The Geographical, Economic and Legal Regionalization of the Changes in Dividend Payments in the World
Bibliographic record
Abstract
The dynamic growth of nominal and real values of dividends paid in the world, observed since the last quarter of the twentieth century, is determined by the companies with the largest capitalization. However, the increase in global dividend payments is not the same in all countries and is subject to geographical, economic and legal regionalization. It is also disturbed by economic fluctuations (especially the 2008 crisis) and, more recently, by the COVID-19 pandemic. The paper, using the data from the survey of Janus Henderson Investments, analyses changes in dividend payments in geographical (continents) economic (countries with a similar level of economic development) and legal (countries with similar legal systems) regions by the 1,200 largest companies in the world between 2009 and 2021. Linnear trend models taking into account the COVID-19 pandemic in the world and in separate regions and subregions, as well as the panel partial adjustment model of dividends vs. GDP, were estimated. The conducted research confirmed the impact of different forms of regionalization on the rate of dividend payments by the world’s largest companies. In the years 2009–2021 dividend payments in Australia and Asia grew the fastest. COVID-19 significantly reduced dividend payments in 2020 in Europe. Dividend payments in emerging markets countries grew faster than in developed markets countries and COVID-19 did not significantly reduce payouts on emerging markets. However, it is the developed markets that still provide the vast majority of dividends. The common law system is more favorable to dividend payments.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".