Government expectation and firm performance nexus in the context of a developing country: does non-mandatory disclosure matter?
Bibliographic record
Abstract
In developing economies like Nigeria, where government expectations on firms intensify amid underdeveloped institutional frameworks, the performance implications of fiscal obligations and voluntary transparency remain poorly understood. This study investigates whether government expectations influence firm performance and whether non-mandatory disclosure moderates this relationship among 80 listed Nigerian firms from 2011 to 2023. Using panel data regression techniques—specifically fixed and random effects models, the study analyzes how fiscal pressure and voluntary environmental, social, and governance disclosures jointly shape firm performance. The findings reveal that higher government expectations are significantly and negatively associated with firm value, suggesting that increasing tax burdens diminish corporate performance. Contrary to theoretical assumptions, non-mandatory disclosure was also negatively associated with firm performance under fixed effects estimation, indicating that voluntary ESG transparency may be perceived as costly or symbolic rather than performance-enhancing in Nigeria’s capital market context. More critically, the interaction between government expectations and non-mandatory disclosure shows a significant negative moderating effect, implying that the combination of tax pressure and voluntary disclosure jointly exacerbates performance erosion rather than mitigating it. These results suggest that without institutional support, investor maturity, and stakeholder awareness, even well-intentioned disclosures may backfire. The study recommends that firms embed ESG practices into core business strategy rather than treat them as compliance rituals, and that policymakers harmonize tax and disclosure policies to avoid disincentivizing transparency. Investors are encouraged to evaluate the strategic substance behind disclosures rather than their volume alone. Future research should explore sector-specific dynamics, stakeholder interpretations of voluntary disclosures, and cross-country comparisons to uncover when and how ESG transparency translates into sustainable firm value under fiscal constraint.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".