Corporate governance and wealth and income inequality
Bibliographic record
Abstract
Abstract Research Question/Issue There has been growing concern about rising social inequality and its effects on general well‐being and the polity. Much of this rise can be traced to changes in the manner in which corporations or firms are governed and how this impacts on income and wealth dispersion. This study systematically reviews the most recent literature on external and internal corporate governance (CG) that deals with the issue of income and wealth inequality. Research Findings/Insights External mechanisms such as institutional regime (defined in terms of varieties of capitalism—liberal or coordinated markets) and financialization reveal important insights, often implicitly, into what makes or sustains inequality. The rise of the platform business model raises explicit concerns about increasing wealth and wage inequality. This is because it is associated with a rapidly growing precariat of gig workers, Big Tech entrepreneurs with untrammeled levels of control and extreme levels of personal wealth, and widespread tax avoidance despite record profits. The literature on internal CG is somewhat constrained in its reliance on agency theory and a focus on shareholder primacy. This only provides limited insights on how internal CG mechanisms impact on inequality. However, in recent work, the issue of perverse incentives posed by CEO reward systems and their impact on organizational sustainability and wage dispersion are receiving increasing attention. Theoretical/Academic Implications Some studies do attempt to widen the lens, and we suggest a greater focus on theorizing codetermination and alternate forms of ownership, non‐monetary incentives, the power of the Big Tech companies, and those strands of comparative institutional analyses that explore the determinants of internal CG structures. Practitioner/Policy Implications The study reasserts the importance of the firm as a central analytical paradigm in understanding income and wealth inequality and that, in seeking to ameliorate the latter's negative consequences, more attention needs to be accorded to the governance and regulation of firms.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".