The Impact of the Legal Environment on Bank Profitability: An Empirical Analysis of the Angolan Banking Sector
Bibliographic record
Abstract
An efficient legal system facilitates the enforcement of guarantees, enables the recovery of non-performing loans and increases trust between creditors and borrowers. This study examines the effect of the legal environment and the profitability of the Angolan banking sector. Specifically, it analyses the influence of property rights and the rule of law on bank profitability in Angola. The study employs various econometric methods for analyzing panel data, such as Feasible Generalized Least Squares (FGLS), and instrumental variables models such as Two-Stage Least Squares (IV-2SLS), Generalized Method of Moments (IV-GMM) and Quantile Regression (MQREG). The study concludes that improving the legal environment by strengthening property rights and promoting the rule of law favours the profitability of Angolan banks. In terms of practical implications, this study shows that the legal environment in Angola is an important barrier to the promotion of credit in Angola, and, above all, to improving the profitability of banks. This study contributes to the scarce literature highlighting the relationship between the legal system and the Angolan banking sector, a topic that has been little explored in the context of African countries. Furthermore, the study awakens the dormant debate on the legal system and finance.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".