Measuring the Efficiency of Tax Collection among Economic Sectors in Paraíba State Northeastern Brazil (2013-2015)
Bibliographic record
Abstract
This study investigates the efficiency of tax collection on operations related to the circulation of goods and interstate services (ICMS) in far east of Brazil, Paraíba State. The efficiency was estimated using quarterly data of the electronic invoices from the period of January 2013 to December 2015. In addition, we aim to identify levy’s key factors among distinct sectors, disaggregated into 489 sub-classes, according to the national classification of economic activities. It was used a stochastic frontier analysis which suggests that the average of the technical efficiency of the tax collection among sectors was 73.75% of the potential tax revenues. The amount of uncollected tax during the studied period were approximately US$7 billion. There is technical inefficiency of tax levy among important sectors of the economy of the state of Paraíba, demonstrated by 88.88% of inefficiency of tax collection itself. The sector comprehends clothing, wholesale of personal care products and leather shoes, among others. We verify that an increase of oversight actions by the tax collection agency helps to inhibit the inefficiency of tax levy.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".