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Record W4324139622 · doi:10.5380/re.v43i81.76420

Consumer Behavior in the Face of the Coronavirus Pandemic: An Analysis of Credit and Debit Card Spending at Brazilian States

2023· article· en· W4324139622 on OpenAlexaff
Andrea Felippe Cabello, Lorena Silva Brandão, Deise Maria Bourscheidt

Bibliographic record

VenueRevista de Economia · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsConsumer spendingConsumption (sociology)Social distancePandemicCoronavirus disease 2019 (COVID-19)BusinessDebit cardCredit cardCoronavirusGoods and servicesDemographic economicsEconomicsPublic economicsDevelopment economicsFinanceEconomyMacroeconomicsPaymentInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The paper analyzes credit and debit card consumer spending and correlates it with the social isolation measures enacted as a result of the 2020 coronavirus pandemic. Correlations were estimated between the variation in spending in different economic sectors and the Social Distancing Index (IDS) proposed by IPEA. The results show the importance of the essentiality of goods, the mobility of consumers during the pandemic and quarantine and the way in which social isolation measures affect each consumer sector. Essential sectors without mobility problems had an increase in consumption and oscillatory behavior of consumption spending during the period. Non-essential goods sectors saw a sharp reduction in consumption spending followed by a gradual recovery. Sectors associated with mobility (whether essential or not) had a sharp reduction and did not have a clear recovery in the period. In addition, there is strong evidence that possibly institutional variations between states have relevant effects.

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)0.001
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 score0.011
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.079
GPT teacher head0.321
Teacher spread0.242 · 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.

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
Published2023
Admission routes1
Has abstractyes

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