Income Inequality and Multinational Enterprise Expansion Strategy
Bibliographic record
Abstract
In this paper, we examine the extent to which host country income inequality influences multinational enterprises’ (MNEs) expansion strategy for foreign production investment. Applying a transaction cost framework, we predict that income inequality generally attracts foreign production investment, as MNEs prefer countries where they can achieve their strategic objectives while incurring lower levels of transaction costs arising from interactions with various market and non-market actors. We also hypothesize that the positive effect of income inequality on location attractiveness will diminish at higher levels of inequality, when the attraction effect is increasingly offset by additional monitoring, bargaining and security costs owing to the more fractious nature of high inequality societies. Finally, we argue that the effect of income inequality on location choice is contingent on investment motives: the positive effect is stronger for efficiency-seeking investment but weaker for market-seeking and competence-enhancing investments. We find substantial support for our hypotheses through an analysis of 27 years (1986-2012) of data on Japanese MNEs’ overseas production entries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".