Income Inequality and Stock Returns: Asymmetric Effects on Canadian Provinces
Bibliographic record
Abstract
Abstract Research purpose. Most countries with highly capitalised free markets and countries with dominant state-owned economies may have difficulty in preventing income inequality. Productivity and income distribution are expected to have an impact on income inequality. The present paper aims to discover whether there is an asymmetric effect between Stock Return and Income inequality in Canada at the provincial level. Design / Methodology / Approach. We analyse annual data from 1976 to 2021 from ten representative Canadian provinces employing an autoregressive distributed lag model (ARDL) for linear and nonlinear model approaches, and reconstruct the error correction model (ECM) to identify aggregation bias. Findings. While in the long run, the impact on income inequality is not significant due to aggregation, our short-run results indicate a significant relationship between stock returns and income inequality in most of the Canadian provinces. Originality / Value / Practical implications. There is evidence of these effects of stock returns on income inequality being asymmetric (partial sums have different coefficients in sign and size) in most cases, and this effect may persist in the long term. This asymmetry suggests that policy measures addressing income inequality should account for temporal variations and the differential impacts of financial market developments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".