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Record W2793178281 · doi:10.1086/696214

Risks, Returns, and Relational Lending: Personal Ties in Microfinance

2018· article· en· W2793178281 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Sociology · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofinanceInterpersonal tiesBusinessPaymentFinanceEconomicsPublic relationsSocial psychologyEconomic growthPsychologyPolitical science

Abstract

fetched live from OpenAlex

Personal relationships often facilitate credit transactions. However, existing research provides different expectations about whether personal ties prove detrimental or beneficial for lenders. Economic sociology highlights the advantages lenders accrue when they have personal ties with borrowers. Yet research from social psychology suggests that personal ties can be costly because lenders may “escalate commitment” to poor performers. This study uses data from a microfinance bank to ask, When are personal relationships detrimental or beneficial for lenders? It shows that lenders with personal ties to borrowers are less likely to cut those ties and their borrowers miss fewer payments. However, these trends vary with frequency of contract. When lenders and borrowers interact infrequently, lenders continue to show strong commitment, but borrowers become less compliant, creating potential problems for lenders. This study integrates theories from economic sociology and social psychology to offer a more nuanced, temporally informed understanding of personal ties in finance.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.455

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 studies0.0000.001
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 head0.051
GPT teacher head0.274
Teacher spread0.223 · 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