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Record W2763049311 · doi:10.1002/hec.3572

What factors affect physicians' labour supply: Comparing structural discrete choice and reduced‐form approaches

2017· article· en· W2763049311 on OpenAlexaff
Guyonne Kalb, Daniel Kuehnle, Anthony Scott, Terence Chai Cheng, Sung‐Hee Jeon

Bibliographic record

VenueHealth Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsStatistics Canada
FundersNational Health and Medical Research CouncilMonash UniversityMedical Research CouncilMichigan Department of Health and Human Services
KeywordsWageEarningsEconomicsMargin (machine learning)EconometricsAffect (linguistics)Discrete choiceLabour supplyDemographic economicsLabour economicsCompensation (psychology)PopulationMedicineComputer scienceAccountingPsychology

Abstract

fetched live from OpenAlex

Little is known about the response of physicians to changes in compensation: Do increases in compensation increase or decrease labour supply? In this paper, we estimate wage elasticities for physicians. We apply both a structural discrete choice approach and a reduced-form approach to examine how these different approaches affect wage elasticities at the intensive margin. Using uniquely rich data collected from a large sample of general practitioners (GPs) and specialists in Australia, we estimate 3 alternative utility specifications (quadratic, translog, and box-cox utility functions) in the structural approach, as well as a reduced-form specification, separately for men and women. Australian data is particularly suited for this analysis due to a lack of regulation of physicians' fees leading to variation in earnings. All models predict small negative wage elasticities for male and female GPs and specialists passing several sensitivity checks. For this high-income and long-working-hours population, the translog and box-cox utility functions outperform the quadratic utility function. Simulating the effects of 5% and 10% wage increases at the intensive margin slightly reduces the full-time equivalent supply of male GPs, and to a lesser extent of male specialists and female GPs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.158
GPT teacher head0.324
Teacher spread0.165 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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