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Record W3153103762 · doi:10.1111/rode.12706

Beyond technical skills training: The impact of credit counseling on the entrepreneurial behavior of Ugandan youth

2020· article· en· W3153103762 on OpenAlexaff
María Laura Alzúa, Maria Josefina Baez, Samuel Galiwango, Daniel Joloba, Benjamin Kachero, Maria Adelaida Lopera, Juliet Ssekandi, Zeridah Zigiti

Bibliographic record

VenueReview of Development Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEntrepreneurshipInvestment (military)Intervention (counseling)FinanceControl (management)Credit riskBusinessCredit historyEconomicsPsychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Abstract There is low take‐up of financial credit among youth in Uganda because potential beneficiaries perceive associated risks as high. This study assesses the determinants of entrepreneurial risk tolerance among Ugandan youth using experimental data from a randomized control trial and a real‐life investment‐risk experiment. Credit counseling was provided to young men and women aged 18–35 who owned a business to educate them about the obligations and commitments associated with financial credit. The intervention had a significant impact on demand for credit and related intermediate outcomes such as ownership of a bank account and investment in assets. The study finds that youth exhibited lower demand for credit after business training because of increased awareness regarding the actual risks associated with receiving credit. Our findings reinforce national strategies to promote soft skills for business entrepreneurship that extend beyond standard business training.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.257
Teacher spread0.208 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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