Long-term dynamics of informal employment and its relationship with the poverty of the Russian population against the backdrop of the COVID-19 pandemic
Bibliographic record
Abstract
The study aims at assessing the prevalence of informal employment in the Russian labour market and evaluating its relationship with the risks of monetary poverty. Empirically, the study bases on the data of the Russian Longitudinal Monitoring Survey (RLMS HSE) for 2000-2020. Calculations have shown that over the past 20 years, on average, about a quarter of Russian employees were included in the informal labour market for their main or secondary employment. The results of the study provide some evidence on the existence of several zones of informality in the Russian labour market, in which there are different motives for deformalization, in particular: low-skilled employment in the informal sector, employment only in the format of informal part-time / side jobs (“casual employment”) and partial departure to the informal sector while maintaining an official employment contract at the main place of work. Employment with part or all of the pay for the main job received informally — that is, without a formal contract or with declared wages below the actual wage received, in violation of current regulations — is more common among men, young people and people of early working age, and as well as citizens with education below vocational secondary. At the same time, women, people aged 30–49, and citizens with vocational secondary education predominate in the structure of informally employed, although with a slight preponderance. Regression analysis shows that there is a statistically significant relationship between involvement in the informal labour market and the risks of monetary poverty: fully informal employment in 2019 is associated with higher chances of the respondent’s household falling into poverty, and with lower chances in 2020.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".