Bibliographic record
Abstract
In a time of increasingly growing income inequality and a shrinking middle class, many governments are proposing redistributing wealth using a personal wealth tax. The wealth inequality has further been exasperated by the COVID-19 pandemic resulting in loss of employment. Wealth inequality is far greater than income inequality as wealth accumulation operates in a self-reinforcing way and likely to increase in the absence of taxation. Investment returns tend to increase with wealth and high earners can save more due to their lower marginal propensity to consume. Governments globally have responded to the crisis through stimulus packages which resulted in an increased budget and government deficit to keep the country’s economy from falling into an economic collapse (i.e., a recession or stagflation). This has resulted in ballooning government deficits and a response from governments to find avenues to fund the increased budgets. The wealth tax is a tool that is proposed by politicians to raise tax revenues considering the growing deficit and expanding budgets. While the tax system should help address wealth inequality, the question is whether the wealth tax is the most effective way to do so. This study will examine the impact of a wealth tax adopted by countries and entrepreneurial activity in the country. Will a wealth tax be more beneficial or harmful to a country’s entrepreneurship size? The findings from the longitudinal study showed mixed results as to whether a wealth tax had a negative or positive impact on entrepreneurship. Four main indices which the study found had an interesting relationship with wealth tax were 1) Self-Employed with and without employees, 2) Self-Employed Manufacturing versus Services sector, 3) Self-Employed Youth rates for men versus women, and 4) Self-Employment rates for men versus women. The case event study has also shown that a wealth tax may not be all beneficial and resulted in France repealing their wealth tax.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.048 | 0.010 |
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".