The Two-Tier Bargaining Model Revisited: Theory and Evidence from China's Natural Resource Investments in Africa
Bibliographic record
Abstract
In recent years, foreign direct investment (FDI) in natural resource industries by Chinese firms in Africa has increased rapidly. The strategic importance of the natural resource sector to host country governments produces considerable bargaining over entry and operating terms, with attendant political risks. Using case studies in Tanzania, we find that the Chinese government and firms engage in a bargaining model different from the traditional models. Specifically, they engage in a modified one-tier bargaining model in which the Chinese government represents the collective interests of Chinese natural resource firms to negotiate with the host country government. In exchange for investment deals in the natural resource sector, the Chinese government offers a package with loans that support multiple-purpose development projects in various sectors, with a focus on infrastructure. Chinese firms act as a group to fulfill the Chinese government’s commitments to the host country government. We discuss the boundary conditions for this Chinese-style bargaining model and its relationship to political risk. We conclude that the Chinese model has unique elements, although they are likely limited to resource investments in developing countries.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".