Earnings Quality and Legal Origin: Evidence from Quebec *
Bibliographic record
Abstract
This paper provides evidence on the link between legal origin and earnings attributes often associated with earnings quality. Prior studies perform country-level analyses and find evidence that earnings quality is higher in counties with common law (English legal origin) than in countries with civil law (which includes French legal origin). These studies classify Canada as having common law, which prevails at the federal (country) level. This paper takes a different approach and exploits within-country variation in Canada. Specifically, our research is motivated by the observation that all Canadian providences have common law with the exception of Quebec which has civil law. We identify matched pairs for each publicly-traded company headquartered in Quebec. We then investigate whether common earnings attributes typically associated with earnings quality vary at the firm-level within country. We find that earnings attributes do vary with legal origin (civil vs. common law). Further, we analyze Canadian firms’ decision regarding where to incorporate (i.e., at either the federal or provincial level). Our evidence suggests that Quebec firms ’ incorporation decision at either the federal (common law) or provincial (civil law) level leads to some differences in earnings quality, after controlling for differences in accounting standards hypothesized by Ball, Kothari and Robin (2000).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".