Political Transition, Structural Inequality, and the Persistence of Bribery in Ghana (1999–2022)
Bibliographic record
Abstract
Bribery and corruption take diverse forms worldwide yet share common drivers. This study examines the persistence of petty bribery in Ghana across six electoral cycles from 1999 to 2022, drawing on nationally representative Afrobarometer survey data. It integrates insights from neo-patrimonialism, structural inequality, rational choice, and relative deprivation theories to explain how political transitions, economic disparities, and perceptions of unfairness contribute to the normalization of bribery in public service delivery. Using logistic regression models and longitudinal trend analysis, the article shows that bribery surges during democratic transitions, particularly when ruling parties change. The findings also reveal that structurally marginalized populations—especially rural, low-income, and less-educated groups—face disproportionately high exposure to bribery, though this pattern has shifted over time. The study argues that democratic institutions alone cannot curb corruption when underlying structural inequalities and informal governance networks remain intact. By combining institutional analysis with sociological theories of inequality, this article contributes to current debates on governance failure, political accountability, and corruption in lower-middle income countries. The Ghanaian case offers broader implications for understanding why anti-corruption reforms often stall in electorally competitive but structurally unequal societies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".