15 % of GDP. MEASURING THE SIZE OF THE HIDDEN ECONOMY IN CANADA: A LATENT VARIABLE/MIMIC MODEL APPROACH
Bibliographic record
Abstract
This paper presents some preliminary results relating to the size of the hidden economy in Canada. There appears to have been very little formal analysis of this phenomenon in the context of the Canadian economy in the past, but, internationally, there has been a wealth of work published for different countries, especially the United States and many European countries. These studies have used a variety of techniques to measure the size of the hidden economy, the most sophisticated of which is the MIMIC model approach. This modeling technique recognizes the inherent invisibility of the hidden economy and it is the method that is employed in this paper. The MIMIC model uses information contained within relevant indicator and causal variables to estimate an ordinal time-path of the size of the hidden economy over the sample period (1976 to 1995). The index series is easily converted into a cardinal time-path by using an average of the estimates of the Canadian hidden economy obtained in other studies to create a “benchmark ” for the series. The end result is a time-path for the hidden economy, expressed as a percentage of measured real GDP. By allowing for different combinations of causal and indicator variables in the model, three separate yet very similar time-paths are presented. The results suggest that the size of the hidden economy in Canada for 1995 is approximately
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".