MétaCan
Menu
Back to cohort
Record W4285582584 · doi:10.1257/rct.6797-1.0

Consumer Surplus of Alternative Payment Methods: Paying Uber with Cash

2020· dataset· en· W4285582584 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAEA Randomized Controlled Trials · 2020
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsBank of Canada
Fundersnot available
KeywordsPaymentCashEconomic surplusEconomicsBusinessCommerceFinanceMarket economy

Abstract

fetched live from OpenAlex

We estimate the private benefits for Uber riders from using alternative payment methods.We focus on Mexico where riders have the option to use cash or credit cards to pay for rides.We use three field experiments involving approximately 400,000 riders to estimate the loss of private benefits for riders if a ban on cash payments is implemented.We find that Uber riders, using cash as means of payment either sometimes or exclusively, suffer an average loss of approximately 50% of their expenditures on trips paid in cash before the ban.

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.

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)0.038
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0230.003
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.106
GPT teacher head0.367
Teacher spread0.261 · 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