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Record W2502918580 · doi:10.3386/w22417

Bride Price and Female Education

2016· report· en· W2502918580 on OpenAlexaff
Nava Ashraf, Natalie Bau, Nathan Nunn, Alessandra Voena

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typereport
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomicsBrideArtArt history

Abstract

fetched live from OpenAlex

Although it is well known that traditional cultural practices can play an important role in development, we still have little understanding of what this means for development policy. To improve our understanding of this issue, we examine how the effects of school construction on girls' education vary with a widely-practiced marriage custom called bride price, which is a payment made by the husband and/or his family to the wife's parents at marriage. We begin by developing a model of educational choice with and without bride price. The model generates a number of predictions that we test in two countries that have had large-scale school construction projects, Indonesia and Zambia. Consistent with the model, we find that for groups that practice the custom of bride price, the value of bride price payments that the parents receive tend to increase with their daughter's education. As a consequence, the probability of a girl being educated is higher among bride price groups. The model also predicts that families from bride price groups will be the most responsive to policies, like school construction, that are aimed at increasing female education. Studying the INPRES school construction program in Indonesia, as well as a similar program in Zambia, we find evidence consistent with this prediction. Although the program had no discernible effect on the education of girls from groups without bride price, it had large positive effects for girls from groups with a bride price. The findings emphasize the importance of the marriage market as a driver of educational investment and provide an example of how the cultural context of a society can be crucial for the effectiveness of development policy.

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)0.007
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.480
GPT teacher head0.579
Teacher spread0.099 · 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
GenreOther

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

Citations48
Published2016
Admission routes1
Has abstractyes

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