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一大障碍。然而,确定和衡量监管障碍仍然是学者们的一项艰巨任务。我提议采用新颖的方法量化国家层面的监管障碍。利用美国上市公司年度报告的信息,我查明监管企业从业人员遇到的壁垒。障碍信息首先通过经过手工编码培训的尖端神经语言模式从文本文件中提取。然后,我将抽取的屏障信息输入一个动态项目反应理论模型,以估计2006年至2015年40个国家的数字障碍水平,同时控制各种混杂渠道。我主张,这一方法产生的结果应该较少受到国际政治等主要混杂者的污染。因此,它们对于未来的政治科学研究来说是十分合适的。