标题： 无法测量的测量？ 主观调查数据中尺度转换的系统性证据 显示英文标题

标题： Measuring the Unmeasurable? Systematic Evidence on Scale Transformations in Subjective Survey Data

摘要： 经济学家通常使用调查测量，例如风险偏好、信任、政治态度或幸福感。 文献通常将数值回答类别视为代表相等的心理区间。 我们提供了对此假设的首次系统性检验，开发了一个通用框架，以量化当放松这种线性假设时结果被推翻的难易程度。 使用原始实验数据，我们表明受访者对调查量表的理解确实偏离了线性，但程度很轻。 专注于幸福感研究，我们随后在超过80篇发表在顶级经济学期刊上的论文中复制了30,000多个系数估计。 复制的系数符号在轻微偏离线性使用的情况下表现出显著的稳健性。 然而，统计推断和相对效应大小的估计在适度偏离线性时变得不可靠。 这对政策应用尤其有问题。 我们证明这些担忧可以推广到许多其他广泛使用的基于调查的构念。 显示英文摘要

摘要： Economists routinely use survey measures of, for example, risk preferences, trust, political attitudes, or wellbeing. The literature generally treats numerical response categories as if they represent equal psychological intervals. We provide the first systematic test of this assumption, developing a general framework to quantify how easily results can be overturned when this linearity assumption is relaxed. Using original experimental data, we show that respondents interpret survey scales in ways that do deviate from linearity, but only mildly. Focusing on wellbeing research, we then replicate 30,000+ coefficient estimates across more than 80 papers published in top economics journals. Replicated coefficient signs are remarkably robust to mild departures from linear scale-use. However, statistical inference and estimates of relative effect magnitudes become unreliable, even under modest departures from linearity. This is especially problematic for policy applications. We show that these concerns generalise to many other widely used survey-based constructs.