Competition and Market Contestability in Ghanaian banking industry: A Panzar-Rosse Approach
Bibliographic record
Abstract
The present study evaluates the market structure of Ghana’s banking industry and estimates the nature and degree of competition. This study uses non-structural methodology proposed by Panzar-Rosse Model known as “H-statistic” to empirically assess competitiveness in the Ghanaian banking market. The study uses 23 banks in Ghana from 2000 to 2019, compiled and reported by Ghana Association of Bankers (GAB). The study results show that banks in Ghana derive their revenue in conditions of monopolistic competition. Thus, Contestable markets theory and Chamberlainian competition theory are validated by the study results. Furthermore, the study results revealed that from 2000 to 2019, after various structural reforms including the implementation of the FINSAP, competition in the Ghanaian banking sector increased. Finally, when the dataset was decomposed into local and foreign banks, the results indicate that monopolistic competition market conditions are found for both local and foreign banks. Managerially, the presence of a monopolistic market condition adds to the call for managers of the banks to consider factor input prices in an attempt to generate more revenues. Second, to avoid negative consequences of competition, managers of these banks should not rely on a single income source but also indulge in non-intermediation activities. In terms of policy, pro-structural shift policies that have helped with the transition from a monopoly structure to a monopolistic competition free entry or contestable market structure should be rigorously pursued by the policymakers. Besides, policy directives that enhance greater consolidation in the banking sector shouldbe pursued rigorously. Finally, the results from this study could help policy-makers to fashion an appropriate optimal intervention and stability policies geared towards enhancing banking stability at different levels of bank competition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".