Collaborative Research: Origins of Serial Sovereign Default

几个世纪以来，主权国家一直从国际金融市场借款，这些外债直到今天仍然是发达国家和发展中国家的重要资金来源。在没有正式的法律手段强制还款的情况下，是什么支撑着这个市场的存在？标准经济模型预测，投资者拒绝参与未来贷款的威胁会阻止主权债务违约。然而，从历史上看，各国在违约可能性和违约后重新进入金融市场的能力方面都存在显著差异。这项研究将考察是什么导致一些国家成为连环违约国--经历反复的借款和违约循环--而另一些国家则不是。该项目将汇编一个新的关于主权借款和违约的数据库，包括数字和文本数据。这些数据将记录详细的违约和债务发行历史，以及普通公众、投资者和分析师的观点。该项目将通过提高对主权违约原因和投资者放贷意愿的理解，对世界各地的主权借款决策产生潜在的政策影响。这项研究将调查为什么一些国家会经历反复借款和违约的周期。该项目将建立一个关于主权借款、违约、与贷款人的谈判以及与主权债务相关的经济和政治情况的新的量化和文本数据库。为了收集这些数据，该项目将开发用于自然语言处理(NLP)的机器学习方法的新改编，以提取和分析文本。这些数据将从主要金融报纸的文章、投资者的年度报告和金融分析师的期刊出版物中获得。该项目将开发一种新的深度学习模型，以纠正常见的光学字符识别(OCR)错误。该项目将进一步构建新的文本库，以帮助对NLP内容进行分类，这将使投资者能够确定投资者如何看待主权国家发行或违约债务的动机，如经济或政治动机。该项目将根据1820年至1939年违约的频率和持续时间将国家归类为连续违约国家。然后，这项研究将记录借款和违约的政治和经济环境，包括公众、投资者和专家对主权动机的看法，如何影响一系列违约决定。决定主权违约和投资者放贷意愿的因素不仅与了解主权债务的历史相关，而且对正在进行的关于债务重组、暂停付款和救助的政策辩论也有影响。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractSovereign nations have consistently borrowed from international financial markets for centuries, and this foreign debt remains an important source of funding for advanced and developing countries to the modern day. What sustains the existence of this market when there is no formal legal means of enforcing repayment? Standard economic models predict that defaulting on sovereign debt is deterred by the threat of investors refusing to engage in future lending. However, historically countries differ significantly in both their likelihood of default and ability to re-access financial markets after default. This research will examine what leads some countries to become serial defaulters – experiencing repeated cycles of borrowing and default – while others do not. This project will assemble a new database on sovereign borrowing and default consisting of both numerical and textual data. These data will document detailed default and debt issuance history as well as the perspectives of the general public, investors, and analysts. The project will have potential policy implications for sovereign borrowing decisions around the world, by improving the understanding of the reasons for sovereign defaults and investor willingness to lend. This research will investigate why some countries experience cycles of repeated borrowing and default. The project will build a new quantitative and textual database on sovereign borrowing, default, negotiations with lenders, and the economic and political circumstances relevant to sovereign debt. To assemble these data, the project will develop new adaptations of machine learning methods for natural language processing (NLP) to extract and analyze text. The data will be obtained from the universe of articles from major financial newspapers, annual reports by investors, and periodical publications by financial analysts. The project will develop a new deep learning model to correct for common Optical Character Recognition (OCR) errors. The project will further construct new text libraries to aide in NLP content classification, which will allow determining how investors perceive the motive of the sovereign, such as economic or political, in issuing or defaulting debt. The project will categorize countries as being a serial defaulter based on the frequency and duration of their defaults from 1820 to 1939. The research will then document how the political and economic circumstances of borrowing and default, including public, investor, and expert perceptions of the sovereign's motives, influence serial default decisions. What determines sovereign default and investor willingness to lend is not only relevant for understanding the history of sovereign debt but has implications for ongoing policy debates over debt restructuring, payment moratoriums, and bailouts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.