The Effects of Financial Innovation, Sustainable Development of the Stock Market, and Economic Growth in Mexico
Bibliographic record
Abstract
In this article, we analyze the dynamic causal relationship between financial innovation, sustainable development of the stock market, and economic growth in Mexico for the period from 1990 to 2020. We utilize the AutoRegressive Distributed Lag (ARDL) bounds testing procedure and the Granger causality test to generate a model that determines the direction of causality among the variables. This model allows for the inclusion of explanatory variables to analyze dynamic relationships between them. To this end, financial innovation is incorporated into a trivariate model involving financial development and economic growth, creating a bidirectional causality model. Another advantage is that it provides consistent and efficient estimates even with small samples. Our results indicate that, overall, access to international financing has a more significant impact on the sustainable performance of Mexican companies compared to domestic financing. This effect is maximized when companies use financial resources to foster innovation, which acts as a key catalyst for sustainability. Furthermore, although direct government support is not statistically significant, the training and advisory services provided by the government facilitate a more efficient use of financing, promoting both sustainability and business innovation. These findings emphasize the importance of a strategic approach that combines diversified financing, innovation, and appropriate public policies to encourage a more competitive and sustainable business development in Mexico.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".