Do Better Political Institutions Help in Reducing Political Pressure on State-Owned Banks? Evidence from Developing Countries
Bibliographic record
Abstract
This study examines whether state-owned banks face political pressure and whether the improvement in political institutions alleviates this pressure. The theory of political benefits argues that politicians use state-owned banks for political purposes such as obtaining and maintaining political support. We reviewed extant empirical research and found that the existing evidence is mixed; some studies support while others reject the theory. In this backdrop, we analyzed a sample of 185 state-owned banks from 51 developing countries over the period 1998–2012 and provide renewed evidence supporting the theory. Specifically, we found that state-owned banks face significant political pressure in developing countries; that is, they lend more and earn less in election years. Next, we observed that the political pressure is prevalent only in the countries with weak political institutions. Strong political institutions in the form of higher constraints on policy change decisions of incumbent government and higher democratic accountability are helpful in eliminating political pressure on state-owned banks in developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".