Drought risk and audit pricing: a mixed-methods study
Bibliographic record
Abstract
Purpose Climate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis. Design/methodology/approach This study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners. Findings The results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field. Originality/value This research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".