There is causal evidence that mortgage credit expansions increase house prices. Does an expansion of margin lending increase stock prices? Because unconstrained arbitrageurs are more important for pricing stocks than homes, the impact is not obvious. Tests are limited because sizable shocks to margin lending are rare. We examine a major Chinese margin-lending expansion between 2010 and 2015. Institutional holding, regression discontinuity, and event study evidence-exploiting the rollout of margin lending across stocks-shows that arbitrageurs anticipated and bought in advance of a significant causal effect of credit. We develop a model to rationalize our findings. Our estimates suggest that margin debt contributes to stock market fluctuations.
In this paper, we propose a novel self-supervised transfer learning method called Distribution Matching (DM), which drives the representation distribution toward a predefined reference distribution while preserving augmentation invariance. The design of DM results in a learned representation space that is intuitively structured and offers easily interpretable hyperparameters. Experimental results across multiple real-world datasets and evaluation metrics demonstrate that DM performs competitively on target classification tasks compared to existing self-supervised transfer learning methods. Additionally, we provide robust theoretical guarantees for DM, including a population theorem and an end-to-end sample theorem. The population theorem bridges the gap between the self-supervised learning task and target classification accuracy, while the sample theorem shows that, even with a limited number of samples from the target domain, DM can deliver exceptional classification performance, provided the unlabeled sample size is sufficiently large. Copyright ?? 2025, The Authors. All rights reserved.
Research SummaryThe impact of political connections on firm innovation remains unclear. Utilizing the unexpected removal of high-ranking officials during China's anti-corruption campaigns between 1999 and 2015 as an exogenous shock, we analyze this relationship to provide greater clarity. Employing a difference-in-differences approach, we find that firms that experience unexpected loss of political connections file significantly more patent applications than those that do not experience such disruption. Further analyses indicate that this innovation boost is more pronounced for firms operating in highly competitive markets and those with substantial knowledge stocks. Our research therefore contributes novel empirical evidence to the discourse on political connections and firm innovation, highlighting the inhibitory effect of political connections and offering insights into the political economy of innovation.Managerial SummaryThis study explores the impact of anti-corruption campaigns on the innovation efforts of firms with political connections in China. We discover that when such firms experience the unexpected loss of their political connections, they tend to file more patent applications, although this does not necessarily result in more patents being granted. It is important for firms and policymakers to understand that the response to such losses is not uniform. Firms are more likely to enhance their innovation efforts through increased patent applications if they are facing intense market competition or possess a rich knowledge stock. These findings underscore the importance of fostering a competitive market environment alongside anti-corruption campaigns to stimulate innovation. Our study therefore sheds light on how to precisely assess the impact of anti-corruption campaigns on firm behaviors and strategies.
通过明确生态产品价值、优化资源配置和强化市场交易,推动乡村生态优势转化为经济优势,实现生态保护与经济发展的良性互动。生态产品价值实现机制是指通过市场经营开发、生态保护补偿等手段,将生态产品价值转化为经济价值,进而打通“绿水青山到金山银山”转化路径的一种制度安排。有效地建立健全农村生态产品价值实现机制,是培育绿色发展新动能、发展新质生产力的关键,对于促进农村经济的可持续发展,实现生态环境保护与经济发展的双赢具有重要意义。