Estimating the size of Romanian shadow economy using Gutmann's simple currency ratio approach
Bibliographic record
Abstract
Currency is widely assumed to have a comparative advantage over checks for the payment of purchases of goods and services that individuals wish to conceal from the authorities. A rise in currency stocks and payments may be taken as a rough indicator of the extent to which these transactions may not be reported to government authorities. The paper aims to estimate the size of subterranean economy using the simple currency ratio method of Gutmann for quarterly data covering the period 2000-2010. Thus, the study analyzes the ratio of currency to demand deposits in order to estimate the amount of economic activity in the subterranean economy. The empirical results point out that the illegal economic activities are about four billion RON at the middle of 2000; it constitutes 17.4 percent of the official GNP. During the period 2001-2004, illegal economic activities follow a downward path reaching 9.5% of official GNP at the end of 2004. For the period 2004-2006, unofficial economic activities fit a slow upward trend until the second quarter of 2006, for which the size of subterranean economy reaches the value of 12.3% of official GNP. Beginning with 2007, the amount of illegal activities as % of official GNP begin to decrease until the third quarter of 2008, which is the base year in which no shadow economy is supposed to exist. For the last years, the ratio of subterranean economy to official economy increased slowly, reaching about 9.3% in the second quarter of 2010.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".