Bibliographic record
Abstract
ABSTRACT Payroll taxes, such as contributions mandated through the Federal Insurance Contributions Act (FICA), are a considerable expense for businesses and a large source of government revenue. Despite the significant cost, little is known about the determinants of payroll tax avoidance. By misclassifying employees as independent contractors, firms can avoid their portion of FICA contributions and other employee-related costs. This paper uses publicly available Wage and Hour Division (WHD) compliance action data from the U.S. Department of Labor (DOL) to identify employee misclassification and examine whether firms that avoid income taxes also avoid payroll taxes. This study documents two main results. First, firms with higher CashETR, indicating lower income tax avoidance, are more likely to have Fair Labor Standards Act (FLSA) violations detected during a WHD audit. Second, firms increase their CashETR following the discovery of FLSA violations, indicating a reduction in income tax avoidance. Data Availability: The data that support the findings of this study are available from the U.S. Department of Labor's Wage and Hour Division. JEL Classifications: H25; H26; K31; K34.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".