Tax Compliance: How Trust in Government Can Increase Federal Tax Revenues
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,010 |
| Communication savante | 0,014 | 0,008 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».