Where do I rank? Am I happy?: learning income position and subjective-wellbeing in an internet experiment

Eiji Yamamura

Home/Archive/arXiv

arXiv · 2107.11185

Where do I rank? Am I happy?: learning income position and subjective-wellbeing in an internet experiment

Eiji Yamamura

View original source ↗Read source document ↗Export metadata ↓

Abstract

A tailor-made internet survey experiment provides individuals with information on their income positions to examine their effects on subjective well-being. In the first survey, respondents were asked about their household income and subjective well-being. Based on the data collected, three different respondents' income positions within the residential locality, within a group of the same educational background, and cohort were obtained. In the follow-up survey for the treatment group, respondents are informed of their income positions and then asked for subjective well-being. Key findings are that, after obtaining information, a higher individual's income position improves their subjective well-being. The effects varied according to individual characteristics and proxies.

Explore related subjects

econ.GN q-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Eiji Yamamura. 2021-07-21. Where do I rank? Am I happy?: learning income position and subjective-wellbeing in an internet experiment. https://arxiv.org/abs/2107.11185

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

KEEP EXPLORING

Related papers

arXiv2026

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN↗

arXiv2026

Bricks or Cash? Externalities of Housing Upgrading in High-density Cities

We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.

econ.GN↗

arXiv2026

The Joneses Visit an Economics Lab

Existing literature offers persuasive evidence that individuals care about how their consumption compares to that of peers, and proposes a large variety of explanatory models. The present paper proposes a common framework for many of those models, and compares their ability to predict behavior in a laboratory experiment. We find evidence of Keeping up with the Joneses motivations but also find that conspicuous consumption is enhanced by Veblen motivations arising from peers' ability to observe one's own choice. Among the seven quasi-linear preference models we compare, our data are best explained by a model that contrasts envy and pride (upward vs downward comparisons) using a value function borrowed from Prospect Theory.

econ.GN↗