标题： 学习调控：资本控制措施的新事件级数据集 显示英文标题

标题： Learning to Regulate: A New Event-Level Dataset of Capital Control Measures

摘要： 我们利用基于提示的大型语言模型（LLMs），构建了一个新颖的事件级资本管制措施（CCM）数据集，涵盖从1999年到2023年的196个国家。 该数据集支持基于丰富政策属性（包括行动类型、强度、方向、实施实体及其他多维特征）的事件研究分析和跨国比较。 使用GPT-4.1的两步提示框架，我们从国际货币基金组织（IMF）的《汇率安排与外汇限制年度报告》（AREAER）中提取结构化信息，生成了5,198个资本管制事件，包含27个标注字段及相关模型推理。 其次，为了促进实时分类和外部资源扩展，我们微调了一个开源的Meta Llama 3.1-8B模型，命名为CCM-Llama，训练数据来自AREAER变更日志和最终状态报告。 该模型在类别分类上的准确率为90.09%，在状态预测上的准确率为99.55%。 最后，我们在一个实证应用中使用了CCM数据集：对中国、澳大利亚和美国进行了事件研究。 结果显示，向内资本管制措施显著减少了一个月内的资金流入，且限制性政策的效果往往强于自由化政策，各国之间存在显著异质性。 我们的工作通过提供一个新颖的高频政策数据集以及从多样化和动态变化的信息源自动分类资本管制事件的可复制框架，为经济学领域中LLMs的应用文献做出了贡献。 显示英文摘要

摘要： We construct a novel event-level Capital Control Measures (CCM) dataset covering 196 countries from 1999 to 2023 by leveraging prompt-based large language models (LLMs). The dataset enables event study analysis and cross-country comparisons based on rich policy attributes, including action type, intensity, direction, implementing entity, and other multidimensional characteristics. Using a two-step prompt framework with GPT-4.1, we extract structured information from the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER), resulting in 5,198 capital control events with 27 annotated fields and corresponding model reasoning. Secondly, to facilitate real-time classification and extension to external sources, we fine-tune an open-source Meta Llama 3.1-8B model, named CCM-Llama, trained on AREAER change logs and final status reports. The model achieves 90.09\% accuracy in category classification and 99.55\% in status prediction. Finally, we apply the CCM dataset in an empirical application: an event study on China, Australia, and the US. The results show that inward capital control measures significantly reduce fund inflows within one month, and restrictive policies tend to have stronger effects than liberalizing ones, with notable heterogeneity across countries. Our work contributes to the growing literature on the use of LLMs in economics by providing both a novel high-frequency policy dataset and a replicable framework for automated classification of capital control events from diverse and evolving information sources.