Does FDI in manufacturing cause FDI in business services? Evidence from French firm‐level data
Bibliographic record
Abstract
Abstract This paper uses a large French firm‐level data set to evaluate the determinants of location choices in services. In a first step, estimates for four broad services sectors are compared with the estimates for the manufacturing sector. Using a discrete choice model, we find that this framework does fairly well in explaining location choices in services and that the parameter estimates for services are close to the ones for manufacturing. We then investigate whether the similarity in estimated parameters is due to a complementarity between location choices in manufacturing and in services, in the sense that manufacturing location choices may cause the location of services. A particularly appropriate services sector, for this purpose is the business services sector, for which input‐output linkages with the manufacturing sector are particularly strong. It is found that the downstream demand of French manufacturing firms has a positive effect on the location choice probabilities of French business services firms. This effect is robust.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".