Do the Origins of Foreign Direct Investment Matter For Target Firms in Developed Host Countries?
Bibliographic record
Abstract
Patterns of foreign direct investment show that multinationals originating from developing countries and with state ownership are increasingly engaging in foreign direct investment (FDI). Yet, the impact of such FDI on target firms in developed host countries has received limited empirical attention. This article contributes to the debate by examining if there are differences in the productivity of target firms where FDI, in the form of partial acquisitions, originates from firms with state ownership or firms from less economically developed home markets. Based on an internalization theory perspective, we assume that the success of a multinational enterprise is contingent on its ability to recombine its own firm-specific advantages with local complementary resources and capabilities. We argue that the level of control obtained by firms of different origins will influence the incentives of the local partner to cooperate, which can negatively impact the productivity pattern of the target firm. We collected data from the oil and gas industry in Canada and the US from 2008-2013. Our panel data analysis suggests that the country of origin affects the productivity pattern of FDI. However, there is no significant difference between the productivity of FDI from state-owned versus non state-owned MNEs, suggesting alternate sources of firm-specific advantages for state-owned MNEs. Our study contributes to the debate of whether the institutional dimensions reflected in different FDI origins impact the sources of FDI benefits.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".