State-owned Development Banks
Bibliographic record
Abstract
Abstract State-owned development banks are either seen as solving market imperfections or criticized for crowding out private lenders and encouraging politically motivated lending. We study six development banks—Chile’s Corfo, Brazil’s BNDES, Canada’s BDC, Germany’s KfW, Korea’s KDB, and China’s CDB, analyze the tools they use to address market failures, and create a typology according to their strategic focus. We identify two general strategic orientations of development banks—national-champion oriented (with a focus on large firms and with direct mechanisms of lending and equity investment) and entrepreneurship-oriented (with a focus on smaller firms and with more indirect tools to reduce market failure, such as credit guarantees). We conclude with several suggestions for research and public policy. For instance, we argue that, as local capital markets and institutions evolve, development banks should progressively become more entrepreneurship-oriented, focused on smaller entrepreneurial firms, and use of more indirect mechanisms to promote equity investment and alleviate credit constraints (such as credit guarantees). From a research standpoint, scholars should examine conditions and mechanisms through which development banks help develop novel and valuable private capabilities and how their internal organizational patterns influence the impact of their policies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".