The Knowledge Deficit about Taxes: Who It Affects and What to Do About It
Bibliographic record
Abstract
Tax professionals argue that the tax system is too complex for ordinary taxpayers and that this sometimes makes their work for clients more educational than strategic. Tax complexity is not only a headache for these professionals but also a source of inefficiency and unfairness in our tax system as not every citizen understands it to the same degree. We use the tax system to support our retirement, education and poverty-reduction public programs. Failures of the tax system affect these programs as well. Weak understanding of taxes has also been shown to lower the level of trust of citizens in the tax system. This lower level of trust can translate into higher rates of tax evasion or avoidance, raising the cost of taxation for everyone. Canada has experience assessing the financial literacy of its citizens and is developing policies to raise it. This issue is considered strategic as financial tools and markets become more and more complicated and our population is aging. The tax system is a financial tool that citizens must know how to use as much as mortgages or pension funds are. Drawing from the research on financial literacy, we aim to develop a method to measure “tax literacy.” We evaluate, with the use of a survey administered by the polling firm Crop to Quebec citizens, the knowledge and skills of citizens concerning fiscal matters, understood broadly to include direct and indirect taxation as well as social transfers. Age, education and income are associated with higher knowledge of these matters, but not gender or being self-employed. Higher tax literacy is associated with a higher propensity for taxpayers to produce their tax return themselves rather than with the help of a professional. It also appears that women tend to underestimate their understanding of tax. Tax literacy, and the methods for its measurement, are a new tool to assess the failures of certain policies such as the Children Fitness Tax Credit or the Canada Learning Bond to reach their target audiences. More generally, weak understanding of taxes contributes to lowered trust in our tax system, which underpins our social bonds. Assessing this issue is a first step towards making our tax system fairer and more efficient.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".