The Evidence of the Fiscal Incentive to Increase the HDI: The Case of Maranhão
Bibliographic record
Abstract
This article is the result of an investigation about tax incentives in the State of Maranhão, where the purpose was to answer the question: what is the efficiency of tax incentives in relation to the impact on the state of employment? The hypothesis behind this research is that the idea of the Maranese government to create fiscal incentives to stimulate the generation of employment and income can not impact the regions with low human development index (HDI). The general objective of this article is to verify the relationship between the fiscal incentive and the balance of jobs between 2010 and 2016 in Maranhão in regions with lower HDI. The specific objectives are: to investigate the policy of granting the fiscal incentive "more companies" in Maranhão; compare the number of jobs generated with the number of companies that benefited from the tax incentive; to verify the evolution of the jobs generated in each mesoregion and to know the economic activities that generate more jobs in the state. This study is justified because it believes in the relevance it can bring to the academic environment and to the society that encompasses government, companies and professionals, since it presents a more comprehensive perspective regarding economic development in the local regional government of Maranhão. It considers the methodology of this study, a field research, but of quantitative-descriptive character. Among the results, it was identified that the fiscal incentive had no impact on job creation in the Meso-regions that have the lowest HDI indices in the state of Maranhão between 2010 and 2016.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".