The econometrics of happiness: Are we underestimating the returns to education and income?
中文摘要
本文描述了在以数值量表获取主观回答的人类感受与观点调查中固有的一项根本性且经验上极为显著的问题，并提出了一种解决方案。该问题在于，部分个体——尤其是受教育程度较低者——倾向于通过仅考虑可能回答中的某个子集（如最低、中间和最高）来简化回答量表。原则上，这种“焦点值取整”（focal value rounding, FVR）行为甚至会使此类数据分析中常用的弱序数假设失效。以“幸福感”或生活满意度数据为例，描述性方法和多项logit模型均表明，该效应较大，且教育程度以及在较小程度上收入水平是FVR行为的预测因素。一个同时考虑潜在福祉和FVR程度的模型能够估计潜在的主观福祉，即所有受访者的反事实全量表回答、与传统估计相关的偏误，以及表现出FVR的受访者比例。解决这一问题有助于化解生活满意度文献中一个长期存在的谜题，即在调整收入后，教育回报似乎很小或为负。由于同样的计量经济学问题，主观福祉意义上的收入边际效用一直被低估。
Abstract
This paper describes a fundamental and empirically conspicuous problem inherent to surveys of human feelings and opinions in which subjective responses are elicited on numerical scales. The paper also proposes a solution. The problem is a tendency by some individuals — particularly those with low levels of education — to simplify the response scale by considering only a subset of possible responses such as the lowest, middle, and highest. In principle, this “focal value rounding” (FVR) behavior renders invalid even the weak ordinality assumption often used in analysis of such data. With “happiness” or life satisfaction data as an example, descriptive methods and a multinomial logit model both show that the effect is large and that education and, to a lesser extent, income level are predictors of FVR behavior. A model simultaneously accounting for the underlying wellbeing and for the degree of FVR is able to estimate the latent subjective wellbeing, i.e. the counterfactual full-scale responses for all respondents, the biases associated with traditional estimates, and the fraction of respondents who exhibit FVR. Addressing this problem helps to resolve a longstanding puzzle in the life satisfaction literature, namely that the returns to education, after adjusting for income, appear to be small or negative. Due to the same econometric problem, the marginal utility of income in a subjective wellbeing sense has been consistently underestimated.