How Do the CEO Political Leanings Affect REIT Business Decisions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it