Opting or Not Opting to Share Income Tax Information with the Census: Does it Affect Research Findings?
Bibliographic record
Abstract
This paper examines the implication of the decision to give 2006 Census respondent the option of letting Statistics Canada access their income tax files rather than answering income related questions directly. We find that giving respondents the option to share their income tax files (or not) adds a confounding factor when it comes to measuring family income inequality, particularly for the bottom tail of the distribution. The consent decision does not, however, materially affect the estimation of standard wage equations. / Ce document examine l’implication de la décision de donner aux répondants du recensement de 2006 l’option de donner permission à Statistique Canada de consulter leur déclaration d’impôt plutôt que de répondre directement aux questions portant sur le revenu. Nous trouvons que donner l’option aux répondants de partager (ou pas) leur rapport d’impôt ajoute un facteur de confusion lorsqu’il s’agit de mesurer l’inégalité du revenu des familles, particulièrement dans le bas de la distribution. Toutefois, la décision de consentement ne touche pas de façon importante l’estimation des équations salariales standard.
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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 | 0.265 | 0.585 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".