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Record W4241686380 · doi:10.31235/osf.io/edt8g

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

2017· preprint· en· W4241686380 on OpenAlexaff
Laura Doering

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmbeddednessMicrofinanceInterpersonal tiesIntermediaryFinancial intermediaryBusinessSuspectPublic relationsFinanceEconomicsSocial psychologyPolitical scienceSociologyPsychologyEconomic growthLaw

Abstract

fetched live from OpenAlex

Personal relationships are a common feature of financial intermediation. However, existing research offers different expectations about whether personal ties prove detrimental or beneficial for lenders. Research on embeddedness from economic sociology highlights the advantages lenders accrue when they develop personal ties with borrowers, including enhanced trust, information-sharing and greater social control. Yet research from social psychology offers reason to suspect that personal relationships can be costly because lenders who feel personally tied to borrowers run the risk of escalating commitment to poor performers. Drawing on these lines of research, this study uses data from a Latin American microfinance bank to ask: When are personal relationships detrimental or beneficial for financial intermediaries? It shows that, when lenders and borrowers have personal relationships, lenders are less likely to cut ties with poor performers and borrowers miss fewer payments, consistent with expectations from both literatures. However, these trends vary with frequency of contact. When lenders and borrowers interact less frequently, lenders continue to show heightened commitment, but borrowers become less compliant, creating potential problems for lenders. Overall, this study integrates theories from economic sociology and social psychology to offer a more balanced, 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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.139
GPT teacher head0.286
Teacher spread0.147 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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