Publications
The Political Psychology of Economic Inequality (with Nick Chater and George Loewenstein)
The high level of inequality of income and wealth across individuals, groups, and nations is widely regarded as among the most fundamental problems facing humanity. Yet democracies often elect and re-elect politicians who deliver policies that exacerbate rather than reduce inequality. We argue that this disconnect arises from three basic features of human social cognition which evolved for interpersonal interactions in small groups rather than for navigating the vast political, economic, and technological systems that shape contemporary life: (i) a focus on local notions of equality and equity between socially connected individuals, rather than across society large; (ii) group identification, which directs attention to inequality between groups rather than “pure” inequality across the broader population; and (iii) what we call the “i-frame bias”—a tendency to explain social outcomes, including inequality, as the product of individual behaviors, taking the system within which those individuals operate as a given. We extend our analysis to examine how economic elites exploit these features of social cognition to shape the political and policy landscape, and examine the role psychology can play in reducing inequality.
Psychological Science in the Public Interest, in press
An S-Frame Agenda for Behavioral Public Policy Research (with Nick Chater and George Loewenstein)
Chater and Loewenstein (2023) argue that behavioral scientists have been testing and advocating individualistic (i-frame) solutions to policy problems that have systemic (sframe) causes and require systemic solutions. Here, we consider the implications of adopting an s-frame approach for research. We argue that an s-frame approach will involve addressing different types of questions, which will, in turn, require a different toolbox of research methods.
Behavioural Public Policy, 9(3), 593–613. doi:10.1017/bpp.2024.58
Working Papers
(inaccurate) Beliefs about Skill Decay (with Sami Horn & George Loewenstein)
Across five experiments, we investigate the accuracy of beliefs about skill decay in oneself and others. Participants consistently underestimated their own skill decay by 28% to 59% across tasks. Even after experiencing skill decay firsthand, participants still underpredicted its extent, though accuracy improved. Participants were more accurate when predicting declines in others' skills than in their own, but still underestimated decay. We find significant heterogeneity by age: older participants exhibit greater declines, but their predictions fail to account for this. Taken together, these findings reveal a consistent underestimation of skill decay, suggesting a potential for errors in human capital investment.
Available at SSRN, updated November 9, 2025
How Memory Crystallizes the Past: Memories Become More Consistent Over Time Due to Differential, Nonconstant Change
How do memories of real-life events change over time, and why do some aspects persist while others fade? We propose that two basic properties of memory - nonconstant change and differential decay across dimensions - jointly give rise to ”crystallization,” a process by which memories become increasingly stable and selected over time. Under the crystallization account, changes in all dimensions of memory occur disproportionately early, but different dimensions of memory change at different rates. Taken together, these processes lead to systematic transformations of memories over time. Across five studies, we examine how event memories evolve using the Memory Experiences Questionnaire (MEQ), a multidimensional measure that captures qualities such as: vividness, sensory detail, coherence, and emotional intensity. We find that change is concentrated in the early period following an event and that the dimensions associated with reliving—vividness, sensory detail, and coherence—decline more quickly than others. In three studies, we also find that, consistent with our account, multiple reports of the same event become more similar to each other over time. These findings extend existing theories of memory change and suggest a mechanism for the emergence of stable long-term representations from complex, multidimensional experience.
Available at SSRN, updated January 9, 2026
Work in Progress
Automatic Processing Facilitates Ideological Divides in Inequality Perceptions (with Hannah Waldfogel and Eldar Shafir)
A central puzzle in the social sciences is that democracies consistently under-deliver redistributive policy despite high and growing inequality. One reason is that ideology shapes perceived inequality: liberals perceive more than conservatives. We propose this gap is driven partly by processing context — people typically encounter inequality-relevant stimuli while occupied with other things. Drawing on dual-process theory, we find that automatic processing accentuates ideological divides in inequality perception while deliberative processing attenuates them. Across five studies, we exposed participants to equivalent inequality-relevant stimuli under automatic versus reflective conditions. Under automatic processing, the left perceives more inequality than the right; under reflective processing, the two become more aligned. In automatic contexts, these perceptual differences also predict support for redistribution and judgments of fairness, suggesting a link between our proposed mechanism and political-economic outcomes.
Unequal Outcomes are Fair When the Rules Are Pre-Agreed (with Anuj Shah)
We propose that perceived "pre-agreement" — whether people believe that the individuals affected by an unequal outcome knew about and consented to the outcome-generating rules in advance — is a key psychological mechanism behind the perceived fairness of unequal outcomes. Across four studies (N = 1,304 US citizens), we exposed participants to vignettes describing unequal outcomes under high or low pre-agreement conditions, spanning private (tournaments, workplaces) and policy domains. High pre-agreement is reliably associated with greater fairness. It is also associated with perceptions of inequality, even for fixed material outcomes. Perceived pre-agreement is also highly idiosyncratic, and there is substantial between-participant disagreement about the level of preagreement in a given scenario. While political ideology independently predicts perceptions of fairness and inequality, it does not predict perceived pre-agreement. These findings suggest that people bring heterogeneous schema to judgements of preagreement in a nonideological fashion.
Private Giving and Public Rules: Evidence from 20 Years of Major US Philanthropy
We document a fundamental reorientation in the grant-making strategies of 44 major U.S. foundations over two decades. Using a novel classification of 365,735 grant descriptions by intervention frame — whether grants act directly on individuals or on systems — we track the dollar-weighted composition of philanthropic giving from 2003 to 2023. Individual-frame giving declined from approximately 33% to 20% of grant dollars, while system-frame giving rose from roughly 14% to 35%, with the steepest acceleration occurring after 2015. To produce reliable estimates, we apply the Design-Based Supervised Learning with LLMs (DSL; Egami, 2024 ) estimator, which corrects for systematic biases in LLM classification using a human-coded validation sample. Without this correction, system-frame giving appears roughly 70% larger than it is. Descriptive decompositions suggest the shift is present across both donor-directed and institutionalized foundations, though patterns differ by foundation type. These findings extend prior work on advocacy philanthropy — largely focused on single policy domains or qualitative case studies — by providing the first longitudinal, dollar-weighted classification of intervention frame across a broad cross-section of major American foundations.
Errors, Noise, & Partisan Difference In Voter Beliefs: Evidence and Implications (with Saurabh Bhargava)
We document a substantial degree of error, imprecision and partisan difference in beliefs about three recent major policy events in the United States. Using contemporaneous surveys of American voters during the Trump tax cuts, Biden stimulus, and the 2020 presidential election, we show that voters systematically misunderstand the effects of these major policy events on their personal welfare. We also show that the effect of partisanship is small relative to the average error in beliefs across the sample. We simulate a Bayesian spatial model in which voters receive noisy signals about candidate policies and show that noisier signals lead to less candidate convergence to the median voter.
Poster, presented at the Behavioral Models of Politics Conference, May 29, 2024
Other Work
Argument Visualization Improves Comprehension & Persuasive Effects of Economic Arguments (with Danny Oppenheimer & Simon Cullen)
We show via a series of online experiments that argument visualization (a novel diagramming technique from philosophy) improves people's comprehension of explanations of economic policies. It also improves participants' comprehension or arguments for and against those policies and changes evaluations of the intelligence of these arguments. We are currently investigating the effects of argument visualization on argument persuasiveness.
Poster, presented at the Society for Judgement and Decision Making Annual Conference, November 20, 2023