Cross-listing and foreign direct investment: an institutional arbitrage perspective on Chinese multinational enterprises
Bibliographic record
Abstract
Purpose Through the lens of institutional arbitrage, this study considers the impact of cross-listing on foreign direct investment of multinational enterprises (MNEs) based in China. This study aims to propose that the advantages associated with cross-listing, identified as the credibility premium and mobility premium, significantly contribute to the international competitiveness of MNEs in constructing trade networks and overcoming market entry challenges. Design/methodology/approach Drawing on an analysis of over 20,000 foreign subsidiaries from China, it is found that cross-listing firms are more likely to have larger FDI portfolios and broader geographic dispersion than non-cross-listing firms. Moreover, the magnitude of these advantages depends on the institutional distance between the home and host countries. Findings The findings suggest that the benefits of cross-listing, particularly in terms of portfolio scope and geographic dispersion, are more pronounced in host countries with mature institutional frameworks. Originality/value This study contributes to the literature by enriching understanding of cross-listing beyond financial outcomes, thereby manifesting its effect on a firm’s international strategy. The research provides valuable insights into the internationalization strategies of Chinese multinationals and how a cross-listing strategy can provide a competitive advantage over domestic peers.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".