Synthetic surveys of monetary policymakers: perceptions, narratives and transparency

We propose a method to generate “synthetic surveys” that reveal policymakers’ perceptions and narratives. This exercise is implemented using 80 time-stamped Large Language Models (LLMs) fine-tuned with FOMC meetings’ transcripts. Given a text input, fine-tuned models identify highly likely responses for the corresponding FOMC meeting. We demonstrate the value of this tool in three different tasks: measurement of perceived economic conditions, evaluation of transparency in Central Bank communication and extraction of policymaking narratives. Our analysis covers the housing bubble and the subsequent Great Recession (2003-2012). For the first task, LLMs are prompted to generate phrases that describe economic conditions. The resulting outputs show policymakers informational advantage. Anticipatory ability increases as models are prompted to discuss future scenarios and financial conditions. To analyze transparency, we compare the content of each FOMC meeting minutes to content generated synthetically through the corresponding fine-tuned LLM. The evaluation suggests the tone of each meeting is transmitted adequately by the corresponding minutes. In the third task, LLMs produce narratives that show policymakers’ views on their responsibilities and their understanding of main forces shaping macroeconomic dynamics.