How Do the CEO Political Leanings Affect REIT Business Decisions?
Bibliographic record
Abstract
Business decisions made by the real estate industry have a profound effect on the well-being of people who live, work, or shop in these buildings. While these decisions may be informed by evidence, the available evidence is often incomplete or imperfect. Therefore, the personal opinions or judgments of senior executives can have an effect. In this paper, we study these effects in two parts: risk-taking and environmental, social, and governance (ESG) activities. Since a person’s political learning is a relatively stable measure, and is associated with preferences for risk and ESG activities, we examine how the political leanings of the CEOs are related to these effects. Using the data from 2003 to 2016, we find that real estate investment trusts with Democratic-leaning CEOs tend to take more risks, as evidenced by higher levels of leverage and more risk in stock prices. We further find that Democratic-leaning CEOs are more broadly engaged in environmentally oriented ESG activities.
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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 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".