基于调查的AI辅助经济学测量：来自公共员工养老金选择的证据
AI Assisted Economics Measurement From Survey: Evidence from Public Employee Pension Choice
AI总结：
本文提出一种基于AI的迭代框架，用于从调查中提取经济测量结构，通过软映射和有效性测试优化分类法，揭示养老金选择中的行为信号与经济机制。
摘要：
我们开发了一个迭代框架，用于经济测量，该框架利用大型语言模型直接从调查工具中提取测量结构。该方法通过我们称为软映射的方式，将调查项目映射到潜在构念的稀疏分布上，将协调响应汇总为受访者层面的子维度分数，并通过离样增量有效性测试和判别有效性诊断来规范由此产生的分类法。该框架明确将迭代整合到测量构建过程中。重叠和冗余诊断会触发有针对性的分类法细化和受限重新映射，确保只有在提供稳定的离样性能提升时，才保留增加的测量灵活性。应用于大规模的公共员工退休计划调查，该框架识别出哪些语义成分包含行为信号，并澄清了对退休选择至关重要的经济机制，如信念与约束。该方法提供了一种可移植的调查工具测量审计，可以指导实证分析和调查设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
