用大模型解析调查问卷，自动识别影响退休决策的关键因素。
AI Assisted Economics Measurement From Survey: Evidence from Public Employee Pension Choice
- 用大模型将问卷项映射到潜在构念，实现软映射与动态优化
- 在大规模公务员养老金调查中识别出行为信号的语义成分
- 可复用于其他调查，提升测量信度与设计科学性
我们提出一种迭代式经济测量框架，利用大语言模型直接从调查工具中提取测量结构。该方法通过软映射将问卷条目映射到潜在构念的稀疏分布上，聚合标准化响应生成受访者层面的子维度得分，并通过样本外增量效度检验与判别效度诊断来约束分类体系。框架显式融入迭代机制，冗余与重叠诊断触发针对性的分类优化与受限重映射，仅当新灵活性带来稳定样本外表现提升时才予以保留。应用于大规模公共部门员工养老计划调查，该方法识别出蕴含行为信号的语义成分，厘清了信念与约束等经济机制对退休决策的影响。该方法提供可移植的调查工具测量审计，可指导实证分析与问卷设计。
原文摘要 · Abstract (English)
We develop an iterative framework for economic measurement that leverages large language models to extract measurement structure directly from survey instruments. The approach maps survey items to a sparse distribution over latent constructs through what we term a soft mapping, aggregates harmonized responses into respondent level sub dimension scores, and disciplines the resulting taxonomy through out of sample incremental validity tests and discriminant validity diagnostics. The framework explicitly integrates iteration into the measurement construction process. Overlap and redundancy diagnostics trigger targeted taxonomy refinement and constrained remapping, ensuring that added measurement flexibility is retained only when it delivers stable out of sample performance gains. Applied to a large scale public employee retirement plan survey, the framework identifies which semantic components contain behavioral signal and clarifies the economic mechanisms, such as beliefs versus constraints, that matter for retirement choices. The methodology provides a portable measurement audit of survey instruments that can guide both empirical analysis and survey design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。