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Record W3014498153 · doi:10.1111/jems.12346

Inefficient incentives and nonprice allocations: Experimental evidence from big‐box restaurants

2020· article· en· W3014498153 on OpenAlexfundno aff
Sacha Kapoor

Bibliographic record

VenueJournal of Economics & Management Strategy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIncentiveProfit (economics)EconomicsMicroeconomicsQuality (philosophy)WageQueueLabour economics

Abstract

fetched live from OpenAlex

Abstract Queues are puzzling because they are consistent with wasted profit in equilibrium. Standard rationales trace the puzzle to the pricing of goods. This article uses field experimental evidence from large‐scale restaurants to trace the puzzle to the pricing of labor. The customary wage contract in these settings fosters congestion and longer queues because it can encourage workers to emphasize the quality rather than quantity of output. To study this problem, the field experiment pays waiters bonuses for customer volume on days with excess demand, in addition to the tips and hourly wages they customarily receive. The experimental contract shortens queues substantially, generating surplus gains for consumers with no discernible cost in terms of perceived service quality. Workers earn more via the bonuses and because they earn more in tips. Short‐run profits increase by at least 49%. There is no discernible reduction in long‐run profit. The firm reverted to the baseline contract on excess demand days after many months of evidence, even after acknowledging the gains from the experimental contract. The evidence suggests the puzzle may partly be explained by inefficient wage contracting.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.695

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 communication0.0000.001
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 head0.107
GPT teacher head0.340
Teacher spread0.233 · 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 designQualitative
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

Citations1
Published2020
Admission routes1
Has abstractyes

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