Local exposure to inequality raises support of people of low wealth for taxing the wealthy

Abstract

Psychological research shows that social comparison of individuals with peers or others shapes attitude formation1,2. Opportunities for such comparisons have increased with global inequality3,4; everyday experiences can make economic disparities more salient through signals of social class5,6. Here we show that, among individuals with a lower socioeconomic status, such local exposure to inequality drives support for the redistribution of wealth. We designed a placebo-controlled field experiment conducted in South African neighbourhoods in which individuals with a low socioeconomic status encountered real-world reminders of inequality through the randomized presence of a high-status car. Pedestrians were asked to sign a petition to increase taxes on wealthy individuals to help with the redistribution of wealth. We found an increase of eleven percentage points in the probability of signing the petition in the presence of inequality, when taking into account the experimental placebo effect. The placebo effect suppresses the probability that an individual signs the petition in general, which is consistent with evidence that upward social comparison reduces political efficacy4. Measures of economic inequality were constructed at the neighbourhood level and connected to a survey of individuals with a low socioeconomic status. We found that local exposure to inequality was positively associated with support for a tax on wealthy individuals to address economic disparities. Inequality seems to affect preferences for the redistribution of wealth through local exposure. However, our results indicate that inequality may also suppress participation; the political implications of our findings at regional or country-wide scales therefore remain uncertain.

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Fig. 1: Experimental setup.
Fig. 2: Effect of exposure to inequality on support for the tax petition.
Fig. 3: Local inequality and support for a tax on wealthy individuals.

Data availability

Research design, protocols, hypotheses and analyses were preregistered before data analysis with EGAP and are available in the Open Science Framework (OSF) repository (https://osf.io/s4bd3). Supporting information is available at https://doi.org/10.7910/DVN/C9UQVW. All data necessary to replicate the analyses and figures in this paper and Supplementary Information are available at https://doi.org/10.7910/DVN/C9UQVW.

Code availability

All codes necessary to replicate the analyses and figures in this paper and the Supplementary Information are available at https://doi.org/10.7910/DVN/C9UQVW. R (open source, version 3.6.1) and Stata (versions 14 and 15) were used for data analysis.

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Acknowledgements

We thank T. Manyathi and P. Mosia for their work in implementing the field experiment; M. Bryer for her management of the field experiment; J. de Kadt and the Gauteng City-Region Observatory for making the Quality of Life IV module available to us; R. Carney, R. Enos, J. Rodden, J. Trounstine and K.-S. Trump for feedback on drafts of this paper; and the audiences at Evidence in Governance and Politics (EGAP) 24, University of California, Merced, University of California, Berkeley, Stanford University and the Helen Suzman Foundation for feedback. All errors are our own.

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M.S. and D.d.K. contributed equally to this work.

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Correspondence to Melissa L. Sands or Daniel de Kadt.

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Extended data figures and tables

Extended Data Fig. 1 Location of experimental sites within South Africa.

a, Map of Soweto. The seven experimental sites are indicated by blue crosses: Protea Glen, Jabulani Mall, Meadowlands, Dube, Pimville, Kliptown and Diepkloof. b, Map of the greater Johannesburg area, in which Soweto is highlighted by a blue box. c, Map of South Africa, in which Johannesburg is highlighted by a blue box.

Extended Data Fig. 2 Local inequality by SAL for Gauteng.

Local inequality is calculated as the Gini coefficient for each SAL (polygon), using census data from 2011 for the entire Gauteng region obtained from Statistics South Africa.

Extended Data Fig. 3 Personal wealth and the association between the presence of a top-decile household and preferences regarding taxation.

a, A kernel-based nonparametric estimate showing the marginal effect of a change in the presence of a top-decile household in a SAL on the support for a tax on wealthy individuals (y axis) for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped standard errors (n = 19,884, M = 6,775). b, A linear estimate showing the marginal effect of a change in the presence of a top-decile household in a SAL on the support for a tax on wealthy individuals (y axis) for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped clustered standard errors (n = 19,884, M = 6,775). The points are conditional estimates for three terciles of the moderator wealth, low (L), middle (M) and high (H); the red lines show the 95% confidence intervals based on bootstrapped clustered standard errors. Two-sided pairwise tests of the differences between the tercile point estimates yield the following P values for comparisons of low versus middle (P = 0.836), middle versus high (P = 0.167), and low versus high (P = 0.070).

Extended Data Fig. 4 Personal wealth and the association between local Gini inequality and preferences regarding taxation.

a, A kernel-based nonparametric estimate showing the marginal effect of a change in local Gini inequality in a SAL on the support for a tax on wealthy individuals (y axis) for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped clustered standard errors (n = 19,884, M = 6,775). b, A linear estimate showing the marginal effect of a change in local Gini inequality in a SAL on the support for a tax on wealthy individuals (y axis), for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped clustered standard errors (n = 19,884, M = 6,775). The points are conditional estimates for three terciles of the moderator wealth, low, middle and high; the red lines show the 95% confidence intervals based on bootstrapped clustered standard errors. Two-sided pairwise tests of the differences between the tercile point estimates yield the following P values for comparisons of low versus middle (P = 0.657), middle versus high (P = 0.481) and low versus high (P = 0.662).

Extended Data Fig. 5 Personal wealth and the association between local Gini inequality and self-assessed socioeconomic status.

a, A kernel-based nonparametric estimate showing the marginal effect of a change in local Gini inequality in a SAL on self-assessed socioeconomic status (y axis) for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped clustered standard errors (n = 19,884, M = 6,775). b, A linear estimate showing the marginal effect of a change in local Gini inequality in a SAL on self-assessed socioeconomic status (y axis) for different levels of wealth (x axis). The shaded area indicates the 95% confidence interval based on bootstrapped clustered standard errors (n = 19,884, M = 6,775). The points are conditional estimates for three terciles of the moderator wealth, low, middle and high; the red lines show the 95% confidence intervals based on bootstrapped clustered standard errors. Two-sided pairwise tests of the differences between the tercile point estimates yield the following P values for comparisons of low versus middle (P = 0.657), middle versus high (P = 0.481) and low versus high (P = 0.662).

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Sands, M.L., de Kadt, D. Local exposure to inequality raises support of people of low wealth for taxing the wealthy. Nature 586, 257–261 (2020). https://doi.org/10.1038/s41586-020-2763-1

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