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Record W3014828573 · doi:10.1093/wber/lhaa010

Increasing Financial Inclusion in the Muslim World: Evidence from an Islamic Finance Marketing Experiment

2020· article· en· W3014828573 on OpenAlexaff
Dean Karlan, Adam Osman, Nour Shammout

Bibliographic record

VenueThe World Bank Economic Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsLoanShariaFinancial inclusionPaymentProduct (mathematics)EconomicsIslamInterest rateFinancial servicesBusinessCommercial lawAccountingFinanceLaw

Abstract

fetched live from OpenAlex

Abstract Low utilization of household credit in developing countries may be partially due to religious considerations. In a randomized marketing experiment in Jordan, this paper estimates the effect of sharia-compliant loan features on demand for credit. To comply with Islamic law, the sharia-compliant product uses a bank fee rather than an interest payment structure, while keeping the rest of the product features very similar. Sharia-compliance increased the application rate for loans from 18 percent to 22 percent, an increase in demand that is equivalent to a 10 percent decrease in interest rates. This study also randomly varied the price of the sharia-compliant loan and finds that less religious individuals are twice as elastic with respect to price as the more religious. By comparing reasons for refusal across treatment groups, this paper estimates that survey measures that try to assess the importance of religious objections to conventional credit overestimate the importance of this type of objection by a third.

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)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.028
GPT teacher head0.271
Teacher spread0.243 · 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 designNon-randomized trial
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

Citations16
Published2020
Admission routes1
Has abstractyes

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