self-rated health, and chronic disease status, mediation analysis showed that the direct effect of educational attainment on depressive symptom severity was −0.297 (95% CI: −0.424 to −0.174), while the indirect effect via self-reported memory function was −0.109 (95% CI: −0.139 to −0.082). The direct and indirect effects accounted for 73.20% and 26.80% of the total effect, respectively; the results of the quasi-Bayesian analysis and multiple sensitivity analyses were consistent in direction, further validating the stability of this mediation effect. Conclusions: Among young-old adults aged 60–74 years, self-reported memory function plays a partial statistical mediating role in the association between educational attainment and depressive symptom severity. This suggests that strengthening attention to memory status and early screening among individuals with lower educational attainment may offer a potential intervention approach for alleviating depressive symptom severity in this population in the future. Full article

China’s rising household leverage has intensified concern about household-level financial risk, yet the role of digital financial inclusion (DFI) remains ambiguous. This study examines the association between DFI and household financial fragility through two channels: an information channel and a credit-constraint channel. We develop a three-period household decision framework and test its implications using China Family Panel Studies (CFPS) data from 2014 to 2022, matched with a county-level DFI index. The results show a U-shaped association between DFI and household financial fragility. Mechanism tests are consistent with a leverage channel: broader credit availability is associated with higher household leverage and higher distress risk. Evidence for the risk-taking channel is more conditional and becomes more visible at higher levels of DFI development. Instrumental-variable estimates, lagged specifications, alternative fragility measures, and double machine learning produce the same nonlinear sign pattern, although the exact turning point is specification-sensitive and the exclusion restriction remains an identifying assumption. The findings support risk-based monitoring of digital credit and consumer-protection policies targeted at households with weak liquidity buffers and high debt-service burdens. Full article

China’s transition from strict fertility control toward a pronatalist regime raises important questions regarding how institutional regime changes are associated with wage outcomes across different policy stages. While previous studies primarily examine contemporaneous labor-market outcomes, this study evaluates wage associations across fertility-policy regime stages using a dynamic panel specification. Using five waves of the China Family Panel Studies (CFPS) from 2014 to 2022, the analysis constructs a short, unbalanced panel and estimates a wage equation using two-step System Generalized Method of Moments (System GMM) with collapsed instruments and restricted lag depth to address dynamic endogeneity and unobserved heterogeneity. To improve representativeness and mitigate observed wage-observability differences, survey weights are combined with inverse-probability weighting. The preferred baseline estimates indicate a positive but modest lagged-wage coefficient. Monte Carlo and sensitivity analyses further suggest that the persistence estimate is fragile and may overstate the degree of true persistence in this short-panel setting. Accordingly, the findings do not support strong intertemporal wage persistence and instead indicate only limited dependence of current wages on past wage realizations. The dynamic specification is therefore informative primarily as a diagnostic framework for assessing whether regime-stage wage associations exhibit meaningful persistence. Additional exposure-based heterogeneity analyses show negative interaction coefficients for Female and childbearing women (CBW). Married women aged 20–39 experience additional negative wage associations during fertility-policy regime stages, with similar results obtained under a narrower CBW20–35 robustness definition. These findings suggest that positive aggregate regime-stage associations may conceal relative wage disadvantages among women in demographic groups more plausibly exposed to fertility-policy-related labor-market conditions. The CBW indicator is interpreted as a demographic-exposure proxy rather than as a direct measure of employer expectations, fertility intentions, or discrimination. Overall, the results highlight exposure-based heterogeneity in regime-stage wage associations while emphasizing that the estimates should be interpreted as conditional associations embedded within broader institutional transitions rather than as causal fertility-policy effects. Full article

Adolescents’ social development is shaped by everyday family interactions, yet evidence on whether routine family practices are associated with social and emotional skills remains limited, particularly in China. Using data from the 2016–2022 China Family Panel Studies, this study examined the association between family meal frequency and five CFPS-based proxy indicators of social and emotional skills among 3727 adolescent-wave observations from adolescents aged 10–16 years. Ordinary least squares models were estimated, with propensity score matching used as a robustness check and subgroup analyses supplemented by formal interaction tests by school stage, residence, and family socioeconomic status (SES). More frequent family meals were positively associated with the proxy indicators for agreeableness, extraversion, and emotional stability, while conscientiousness showed weaker but positive evidence of association; no significant association was found for openness. The subgroup and interaction analyses suggested some group-specific patterns, including tentative differences by school stage and somewhat broader positive associations among rural and low-SES adolescents, although the interaction evidence was limited. The findings suggest that regular family meals may represent a routine family interaction context modestly associated with selected proxy indicators of adolescents’ social and emotional skills. Full article

The rapid expansion of e-commerce into rural areas has emerged as a prominent policy initiative aimed at promoting digital economic development and facilitating structural transformation in developing countries. However, empirical evidence on whether and how such policies affect rural labor markets remains limited. This paper investigates the impact of China’s E-commerce into Rural Areas Policy on non-agricultural employment among rural residents. We develop a theoretical framework in which an individual’s utility derives from commodity consumption, leisure, and social recognition associated with e-commerce participation. The model predicts that reducing the investment price of e-commerce activities—the primary intervention of the policy—increases non-agricultural labor supply through both direct and indirect channels. Using panel data from the China Family Panel Studies (CFPS) spanning 2010 to 2022 and exploiting the staggered rollout of the Rural E-commerce Demonstration Pilot as a quasi-natural experiment, we employ a difference-in-differences (DID) approach to estimate the policy’s causal effects. The results show that the policy marginally significantly increases the probability of non-agricultural employment by approximately 1.1 percentage points, an effect that remains robust after parallel trend tests, placebo tests, and controlling for concurrent policies. Mechanism analysis reveals that the policy operates through two distinct channels: promoting regional industrial development, particularly the growth of the tertiary sector, and generating village-level peer economic incentives that encourage participation through social networks and information spillovers. Heterogeneity analysis indicates that the policy’s effects are larger for males, younger individuals, those with higher educational attainment, and residents of poor counties or regions with e-commerce potential. These findings contribute to the literature on digital development and rural labor markets by providing rigorous causal evidence and identifying the mechanisms underlying the policy’s effectiveness. The results also offer practical insights for policymakers seeking to leverage digital technologies for rural employment generation and structural transformation. Full article

The Easterlin paradox and recent distributional reassessments suggest that average effects obscure how subjective disadvantage is generated and reproduced over time. We propose the Social Comparison System (SCS), a framework that represents subjective well-being (SWB) as an internal state and relative income rank as an external conditioning variable within a feedback structure, with three structural properties: threshold activation, state-dependent gain, and rank-conditioned attractor displacement. The properties are recovered through a sample-isolated three-stage framework integrating tree-based machine learning, forest-based heterogeneity estimation, panel-data estimation, and hierarchical Bayesian Markov modeling on a balanced four-wave panel of the China Family Panel Studies (CFPS; 8099 individuals; 32,396 person-wave observations). Stage 1 locates a discrete predictive discontinuity in relative income rank between rank 2 and rank 3 (SHAP jump = 0.383, permutation p < 0.001). Stage 2 carries this boundary into a disjoint validation panel and recovers a negative rank-by-prior-SWB interaction (β = −0.036) and a 2.30-fold larger conditional effect in low- than in high-prior-SWB strata. Stage 3 recovers a 22.6-percentage-point gap in the rank-conditioned occupancy of the lowest within-wave SWB quartile between low- and high-rank subsystems, which under a first-order Markov approximation corresponds to a long-run stationary gap, robust to alternative state-space discretizations. Throughout this paper, relative income rank is treated as a conditioning variable, and the rank-conditioned patterns are interpreted as associational; the long-run quantities are reported under a first-order dynamical approximation rather than as identified causal or fully validated long-run effects. Persistent subjective disadvantage is therefore characterized by unequal dynamics of activation, amplification, and escape, rather than by unequal resources alone. This reframing provides a methodological template for identifying rank-conditioned feedback structures in social-systems data. Full article

Climate change has increased the frequency of extreme weather events, posing a major threat to the sustainable development of agriculture and farmers’ welfare. Based on provincial meteorological data and China Family Panel Studies (CFPS) data from 2014 to 2022, this study systematically investigates the impact of climate shocks on farmers’ welfare, heterogeneity characteristics, and the buffering role of off-farm employment, using a two-way fixed-effect model. The results show that climate shocks significantly reduce farmers’ welfare, with greater welfare losses in northern regions, major grain-producing areas, and plain areas. Extreme low temperatures, extreme high temperatures, and drought are the three dominant climate hazards. In response to climate shocks, off-farm employment effectively buffers welfare losses. This study clarifies the logic of changes in farmers’ welfare and livelihood adaptation mechanisms under climate change, providing micro-empirical support for improving differentiated climate adaptation policies, strengthening agricultural risk management systems, enhancing agricultural system resilience, and promoting high-quality and sustainable agricultural development. However, constrained by the matching precision between micro-level data and meteorological indicators, future research should further refine the measurement of climate shock exposure at the individual farmer level. Full article

As digital technology has become increasingly integrated into rural governance and daily life in China, Internet use among rural residents exerts a multifaceted influence on their mental health. A key mechanism lies in its restructuring of the neighborhood social environment. Uncovering this mechanism is essential for understanding the theoretical and practical connections between rural social transformation and individual well-being in the digital age. This study applied a binary probit model to data from the 2020 China Family Panel Studies (CFPS) to examine the impact of Internet use on the mental health of rural residents. Mediation analysis was used to examine the role of the neighborhood social environment, and the conditional mixed process method was applied to address potential endogeneity issues. Empirical results demonstrate that access to the Internet, along with the breadth and depth of its use all significantly improve the mental health of rural residents. Internet use promotes mental health by strengthening neighborhood relationship and trust, whereas it also negatively affects mental health by suppressing neighborhood identity. Heterogeneity analyses reveal three key dimensions of variation. (1) By usage type: Activities such as gaming, short-video consumption, and WeChat communication show positive associations with mental health, whereas online shopping and learning exhibit non-significant effects. (2) By user group: The mental health benefits are more pronounced among women, less-educated individuals, and middle-aged to older adults. (3) By region: Positive associations are observed in central and western China, with the most substantial effect in the central region. This study elucidates the mechanism through which Internet use affects mental health: the restructuring of traditional, place-based social capital in rural neighborhoods. These findings offer robust empirical support for policies that integrate digital initiatives with the nurturing of local community bonds to improve rural mental health and foster livable and harmonious villages. Full article