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Record W4392757101 · doi:10.1002/hec.4822

The optimal design of assisted reproductive technologies policies

2024· article· en· W4392757101 on OpenAlexafffund
Marie‐Louise Leroux, Pierre Pestieau, Grégory Ponthière

Bibliographic record

VenueHealth Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFecundityEconomicsUtilitarianismEgalitarianismFertilityWageIncentiveAssisted reproductive technologyWelfarePublic economicsMicroeconomicsLabour economicsDemographyBiologyPopulationInfertilityMarket economySociologyPolitics

Abstract

fetched live from OpenAlex

This paper studies the optimal fiscal treatment of assisted reproductive technologies (ART) in an economy where individuals differ in their reproductive capacity (or fecundity) and in their wage. We find that the optimal ART tax policy varies with the postulated social welfare criterion. Utilitarianism redistributes only between individuals with unequal fecundity and wages but not between parents and childless individuals. To the opposite, ex post egalitarianism (which gives absolute priority to the worst-off in realized terms) redistributes from individuals with children toward those without children, and from individuals with high fecundity toward those with low fecundity, so as to compensate for both the monetary cost of ART and the disutility from involuntary childlessness resulting from unsuccessful ART investments. Under asymmetric information and in order to solve for the incentive problem, utilitarianism recommends to either tax or subsidize ART investments of low-fecundity-low-productivity individuals at the margin, depending on the degree of complementarity between fecundity and ART in the fertility technology. On the opposite, ex post egalitarianism always recommends marginal taxation of ART.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.070
GPT teacher head0.339
Teacher spread0.269 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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