Investigating if the Financial Markets Performance Affects the Shadow Economy: Additional Evidence From EU Countries
Bibliographic record
Abstract
The shadow economy also known as the informal or unobserved or underground economy, is a phenomenon that affects not only emerging markets and developing countries but also advanced economies. In general, this undeclared economic activity is hard to measure given its hidden nature in addition to its relation with unlawful activities. Nevertheless, apart from the legal aspects that may appear, shadow economy has negative implications in terms of tax revenue and social security contributions for the nations. To this end, an extensive literature has explored the measurement issues as well as the root causes of this phenomenon proving that the underground economy constitutes a significant portion of the overall economy in a number of countries. This paper tries to investigate the relationship between the shadow economy and the financial markets. This paper employs a number of panel data regression models to detect the association between the financial market metrics and the shadow economy (as a% of GDP). The outcome of this paper is that it finds evidence that increased market capitalization, GDP per capita and FDI as well as low unemployment and inflation rates contribute to low levels of shadow economy. This can be of value to policy makers and the competent authorities of the countries that wish to find means to contain their shadow economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".