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Record W3173467979 · doi:10.24036/jkep.v2i1.8857

Analisis Pengaruh Financial Development Terhadap Pertumbuhan Ekonomi di Indonesia

2020· article· en· W3173467979 on OpenAlexaboutno aff
Nia Putri Kunanti, Melti Roza Adry

Bibliographic record

VenueJurnal Kajian Ekonomi dan Pembangunan · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsOpenness to experienceInflation (cosmology)Quarter (Canadian coin)Money supplyEconomicsBank creditVariablesMonetary economicsRegression analysisFinancial systemBusinessMonetary policy

Abstract

fetched live from OpenAlex

This study aims to determine how the influence of financial development on economic growth in Indonesia. Financial development indicators are M2 money supply, bank assets, private credit and trade openness. Where inflation and trade openness as a control variable and economic growth as the dependent variable. The data used in this study are secondary data from 2005 quarter 1 to 2018 quarter 4 which were collected through documentation and related agencies. This study uses multiple linear regression analysis and error correction models. The results of this study indicate that: (1) the money supply M2 has a negative effect on economic growth in Indonesia; (2) Bank assets have a negative effect on economic growth in Indonesia; (3) Private credit has a positive effect on economic growth in Indonesia; (4)) trade openness has a positive effect on economic growth in Indonesia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.034
GPT teacher head0.203
Teacher spread0.169 · 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.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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