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Economic Inequality: Implications for Society and Organizations

2020· article· en· W3045992347 on OpenAlexaff
Daniela Goya‐Tocchetto, Jon Jachimowicz, Shai Davidai, Hemant Kakkar, Hannah Benner Waldfogel, Oliver Hauser, Arnold K. Ho, Xiaoran Hu, Aaron C. Kay, Nour Kteily, Martino Ongis, Keith Payne, Jennifer Sheehy‐Skeffington, Niro Sivanathan

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsInequalityEconomic inequalityPositive economicsSocial psychologySocial inequalityPresentation (obstetrics)IdeologyDominance (genetics)Social dominance orientationSociologyRelative deprivationPsychologyEconomicsPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

In this symposium, we present some of the latest research findings in regard to economic inequality and the ways in which it is shaping beliefs and behaviors, both at the societal and the organizational levels. These findings are timely and relevant, as the consequences of economic inequality are heightened by current trends, and the effects for society and organizations are of great consequence, as demonstrated by the work presented in this symposium. In the first presentation, Ongis and Davidai discuss the antecedents of a destructive thinking pattern known as zero-sum thinking, i.e., the belief that in order for one group to win another one has to lose. More specifically, they explore the belief that wealth is zero-sum, such that one’s economic gains must also come at the expense of another’s loss. Their work reveals a surprising factor that leads to zero-sum thinking: the experience of personal relative deprivation. In the second presentation, Waldfogel, Kteily, Sheehy-Skeffington, Ho, and Hauser explore the ways in which social dominance orientation (SDO), an inequality-relevant ideology, predicts the extent to which people notice the presence of economic inequality. Their work shows that social egalitarians pay more attention to inequality relative to anti-egalitarians, noticing it to a greater extent. In the third presentation, Kakkar et al. contend that the relationship between social class and unethical behavior depends on the immediate economic environment an individual resides in. While for high SES individual’s the propensity to behave unethically reduces with the worsening of their economic environment, for low SES individuals the tendency to behave unethically increases as the economic environment worsens. Their work shows that this is driven by comparing similar others as points of reference to gauge one’s own social standing. In the fourth and final presentation, Goya-Tocchetto, Kay, and Payne discuss the effects of economic inequality on perceptions of the fairness of organizational processes and outcomes. They show that, when it comes to economic inequality, it is impossible to disentangle the fairness evaluation of processes versus outcomes. More specifically, their work reveals that inequality aversion exists not only when unequal outcomes are the result of unfair processes, but also when people realize that unequal outcomes are inter-temporally undermining the landscape of opportunities for income generation. The Influence of Personal Relative Deprivation on the Belief that Wealth is Zero-Sum Presenter: Martino Ongis; The New School for Social Research Presenter: Shai Davidai; Columbia Business School (Anti-)Egalitarianism Predicts Attention to Inequality Presenter: Hannah Benner Waldfogel; Northwestern Kellogg School of Management Presenter: Nour Kteily; Northwestern Kellogg School of Management Presenter: Jennifer Sheehy-Skeffington; London School of Economics and Political Science Presenter: Arnold Ho; U. of Michigan Presenter: Oliver Hauser; U. of Exeter Business School The Critical Role of the Economic Environment in Influencing Unethical Behavior Presenter: Hemant Kakkar; Fuqua School of Business, Duke U. Presenter: Niro Sivanathan; London Business School Presenter: Jon Michael Jachimowicz; Harvard Business School Presenter: Xiaoran Hu; London Business School The Role of Economic Inequality in Perceptions of Process and Outcome Fairness in the Workplace Presenter: Daniela Goya-Tocchetto; Fuqua School of Business, Duke U. Presenter: Aaron Kay; Duke U. Presenter: Keith Payne; U. of North Carolina, Chapel Hill

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.257
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
Admission routes1
Has abstractyes

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