Narratives as macroeconomic signals: Shaping expectations, confidence, and collective action

Christos Christodoulou-Volos ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 9, 1895-1923

Abstract: This paper reviews the emerging literature on how macroeconomic narratives—systematized, socially agreed-upon stories of the economy—function as signals that shape expectations, impact confidence, and drive collective economic behavior. Based on rational expectations, behavioral economics, signaling theory, narrative economics, and sociological methods, we examine how stories arise, disseminate through multiple channels, and gain strength through contagion and feedback loops. Empirical evidence demonstrates that policy communication stories, media framing, market commentaries, and public discourse can influence consumption, investment, asset prices, and political opinions individually. The literature's primary shortcomings include vagueness of definitions, measurement problems, causality issues, and a lack of cross-cultural and non-crisis research. Future research directions involve conceptual standardization, richer narrative measurement, improved causal inference, channel attribution, and integration into macroeconomic models. The paper concludes with insights into the strategic potential of narrative management for policymakers, market participants, and media outlets, as well as associated risks in policy and practice.

Keywords: Behavioral economics; Economic signals; Macroeconomic narratives; Narrative contagion; Policy communication; Sentiment analysis. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/10237/3340 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:9:p:1895-1923:id:10237

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().