U.S. REIT INDUSTRY PROFITABILITY: A BENNET DECOMPOSITION OF INDUSTRY DYNAMICS
Bibliographic record
Abstract
This paper considers the aggregate profitability performance of the REIT industry. The aggregate performance depends on the underlying microeconomic dynamics within an industry – the growth of individual REITs (the within effect), the reallocation between existing REITs (the between effect), the entry of new REITs (the entry effect), and the exit of the existing REITs (the exit effect). We apply an extended Bennet (1920) dynamic decomposition on the REIT industry’s return on equity (ROE) and study the annual data on U.S. Equity REITs for the 1989 to 2015 period and various REIT industry specific sub-sample periods. Bailey et al.’s (1992) and Haltiwanger’s (1997) dynamic industry performance decompositions are special cases of the Bennet decomposition. The “within” and “between” effects dominate the annual changes in this industry’s ROE. To the extent that our Equity REIT sample proxies for the FTSE NAREIT All Equity Index, our conclusions also relate to this index’s profitability performance between 1989 and 2015.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".