Bibliographic record
Abstract
There is considerable agreement internationally about the factors that determine the relative size of the underground economy (tax burden, regulation, enforcement, confidence in government, labour force characteristics, and morality) and that evidence of underground activity will be captured in several economic indicators (GDP, currency in circulation, and consumption rates). Until recently, however, the methods that have been employed to measure the underground economy focused on only a few causal factors, one indicator, and only produced an estimate for one particular point in time. There exists a modeling technique that treats the underground economy as an unobservable or latent variable and incorporates multiple indicator and multiple causal (MIMIC) variables. The MIMIC model uses information contained within relevant indicator and causal variables to estimate a time-path of the size of the hidden economy. In applying this estimation technique to Canada data, my results indicate that, the underground economy in Canada grew steadily relative to measured GDP over the period 1976 to 2001. The value of the broadly defined underground economy grew from about 7.5% of GDP in 1976 to about 15.3% in 2001. In real (1997) dollar terms, it increased from about $38 billion to $159 billion.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".