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Record W4388989551 · doi:10.1016/j.lana.2023.100637

Taxing women’s bodies: the state of menstrual product taxes in the Americas

2023· review· en· W4388989551 on OpenAlexaboutno aff
Alhelí Calderón-Villarreal

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typereview
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
FundersNational Institute on Drug AbuseConsejo Nacional de Ciencia y TecnologíaUniversity of California Institute for Mexico and the United StatesUniversity of California
KeywordsPovertyState (computer science)Product (mathematics)Development economicsGeographyDeveloped countryPolitical scienceEconomic growthDemographyEconomicsPopulationSociology

Abstract

fetched live from OpenAlex

The taxation of menstrual products has been identified as unfair, imposing economic burden on people who menstruate based simply on a biological difference. These taxes have been described as major contributors to menstrual poverty. Although they have been debated among governments, and a focus of political activism, academic literature has largely neglected the issue. Here I comprehensively reviewed the status of menstrual product taxes for all countries and populated territories in the Americas in 2022. Data from 57 countries and territories, and 78 states (those of the United States and Brazil) were included. Since 2012, 10 countries and territories have eliminated taxation on menstrual products—Jamaica, Canada, Saint Kitts & Nevis, Trinidad & Tobago, Guyana, Colombia, Puerto Rico, Mexico, Ecuador, and Barbados. Nevertheless, menstrual product taxes were still applied in 63.2% of locations in 2022, with an average tax rate of 11.2% (ranging from 1.0% in Costa Rica to 22.0% in Uruguay). The average woman of reproductive age in the Americas experienced a menstrual product tax rate of 5.8% in 2022. In sum, despite activism and progress, most of the region continues to employ discriminatory taxation against people who menstruate, with particularly high taxation rates concentrated in South America.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.262
GPT teacher head0.471
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes1
Has abstractyes

Explore more

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