Capital and Firm Specific Characteristics: An Examination of the Survival of Deposit-Taking Financial Institutions (DFI) in Latin America and the Caribbean during the Financial Crisis
Bibliographic record
Abstract
This paper aims to assess how capital influences the likelihood of survival of Latin American and Caribbean financial institutions during normal economic times, and the 2008 financial crisis. These financial institutions operate in developing economies that are vulnerable to externals shocks, characterized by large foreign institutions, small indigenous institutions, and high dependence on bank financing due to underdeveloped capital markets. The study uses logistic regressions to estimate the likelihood of survival for three non-overlapping periods – pre, post, and during the 2008 recession. Bankscope provided both financial and firm characteristics (ownership and organizational structures) data. Separate analyses were done based on the size of the institutions. The paper provides empirical indicating that different factors influence the survival of large and small institutions. Higher capital ratios increase the likelihood of survival during the post-recession period especially for smaller institutions. In general, smaller indigenous institutions were less likely to survive. Bank regulators in the regions can use the results of this study to increase their understanding of the factors that influence the failure of financial institutions during different economic periods. This knowledge can be used to implement modifications in how existing regulations are applied to different types of financial institutions. This paper identifies the role of capital, ownership and institutional structures, and size in assessing the likelihood of survival during periods of varying economic activities in developing economies. The paper also highlights similarities and differences with studies conducted in developed economies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".