Investor Protection, Income Smoothing, and Earnings Informativeness
Bibliographic record
Abstract
This study investigates whether investor protection affects the efficient communication of private information about future prospects through income smoothing. While prior research suggests that the level of earnings management differs between high and low investor protection countries, we examine whether the underlying motive for earnings management differs between high and low investor protection countries. Using firm-level data from 44 countries for 1993 to 2002 and Tucker and Zarowin's (2006) method to measure earnings informativeness, we find that earnings informativeness is more positively associated with income smoothing in countries with strong investor protection than it is in countries with weak investor protection. Our findings suggest that managers in weak investor protection countries are more likely to use income smoothing for opportunistic reasons while managers in strong investor protection countries are more likely to use income smoothing to convey their private information about future earnings. The results are robust through various additional analyses. More broadly, our results suggest that the role of accounting discretion is affected by a country's institutional infrastructure, specifically, its ability to provide protection for outside shareholders.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".