The importance of representing economic inequality saliently and to scale

TY - CONF

T1 - The importance of representing economic inequality saliently and to scale

AU - Elbæk, Christian T.

AU - Mitkidis, Panagiotis

PY - 2024/8/20

Y1 - 2024/8/20

N2 - Over the last decades, economic inequality has escalated across the world(Piketty, 2020). For instance, in the US, the wealthiest 0.1% own almost asmuch wealth as the bottom 90% of American families (Saez & Zucman, 2019). Thisconcentration of capital has detrimental consequences; it reduces socialmobility and economic growth, worsens health and educational outcomes, erodestrust and increases polarization (see Lobeck & Støstad, 2023). Reducingeconomic inequality is thus “a pivotal challenge for modern democracies”(Lobeck & Støstad, 2023, p. 2). One effective way to reduce extreme wealthinequalities is redistributive policies (Saez & Zucman, 2019). Yet, supportfor such policies tends to be low (Kuziemko et al., 2015) with preferencesstrongly influenced by perceptions of inequality (Gimpelson & Treisman, 2018)and political attitudes (Alesina et al., 2018). To aid in understanding thisissue, Walker et al. (2021) in PNAS recently provided evidence that drawingindividuals’ attention towards a wealthy individual (vs. group) lowerssupport for redistribution by leading people to judge the individual’swealth as well-deserved, highlighting a strong “person-positivity” bias.However, do people understand the true extent of economic inequality and mightthe observed bias differ across political ideologies? We address this in threepreregistered conceptual replications of Study 5 in Walker et al. (2021) withUS nationally representative samples (N = 4,308), where we investigate howgraphical and scaled representations of wealth inequality influence fairnessperceptions, attributions of wealth accumulation, and support forredistribution among different political orientations. In study 1,participants (n = 1462) were randomly assigned to one of four conditions: theoriginal written descriptions of individual or group wealth, or new conditionswhere these descriptions were accompanied by graphical representations scaledto a US median income, $1 million, and $1 billion. We replicated theperson-positivity bias in terms of attributions (d = .31) and fairness (d =.45), but further found that graphical and scaled depictions of wealthinequality reduced this bias, making extreme wealth appear less fair (d = .35)and more due to situational factors (d = .25). However, support forredistribution did not significantly differ between any of our conditions.Study 2 aimed at increasing the comparability between conditions by using onescaling point of median household wealth across all conditions. Participants(n = 1479) were assigned to one of four conditions, like Study 1 but withconsistent scaling points, ensuring clearer comparisons between graphical andwritten presentations of wealth inequality. We again replicated theperson-positivity bias for fairness judgments (d = .31) and attributions forwealth accumulation (d = .20), but our results indicated that graphicaldepictions could amplify the person-positivity bias in terms of attributions(d = .34) and fairness judgments (d = 0.50). We again found now significantdifferences across conditions in terms of support for redistribution. Study 3focused solely on isolating the effects of presentation format (written vs.graphical) and scaling reference (median income vs. wealth). We did this byonly focusing on conditions where inequality was framed around a group (vs.individual). In this study we introduced new measures of dispositional envy,compassion, personal gain from redistribution to disentangle individualdifferences in the observed outcomes. We found no significant differences inattributions, perceived fairness or support for redistribution acrossconditions. Yet, dispositional envy (b = .55, p < .001), compassion (b = .67,p < .001), and expected personal gain (b = 1.01, p < .001) were significantpredictors of support for redistribution, while more conservative politicalviews predicted less support (b = -.22, p < .001), hence replicating previouswork by Sznycer et al. (2017) in PNAS. Finally, our computational analysesusing Model Based Recursive Partitioning revealed notable variations insupport for redistribution across political orientations, depending on howeconomic inequality was presented and scaled. For liberals, graphical andscaled representations tended to increase support for redistribution. Formoderates however, graphical representations often resulted in a decrease insupport for redistribution, suggesting potential adverse effects of suchframing. Finally, we found trends indicating that graphical representationscould sometimes increase, and other times decrease support for redistributionamong this group depending on the scaling point used. Collectively, weconclude that (1) the person-positivity bias observed by Walker et al. (2021)is robust, but (2) that graphical and scaled representations of wealthinequality can influence perceptions of fairness and attributions of wealthaccumulation. However, neither of these effects significantly alter supportfor redistributive policies. Our results thus underscore the complexity ofredistributive preferences and suggest that policy framing alone may beinsufficient to shift attitudes. However, our results highlight how wealthinequality can be effectively communicated to different political groups andunderscore the need for tailored approaches that consider the diversepsychological and ideological factors.

AB - Over the last decades, economic inequality has escalated across the world(Piketty, 2020). For instance, in the US, the wealthiest 0.1% own almost asmuch wealth as the bottom 90% of American families (Saez & Zucman, 2019). Thisconcentration of capital has detrimental consequences; it reduces socialmobility and economic growth, worsens health and educational outcomes, erodestrust and increases polarization (see Lobeck & Støstad, 2023). Reducingeconomic inequality is thus “a pivotal challenge for modern democracies”(Lobeck & Støstad, 2023, p. 2). One effective way to reduce extreme wealthinequalities is redistributive policies (Saez & Zucman, 2019). Yet, supportfor such policies tends to be low (Kuziemko et al., 2015) with preferencesstrongly influenced by perceptions of inequality (Gimpelson & Treisman, 2018)and political attitudes (Alesina et al., 2018). To aid in understanding thisissue, Walker et al. (2021) in PNAS recently provided evidence that drawingindividuals’ attention towards a wealthy individual (vs. group) lowerssupport for redistribution by leading people to judge the individual’swealth as well-deserved, highlighting a strong “person-positivity” bias.However, do people understand the true extent of economic inequality and mightthe observed bias differ across political ideologies? We address this in threepreregistered conceptual replications of Study 5 in Walker et al. (2021) withUS nationally representative samples (N = 4,308), where we investigate howgraphical and scaled representations of wealth inequality influence fairnessperceptions, attributions of wealth accumulation, and support forredistribution among different political orientations. In study 1,participants (n = 1462) were randomly assigned to one of four conditions: theoriginal written descriptions of individual or group wealth, or new conditionswhere these descriptions were accompanied by graphical representations scaledto a US median income, $1 million, and $1 billion. We replicated theperson-positivity bias in terms of attributions (d = .31) and fairness (d =.45), but further found that graphical and scaled depictions of wealthinequality reduced this bias, making extreme wealth appear less fair (d = .35)and more due to situational factors (d = .25). However, support forredistribution did not significantly differ between any of our conditions.Study 2 aimed at increasing the comparability between conditions by using onescaling point of median household wealth across all conditions. Participants(n = 1479) were assigned to one of four conditions, like Study 1 but withconsistent scaling points, ensuring clearer comparisons between graphical andwritten presentations of wealth inequality. We again replicated theperson-positivity bias for fairness judgments (d = .31) and attributions forwealth accumulation (d = .20), but our results indicated that graphicaldepictions could amplify the person-positivity bias in terms of attributions(d = .34) and fairness judgments (d = 0.50). We again found now significantdifferences across conditions in terms of support for redistribution. Study 3focused solely on isolating the effects of presentation format (written vs.graphical) and scaling reference (median income vs. wealth). We did this byonly focusing on conditions where inequality was framed around a group (vs.individual). In this study we introduced new measures of dispositional envy,compassion, personal gain from redistribution to disentangle individualdifferences in the observed outcomes. We found no significant differences inattributions, perceived fairness or support for redistribution acrossconditions. Yet, dispositional envy (b = .55, p < .001), compassion (b = .67,p < .001), and expected personal gain (b = 1.01, p < .001) were significantpredictors of support for redistribution, while more conservative politicalviews predicted less support (b = -.22, p < .001), hence replicating previouswork by Sznycer et al. (2017) in PNAS. Finally, our computational analysesusing Model Based Recursive Partitioning revealed notable variations insupport for redistribution across political orientations, depending on howeconomic inequality was presented and scaled. For liberals, graphical andscaled representations tended to increase support for redistribution. Formoderates however, graphical representations often resulted in a decrease insupport for redistribution, suggesting potential adverse effects of suchframing. Finally, we found trends indicating that graphical representationscould sometimes increase, and other times decrease support for redistributionamong this group depending on the scaling point used. Collectively, weconclude that (1) the person-positivity bias observed by Walker et al. (2021)is robust, but (2) that graphical and scaled representations of wealthinequality can influence perceptions of fairness and attributions of wealthaccumulation. However, neither of these effects significantly alter supportfor redistributive policies. Our results thus underscore the complexity ofredistributive preferences and suggest that policy framing alone may beinsufficient to shift attitudes. However, our results highlight how wealthinequality can be effectively communicated to different political groups andunderscore the need for tailored approaches that consider the diversepsychological and ideological factors.

M3 - Poster

T2 - Annual Meeting of the Society for Judgment and Decision Making 2024

Y2 - 22 November 2024 through 25 November 2024

ER -