Modern Economic Conditions and Impact of the Tax Regime on the Legalization of Self-Employment: Russian and Foreign Experience
Bibliographic record
Abstract
This article analyzes Russian and international experience in approaches to taxation of self-employed individuals and their impact on the legalization of activities based on modern economic conditions. The purpose of the study is to conduct a comparative characteristic of the criteria for determining self-employed persons and various forms of their taxation in Russia and abroad at the present stage. The special tax regime for the self-employed in Russia has been in effect since 2019 and is one of the youngest in the tax system, so the analysis of foreign experience in the taxation of self-employed persons is of particular practical interest. The authors analyze global statistics on the number of self-employed persons and identify trends in their changes not only in the Russian Federation, but also in some foreign countries, especially in the light of the development of digital technologies and the emergence of new opportunities for independent activity by individuals. The research made it possible to draw intermediate conclusions for Russia at this stage of the new regime implementation: whether modern tax regimes stimulate the development of self-employment, how optimal these regimes are for legalizing citizens ' income, and what trends of foreign countries in the field of self-employed taxation may be relevant for modern Russia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".