Impact of Political Instability on Financial Reporting Quality in Sub-Saharan African Countries
Bibliographic record
Abstract
Purpose: This study investigates how political instability influences the quality of financial reporting in Sub-Saharan Africa, focusing on discretionary accruals as a measure of earnings management. It aims to assess how fragile political systems contribute to reduced financial transparency and increased manipulation in corporate reporting within emerging market contexts.Methodology: Using a balanced panel of 244 listed firms from seven Sub-Saharan African countries between 2004 and 2023 (4,880 firm-year observations), the study employs pooled OLS, fixed effects, and system GMM estimators. Political risk data are sourced from the Worldwide Governance Indicators, while firm-level financials are extracted from annual reports. Diagnostic tests, including the Hausman test and cross-sectional dependence checks, ensure robust model selection and validity.Findings: The analysis reveals a positive and significant relationship between political instability and discretionary accruals, suggesting greater earnings manipulation in politically unstable environments. Conversely, audit quality and board independence are associated with reduced accrual-based earnings management, supporting agency and institutional theory perspectives on governance and transparency.Practical Implications: The findings emphasize the need for stronger institutional frameworks, independent audit oversight, and effective board governance in politically volatile economies. These measures can help reduce opportunistic reporting and enhance investor confidence in financial disclosures.Originality: This study synthesizes six theoretical perspectives to explain how political risk affects corporate reporting. It offers rare empirical insights from Sub-Saharan Africa and applies rigorous econometric techniques to advance the literature on governance and financial reporting.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".