Bibliographic record
Abstract
Some policies drive economic growth, some act to redistribute income or wealth; however, it is rare to find policy instruments that do both. Investment in improving skills and education may fall into that category. New research from the OECD suggests that competition can also help governments to simultaneously achieve these two goals. While competition has long been known to drive economic growth, there is evidence that competition can also make an important contribution to reducing income and wealth inequality. Given the recent concerns about increased inequality across many countries, this relationship bears further exploration. The reason market power and inequality are related is simple. In the absence of competition, market power drives prices above costs; these higher prices increase everyone’s consumption expenditure and redistribute the extra money spent towards business owners and financial asset holders, who are overwhelmingly concentrated at the top of the income distribution. The dual effect is to increase the income of the upper decile while reducing consumption power and savings for the rest of the population. In the long-run, the accumulated money transfers from consumers to businesses is likely to help the richest accumulating wealth and raising their income, while making it more difficult for the poorest to build their savings or to reduce their dependence on credit. While estimating effects on long-term distributions of wealth and income requires extensive data and complex modelling, in this paper we attempt to calculate a rough measure of the short-run money transfer from poor to rich due to market power, for 12 OECD economies: Australia, Canada, France, Germany, Greece, Japan, Korea, Mexico, Portugal, Spain, United Kingdom and the United States. Our results indicate that, on average, for each dollar of monopoly profits, a total of USD 0.37 is transferred from the 90 percent poorest to the 10 percent richest.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".