Central banks are charged with setting interest rates, and other related policies, to manage the business cycle. But the world in which central banks operate, and the way in which they operate in it, has changed dramatically in the last 25 years. Not that long ago, central banks would operate behind closed doors and not even announce their decisions on these important policies. Now, they are a part of the 24-hour news cycle, make their decision announcements with statements and press conferences, and they actively try to engage not only their traditional audiences, financial markets, but also a wider public.
This revolution of increasing transparency and an increasing role of communication brings challenges to central banks, and to the academics who study them (and their policies). The changing framework shifts the emphasis from interest rates to words. But most of economic analysis is not designed to study words. To do so, requires the use of tools from computer science and the field of natural language processing.
NewMonEc (New Monetary Economics) is my ground-breaking research agenda comprising of pioneering projects across three related themes addressing questions in monetary economics that emphasise the increasingly important role of central bank communication in policy. The projects rise to the challenge by combining new data, new approaches and innovative use of new statistical methodologies from outside economics.
There are a number of key important findings that come from the research.
In Haldane et al. (2021), we show that trust affects the extent to which agents will pay attention to the messages sent by the central bank. We show that simplifying communication can increase the engagement of the public with central bank messages making their expectations more precise. We also describe the key challenges in terms of communicating with the general public as relating to 3Es: Explanation, Engagement, and Education.
In Cieslak et al. (2023), we study how uncertainty impacts decision-making by central bankers. We find that inflation uncertainty, and particularly concerns about upper-tail inflation risks, prompt a more hawkish policy stance. This effect occurs when expected inflation nears or exceeds the target, and we can link this to narrative evidence that suggests a driving concern is a loss of credibility. We link this behaviour to the risk management approach to monetary policy.
Byrne et al. (2022) show the data covering the macroeconomy do not uniquely define the state of the economy. Instead, the central bank, like other people in the economy, need to form an assessment of the state of the economy. We show, using a newly developed methodology from Byrne et al. (2022), that, contrary to standard models, backward looking information reflecting this assessment function is important for the market reaction to central bank communication.
Cieslak and McMahon (2024) look at how alternative-policy scenarios, expressed by policymakers in speeches, impact risk premia in financial markets. We show how market perceptions of policy mistakes can raise risk premia against policy intentions. Communicating a more hawkish policy stance in what an alternative policy might look like predicts lower risk premium in the intermeeting period.
In McMahon and Naylor (2023), we distinguish between the kind of semantic complexity measured by Flesch-Kincaid, and what we term conceptual complexity which relates to the use of economics jargon. Our novel measure, termed the Conceptual Complexity Index (CCI), seeks to better reflect the true information-processing costs identified by theory. We designed and implemented an information provision experiment to distinguish between dimensions of semantic complexity and conceptual complexity and show that managing technical complexity has a bigger impact on understanding and trust than simplifying in a semantic way.
McMahon and Rholes (2023) offers a salutary lesson that precisely when inflation is high, it may be that the publics willingness to listen to what the central bank has to say is reduced. This result comes from an evaluation of how forecast performance, especially the timing of errors, affects the extent to which the public use the central banks forecast to inform their own outlook. Not only does forecast performance matter, we find a form of recency bias when subjects evaluate forecast accuracy. This bias, which applies to both short-term and medium-term forecasts, is especially strong after poor forecasting performance.
