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Status invisibility alleviates the economic gradient in happiness in social network experiments

Nature Mental Health volume 1, pages 990–1000 (2023)Cite this article

Subjects

Abstract

Economic status is positively associated with subjective well-being (SWB), happiness and mental health, but debate persists about the determinants of SWB in circumstances involving economic inequality. Here we implemented online experiments where subjects interact financially, gain or lose wealth, report their SWB and adjust their social ties with others across time. We assigned 1,289 subjects to be initially rich or poor in 100 networked groups and manipulated wealth visibility in the groups. In the visible wealth condition, we showed subjects the wealth of their immediate neighbours, thereby allowing social comparisons, while in the invisible wealth condition, we kept such information hidden. Results show that invisible wealth condition disproportionally improves the SWB of currently poorer subjects and thus alleviated the economic gradient in SWB. Two phenomena may explain the alleviation observed in the invisible wealth condition: initially rich subjects who become relatively poorer do not experience substantial damage to their SWB, and initially poor subjects who remain poorer may still experience an SWB gain similar to those who become richer.

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Fig. 1: The experimental social networks in our set-up (experiment 1).
Fig. 2: Trajectories of average SWB over the 15 game rounds for initially poor and initially rich subjects in different settings.
Fig. 3: Trajectories of various outcome variables for initially poor and rich individuals in visible and invisible wealth conditions.
Fig. 4: Wealth visibility decreases SWB and exacerbates the economic gradient in SWB.

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Data availability

The data for replicating the main results are available on A.N.’s GitHub page (https://github.com/akihironishi).

Code availability

The details of the analysis along with the R code are provided in our Supplementary Information Extended Data Methods and Results.

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Acknowledgements

We gratefully acknowledge support from Japan Science and Technology Agency (JPMJPR21R8), UCLA Fielding School of Public Health, the SEI Group CSR Foundation and the Murata Science Foundation (A.N.) and the Robert Wood Johnson Foundation (N.A.C.). C. Muthappan, H. Ando and M. McKnight provided expert programming assistance and technical support.

Author information

Authors and Affiliations

  1. Department of Epidemiology, University of California, Los Angeles, Los Angeles, CA, USA

    Akihiro Nishi

  2. California Center for Population Research, University of California, Los Angeles, Los Angeles, CA, USA

    Akihiro Nishi

  3. Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA

    Christopher A. German

  4. Vanderbilt University School of Medicine, Nashville, TN, USA

    Sage K. Iwamoto

  5. Yale Institute for Network Science, Yale University, New Haven, CT, USA

    Nicholas A. Christakis

  6. Department of Sociology, Yale University, New Haven, CT, USA

    Nicholas A. Christakis

  7. Department of Medicine, Yale University, New Haven, CT, USA

    Nicholas A. Christakis

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Contributions

A.N. designed the project. A.N. and N.A.C. secured funding. A.N. and C.A.G. conducted the experiment. A.N., S.K.I. and C.A.G. conducted the statistical analyses. A.N., C.A.G., S.K.I. and N.A.C. analysed the findings and wrote the manuscript.

Corresponding author

Correspondence to Akihiro Nishi.

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Competing interests

A.N. is a consultant to Vacan, Inc. and obtained an honorarium from Taisho Pharmaceutical Co., Ltd., which had no role in the project. C.A.G. is an employee of 23andMe, which had no role in the project. The other authors declare no competing interests.

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Nature Mental Health thanks Osea Giuntella and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The flow of our social network experiments.

A session consisting of a networked group lasted 15 rounds. The timing of reporting subjective well-being (SWB) was different before the actual game rounds started.

Extended Data Fig. 2 CONSORT participant flow diagram.

A, Experiment 1. B, Experiment 2.

Extended Data Fig. 3 Trajectories of standardized and actual in-game units for the initially poor and rich subjects.

a–d. Experiment 1 (n = 570). e–h. Experiment 2 (n = 719). Lines with dots represent medians, light shading represents range between minimum and maximum, dark shading represents interquartile range (IQR).

Source data

Extended Data Table 1 Socio-demographic factors of the Amazon Mechanical Turk (Mturk) study participants
Full size table
Extended Data Table 2 Regression results with various models using the data of Experiment 1 (n = 570) and Experiment 2 (n = 719)
Full size table

Supplementary information

Supplementary Information

Supplementary Additional Analyses 1–8 and references.

Reporting Summary

Supplementary Code

R code.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Table 1

Raw result table.

Source Data Extended Data Table 2

Raw result table.

Source Data Extended Data Fig. 3

Statistical source data.

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Nishi, A., German, C.A., Iwamoto, S.K. et al. Status invisibility alleviates the economic gradient in happiness in social network experiments. Nat. Mental Health 1, 990–1000 (2023). https://doi.org/10.1038/s44220-023-00159-0

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