Do Privatized Banks in Middle- and Low-Income Countries Perform Better than Rival Banks? An Intra-Industry Analysis of Bank Privatization
Bibliographic record
Abstract
This paper presents a comprehensive analysis of the pre- and post-privatization operating performance and stock market performance of privatized banks and their rivals in middle- and low-income countries. First, we find that privatization announcements elicit negative abnormal returns for rival banks. The effects are more pronounced for subsequent tranche sales where the proportion of government ownership in the privatized bank is reduced. Second, we observe that the privatized banks underperformed the benchmark index in the long run. Investors who bought shares of the privatized banks on the first day of trading and held them for 5 years (instead of investing in the market index) lost 24% of their wealth. The underperformance is consistent with the negative long run returns that have been documented for initial public offerings. Third, we document marginal improvements in the post-privatization operating performance of the privatized banks. Though the privatized banks in middle- and low-income countries are better capitalized than rival banks, they carry higher problem loans and are overstaffed relative to other private banks in the post-privatization period. Since most of the sample firms are partially privatized, we submit that perhaps the continued government ownership of the privatized banks might have hindered managers' ability to restructure the firms.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".