A firm-level analysis of the impact of foreign direct investment in research and development on the innovation output of Indian firms
Bibliographic record
Abstract
Using the firm-level data, this paper investigates the impact of Foreign Direct Investment in Research and Development (FDI-R&D) on firms' innovation output in the Indian context during 2010–2020. We hypothesize that foreign investment brings intangible knowledge about the recent technological advancements, international scientific practices, and working culture of global laboratories to domestic firms and thus improves the innovation performance of such firms. The major challenge in conducting the empirical studies is the selection problem as productive efficient host country firms are likely to attract foreign investments. Hence, we employ Propensity Score Matching (PSM) and Difference in Differences (DID) to capture the influence of foreign investment on innovation output namely patents. After controlling the selection problem, we find that firms with FDI-R&D patent more than the non-FDI-R&D firms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".