Reform chatter and democracy
It is often argued that democracy is the least imperfect form of government mainly because of the existence of a ‘self-correcting’ mechanism stemming from voice and accountability embedded in democracies. Using text analysis from about a billion newspaper articles in 28 languages, this column shows that the intensity of reform chatter increases during economic downturns and that the increase is more significant in democracies. During downturns, democracies appear to benefit disproportionately from changing popular attitudes translating into actual reforms.
The debate over the relative benefits of democracies over autocracies has become heated (Wolf 2019). Democracies are often criticised for being less responsive. It is often argued that there is an apparent trade-off between legitimacy and efficiency in democracies. But is this really so? In a recent paper (Arezki et al. 2020), we provide evidence against that trade-off. Indeed, we uncover one important channel through which democracies display a built-in mechanism for self-correction, namely, ‘reform chatter’. We find that reform chatter peaks during economic downturns, followed by improved popular attitudes in turn leading to actual reforms.
The political debate over reforms had traditionally revolved around their timing and consequences. The pioneering work on the political economy of reforms steered the debate toward the determinant of reforms and issues of political feasibility (Alesina and Drazen 1991). Yet little is known about attitudes towards reform in the public square. Arguably, understanding public chatter illuminates the presence of support for public policy.
In our research, we explore the dynamics of reform chatter. To do so, we use text analysis from about a billion newspaper articles. Our measure of chatter intensity uses Dow Jones FACTIVA, a global repository of newspaper articles. The approach is akin to Baker et al. (2016), who use the same data to construct an index of economic policy uncertainty. We refine the search method in several directions, beyond the search of keywords like “economic reform” in press articles. In particular, we distinguish between international and local media sources, and across media in 28 different languages. Also, we use machine learning techniques such as topic modelling to ascertain the informational content of reform chatter (Blei et al. 2003).
We show that the intensity of reform chatter increases during economic downturns. This increase is more significant in democracies (for example, see Figure 1 for “Reform Chatter” for the US). We find that the relationship between democracy and reform chatter is causal, using instrumental variable techniques. We finally document that reform chatter is followed by actual reforms, suggesting that democracies benefit from a ‘self-correcting’ mechanism stemming from media chatter.
Figure 1 Reform chatter and the US business cycle
Notes: This figure presents the trends in two measure of normalized reform articles in the United States over the period 1980-2019. The line connected by circles denotes economic reform articles in all languages divided by total articles in all languages. The line connected by squares only considers articles published in English. Articles relating to the US are identified by Dow Jones’ country codes assignment to articles. Vertical dashed lines represent recession years as defined by the NBER’s Business Cycle Dating Committee.
To further explore the informational content of the reform chatter, we use a learning algorithm to uncover the underlying topics in the media coverage. Topic models are a class of algorithms which can be used to understand the content of large corpora of text. The core insight is that distinct topics use different vocabularies and keywords. For example, a topic that discusses budget is likely to use words such as “expenditures”, “tax”, and “deficit” with high frequency, whereas a topic discussing financial markets is more likely to refer to terms such “interest rate”, “stocks”, and “bonds”. Word clouds are one convenient way to summarise the data, where the relative size of each word corresponds to its weight in a topic. Figure 2 presents word clouds for the five distinct clusters that emerged from the learning algorithm.
Figure 2 Word clouds around reform chatter
Topic 1 Financial I and European
Topic 2 Political and American
Topic 3 Financial II
Topic 4 Trade/International
Topic 5 Business/Investment
Sources: Factiva Dow Jones and authors’ own calculations.
Notes: The word clouds are obtained using a learning algorithm to uncover the underlying topics in the media coverage of reform chatter articles. The relative size of each word corresponds to its weight in a topic.
We compute a measure of attitudes around reform around the world to get a sense of whether or not reform is put forth with a positive lens. Our paper contributes to the large and growing economic literature using text analysis and machine learning techniques to analyse data (for a review, see Gentzkow et al. 2019). The existing literature on sentiment focuses mostly on the US and on the predictive power of sentiment on economic activity (for examples, see Tetlock 2007 and Shapiro et al. 2019). Figure 3 presents the evolution of these attitudes across all languages, divided across seven world regions. One interesting pattern is the collapse in reform attitudes in Latin America in the 1990s. This pattern coincides with a period of upheavals with crisis followed by reforms aimed at liberalising the region according to the ‘Washington Consensus’ (Williamson 1990). We find that attitudes towards reform increase in stronger democracies during economic downturns. This finding supports the presence of a ‘self-correcting mechanism’ in democracies: attitudes toward reform improve in bad times, paving the way for actual reform. While rising intensity of reform chatter in democracies during downturns provides evidence at the ‘extensive margin’, rising sentiment provides evidence at the ‘intensive margin’ that there is more support for reform on top of the increase in intensity.
Figure 3 Reform sentiment over time
We also find evidence of a strong association between reform attitudes and actual reforms irrespective of using World Bank or IMF measures of reforms. Indeed, the fact that democracies experience higher intensity and sentiment of reform chatter following downturns suggests that they disproportionately benefit from the self-correcting channel that link reform chatter and actual reform.
Alesina, A and A Drazen (1991), “Why Are Stabilizations Delayed?”, American Economic Review 81(5): 1170-88.
Arezki, R, S Djankov, H Nguyen and I Yotzov (2020), “Reform Chatter”, World Bank Policy Research Working Paper 9319.
Baker, S R, N Bloom and S J Davis (2016), “Measuring economic policy uncertainty”, The Quarterly Journal of Economics 131(4): 1593-1636.
Blei, D, A Ng and M Jordan (2003), “Latent Dirichlet Allocation”, Journal of Machine Learning Research 3: 993-1022.
Gentzkow, M, B Kelly and M Taddy (2019), “Text as data”, Journal of Economic Literature 57(3): 535–574.
Shapiro, A H, M Sudhof and D Wilson (2019), “Measuring news sentiment”, Federal Reserve Bank of San Francisco Working Paper Series.
Tetlock, P C (2007), “Giving content to investor sentiment: The role of media in the stock market”, The Journal of Finance 62(3): 1139-1168.
Williamson, J (ed.) (1990). Latin American Adjustment: How Much Has Happened?, Peterson Institute for International Economics.
Wolf, M (2019), “How to reform today’s rigged capitalism”, Financial Times, 3 December.