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Record W1507908316

The Two-Tier Bargaining Model Revisited: Theory and Evidence from China's Natural Resource Investments in Africa

2013· article· en· W1507908316 on OpenAlexaff
Jing Li, Aloysius Newenham‐Kahindi, Daniel M. Shapiro, Victor Zitian Chen

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of SaskatchewanSimon Fraser University
Fundersnot available
KeywordsGovernment (linguistics)Resource (disambiguation)Foreign direct investmentNatural resourceChinaNegotiationBusinessBargaining powerPolitical riskInvestment (military)PoliticsEconomicsEconomic systemMarket economyPolitical scienceMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.271
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

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