MétaCan
Menu
Back to cohort
Record W4387937373 · doi:10.1287/mnsc.2023.4956

Incentives from Career Concerns in a Contract Package: An Empirical Investigation

2023· article· en· W4387937373 on OpenAlexaboutno aff
Bicheng Yang, Tat Y. Chan, Hideo Owan, T. Tsuru

Bibliographic record

VenueManagement Science · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveCounterfactual thinkingWagePromotion (chess)EconomicsWork (physics)Profit (economics)CommissionBusinessMicroeconomicsLabour economicsFinancePolitical science

Abstract

fetched live from OpenAlex

This paper empirically studies the extent to which career concerns as part of a typical contract offer influence employees’ work performance in a Japanese auto dealership firm. Because career movements and base-wage adjustments rely on performance evaluation over time, we develop a dynamic structural model that allows concerns for future payoffs to impact an employee’s current work effort. A reform in personnel-management policies of the firm during the data period enables us not only to compare the performances across individuals, but also to compare within an individual the performance before and after the reform—this enhances the model identification. Our estimation results show that the added value from career movements on top of the monetary payoffs is more important than the monetary payoffs. Individuals respond to career movements and commissions differently, mainly due to varying cost of effort and different payoffs from career movements. Our counterfactual exercises suggest that, compared with the scenario when there is only monetary compensation, adding career movements in a contract package will greatly improve the firm’s gross profit. The firm can improve its net profit by making commission and promotion more performance-based. This paper was accepted by Matt Shum, marketing. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada [Grant 20190410-01] is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4956 .

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.070
GPT teacher head0.313
Teacher spread0.243 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueManagement ScienceSame topicConsumer Market Behavior and PricingFrench-language works237,207