SearcharxivSearch

arXiv · 1807.11835

The econometrics of happiness: Are we underestimating the returns to education and income?

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Christopher P Barrington-Leigh. 2018-07-31. The econometrics of happiness: Are we underestimating the returns to education and income?. https://doi.org/10.1016/j.jpubeco.2023.105052

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM