MétaCan
Menu
Back to cohort
Record W2563629122 · doi:10.1080/00036846.2016.1153786

The determinants of FDI location choice in China: a discrete-choice analysis

2016· article· en· W2563629122 on OpenAlex
Omar Belkhodja, Muhammad Mohiuddin, Égide Karuranga

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Economics · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversité LavalThompson Rivers University
Fundersnot available
KeywordsForeign direct investmentChinaEndowmentEconomicsEconomies of agglomerationDiscrete choiceInternational economicsEconomic geographyInternational tradeEconometricsMacroeconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

This study addresses two questions: What are the determinants of foreign direct investment (FDI) location choice in China? What are the factors that determine investors’ choice between ‘Economic zones’ in China on one hand, and ‘other cities’ of China on the other hand? This study shows that FDI location choice is sensitive both on the endowment conditions in different regions/cities/economic zones in China as well as on the country of origin of the FDI. Based on a data set of 1218 observations, the results of the binary logit regressions indicate that the protection of intellectual rights, agglomeration economies, investments in education and gross regional product affect the location choice of FDI in China. This choices, however, varies depending on the origin of the FDI. Policy makers can use these findings to channel FDI to targeted regions/ cities.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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