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Record W3122249789

Peer Pressure, Incentives, and Gender: An Experimental Analysis of Motivation in the Workplace

2009· preprint· en· W3122249789 on OpenAlexafffund
Charles Bellemare, Patrick Lepage, Bruce Shearer

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsCégep LimoilouUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaRheinische Friedrich-Wilhelms-Universität Bonn
KeywordsProductivityIncentivePeer effectsPeer pressurePaymentWork (physics)Labour economicsPiece workPeer groupDemographic economicsEconomicsBusinessPsychologyMicroeconomicsSocial psychologyEngineeringEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

We present results from a real-effort experiment comparing the productivity of workers under fixed wages and piece rates. Workers working under both payment systems were exposed to different degrees of peer pressure in the form of private information about the productivity of their peers. We have three main results. First, under fixed wages, we find a significant inverted U relationship between peer pressure intensity and the productiv-ity of men. We argue that this relationship is consistent with theories of self-assessment and motivation. Second, we find no significant effect of peer pressure on productivity of women paid a fixed wage. Finally, we find no significant effect of peer pressure intensity on productivity of men or women when workers are paid a piece rate. JEL Codes:

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.091
GPT teacher head0.410
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations0
Published2009
Admission routes2
Has abstractyes

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