Are Economists Open to AI? A Text-as-Data-as-Survey Approach via Language Models

Abstract

Traditional surveys yield comparable measures but are costly to field, difficult to reconstruct retrospectively, and often ill-suited to fast-moving or sensitive topics. While large-scale internet text is often noisy and weakly structured. To bridge this gap, we introduce Text-as-Data-as-Survey (TaDaS). TaDaS employs Reference-Anchored Semantic Reparameterization (RAS) to project unstructured main text into survey-like evidence, leveraging structured auxiliary text as semantic anchors. Applying TaDaS to 1.25 million Economics Job Market Rumors posts linked with 53,585 top economics and finance publications, we track economists' evolving research sentiment toward AI. Cross-sectionally, AI-related research discussions are less open, with openness and curiosity declining rapidly at first years. Over time, however, economists have become increasingly open and curious, with a notable shift around 2018. Ultimately, TaDaS provides a scalable, non-reactive method to extract longitudinal insights from digital archives, unlocking diverse applications across industry and academia.