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LLM Survey Framework: Coverage, Reasoning, Dynamics, Identification

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  • Jing Cynthia Wu
  • Jin Xi
  • Shihan Xie

Abstract

We propose a new LLM-based survey framework that enables retrospective coverage, economic reasoning, dynamic effects, and clean identification. We recover human-comparable treatment effects in a multi-wave randomized controlled trial of inflation expectations surveys, at 1/1000 the cost. To demonstrate the framework’s full potential, we extend the benchmark human survey (10 waves, 2018–2023) to over 50 waves dating back to 1990. We further examine the economic mechanisms underlying agents’ expectation formation, identifying the mean-reversion and individual-attention channels. Finally, we trace dynamic treatment effects and demonstrate clean identification. Together, these innovations demonstrate that LLM surveys enable research designs unattainable with human surveys.

Suggested Citation

  • Jing Cynthia Wu & Jin Xi & Shihan Xie, 2025. "LLM Survey Framework: Coverage, Reasoning, Dynamics, Identification," NBER Working Papers 34308, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:34308
    Note: AP EFG ME
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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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