Keeping it off the books: an empirical investigation of firms that engage in tax evasion
Bibliographic record
Abstract
This article uses a unique dataset that contains detailed information on firms from around the world to investigate factors that affect under-reporting behaviour. The empirical strategy employed exploits the nature of the dependent variable, which is interval coded, and uses interval regression which provides an asymptotically efficient estimator provided that the classical linear model assumptions hold. These assumptions are investigated using standard diagnostic tests that have been modified for the interval regression model. Evidence is presented that shows that the firms in all regions engage in under-reporting. Regression results indicate that government corruption has the single largest causal effect on under-reporting, resulting in the percentage of sales not reported to the tax authority being 51.3% higher. Taxes have the second single largest causal effect on under-reporting, resulting in the percentage of sales not reported to the tax authority being 18.0% higher, followed by access to financing at 8.9% higher and organized crime at 7.6% higher. Inflation, political instability, exchange rates and the fairness of the legal system were found to have no effect on under-reporting. It is also found that there is a significant correlation between under-reporting and the legal organization of the business, size, age, ownership, competition and audit controls.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".