Mandatory extraction payment disclosures and tax haven use: Evidence from United Kingdom
Bibliographic record
Abstract
Research Question: Does public country by country reporting (CbCr) deter multinationals' tax avoidance practices operating in extractive industries? Motivation: Public CbCr has already been implemented for two specific sectors, namely the financial and extractive sectors. Prior studies have focused on tax avoidance of EU banks around the implementation of public CbCr requirement (Joshi et al., 2020; Eberhartinger et al., 2020; Overesch & Wolff, 2021). However, studies on how resource-extracting multinationals respond to the CbCr regulation are scarce. This study seeks to fill this gap by examining the effect of public CbCr on tax avoidance with a special focus on extractive industries. Idea: To improve fiscal transparency, Canadian and European legislators have adopted regulations requiring multinational corporations (MNCs) to provide, annually, their Extraction Payment Disclosures (EPD) (Public CbCr standard for extractive industries) to governments (EC, 2013; Natural Resource Canada, 2014). This study examines the effect of mandatory EPD adoption on the extent of tax haven use. Data: For a 10-year period surrounding the mandatory EPD adoption (2010-2019), we selected a sample of UK MNCs operating in the oil, gas, and mining sectors and listed on the London Stock Exchange. The analysis is mainly based on firm-level information taken from DATASTREAM database. Based on hand-collected data from annual reports, we measured the extent of tax haven use using the percentage of multinational subsidiaries located in tax haven jurisdictions/countries as listed in Dyreng and Lindsey (2009). An alternative list identified by the Organization for Economic Co-operation and Development (OECD) (2006) was also used in a robustness test. Tools: To examine our research question, we estimated a linear regression model with panel data using STATA software. Findings: The results show that the increased transparency resulting from public EPD does not appear to significantly affect the intensity of tax haven use. Contribution: This study extends and complements prior literature examining the effect of CbCr on tax avoidance and profit shifting by focusing on a specific setting i.e. extractive sector. To the best of our knowledge, apart from Johannesen and Larsen (2016) and Rauter (2020), no studies have provided empirical evidence on how resource-extracting multinationals respond to the EPD regulation.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".