MétaCan
Menu
Back to cohort
Record W3207984002 · doi:10.15353/rea.v13i3.4522

Will the Widespread Use of Cashless Payments Reduce the Frequency of the Use of Cash Payments?

2021· article· en· W3207984002 on OpenAlexvenueno aff
Hiroshi Fujiki

Bibliographic record

VenueReview of Economic Analysis · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsCashPaymentBusinessCash and cash equivalentsCounterfactual thinkingCash managementActuarial scienceEconomicsFinance

Abstract

fetched live from OpenAlex

Will the widespread use of cashless payments reduce the frequency of the use of cash payments? This question is important because the major costs of cash use are fixed costs that would only be reduced if the frequency of cash payments substantially decreased, and thus the extent of the reduction of the cost of cash use depends on the frequency of cash use after the widespread use of cashless payment methods. Using the data from the Financial Literacy Survey 2019 in Japan, this paper shows that the frequency of cash use for those who use both cash and noncash payment methods and that of those who exclusively use cash are about once in 2.3 days and about once in 2 days, respectively, and thus there is only a slight difference. The result did not change even if a regression model for cash usage was used that considers the endogenous choice of payment methods or if counterfactual simulations of the decrease in consumers’ willingness to use cash were conducted. The results suggest that the benefit of reducing the cost of cash use due to the widespread use of cashless payment methods is overestimated because the frequency of the use of cash payments is unlikely to decrease despite the use of cashless payment methods.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.061
GPT teacher head0.252
Teacher spread0.192 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueReview of Economic AnalysisSame topicDigital Platforms and EconomicsFrench-language works237,207