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This paper aims to conduct research to examine the impact of Internet adoption on the productivity of firms in non-urban China.

The study investigates the impact of Internet adoption on firms' productivity in non-urban China. More specifically, the authors conduct a comprehensive and rigorous study while addressing concerns related to firm-level endogeneity by utilizing firm-level panel data. Information on firms in non-urban areas is collected from China's Annual Surveys of Industrial Firm data. For robustness, the authors implement the instrumental variables approach and propensity score matching estimations to strengthen the evidence for suggestive causal inference. Furthermore, the authors also examine the mechanisms and group heterogeneity.

Evidence indicate that the adoption of Internet technology positively impacts the total factor productivity (TFP) of firms in non-urban areas. According to the heterogeneity analysis, the marginal effect of Internet adoption is more significant and pronounced for labor-intensive, private and small-scale manufacturing firms. Moreover, additional evidence suggest that Internet adoption is beneficial for non-urban firms in expanding their business and enlarging their market. It has also been found that the positive effect of Internet adoption on firms' TFP is amplified by expanding public infrastructure.

The current study supports that the informatization strategy benefits non-urban firms and promotes rural revitalization. The findings suggest the possibility of firms borrowing market size from the closest cities and supporting the ongoing policies of investing in broadband infrastructure to narrow the urban-rural digital gap in China.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.