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Record W3136793951 · doi:10.1111/jfir.12240

Inequality, autocracy, and sovereign funds as determinants of foreign portfolio equity flows

2021· article· en· W3136793951 on OpenAlexaff
David M. Kemme, Bhavik Parikh, Tanja Steigner

Bibliographic record

VenueThe Journal of Financial Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsAutocracyEconomicsEquity (law)PortfolioForeign direct investmentInequalitySovereign wealth fundForeign portfolio investmentPortfolio investmentSovereigntyPolitical riskMonetary economicsBusinessInternational economicsPoliticsFinancial economicsMacroeconomicsReturn on investmentDemocracy

Abstract

fetched live from OpenAlex

Abstract We identify new country characteristics that influence cross‐border equity flows: income inequality, autocracy, and sovereign wealth funds. Using 149 source countries and 34 Organisation for Economic Co‐operation and Development (OECD) host countries for 2002–2013, we find that OECD host countries receive higher foreign portfolio investment (FPI) inflows from source countries with high income inequality and source countries with a sovereign wealth fund, but lower inflows from autocratic source countries. We argue that concentrated wealth in a society, overseas investments for the benefit of future generations, and political regimes influence FPI. These variables have been understudied though they provide valuable indicators for policy makers and investors. We supplement our core analysis with realistic interactions between these new variables and with the increasing importance of tax havens. Our results hold throughout a variety of robustness checks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.101
GPT teacher head0.371
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2021
Admission routes1
Has abstractyes

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