Studying Individuals in Same-Sex Couples using Longitudinal Administrative Data from Canadian Tax Records: Opportunities and Challenges
Bibliographic record
Abstract
Background: Quantitative research on the social, demographic, and economic outcomes of sexual minorities has long been hampered by data shortfalls, with most surveys and censuses limited by sample sizes and/or a lack of direct questions on sexual identity. The growing availability of administrative data presents an opportunity to fill some of these gaps.Objective: This article highlights the challenges and opportunities involved with using a novel administrative dataset – the Longitudinal Administrative Databank, which includes 20% of Canadian tax filers – to study sexual minority populations in Canada. We identify three sources of bias, propose strategies to adjust for this bias, and introduce a measure of “inferred sexual minority status” to improve the identification of sexual minorities in tax data.Results: Administrative tax data offers significant advantages, including a large sample size, high-quality income data for individuals and linked family members, a longitudinal design, and the ability to trace individuals’ same-/different-sex partnership histories. Our adjustment strategies mitigate some biases in identifying same-sex couples, including underreporting, misclassification, and measurement errors. The estimated proportion of individuals in same-sex marriages closely aligns with Canadian census estimates from 2006–2021, while the proportion in same-sex common-law partnerships is underestimated. Finally, our earnings gaps analyses highlight the utility of the inferred sexual minority status measure.Contribution: This article contributes to research on sexual minority data landscapes, offering new insights into the identification and measures of sexual minority populations using longitudinal administrative tax data. Our approach points to new opportunities for studying the long-term longitudinal income and family dynamics of sexual minority populations on the national level.
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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 | 0.059 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".