Do “Newly Oligopolistic Reaction” and Host Technology Resources Matter for MNC’s Location? —A Study in China’s Technology Industries
Bibliographic record
Abstract
This paper aims at studying the determinants of inward Foreign Direct Investment (FDI) varying with sectors, by considering particularly multinational corporation (MNC)’s location strategies and local technology resources in host industries. Using data from China’s National Bureau of Statistics and National Development and Reform Commission, we empirically analyze the main determinants of industrial inward FDI, across 20 manufacturing sectors (2-digit) in China, over the period 2001-2008, and we are particularly interested in 9 high-technology (HT) and medium-high-technology (MHT) industries. The random effect panel estimations reveal that when industrial technological intensity is controlled, host technology resources are significantly positive determinants for newly inward FDI. The dynamic econometrical approach by System Generalized Method of Moment (GMM) estimations for HT and MHT industries obtain interesting results, which show evident impacts on MNC’s strategic behaviors, brought about by geographic agglomeration (or industrial concentration) effects and local protection (that we will call “new oligopolistic reactions”). Besides, FDI in HT and MHT industries are both market and export seeking. High productivity, large economies of scale, and abundant technology resources attract newly FDI in these industries. This study has two contributions: firstly, it covers the deficiency that many researches on FDI in China only focus on aggregate flow without distinguishing host sector’s characteristics; secondly, it provide the local government some useful suggestions on regional development and industrial policies, especially in technology industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".