What is the Relationship Between REIT Governance and Earnings Management
Bibliographic record
Abstract
The empirical corporate finance literature claims that better corporate governance constrains earnings management, while others argue that the unique legal and reporting structure of REITs may reduce the need for such internal corporate governance. Using a sample of publicly traded REITs for the 2004-2008 time period, we examine the relationships amongst corporate governance, accruals earnings management, manipulation of Funds from Operations (FFO), and real earnings management. We find that corporate governance quality is unrelated to accruals earnings management and manipulation of FFO. At first glance, the findings suggest that managers need less internal oversight because of the more transparent reporting structure of REITs. However, we document that REITs engage in significant real activities manipulation for earnings management purposes. Our empirical findings further show that corporate governance characteristics, in particular board size, independence, number of board meetings and audit committee financial expertise, are essential for constraining such activities. Finally, by focusing on a subset of REITs that act in ways which previous research has identified as more susceptible to earnings management activities, we demonstrate that good corporate governance effectively reduces accruals earnings management and manipulation of FFO for these REITs. Overall, our findings indicate that, despite the unique legal and reporting structure, REITs engage in certain forms of earnings management, and that the ability for REITs to manipulate earnings is reduced when corporate governance is more effective.
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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 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".