The percentage of institutional ownership in China has increased gradually. Does this characteristic of Chinese institutional investors affect their participation in corporate governance, which in turn, affects corporate tax avoidance activities? We take 1108 listed Chinese companies from 2009 to 2017 to study this issue. The paper uses average daily total market capitalization as an instrumental variable to test the endogeneity problems. We run a quantile regression (QR) at the median level as robustness testing to overcome the fat tail of financial data. Then, the shareholding ratio of majority shareholders is introduced to verify that ownership concentration is one of the mechanisms through which institutional shareholders influence corporate tax avoidance decisions. The findings are as follows: The proportion of institutional investors’ shareholdings is positively related to the degree of enterprise tax avoidance. The increase in institutional investors’ shareholdings is likely to promote corporate tax avoidance. When the level of ownership concentration is low, the increase in institutional ownership can play a greater role in promoting tax avoidance.

Econometricians construct panel logit-based early warning systems (EWSs) as the primary predictive tool to prevent incoming systemic banking crises. Considering the actual scenario of systemic banking crises, we argue that changes in economic indicators under the crisis may impact the information extraction of EWSs based on logistic regression. According to the potential limitations of the conventional EWS and properties of the machine learning algorithm, we assume that an ‘experts voting EWS’ framework can better fit characteristics of data of systemic banking crisis. Indeed, among other machine learning classifiers tested in this paper, random forest classifier simulating experts voting process is the most efficient classifier showing relatively high generalization above 80% area under the receiver operating characteristic curve on constructing the EWS. In contrast to the conventional system, an image of evidence shows that the experts voting EWS synthesizing multivariate information may be suitable for providing systemic banking systemic crises alerts in varied contexts.

The causal effect of public pensions on the mental wellbeing of the elderly in lower and middle-income countries deserves further investigation. This paper first constructed a theoretical framework for the impact of New Rural Society Pension Insurance pensions in China on the mental wellbeing of the rural elderly, and described potential channels through which pension income may affect mental wellbeing. We then used the fixed effect model and the instrument variable approach to estimate the casual effects of pension income on the mental wellbeing of the rural elderly. The results reveal that pension income improves mental wellbeing by relieving depression of the rural elderly; however, the beneficial effects of pension income are very limited. Pension income has no beneficial effects on the mental health of the rural elderly in the east region, whereas it slightly relieves depression of those in the middle and west regions. We also found that pension income produces small improvements in the mental health of older females, elderly persons living independently, and those with relatively poor economic conditions.

In cities’ policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities’ emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs) to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.

Issues concerning which factors that influence carbon dioxide emission, and which administrative measures should be imposed to reduce carbon emission in Chinese cities, have been on the agenda in cities’ policy-making. Yet little literature has studied this topic from the city level. This paper first measures CO₂ emission of 73 Chinese cities. We find heterogeneity embedded in the cross-city distribution of CO₂ emission per capita and a nonlinear structure in the relationship between carbon emission and GDP per capita. To describe such multimodality and examine the determinants of CO₂ emission in these cities, this article applies a linear mixed effect model covering the quadratic term of GDP per capita to extend the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The empirical results demonstrate that population size, secondary industry proportion, energy consumption structure, urbanization level and economic level have generally shown a positive influence on CO₂ emissions in Chinese cities. However, the urbanization level is of no significance. The phenomenon of the environmental Kuznets curve varies across Chinese cities, according to which three city groups are formed. Specific policy recommendations are given to each city group in light of their unique influencing modes on carbon emissions.