Objective: To determine whether or not chronic disease positively impacts medical costs among the rural elderly in China and to calculate medical expenditure induced by chronic disease between different groups of the rural elderly, as well as provide insight into the factors that affect medical losses induced by chronic disease among different household registration groups and different New Rural Pension Scheme statuses. Design: To estimate the share of medical expenses induced by chronic disease, this article uses a two-part model and a four-part model to analyze the causal effect of chronic disease on medical services and then uses a counter-factual method to estimate the share of medical expenses. Setting: The rapid development of China has changed nearly every aspect of life for the rural elderly. Many are concerned about the increasing prevalence of physical health issues, particularly chronic diseases, among the rural elderly. Nevertheless, there are no articles using nationally representative panel datasets that report differences in the cost of chronic disease between sub-populations. Participants: The panel dataset used for this study comes from the China Family Panel Studies (CFPS) data. In this study, we use the CFPS data for the years 2012, 2014 and 2016 to create a panel dataset that includes 2730 rural elderly for 3 years. Interventions: This article reports the representative estimate of medical expenditures attributable to chronic disease among rural elderly in China. On this basis, we estimate per capita medical spending among rural elderly under the condition of logarithmic normal distribution of different parameters between urban and rural or between groups that participate in the New Rural Pension Program and groups that do not. Main Outcome Measures: This study indicates that physical health status has a significant impact on both the probability of undergoing medical care and the size of medical expenditures among the rural elderly, and the influence was significant for all patients, including outpatients and inpatients. Results: Chronic illness has significant effects on individual medical expenses, and they aggregately contribute to 63.96% of total personal expected medical expenditure. Specifically, the medical spending caused by chronic disease was part of a non-uniform distribution, with the rural, male, older, married and higher educated groups spending more money on medical costs induced by chronic disease. Conclusions: Examining trends in the prevalence of chronic diseases and evaluating medical spending on chronic diseases can prevent and control potential medical costs among rural elderly, especially for vulnerable groups, which helps to predict future health-care needs.

Due to China’s socioeconomic development, labor force transfer from rural areas has become more common, the income of rural households has increased, and the structure of rural household clean living energy consumption has changed. However, few studies have explored the correlation between non-farm employment and clean energy adoption in rural households. Using survey data from 1175 farmers in 106 villages from a 2018 Survey in Liaoning Province, this study uses a Probit model to analyze the effect of non-farm work on clean energy adoption, as well as an effect decomposition model to examine the specific mechanism of their interaction. Robustness tests were performed using extended regression models (ERMs), propensity score matching (PSM), and variation of the core explanatory variable measures. The results found that: (1) Rural residents’ non-farm work has a significant positive effect on their household clean energy adoption. (2) Increasing rural residents’ household income and promoting the growth of their health knowledge are the main channels through which non-farm work influences their clean energy adoption. (3) Non-farm work has a more positive impact on household clean energy adoption for young or male farmers, those who had a junior high school education or above, and those who had a village head in the family. This study provides an understanding of rural non-farm work and clean energy adoption decisions and provides references for the effective allocation of rural labor resources and the formulation of policies related to rural energy adoption.