The Effect of Mandatory Extraction Payment Disclosures on Corporate Payment and Investment Policies Abroad
Bibliographic record
Abstract
ABSTRACT I examine how mandatory extraction payment disclosures (EPD)—a policy solution intended to discourage corporate payment avoidance in the oil, gas, and mining industries—affect fiscal revenue contributions and investments by multinational firms in foreign host countries. Using the staggered adoption of EPD across firms headquartered in Europe and Canada, I find that disclosing companies increase their payments to host governments, decrease investments, and obtain fewer extraction licenses relative to non‐disclosing competitors. These effects are stronger for firms that face a high risk of public shaming, operate in corrupt host countries, and have a high exposure to bribery‐prone payments, suggesting that EPD increases the reputational cost of corporate behavior that could be perceived as exploitative. The resulting reallocation of investments from disclosing to non‐disclosing firms reduces drilling productivity and resource production in host countries, consistent with uneven disclosure regulation distorting capital allocation.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".