Wage Sentiment Indices Derived from Survey Comments via Large Language Models
This work addresses the problem of improving wage forecasting for governments and central banks, though it is incremental as it extends an existing framework to a new domain.
The study tackled forecasting wage dynamics in Japan by constructing a Wage Sentiment Index (WSI) using Large Language Models (LLMs) on survey comments, and found that WSI models based on LLMs significantly outperformed baseline approaches and pretrained models.
The emergence of generative Artificial Intelligence (AI) has created new opportunities for economic text analysis. This study proposes a Wage Sentiment Index (WSI) constructed with Large Language Models (LLMs) to forecast wage dynamics in Japan. The analysis is based on the Economy Watchers Survey (EWS), a monthly survey conducted by the Cabinet Office of Japan that captures real-time economic assessments from workers in industries highly sensitive to business conditions. The WSI extends the framework of the Price Sentiment Index (PSI) used in prior studies, adapting it specifically to wage related sentiment. To ensure scalability and adaptability, a data architecture is also developed that enables integration of additional sources such as newspapers and social media. Experimental results demonstrate that WSI models based on LLMs significantly outperform both baseline approaches and pretrained models. These findings highlight the potential of LLM-driven sentiment indices to enhance the timeliness and effectiveness of economic policy design by governments and central banks.