Essays on economic inequality, income taxes, and intergenerational mobility
Bibliographic record
Abstract
The first chapter provides the first consistent estimates of intergenerational earnings mobility in Chile, based on administrative records that link a child's and their parent's earnings from the formal private labour sector. We estimate that the intergenerational earnings elasticity is between 0.288 and 0.323, whereas the rank-rank slope is between 0.254 and 0.275. We find significant non-linearities in the intergenerational mobility measures, where intergenerational mobility is very high in the bottom 80\\% of the parents' distribution but with extremely high intergenerational persistence in the upper part of the earnings distribution. In addition, we find remarkable heterogeneity in intergenerational mobility at the regional level, where Antofagasta, a mining region, is the most upwardly-mobile region. Finally, we estimate significant differences across municipalities in the Metropolitan Region, where our estimates suggest that the place of residence makes a significant difference in intergenerational mobility for children of upper-class families, while it is less relatively important for children of lower- and middle-class families. \n \nThe second chapter proposes a new methodology to value retained earnings as income by transforming them into accrued capital gains and develops a parametric procedure to impute corporate retained earnings to households. We use this approach to estimate income inequality for Canada using household survey data, and aggregate retained earnings information from national accounts. We show that including retained earnings by transforming it into accrued capital gains increases income inequality in Canada and changes the trend in income inequality, exhibiting more consistency with the decline in capital income after the Great Recession. \n \n \nThe third chapter investigates consequences of top-distribution undercoverage on the Gini coefficient. It shows that not correcting for underreporting and nonresponse at the top does not necessarily result in an underestimated Gini coefficient. In addition, this paper proposes a Gini approximation based on the Atkinson approximation to correct for underreporting at the top. Under plausible assumptions, the approximation proposed for correcting underreporting at the top is near exact. To evaluate this methodology, this paper uses Chile and Canada as examples where we include undistributed business profits to measure income inequality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".