Existing studies show that regulation is a major barrier to global economic integration. Nonetheless, identifying and measuring regulatory barriers remains a challenging task for scholars. I propose a novel approach to quantify regulatory barriers at the country-year level. Utilizing information from annual reports of publicly listed companies in the U.S., I identify regulatory barriers business practitioners encounter. The barrier information is first extracted from the text documents by a cutting-edge neural language model trained on a hand-coded training set. Then, I feed the extracted barrier information into a dynamic item response theory model to estimate the numerical barrier level of 40 countries between 2006 and 2015 while controlling for various channels of confounding. I argue that the results returned by this approach should be less likely to be contaminated by major confounders such as international politics. Thus, they are well-suited for future political science research.

翻译：现有研究表明，监管是全球经济一体化的主要障碍。然而，识别并衡量监管壁垒对学者而言仍是艰巨任务。本文提出一种在国家-年度层面量化监管壁垒的新方法。通过利用美国上市公司年报信息，本文识别出商业从业者实际面临的监管壁垒。首先，利用基于人工编码训练集训练的尖端神经语言模型从文本文件中提取壁垒信息；随后，将提取的壁垒信息输入动态项目反应理论模型，在控制多重混杂因素的同时，估算2006至2015年间40个国家的监管壁垒数值水平。本文论证该方法得出的结果不易受国际政治等主要混杂因素干扰，因此特别适用于未来的政治科学研究。