Understanding Reform Narratives: Research Proposal

Lead Research Organisation: University of Warwick
Department Name: Economics

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000711/1 01/10/2017 30/09/2027
2271863 Studentship ES/P000711/1 01/10/2019 31/03/2023 Ivan Yotzov