The Effect of Changes in Labor Demand and Entrepreneurship on Income Inequality Through Innovation
Bibliographic record
Abstract
The highly skill-biased technological changes brought about by innovation have changed the employment market greatly. This paper examines the impact of changes in labor demand and entrepreneurship, as a result of technological innovation, on income inequality in the form of a literature review, with analysis of related theoretical and empirical research studies. Innovation is positively correlated with income inequality from two points of view – labor demand and entrepreneurship. Firstly, innovations alter the demand for high-skilled and unskilled labor, and thereby change the skill premia– developments which, in turn, influence income inequality. Secondly, increased entrepreneurship enables entrepreneurs to accumulate more wealth due to higher financial returns. The paper departs from the approach adopted in most traditional papers, which analyze the relationship from a single perspective, by taking a multi-angled approach, with examination of the effect of labor demand and entrepreneurship on income equality from innovations. The study also identifies research gaps in the current literature and direction exploring the effects of innovation on income inequality going forward.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".