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Record W3186625282 · doi:10.1002/jae.2840

Labour supply, service intensity, and contracts: Theory and evidence on physicians

2021· article· en· W3186625282 on OpenAlexafffund
Bernard Fortin, Nicolas Jacquemet, Bruce Shearer

Bibliographic record

VenueJournal of Applied Econometrics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité Laval
FundersCanadian HIV Trials Network, Canadian Institutes of Health ResearchAgence Nationale de la Recherche
KeywordsRemunerationSalaryIncentiveWelfareService (business)BusinessLabour supplyPanel dataEconomicsLabour economicsActuarial scienceFinanceMicroeconomicsEconometricsMarketing

Abstract

fetched live from OpenAlex

Summary Based on linked administrative and survey panel data, we analyse the labour supply behaviour of physicians who could adopt either a standard fee‐for‐service contract or a mixed remuneration (MR) contract. Under MR, physicians received a per diem and a reduced fee for services provided. We present estimates of a structural discrete choice model that incorporates service intensity (services provided per hour) and contract choice into a labour supply framework. We use our estimates to predict (ex ante) the effects of contracts on physician behaviour and welfare, as measured by average equivalent variations. The supply of services is reduced under an MR contract, suggesting incentives matter. Hours spent seeing patients is less sensitive to incentives than the supply of services. Our results suggest that a reform forcing all physicians to adopt the MR system would have substantially larger effects on physician behaviour than were measured under the observed reform. A pure salary (per diem) reform would sharply reduce services but would increase time spent seeing patients.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.061
GPT teacher head0.263
Teacher spread0.203 · 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 designTheoretical or conceptual
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

Citations6
Published2021
Admission routes2
Has abstractyes

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