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Record W3123006309

Psychological Incentives, Financial Incentives, and Risk Attitudes in Tournaments: An Artefactual Field Experiment

2015· article· en· W3123006309 on OpenAlexaff
Jim Engle‐Warnick, Tony Fang, Fei Song

Bibliographic record

VenueEconstor (Econstor) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsYork UniversityToronto Metropolitan UniversityMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsIncentiveField (mathematics)FinanceBusinessPsychologyActuarial scienceEconomicsMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Tournaments are widely used to assign bonuses and determine promotions because of the link between relative performance and rewards. However, performing relatively well (poorly) may also yield psychological benefits (pain). This may also stimulate effort. Through a real-effort artefactual field experiment with factory workers and university students as a comparison group in China, we examine how both psychological and financial incentives, together with attitudes toward risk, may influence motivation and performance. We provided performance-ranking information both privately and publicly, with and without rank-based financial incentives. Our results show that performance-ranking information had a significant motivational effect on average performance for students, but not for that of workers. Adding financial incentives based on rank provided little evidence of further improvement. Much of the difference between workers and students can be explained by differences in attitudes toward risk. Indeed, for both groups financial and psychological incentive effects are both inversely related to individual levels of risk aversion, and are positive and significant both for workers and for students who are sufficiently risk-tolerant.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.355
Teacher spread0.307 · 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.

Study designObservational
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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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