Análisis de las Tendencias Recaudatorias y las Contribuciones en México
Bibliographic record
Abstract
Public contributions in Mexico are governed by the constitutional framework, which mandates citizens to contribute proportionally and equitably to public spending. In 2024, the primary sources of tax revenue were the Income Tax (ISR), the Value Added Tax (IVA), and the Special Tax on Production and Services (IEPS), with increases observed in IVA and IEPS collections. This study presents a descriptive and trend analysis of the evolution of public contributions in Mexico, employing a mixed-method approach based on secondary data sources. The results project an increase in federal taxes, social security contributions, and fees by 2031, while other revenue streams and initial availability are expected to decline. The ABCD strategy implemented by the Servicio de Administración Tributaria (SAT), aimed at optimizing tax collection and reducing tax evasion, led to an increase of 92.8 billion pesos in the first quarter of 2024 compared to 2023. However, challenges remain, particularly in expanding the taxpayer base. These findings highlight the crucial role of tax collection in sustaining Mexico’s social and economic development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".