Innovation and Productivity in Services: Evidence from Chile
Why this work is in the frame
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Bibliographic record
Abstract
This paper analyzes empirically the relationship between innovation and productivity in the Chilean services sector. Consistent with recent evidence on developed countries, we find that services firms are as innovative as firms in the manufacturing industry. In the basic model, we also find that both industries have similar determinants of the investment in innovation and the probability of introducing innovations (products or process), such as size and export status. In several extensions we find similar roles for technological and non-technological innovation in labor productivity and for determinants such as skill intensity and financial restrictions. In general, our evidence suggests that that innovation input and output is associated with improvements in productivity in both sectors. Since we do not find clear-cut differences, it might be the case that even larger efforts need to be done to have better measures of innovation and productivity in the services industries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it