Social trust and use of banking services across households in 28 transitional countries
Bibliographic record
Abstract
Purpose – The purpose of this paper is to use survey data from a sample of 29,000 households from 28 transitional countries and Turkey to address two main questions: first, is there any effect of social trust on the use of banking services; and second, what are the household-level and country-specific determinants of using banking services in transitional countries. Design/methodology/approach – The authors use binary probit models, instrumental variables methods and various econometric specifications to test the hypothesis. Findings – The authors found that the higher level of trust in people predicts a greater level in use of banking services by households regardless of the model specifications and econometric adjustments employed. This association appears to be more prominent among less educated respondents and in countries with low levels of legal enforcement. The results also suggest that location, income and wealth of households, along with country income level, legal enforcement and inflation rates strongly affect the decisions made by households regarding their use of banking services. In contrast, the authors found either a very small or non-significant impact with regard to bank ownership structure on the use of banking services across households. Originality/value – To the best of the knowledge this is the first study which specifically focusses on social trust and the usage of formal banking services across a large set of transitional countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".