The effects of corruption on Peru's economic growth during the period 1998-2018
Bibliographic record
Abstract
The objective of this research was to determine the effect of corruption on economic growth in Peru for the period 1998-2018. It was determined how economic growth was affected both in the short and long term by corruption. The corruption control index was used to measure corruption and the variation in gross domestic product (GDP) at constant prices was used to measure economic growth. To determine this effect, the hypothetical deductive method was used as a general method since we sought to corroborate a hypothesis and as a specific method, we used the Autoregressive Model of Distributed Lag (ARDL) since this model adapts well to small samples, likewise we had a non-experimental research design – longitudinal explanatory. Data were collected from the World Bank and the Ministry of Education (MINEDU) from 1998 to 2018. As a result of the econometric ARDL model, it was obtained that corruption had a negative effect on economic growth since an improvement of one percentage point in the corruption index would mean an improvement of 0.55 percentage points in economic growth. Therefore, it is concluded that the effect of corruption has a negative effect on economic growth as expected according to the reviewed antecedents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".