Understanding Reform Narratives: Research Proposal

理解改革叙事：研究计划

• 批准号: 2271863
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271863
• 关键词: Understanding; Reform; Narratives; Research; Proposal

Economic reforms are an essential ingredient in growth & development. Reforms resolve crises, correct inefficiencies, and improve productivity. Recent history has been characterized by several large reform episodes, such as the post-Communist transition to market economy, the economic liberalization of Latin America, and the structural reforms following the Global Financial Crisis. Nevertheless, reforms are few & far between. In addition, they are the outcome of long periods of public discussions and political negotiations. This proposal outlines a research agenda to understand the nature of these reform discussions, their evolution over time and across countries, and how they contribute to the success of eventual policy decisions. The main data source will be news articles published in each country from a variety of sources. There are three advantages to this approach: first, news is published daily, and can provide a very high-frequency measure of reform discussions. Second, since news articles are published in every country, this project can use the entire world as the full sample. Finally, large news aggregators already exist which provide access to news data since the 1980s. These sources can be searched across a variety of dimensions in order to identify reform articles.The main research questions this project seeks to address are:1. Are there systematic patterns in reform discussions in response to the business cycle? How do these differ along different institutional characteristics of countries & over time?2. Does higher intensity of reform discussions lead to faster enactment of actual reforms? 3. How does sentiment toward reforms vary over time & across countries? Does negative sentiment predict the failure of proposed reforms? 4. How have major economic crises shifted the rhetoric related to economic reforms? For example, how has the Global Financial Crisis affected attitudes toward liberalization policies? The methodology of this project will involve applying well-known machine learning & natural language processing algorithms to analyze a corpus of millions of newspaper articles relating to economic reforms. One promising approach of dimensionality reduction is dynamic topic modelling: these algorithms allow the researcher to uncover underlying topics and track the evolution of specific terms and topics over time. Although this has been applied in the economics and political science literature, no papers have used this methodology to analyze economic reform narratives. Topic modeling will provide answers to how narratives have evolved over time, particularly in the aftermath of the Great Recession.A second approach to be applied is sentiment analysis, the use of computer algorithms to understand the emotional slant of text data. Many different approaches to sentiment analysis have been proposed in the literature. For this project, studying the attitudes toward economic reforms is crucial, because the sentiments of the public and the media are often decisive for the eventual success or failure of reforms. The main methodological challenge will be designing a structured and consistent approach to conducting topic modeling and sentiment analysis for multiple languages. Focusing exclusively on text in English is a limitation, since local languages dominate in many parts of the world. Constructing algorithms to harness text data from multiple languages will be a major contribution of this project and could thereafter be applied in many different areas. In conclusion, reforms are essential for economic development. However, the literature has given insufficient attention to understanding the narratives and discussions which drive reform efforts. This proposal outlines a research agenda to analyze the dynamics of these narratives, both across countries & over time. It will provide novel evidence on how attitudes toward economic reforms have evolved, and the key ingredients to successful reforms.

经济改革是增长和发展的一个基本要素。改革解决危机，纠正效率低下，提高生产力。近年来的历史以几次大规模的改革为特征，如后共产主义向市场经济的过渡、拉丁美洲的经济自由化以及全球金融危机后的结构性改革。尽管如此，改革却很少，而且间隔很长。此外，它们是长期公共讨论和政治谈判的结果。该提案概述了一个研究议程，以了解这些改革讨论的性质、其随时间和国家的演变，以及它们如何有助于最终政策决定的成功。主要数据来源将是各国从各种来源发表的新闻文章。这种做法有三个好处：第一，新闻是每天发布的，可以提供一个非常高频率的改革讨论的措施。第二，由于每个国家都有新闻报道，这个项目可以把整个世界作为完整的样本。最后，自1980年代以来，已经存在提供新闻数据的大型新闻聚合器。这些来源可以在不同的维度上进行搜索，以识别改革文章。在针对商业周期的改革讨论中是否存在系统模式？随着时间的推移，这些政策如何随着各国不同的体制特点而沿着变化？2.更密集的改革讨论是否会导致更快地实施实际改革？3.各国对改革的态度如何随时间变化？负面情绪是否预示着拟议中的改革会失败？4.重大经济危机如何改变了与经济改革相关的言论？例如，全球金融危机如何影响了人们对自由化政策的态度？该项目的方法将涉及应用著名的机器学习和自然语言处理算法来分析数百万篇与经济改革有关的报纸文章的语料库。降维的一种有前途的方法是动态主题建模：这些算法允许研究人员发现潜在的主题，并跟踪特定术语和主题随时间的演变。虽然这一方法已被应用于经济学和政治学文献，但没有论文使用这种方法来分析经济改革叙事。主题建模将为叙事如何随着时间的推移而演变提供答案，特别是在大衰退之后。第二种应用方法是情感分析，使用计算机算法来理解文本数据的情感倾向。在文献中已经提出了许多不同的情感分析方法。在本课题中，研究民众对经济改革的态度至关重要，因为民众和媒体的情绪往往决定着改革的成败。主要的方法挑战将是设计一种结构化和一致的方法来进行多语言的主题建模和情感分析。只关注英文文本是一种局限，因为在世界许多地方，当地语言占主导地位。构建算法来利用来自多种语言的文本数据将是该项目的主要贡献，此后可以应用于许多不同的领域。最后，改革对经济发展至关重要。然而，文献对理解推动改革努力的叙述和讨论给予的关注不够。该提案概述了一个研究议程，以分析这些叙述的动态，无论是在国家之间还是随着时间的推移。它将提供新的证据，说明人们对经济改革的态度是如何演变的，以及成功改革的关键因素。