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Record W3155426991 · doi:10.2478/midj-2020-0001

Does Economic Growth and Inflation Impact Consumer Confidence during a Pandemic? An Empirical Analysis in EU Countries

2020· article· en· W3155426991 on OpenAlexaboutno aff
Larissa M. Batrancea

Bibliographic record

VenueMarketing – from Information to Decision Journal · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInflation (cosmology)RecessionQuarter (Canadian coin)Consumer confidence indexGross domestic productMonetary economicsPanel dataEuropean unionGlobal recessionMonetary policyMacroeconomicsInternational economics

Abstract

fetched live from OpenAlex

Abstract The study investigates the capacity of European Union member states to face the effects of the economic crisis caused by the COVID-19 pandemic. Namely, by means of a panel data analysis, the study reports on the impact of economic growth (proxied by gross domestic product) and inflation rates (proxied by harmonized indices of consumer prices) on the overall confidence indicator corresponding to 27 EU countries for the period fourth quarter 2019–third quarter 2020. Results showed that inflation had a negative influence on the confidence indicator during the pandemic crisis, while economic growth had no impact. The negative effect triggered by inflation uncovered the impact of monetary policies and fiscal policies on the staggering level of public debt. The study emphasizes that inflation plays a significant role in the market economy, reason for which governments should monitor this factor when trying to stimulate the economy and set appropriate policies for eliminating negative consequences of potential future recession periods.

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)0.007
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.293
Teacher spread0.266 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueMarketing – from Information to Decision JournalSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207