Lifetime Inequality Measures For An Emerging Economy: The Case of Chile
Bibliographic record
Abstract
Cross-section and lifetime measures of inequality are different. While the latter reflects long run resources available to individuals, the former does not. This emphasizes the dynamic dimension of inequality. Many studies have analyzed and compared economies using this lifetime perspective, however, they all focus on the United States, Canada or Europe. Since the literature is scarce for emerging economies, this paper seeks to fill this gap focusing on the analysis of lifetime inequality for an emerging economy using a search-theoretic framework. The model, which is structurally estimated with Chilean data, uses career simulations to construct lifetime measures of inequality. A set of experiments are also performed to isolate the mobility and distribution effects on inequality, the marginal effect on inequality of individual parameters, and the importance of the different ages. Results indicate that inequality is not only high in a cross-section perspective, but also in a lifetime perspective. Low mobility is the main source of lifetime inequality in the Chilean labor market being the older workers who experience the lowest degree of mobility. Finally, regulation of the labor market is important because it affects the degree of mobility in the labor market.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".