The participation of foreign bidders in EU public procurement: Too much or too little?
Bibliographic record
Abstract
This policy brief examines EU public procurement data from the Tenders Electronic Daily (TED) to evaluate foreign bidders' participation and success in winning EU public contracts. Despite data coverage limitations, the available information shows an increase in foreign participation in both full and partial contracts, with most activity concentrated in a few countries. Countries like the United States, Japan, and Canada focus on securing full contracts, while nations such as Norway and Turkey often engage through partial contracts. Notably, since Brexit, UK bidders have faced a significant decline in market share, benefiting other countries. The main conclusion is that foreign participation via cross-border procurement (Mode 1) - the only one available in the TED database - is not very high. Although it increased over time, it remains relatively modest, mainly due to low participation rates, rather than discriminatory practices. Between 2016 and 2019, only about 7 percent of EU procurement authorities received foreign bids. This fact alone largely explains the low level of cross-border procurement taking place via Mode 1: put simply, there is no winning without trying. Another important conclusion is that a comprehensive assessment of the participation of foreign bidders in EU procurement would require two new key metrics in the TED data collection process to capture the more important yet missing modes of international procurement. Having reliable and comprehensive data is not just an academic pursuit but a necessity for shaping effective EU policy in the face of rising global protectionism in public procurement.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".