China is undergoing a campaign which is called “The Targeted Poverty Alleviation Policy” to eradicate extreme poverty from rural China until 2020. Though poverty in rural China has been studied intensively in different objective dimensions, little attention has been paid to poverty line settings and subjective poverty, which are hinged to the policy effects. In order to fill in the research gap, this study employs a nationally representative survey of rural households in 2016, to measure subjective poverty in rural China, and analyze the determinants as well. Our results indicate that the mean subjective poverty line of the rural households is 8297 yuan per capita, which is far higher than the national poverty line (2800 yuan). Statistically, 29% of the surveyed rural households who are not objectively poor feel subjectively poor. The objective poverty line cannot fully reflect the subjective poverty perception. Thus, how to reduce the subjective poverty perception could be a major policy agenda in rural China after 2020, when extreme poverty is no longer a problem.

Background: Alcohol use has been linked to a number of physical conditions, but the relationship between alcohol drinking and depression, one of the most common mental disorders that is a significant contributor to the global burden of disease, is still under debate. We aim to help fill the literature gap on the causal effect of alcohol use on depression by using genetic instruments of ALDH2 rs671 and ADH1B rs1229984 in the Mendelian randomization (MR) framework. Materials and Methods: We collected a sample of 476 middle-aged and older adults from mainland China. The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used to measure the status of depression. The frequency and intensity of alcohol consumption were measured by (1) a binary indicator of drinking or not, (2) the total number of drinking occasions during the past 30 days, and (3) the weekly ethanol consumption in grams. Results: MR estimates indicated that alcohol use was causally associated with a lower risk of depression. Parameter estimates of drinking or not (b = −0.127, p = 0.048), number of drinking occasions (b = −0.012, p = 0.040), and weekly ethanol consumption (b = −0.001, p = 0.039) were all negative and statistically significant. The results were robust after adjustments for potential confounders (e.g., income, smoking, and parental drinking behaviors), and the exclusion of heavy or former drinkers. Conclusions: This is one of the first study to investigate the causal relationship between alcohol use and mental health using an MR design in East Asian populations. Further studies are needed to clarify the mechanisms of this causal link.

Purpose: Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.Design/methodology/approach: Households report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.Findings: The random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.Practical implications: To reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.Originality/value: For the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.