Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Shifting attributions for poverty motivates opposition to inequality and enhances egalitarianism


Amidst rising economic inequality and mounting evidence of its pernicious social effects, what motivates opposition to inequality? Five studies (n = 34,442) show that attributing poverty to situational forces is associated with greater concern about inequality, preference for egalitarian policies and inequality-reducing behaviour. In Study 1, situational attributions for poverty were associated with reduced support for inequality across 34 countries. Study 2 replicated these findings with a nationally representative sample of Americans. Three experiments then tested whether situational attributions for poverty are malleable and motivate egalitarianism. Bolstering situational attributions for poverty through a writing exercise (Study 3) and a computer-based poverty simulation (Studies 4a and b) increased egalitarian action and reduced support for inequality immediately (Studies 3 and 4b), 1 d later and 155 d post-intervention (Study 4b). Causal attributions for poverty offer one accessible means of shaping inequality-reducing attitudes and actions. Situational attributions may be a potent psychological lever for lessening societal inequality.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Situational attributions for poverty and support for economic inequality.
Fig. 2: Effects of SPENT game on support for economic inequality over time.

Data availability

All data supporting the findings in this manuscript are publicly available on the Open Science Framework and can be found here:

Code availability

All custom code for data cleaning and analysis supporting the findings in this manuscript are available on the Open Science Framework and can be found here:


  1. Piketty, T., Saez, E. & Zucman, G. Distributional national accounts: methods and estimates for the United States. Q. J. Econ. 133, 553–609 (2018).

    Google Scholar 

  2. The Rise of Canada’s Richest 1% (Canadian Centre for Policy Alternatives, 2010).

  3. Wilkinson, R. G. & Pickett, K. E. The enemy between us: the psychological and social costs of inequality. Eur. J. Soc. Psychol. 47, 11–24 (2017).

    Google Scholar 

  4. Piff, P. K., Kraus, M. W. & Keltner, D. Unpacking the inequality paradox: the psychological roots of inequality and social class. Adv. Exp. Soc. Psychol. 57, 53–124 (2018).

    Google Scholar 

  5. Shariff, A. F., Wiwad, D. & Aknin, L. B. Income mobility breeds tolerance for income inequality: cross-national and experimental evidence. Perspect. Psychol. Sci. 11, 373–380 (2016).

    PubMed  Google Scholar 

  6. Lamont, M. & Pierson, P. Inequality generation & persistence as multidimensional processes: an interdisciplinary agenda. Daedalus 148, 5–18 (2019).

    Google Scholar 

  7. Jost, J. T., Banaji, M. R. & Nosek, B. A. A decade of system justification theory: accumulated evidence of conscious and unconscious bolstering of the status quo. Polit. Psychol. 25, 881–919 (2004).

    Google Scholar 

  8. Hunt, M. O. & Bullock, H. E. in The Oxford Handbook of the Social Science of Poverty (eds Brady, D. & Burton, L. M.) 93–116 (Oxford Univ. Press, 2016).

  9. Homan, P., Valentino, L. & Weed, E. Being and becoming poor: how cultural schemas shape beliefs about poverty. Soc. Forces 95, 1023–1048 (2017).

    Google Scholar 

  10. Desilver, D. For most workers, real wages have barely budged for decades. Pew Research Center (2018).

  11. Grau, L. Illness-engendered poverty among the elderly. Women Health 12, 103–118 (1988).

    Google Scholar 

  12. Engel, K. C. & McCoy, P. A. A tale of three markets: the law and economics of predatory lending. Tex. Law Rev. 80, 1259–1366 (2002).

    Google Scholar 

  13. Gilens, M. Why Americans Hate Welfare: Race, Media, and the Politics of Antipoverty Policy (Univ. of Chicago Press, 1999).

  14. Mani, A., Mullainathan, S., Shafir, E. & Zhao, J. Poverty impedes cognitive function. Science. 341, 976–980 (2013).

    CAS  PubMed  Google Scholar 

  15. Cozzarelli, C., Wilkinson, A. V. & Tagler, M. J. Attitudes toward the poor and attributions for poverty. J. Soc. Issues 57, 207–227 (2001).

    Google Scholar 

  16. Heiserman, N. & Simpson, B. Higher inequality increases the gap in the perceived merit of the rich and poor. Soc. Psychol. Q. 80, 243–253 (2017).

    Google Scholar 

  17. Feather, N. T. Explanations of poverty in Australian and American samples: the person, society, or fate? Aust. J. Psychol. 26, 199–216 (1974).

    Google Scholar 

  18. Feagin, J. Subordinating the Poor: Welfare and American Beliefs (Prentice-Hall, 1975).

  19. Heider, F. The Psychology of Interpersonal Relations (Wiley, 1958).

  20. Weiner, B. An attributional theory of achievement motivation and emotion. Psychol. Rev. 92, 548–573 (1985).

    CAS  PubMed  Google Scholar 

  21. Gilbert, D. T. & Malone, P. S. The correspondence bias. Psychol. Bull. 117, 21–38 (1995).

    CAS  PubMed  Google Scholar 

  22. Lerner, M. J. & Miller, D. T. Just world research and the attribution process: looking back and ahead. Psychol. Bull. 85, 1030–1051 (1978).

    Google Scholar 

  23. McCoy, S. K. & Major, B. Priming meritocracy and the psychological justification of inequality. J. Exp. Soc. Psychol. 43, 341–351 (2007).

    Google Scholar 

  24. Schmidt, G. & Weiner, B. An attribution–affect–action theory of behavior: replications of judgments of help-giving. Personal. Soc. Psychol. Bull. 14, 610–621 (1988).

    Google Scholar 

  25. Zucker, G. S. & Weiner, B. Conservatism and perceptions of poverty: an attributional analysis. J. Appl. Soc. Psychol. 23, 925–943 (1993).

    Google Scholar 

  26. Bullock, H. E., Williams, W. R. & Limbert, W. M. Decoding responsibility: welfare recipients and the enforcement of parental obligations. J. Poverty 7, 13–33 (2003).

    Google Scholar 

  27. Kluegel, J. R. & Smith, E. R. Beliefs about Inequality: Americans’ Views of What Is and What Ought to Be (Routledge, 1986).

  28. Sznycer, D. et al. Support for redistribution is shaped by compassion, envy, and self-interest, but not a taste for fairness. Proc. Natl Acad. Sci. USA 114, 8420–8425 (2017).

    CAS  PubMed  Google Scholar 

  29. Bullock, H. E. & Lott, B. Building a research and advocacy agenda on issues of economic justice. Anal. Soc. Issues Public Policy 1, 147–162 (2004).

    Google Scholar 

  30. Wong, P. T. & Weiner, B. When people ask ‘why’ questions, and the heuristics of attributional search. J. Pers. Soc. Psychol. 40, 660–663 (1981).

    Google Scholar 

  31. Mendelberg, T., McCabe, K. T. & Thal, A. College socialization and the economic views of affluent Americans. Am. J. Pol. Sci. 61, 606–623 (2017).

    Google Scholar 

  32. Rao, G. Familiarity does not breed contempt: diversity and generosity in Delhi schools. Am. Econ. Rev. 109, 774–809 (2019).

    Google Scholar 

  33. Mo, C. H. & Conn, K. M. When do the advantaged see the disadvantages of others? A quasi-experimental study of National Service. Am. Polit. Sci. Rev. 112, 1016–1035 (2018).

    Google Scholar 

  34. Inglehart, R. et al. World Values Survey: Round three – country-pooled datafile. World Values Survey (2014).

  35. Guimond, S., Begin, G. & Palmer, D. L. Education and causal attributions: the development of “person-blame” and “system-blame” ideology. Soc. Psychol. Q. 52, 126–140 (1989).

    Google Scholar 

  36. Wiwad, D. et al. The support for economic inequality scale: development and adjudication. PLoS ONE 14, 1–29 (2019).

    Google Scholar 

  37. Most see inequality growing, but partisans differ over solutions. Pew Research Center (2014).

  38. Hunt, M. O. The individual, society, or both? A comparison of black, Latino, and white beliefs about the causes of poverty. Soc. Forces 75, 293–322 (1996).

    Google Scholar 

  39. Hunt, M. O. Race/ethnicity and beliefs about wealth and poverty. Soc. Sci. Q. 85, 827–853 (2004).

    Google Scholar 

  40. Osborne, D. & Weiner, B. A latent profile analysis of attributions for poverty: Identifying response patterns underlying people’s willingness to help the poor. Pers. Individ. Dif. 85, 149–154 (2015).

    Google Scholar 

  41. Rasch, D. & Guiard, V. The robustness of parametric statistical methods. Psychol. Sci. 46, 175–208 (2004).

    Google Scholar 

  42. Sawilowsky, S. S. & Blair, R. C. A more realistic look at the robustness and type II error properties of the t-test to departures from population normality. Psychol. Bull. 111, 352–360 (1992).

    Google Scholar 

  43. Rasch, D., Kubinger, K. D. & Moder, K. The two-sample t-test: pre-testing its assumptions does not pay off. Stat. Pap. (Berl.) 52, 219–231 (2011).

    Google Scholar 

  44. Ruxton, G. D. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U-test. Behav. Ecol. 17, 688–690 (2006).

    Google Scholar 

  45. Nickols, S. Y. & Nielsen, R. B. “So many people are struggling”: developing social empathy through a poverty simulation. J. Poverty 15, 22–42 (2011).

    Google Scholar 

  46. World Values Survey (World Values Survey Association, accessed 1 January 2018);

  47. McCall, L., Burk, D., Laperrière, M. & Richeson, J. A. Exposure to rising inequality shapes Americans’ opportunity beliefs and policy support. Proc. Natl Acad. Sci. USA 114, 9593–9598 (2017).

  48. Fehr, E. & Schmidt, K. M. A theory of fairness, competition, and cooperation. Q. J. Econ. 114, 817–868 (1999).

    Google Scholar 

  49. Rand, D. G., Greene, J. D. & Nowak, M. A. Spontaneous giving and calculated greed. Nature 489, 427–430 (2012).

    CAS  PubMed  Google Scholar 

  50. Starmans, C., Sheskin, M. & Bloom, P. Why people prefer unequal societies. Nat. Hum. Behav. 1, 1–7 (2017).

    Google Scholar 

  51. Mummolo, J. & Peterson, E. Demand effects in survey experiments: an empirical assessment. Am. Polit. Sci. Rev. 113, 517–529 (2019).

    Google Scholar 

  52. Weinberg, J. D., Freese, J. & McElhattan, D. Comparing data characteristics and results of an online factorial survey between a population-based and a crowdsource-recruited sample. Sociol. Sci. 1, 292–310 (2014).

    Google Scholar 

  53. Schneider, S. M. & Castillo, J. C. Poverty attributions and the perceived justice of income inequality: a comparison of East and West Germany. Soc. Psychol. Q. 78, 263–282 (2015).

    Google Scholar 

  54. Davidai, S. & Gilovich, T. The headwinds/tailwinds asymmetry: an availability bias in assessments of barriers and blessings. J. Pers. Soc. Psychol. 111, 835–851 (2016).

    PubMed  Google Scholar 

  55. Obama, B. H. Inaugural Address by President Barack Obama (The White House, 2013).

  56. World Bank Open Data (The World Bank Group, 2019);

  57. Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. Increasing transparency through a multiverse analysis. Perspect. Psychol. Sci. 11, 702–712 (2016).

    PubMed  Google Scholar 

  58. van Buuren, S. & Groothuis-Oudshoorn, K. Mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–68 (2010).

    Google Scholar 

  59. Killip, S., Mahfound, Z. & Pearce, K. What Is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Ann. Fam. Med. 2, 204–208 (2004).

    PubMed  PubMed Central  Google Scholar 

  60. Household Income Quintiles (Tax Policy Center, 2019);

  61. Feagin, J. R. Poverty: we still believe that God helps those who help themselves. Psychol. Today 6, 101–110 (1972).

    Google Scholar 

  62. Davis, M. A multidimensional approach to individual differences in empathy. Cat. Sel. Doc. Psychol. 10, 85–104 (1980).

    Google Scholar 

  63. Lakens, D. Performing high-powered studies efficiently with sequential analyses. Eur. J. Soc. Psychol. 44, 701–710 (2014).

    Google Scholar 

Download references


Funding for Study 2 was provided by a Canada 150 research grant to A.S. Funding for Study 3 was provided by a Department of Psychological Science research grant to P.K.P. Funding for Studies 4a and b was provided by a Psychology Department Research grant to L.B.A. None of these funders had a role in the conceptualization, design, data collection, analysis, decision to publish or preparation of any part of this manuscript.

Author information

Authors and Affiliations



P.K.P. and D.W. contributed equally. All authors helped develop the study concepts and contributed to the study designs. Testing and data collection were performed by P.K.P., D.W. and A.R.R. P.K.P., D.W. and A.R.R. analysed and interpreted the data and drafted the manuscript, and B.M., L.B.A. and A.S. provided critical revisions. All authors approved the final version of the manuscript for submission.

Corresponding authors

Correspondence to Paul K. Piff or Dylan Wiwad.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary handling editor: Aisha Bradshaw.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Effect sizes and standard errors for each dependent variable in Study 4a.

* denotes the Guimond et al.35 measure of attributions for poverty and ** denotes the Nickols and Nielsen45 measure of attributions for poverty (as reported in the main text).

Extended Data Fig. 2 Study 4a mediation model.

Mediation model with the Nickols and Nielsen45 measure of situational attributions for poverty in Study 4a (n = 611). Situational attributions for poverty mediated the effect of the poverty simulation on support for economic inequality.

Extended Data Fig. 3 The effects of SPENT on support for economic inequality over time.

Graph illustrating the effects of the poverty simulation (SPENT game) versus no-game control condition on support for economic inequality over days between first (Time 1) and last survey (Time 3; n = 111).

Extended Data Fig. 4 Study 4b time 1 mediation models.

Study 4b mediational models showing that the poverty simulation (SPENT) led to reduced support for economic inequality (top) and increased support for redistribution (bottom) by inducing greater situational attributions for poverty at Time 1 (n = 611).

Extended Data Fig. 5 Study 4b time 2 mediation models.

Study 4b mediational models showing that the poverty simulation (SPENT) led to reduced support for economic inequality (top) and increased support for redistribution (bottom) by inducing greater situational attributions for poverty at Time 2 (n = 555).

Extended Data Fig. 6 Study 4b time 3 mediation models.

Study 4b mediational models showing that the poverty simulation (SPENT) led to reduced support for economic inequality (top) and increased support for redistribution (bottom) by inducing greater situational attributions for poverty at Time 3 (n = 110).

Extended Data Fig. 7

Visual inspection of regression assumptions for linear regression in Study 1.

Extended Data Fig. 8

Visual inspection of regression assumptions for multilevel model in Study 1.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Tables 1–4 and Supplementary References.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Piff, P.K., Wiwad, D., Robinson, A.R. et al. Shifting attributions for poverty motivates opposition to inequality and enhances egalitarianism. Nat Hum Behav 4, 496–505 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing