Transparency, Trust, Transnationality, and Tax Compliance: Lessons from Google's Financial Reporting Practices in New Zealand
Bibliographic record
Abstract
Using New Zealand as an example, this article discusses how tax transparency influences public trust in governing institutions, trust in the private sector, and social trust among taxpayers, with a particular emphasis on the challenges of the corporate income taxation of multinational enterprises and its impact on the voluntary tax compliance of individuals in an open-economy welfare state. Drawing on rational choice and collective action theories, along with the results of empirical research on the psychology of cooperation, the article demonstrates that tax compliance, while appearing to be an individual choice, is fundamentally a collective action dependent on trust in fellow taxpayers and in governing institutions. The article argues that to develop trust, a rational taxpayer requires access to comparable information about other taxpayers' contributions and to information about the government's responses to tax non-compliance. While social trust may drive compliance of people residing within a defined tax jurisdiction, this trust does not extend to non-residents and corporate entities. Furthermore, the default perception of these non-members is rooted in distrust, which increases the demand for information about non-members' tax behaviour and the government's responses to any misbehaviour. The article analyzes two domestic institutional structures in New Zealand—the tax system and the financial reporting regime—alongside a case study of Google's financial reporting practices in the country. The analysis reveals how three key factors create and perpetuate a tax information deficit and undermine the tax system's integrity and the voluntary tax compliance of individuals: insufficient coordination between the tax system and the financial reporting regime, limited transparency in the institutional performance of tax administrators, and inadequate mechanisms for addressing the public distrust of multinational enterprises. The findings emphasize the need to revise New Zealand's regulatory framework for managing and disclosing tax-related information, particularly regarding multinationals operating within the country. These findings are relevant to other countries, including Canada, that protect the confidentiality of corporate tax information.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".