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Record W4404177705 · doi:10.1086/733932

Identity Economics and Intrahousehold Bargaining

2024· article· en· W4404177705 on OpenAlexaff
Sandeep Mohapatra, Leo K. Simon

Bibliographic record

VenueEconomic Development and Cultural Change · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIdentity (music)EconomicsNeoclassical economicsSociologyLabour economicsPositive economicsPublic economicsArt

Abstract

fetched live from OpenAlex

Intrahousehold bargaining theory predicts that an income increase will cause an unambiguous increase in women’s bargaining power. Identity theory, in contrast, predicts that women may voluntarily give up power to compensate their husbands whose identity is challenged by the increase in their wives’ incomes. We outline a model of these competing forces. We then present empirical tests that use amendments to women’s inheritance laws in India to identify variations in female income. We exploit differences across long-standing and deep-rooted social institutions (caste groups and the practice of purdah or veiling) for variation in identity prescriptions. Using a large dataset on married women, we estimate significant identity effects that lead to the loss of bargaining power of women after their income increase. The negative identity effects vary predictably with a household’s stringency of patriarchal prescriptions regarding the “role” of women in a household. Consistent with identity theory, our results suggest that alterations in women’s labor market activities are a plausible mechanism through which the loss of women’s power is mediated and rule out alternative mechanisms, such as the potential rise in domestic violence that some scholars associate with increases in women’s income.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.066
GPT teacher head0.279
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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