Do Credit and Employment Exhibit Long-Run Convergence? Empirical Evidence from the State of Santa Catarina
Bibliographic record
Abstract
Credit and employment are fundamental variables for measuring the socioeconomic development of a region. This study examines the convergence between credit levels and employment in the economy of the state of Santa Catarina, Brazil. A theoretical and empirical review was conducted on the credit market and its connection to economic growth and employment. The initial hypothesis considers that credit and employment exhibit long-run convergence. The methodological approach adopted involves the application of an error correction model (ECM), which provides a comprehensive view of the short- and long-run interactions between employment and credit. In addition, the causality between the variables is assessed. These relationships are crucial for policymakers and economic analysts when making decisions and forecasting economic trends. The results indicate a stable long-run relationship between the variables analyzed. In the short run, credit adjusts more rapidly than employment in response to deviations in the economy of Santa Catarina. The causality effect suggests that employment stock causes credit balances.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".