Do Innovation and Offshoring Make a Difference? An Empirical Exploration of the Effects on the Performance of European Firms
Bibliographic record
Abstract
<p>This study attempts to answer the question of whether European manufacturing firms that undertake offshoring, innovation or both benefit from higher productivity and profitability. From a methodological point of view, the driving forces that push firms to innovate and/or to offshore can be seen as self-selection mechanisms that make the estimation of their economic impact more difficult if the confounding factors affecting these mechanisms also affect the economic performance of the firms. To disentangle the effect of both offshoring and innovation on firms’ performances from the effect of firm characteristics, the propensity score matching methodology in a multi-overlapping treatment setting is used.<strong> </strong>The study targets European countries using the EU-EFIGE/Bruegel-Unicredit dataset. Decisions to offshore and innovate do not seem to have a significant effect on productivity, whereas the decision to innovate only has a significant effect on firm profitability.<strong> </strong></p>
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".