Measuring participation in undeclared work in Europe using survey data: A method for resolving social desirability bias
Bibliographic record
Abstract
Measuring participation in undeclared work using surveys has been criticized for under-estimating the level of engagement due to social desirability bias that leads to an under-reporting of “bad” behavior. Until now, few studies have sought to quantify the amplitude of this bias in surveys of undeclared work. The aim of this paper is to fill this gap by using the most appropriate methodologies for estimating the probability of misleading responses in such surveys. Reporting data from special Eurobarometer survey no. 498 conducted in 2019 and involving 27,565 respondents in EU-27 countries and the UK, only 3.5% openly admitted to participating in undeclared work. The results of a Probit model with correction for misclassified cases (i.e., those undertaking undeclared work but declaring that they do not) reveals that nearly a quarter (23.3%) of the respondents undertaking undeclared work refused to openly admit this during the survey, due to the social desirability bias. The estimated overall proportion of undeclared workers is 17.3%. We obtained this value by correcting for both misclassification and the additional source of negative bias due to the large imbalance in the data (i.e., observations in one class are much lower than the other). The outcome of this new advanced approach in analysing undeclared work is that survey estimates can now report its size and determinants in a more accurate manner than has been previously the case.
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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 | 0.090 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".