PRINCIPLES AND PRACTICES IN THE MEASUREMENT OF THE UNRECORDED ECONOMY: DISCUSSANT’S REMARKS
Bibliographic record
Abstract
A lot of attention is being paid these days to the “hidden economy”. Reports often suggest that the figures published by national statistical offices miss large parts of the economy. They challenge the credibility of national accounts ’ estimates. For example, Schneider and Enste (2000) estimate an average level of the hidden economy in Canada over the period 1990-93 at 13.5 % whereas a Statistics Canada report (1994) concluded that the upper bound to what could have been missed in the 1992 official gross domestic product figures was 2.7%. However, the research papers on the hidden economy are often subject to one or both of two major weaknesses. First, they often fail to define exactly what is to be measured and thus possibly missed. This lack of precision regarding the measurement target is epitomized by the wide range of different terms in common use- hidden economy, shadow economy, parallel economy, subterranean economy, informal economy, cash economy, black market- to mention just a few. There is no common understanding whether they all mean the same thing, and if not, what relationships they have to one another. The second problem is the dependence of most estimation methods upon high level model assumptions that cannot be justified. For example, the model upon which Schneider and many others base their estimates is one that assumes that changes in the patterns of currency demand can be attributed entirely to, and reflect accurately, changes in the hidden economy. Another model used by Lackó (see Schneider, 2000) assumes the hidden economy can be measured through changes in household consumption of
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".