Factores externos e impactos sobre la productividad total factores de Empresas de Manufacturas: Evidencia del Caso Peruano
Bibliographic record
Abstract
En contraste con la literatura sobre la productividad total factorial de empresas, focalizada en factores internos, este trabajo examina el impacto de tres factores externos sobre la tasa de crecimiento de la productividad total factorial (PTF) de empresas manufactureras del Perú, periodo 2002-2019. Usando la Encuesta Económica Anual de firmas manufactureras del Perú, los resultados de las estimaciones de datos de panel con variables instrumentales revelan que países exportadores de productos primarios y derivados de ellos, los términos de intercambio proveen incentivos a las empresas a incrementar la productividad. Contrariamente, shocks domésticos de crecimiento del PBI, en países dependientes de la demanda y producción interna, desincentivan al crecimiento de la PTF dado que la producción interna y PTF se asocian más a los incrementos del capital, en particular aquellos de origen importado. Finalmente, los procesos de liberalización comercial a través de reducciones de los aranceles preferenciales tanto de productos como de insumos ayudan, aunque en menor magnitud y significancia estadística, a incrementar la tasa de crecimiento de las PTF de las empresas, particularmente en las empresas exportadoras.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".