Exploring How Moral History of Money Affects How It Is Valued

There are seemingly trivial properties of money that may have an effect on how much a person wants it. These properties can be physical properties, such as being crumpled up and old or crisp and new, and symbolic properties, such as the moral history of the actions that generated the money. For example, Tasimi and Gelman (2016) showed that people wanted money less when it was associated with a negative moral history (it was stolen) or a negative moral agent (it was offered by a thief). But that is not all there is to moral reasoning: Tasimi and Gelman (2016) consistently used patient language in their study, casting the participant as a passive recipient of the actions of others rather than as a moral agent. To fill this gap, we created a new agent language condition to see if reversing the role of the participant would have an effect on how much they wanted the money. When the roles were revered, the participants became the moral agent and the people in the scenarios became the moral patient. It was hypothesized that we would replicate the basic pattern of findings from Tasimi and Gelman (2016) but that there would be a main effect of language, such that participants exposed to the new, agentive language would desire the money more than those who received the original, more patient-based language. The main results of this study supported this hypothesis, at least for some situations.

Authors

Publication Details

Journal
SUNY Digital Repository Support (State University of New York System)
Published
2026-08-17
Primary Topic
Psychology of Moral and Emotional Judgment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring How Moral History of Money Affects How It Is Valued

Carly LaPlace
SUNY Digital Repository Support (State University of New York System)
Psychology of Moral and Emotional Judgment
article

Exploring How Moral History of Money Affects How It Is Valued

Carly LaPlace
article en

Abstract

There are seemingly trivial properties of money that may have an effect on how much a person wants it. These properties can be physical properties, such as being crumpled up and old or crisp and new, and symbolic properties, such as the moral history of the actions that generated the money. For example, Tasimi and Gelman (2016) showed that people wanted money less when it was associated with a negative moral history (it was stolen) or a negative moral agent (it was offered by a thief). But that is not all there is to moral reasoning: Tasimi and Gelman (2016) consistently used patient language in their study, casting the participant as a passive recipient of the actions of others rather than as a moral agent. To fill this gap, we created a new agent language condition to see if reversing the role of the participant would have an effect on how much they wanted the money. When the roles were revered, the participants became the moral agent and the people in the scenarios became the moral patient. It was hypothesized that we would replicate the basic pattern of findings from Tasimi and Gelman (2016) but that there would be a main effect of language, such that participants exposed to the new, agentive language would desire the money more than those who received the original, more patient-based language. The main results of this study supported this hypothesis, at least for some situations.

SUNY Digital Repository Support (State University of New York System)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Psychology of Moral and Emotional Judgment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.