THREE ESSAYS ON THE UNDERGROUND ECONOMY SHADY TRANSACTIONS: THREE ESSAYS ON THE UNDERGROUND ECONOMY By
Bibliographic record
Abstract
The term "underground economy " refers to output that is produced, and income that is generated, by agents who hide this fact from authorities. There has been a recent resurgence in interest in the underground economy and this interest has predominantly been stimulated by the perception that the underground economy is sizeable and growing. This dissertation is comprised of three essays, the goals of which are to provide empirical measures of underground activity. The first paper in this dissertation applies a modeling technique that treats the underground economy as an unobservable or latent variable and incorporates multiple indicator and multiple causal (MIMIC) variables to estimate a time-path of the size of the broadly defined underground economy. Using macroeconomic Canadian data, the results indicate that the underground economy grew steadily over the sample period: from 7.5% of Gross Domestic Product (GDP) in 1976 to about 15.3 % in 2001. The second paper uses microeconomic data and proposes a nonparametric expenditure-based approach to obtain estimates of income under-reporting by self-employed households. The approach is illustrated by estimating the effect of the Canadian Goods and Services Tax (GST) on income under-reporting. It is found that the difference between true and reported self-employment income is larger for households at the lower end of the self-employment income distribution and that there was no statistically significant change in under-reporting behaviour following the implementation of the GST. iii
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".