Assessing Institutional Dynamics of Governance Compliance in Emerging Markets: The GCC Real Estate Sector
Bibliographic record
Abstract
The real estate sector has emerged as the bedrock of the Gulf Cooperation Council (GCC) economies, and it has remained resilient despite the various unprecedented micro- and macro-economic shocks devouring the world’s economies. However, wavering investor attitudes and minimal exposure to real estate investment vehicles, coupled with weak regulatory frameworks, have led to dramatic downturns in the sector. Transparency about what is happening in real estate is imperative if the success of high-profile initiatives is to continue and much depends on good corporate governance (CG) in the sector. Using the most recent data from 2019, the current study applies the CG Index (CGI) and CG Deviation Index (CGDI) constructs to the real estate (RE) sector in the GCC in an effort to develop vital indicators for future RE investment decisions in the GCC region. The results indicate that the highest CG adherence levels are being achieved in Dubai, followed by Abu Dhabi and Saudi Arabia. The authors attribute these countries’ success in CG adherence to the entrepreneurial identity of them RE firms as well as to their governance capacity, their socio-cognitive capability, and the level of regulatory enforcement within the context of their dominant governance logic. It should be noted that there are variations in adherence levels throughout each region. The results also agree with prior literature that a higher CGS leads to a lower CGD score, and vice versa. At this point, encouraging more real estate investment trust (REIT) formations in the GCC could ensure value propositions, such as liquidity, to both investors and RE companies as well as solid governance fundamentals. This is strongly recommended for increasing the RE presence and its contribution to the GDP of each country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".