Evaluating the Prevalence and Distribution of Quasi-formal Employment in Europe
Bibliographic record
Abstract
To show how formal and informal jobs are not always discrete, this paper uncovers how many formal employees in the European Union are paid two wages by their formal employers, an official declared salary and an additional undeclared wage, thus allowing employers to evade their full social insurance and tax liabilities. Analyzing a 2007 Eurobarometer survey involving 26,659 face-to-face interviews in the 27 member states of the European Union (EU-27), one in 18 formal employees are found to engage in such quasi-formal employment, receiving on average one-quarter of their gross salary on an undeclared basis. Multi-level logistic regression analysis reveals that quasi-formal employment is significantly more prevalent in East-Central Europe, in smaller businesses and the construction sector, and amongst men, younger persons and the lower paid. The dichotomous depiction of employment as either formal or informal therefore needs to be transcended and a finer-grained continuum of types of employment depicted from wholly formal to wholly informal with many varieties in-between. The paper then briefly reviews what might be done to tackle this illegitimate wage practice. This clearly displays that this quasi-formal form of employment needs to be more fully integrated into discussions when discussing how to tackle undeclared work, since some measures that tackle wholly undeclared work, such as reducing the minimum wage, might simply allow formal employers to pay a larger portion of their formal employees’ earnings as an additional undeclared wage, rather than facilitate the creation of fully formal employment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".