Corporate Lobbying, Visibility and Accounting Conservatism
Bibliographic record
Abstract
Abstract In this study, we examine the relationship between a firm's lobbying activities and financial reporting quality using a US setting where public scrutiny of corporate political activities is high. More importantly, we examine whether and how a firm's visibility shapes the relationship between its corporate lobbying activities and accounting conservatism. Adopting annual lobbying expenditure data to measure firms’ lobbying activities, and using a propensity‐score‐matching methodology to control for differences in firm characteristics between lobbying and non‐lobbying firms, we find a positive relationship between a firm's lobbying intensity and the degree of accounting conservatism in its financial reporting. We further find this positive relationship to be more pronounced in lobbying firms with a higher level of visibility. These results are robust after controlling for a firm's political connections, across various conditional conservatism measures, and across a number of visibility measures including firm size, the number of analysts following the firm, the age of the firm, the number of foreign stock exchanges that the firm is cross‐listed in, and the level of the firm's media coverage. Together, our findings add to the literature on how firms’ political activities shape their accounting practices in general, and accounting conservatism in particular. More importantly, our findings suggest that the heightened public attention paid to political activities in the US yields incentives for firms to be more conservative in their accounting practices.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".