Distributional Effects of Income Transfer Programs and Personal Income Taxes
Bibliographic record
Abstract
This thesis studies various distributional effects of social transfers and taxation for Canada over the 1993-2008 period using the longitudinal Survey of Labour and Income Dynamics (SLID). It is comprised of four chapters. Chapter 1 examines how changes in the receipt of program benefits, due to reforms in government transfer programs, have affected the income distribution. Chapter 2 examines the relationship between the post- to pre-fiscal ratio of income instability, and measures of fiscal progressivity and indicators of social insurance. Chapter 3 studies the inequality-reducing role of the tax/transfer system; and chapter 4 presents estimates of earnings dynamics accounting for education. In chapter 1 we apply a nonparametric reweighting decomposition method that reflects changes in the probability of receiving program benefits from 1996 to 2006. This method identifies the redistributive role played by transfer programs, and where in the distribution these programs have their greatest effects. We find reforms to Social Assistance (SA) reduced its redistributive effectiveness, while Child Benefits, Employment Insurance (EI) and Old Age Security became more redistributive. Chapters 2 and 3 decompose the variance of income into a long- and a short-term component as in the literature on earnings dynamics, using a five-year rolling window structure. Chapter 2 examines the relationship of the post- to pre-fiscal ratio of income instability (E-ratio) with a measure of fiscal progressivity, and indicators of social insurance.We find the E-ratio increased substantially after 1998, indicating a turn toward less progressivity, a pattern driven mainly by families with low education earners. The E-ratio of income long-term inequality (permanent variance) is examined in chapter 3. We find the tax/transfer system reduces the permanent variance for earners with less than High School. The provision of SA is more effective in reducing long-term inequality than EI. Chapter 4 presents the first estimates of earnings dynamics accounting for education and using the SLID for Canada. Instability and inequality are the greatest for the less educated, and grew rapidly in the late 1990s. Since 2001, inequality increased among earners with college and university degrees.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".