Foreign Equity and Technological Capabilities: A Comparison of Joint-venture and National Automotive Suppliers in India
Bibliographic record
Abstract
Using the evolutionary framework, this paper examines differences in technological capabilities between joint-ventures with over 10% foreign equity and fully national owned suppliers located in the greater Delhi region. In a country where automotive manufacturing emerged from the relocation of foreign capital and with only 3 automobile assemblers supplying essentially the domestic market until 1991, India has experienced tremendous upward movement in firm-level technological capabilities to generate rapid growth in automotive exports to record a positive trade balance by 2000. Against the background of rapid expansion, this paper uses a sample of 84 tiers one and two suppliers to compare technological capabilities between joint-ventures with foreign equity and wholly national owned suppliers. The results show considerable participation of both joint-venture and national firms in cutting edge technological capabilities. The two tailed ‘t’ tests, and the knowledge intensity exercises show joint-ventures enjoying higher overall technological capabilities, training expense in payroll, process technology expenditure in sales and R&D expenditure in sales than national firms. The Tobit regressions show that once export-intensity and firm size is accounted for, joint-ventures enjoy a statistically significant higher technological capability with its superior process technology over national firms. Overall, the evidence shows that foreign equity is still important in the technological operations of automotive suppliers in India.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".