Tax Compliance: How Trust in Government Can Increase Federal Tax Revenues
Bibliographic record
Abstract
Canada is losing billions of dollars each year to individuals shirking on their taxes (Canada Revenue Agency 2016). The question of how to reduce this substantial amount is both pertinent and difficult. As a liberal democracy, the federal government is constrained by how much coercive force it can use. Voluntary compliance by citizens is essential. This paper will demonstrate how certain measures can be used to build trust in the federal government and its institutions – specifically in the Canada Revenue Agency (CRA), leading to a decrease in tax shirking and increasing tax revenue. Canada, along with many other liberal democracies, have simultaneously experienced declining levels of trust (Dalton 2004). This change erodes the legitimacy of the government and its institutions. In addition, the problem is unlikely to correct itself. Values have changed among younger generations, altering expectations of governments (Inglehart 2008) and further straining trust. Action is needed to respond to the changing relationship between the Canadian government and its citizens. There is a strong correlation between an individual’s trust in government and their likelihood of paying taxes (Kucher and Götte 1998; Shulz and Lubell 1998). This relationship is integral to the paper’s recommendations. If an individual’s trust in government can be increased, more tax revenue will follow. Trust must first be built with the public. Advanced liberal democracies primarily build trust through their institutions (Zucker 1986). Trust in government can be viewed as a collection of trust in its parts. Ideally all federal institutions would follow trust building measures. However, given the infancy of the research and the relative novelty of recommendations, this is unrealistic. This paper will give more pragmatic recommendations focusing on the CRA. As a large institution that deals regularly with taxpayers, the CRA is a clear choice to first implement trust building measures. In order to accurately quantify and analyze trust, the CRA must first conduct its own trust measurements. Perception surveys, the most common measurement type in use, will be used. Popular 3rd party trust measurements ask broad and ambiguous questions (Connolly 2016; Edelman 2021), limiting their efficacy. The CRA should ask clearer and more pointed questions that get to the heart of where Canadian distrust arises. Corruption constitutes the strongest predictor of trust placed in remote political institutions directly (Blind 2006, 12). To address appearances of corruption, steps should be taken that prevent citizens from forming negative views of the CRA. Appearance standards remedy this issue by treating improper appearances as an offence, even if no offence has taken place. Trust is measured by perception, making preemptive action a necessary element. Once trust is lost, it is difficult to gain back. Engagement is linked with increased trust in the government (Wesley 2018). The CRA should foster greater engagement by allowing for e-participation opportunities on its website. Not only will it capture new individuals, but greater levels of engagement will be made available to those already participating. An e-government model will be followed to detail the process.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".