The Effect of Tax Preparation Expenses for Employees: Evidence from Germany
Bibliographic record
Abstract
Abstract Using a panel of German income tax accounting data from taxpayers with no business income (employees), we find a negative relationship between tax preparation expenses and tax liabilities. However, preparation expenses are shown to exceed estimated tax savings. Specifically, one additional Euro spent on preparation yields an estimated tax savings of 72 cents in an OLS regression and 24 cents in a fixed effects regression. In addition, we observe substantial heterogeneity in tax savings among income groups, but even if we account for long‐term tax savings, tax liability reductions exceed tax preparation expenses only for the highest income quintile. In all other income quintiles, average preparation expenses exceed the estimated tax and time savings. Based on these results, we also examine whether other specific benefits affect an individual's decision to purchase tax preparation services, and the results indicate the importance of the benefits of coping with complexity and reduced uncertainty. Overall, our findings illustrate that the current tax compliance process violates at least two of Adam Smith's principles of taxation, taxes are neither certain nor fair.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".