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Record W3015569507 · doi:10.3386/w26956

The Value of Urgency: Evidence from Real-Time Congestion Pricing

2020· report· en· W3015569507 on OpenAlexaff
Antonio M. Bento, Kevin Roth, Andrew Waxman

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsValue of timeTollEconomicsValue (mathematics)MicroeconomicsEconometricsTransaction costDiscrete choiceTime value of moneyWillingness to payConstant (computer programming)Travel timeComputer science

Abstract

fetched live from OpenAlex

In Becker (1965) and neoclassical microeconomic theory the value of time is a constant fraction of the hourly wage. When taken to data, however, this value departs from theoretical predictions, and appears to vary with the amount of time saved. By observing drivers on freeways opting to enter toll lanes with high-frequency, time-varying prices that secure a minimum level-of-service, we uncover a new and fundamental aspect of preferences for travel time savings related to urgency. The presence of preferences for urgency, which reflect the fact that individuals often face discrete penalties for being late, allows us to reconcile the pattern observed in the data with neoclassical theory. Using a rich, repeated-transaction data and individual-level hedonic estimation, we show that the value of urgency accounts for 87 percent of total willingness-to-pay for time savings. As a result, ignoring the value of urgency in cost-benefit analysis severely underestimates the true value of time savings that projects deliver, as such omission will typically ignore non-trivial welfare gains to a potentially large number of individuals.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.558
GPT teacher head0.449
Teacher spread0.109 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations14
Published2020
Admission routes1
Has abstractyes

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