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Record W1996181070 · doi:10.12735/jfe.v1i2p28

Assessing the Effect of the Cameroon’s Investment Charters on Private Investment

2013· article· en· W1996181070 on OpenAlexvenueno aff
Vukenkeng Andrew Wujung

Bibliographic record

VenueJournal of Finance & Economics · 2013
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)BusinessEconomicsFinancePolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

The Cameroon government has implemented a series of investment legislations, the latest of them being that of 2002 with the aim of boosting investments and stimulating economic growth. This paper while focusing on the latest investment code contends that the various investment legislations have had a positive effect on private investments. Data for the study is collected from the World Bank Development Indicators, covering a period of 31 years from 1980 to 2010. The estimation technique used for this study is the Generalized Methods of Moments (GMM) estimation technique. The analyses (both descriptive and empirical) showed that the institution of the investment charter between 1991 and 2002 did not improve the level of private investment. However, we did obtain results indicating that the introduction of the investment charter in April 2002 resulted to an improvement in the level of private investment. Other results obtained showed that domestic credit to the private sector, GDP growth and electricity production play a positive and statistical significant influence on the level of private investment in the country. An important conclusion is that the policy structures of the 2002 investment charter should be fully implemented so as to encourage and enhanced private investment in the country.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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