Storytelling is central to how we interpret economic events. We recall economic history through haunting images of anxious crowds waiting to take money out of banks during the Great Depression or dejected office workers carrying cardboard boxes out of Lehman Brothers in 2008. We gauge inflation by comparing shopping baskets with friends and family. We grapple with the consequences of artificial intelligence by channeling our hopes and fears into science fiction.
But do stories themselves influence the economy? This idea has a long precedent in economic thought. John Maynard Keynes wrote extensively about how “animal spirits”—instincts and emotions that influence behavior—prompt people’s economic actions, like spending or investing in businesses. He argued that these herd emotional urges lie at the heart of economic booms and busts.
Taking this idea one step further, Robert Shiller, an economist at Yale University, has pushed for a more detailed study of economic narratives—the contagious stories that shape how individuals view the economy and make decisions. Shiller hypothesizes that sufficiently popular narratives can go viral and have society-wide impact (Shiller 2020).
Viral narratives could be the missing link between emotions and economic fluctuations. But policymakers, researchers, and practitioners alike currently lack effective tools to identify these narratives, measure their contagiousness, and quantify their contribution toward economic events.
We made a first attempt to understand the macroeconomic consequences of narratives in a recent paper (Flynn and Sastry 2024). We introduced new tools for measuring and quantifying economic narratives and used these tools to assess their importance for the US business cycle. Our findings suggest that narratives play a central role. They also raise fresh questions about how and why such stories emerge and what policymakers might do differently in such a world.
Natural language processing
To measure narratives, we use resources not available to Keynes: large textual databases of what economic decision-makers are saying and natural-language-processing tools that can translate these words into hard data.
The key datasets we study are the text of US public firms’ conference calls, typically held every quarter to review financial results, and Form 10-K filings, regulatory reports filed with the US Securities and Exchange Commission each year. Both are outlets for company management not just to report company results but to offer explanations: They fill in the how and why of business results and offer clues to how management and investors are thinking about broader trends.
To identify narratives, we apply a variety of natural language techniques. These range from simple dictionary-based methods that scan for keywords and phrases to more complex algorithmic methods that uncover less structured topics. The narratives we uncover pertain to varied topics, such as firms’ general optimism about the future, their excitement about artificial intelligence, or their adoption of new digital marketing techniques. Using this database, we can empirically model the extent to which narratives drive firms’ decisions and the process by which such stories spread in the US economy.
Shaping business decisions
We find that companies with more optimistic narratives tend to accelerate hiring and capital investment. In particular, the pace of hiring at a company that uses optimistic language increases by 2.6 percentage points more in a year than a comparable company that uses pessimistic language. This effect is above and beyond what would be predicted by firms’ productivity or recent financial success. These results challenge conventional economic theories, which suggest that these fundamentals, and the “rational” expectations about the future that they embody, should entirely explain firms’ economic decisions.
Strikingly, firms with optimistic narratives do not see higher stock returns or profitability in the future and also make overoptimistic forecasts to investors. This suggests that narratives do not simply capture positive news about the future. In this way, firms’ optimistic and pessimistic narratives bear the hallmarks of Keynes’s animal spirits: forces that drive managers to expand and shrink their businesses but are based on emotions rather than fundamentals.
The data also support the idea that narratives spread contagiously, like a virus. That is, companies tend to adopt the narratives of their peers: When one company adopts an optimistic mood or starts talking up the transformative power of AI, others seem to follow suit. This narrative contagion seems to start within groups of peer firms that directly compete in the same industry and then spread to the aggregate level. Moreover, there is an especially large effect for narratives that arise at large companies. This raises the possibility that large companies are thought leaders in the narrative economy, with more influence than traditional measures of market power might suggest.
