Size and Development of the Shadow Economy of 31 European and 5 other OECD Countries from 2003 to 2013: A Further Decline
Bibliographic record
Abstract
In the Tables 1 to 3 the size and development of 31 European and of five non-European shadow economies over the period 2003-2013 is presented 1 . If we first consider the results of the average size of the shadow economy of the 27 European Union countries, we realize that the shadow economy in the year 2003 was 22.3% (of official GDP), decreased to 19.3% in 2008 and increased to 19.8 % in 2009 and then decreased again to 18.4 % in 2013 2 . If we compare the average of 31 European countries, in 2003 the average size was 22.4%, decreased to 19.4% in 2008, and increased to 19.9% in 2009 and decreased to 18.5 in 2013 (Table 2). If we consider the development of the shadow economy of Australia, Canada, Japan, New Zealand and the USA, we find a similar movement over time (see Table 3.); in 2013 these 5 countries had an average size of the shadow economy of 8.6%, in 2010 this value was 9.7%. If we consider the size of the shadow economies over the last 2 years (2012 and 2013) and compare them with the years 2008/09, we realize that, in most countries, we had again a decrease of the size and development of the shadow economy, which is due to the recovery from the worldwide economic and financial crises. Hence, the most important reason for this decrease is, that, if the official economy is recovering or booming, people have fewer incentives to undertake additional activities in the shadow economy and to earn extra “black” money. The only exceptions are Greece and Spain, where the recession of the official economy is so strong, that it even reduces the demand of the shadow economy activities due to the severe income losses of the Greek and Spanish people; the Greek (Spanish) shadow economy will
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".