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Record W4307713577 · doi:10.1257/pol.20200400

Reminders Work, but for Whom? Evidence from New York City Parking Ticket Recipients

2022· article· en· W4307713577 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

VenueAmerican Economic Journal Economic Policy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsQueen's University
Fundersnot available
KeywordsDisadvantagedPsychological interventionTicketNatural experimentIncentiveBaseline (sea)Work (physics)Demographic economicsPublic economicsVariation (astronomy)EconomicsBusinessPolitical sciencePsychologyMedicineComputer scienceEconomic growthComputer securityMicroeconomicsEngineering

Abstract

fetched live from OpenAlex

We investigate heterogeneity in responsiveness to reminder letters among New York City parking ticket recipients. Using variation in the timing of letters, we find a strong aggregate response. But we find large differences across individuals: those with a low baseline propensity to respond to tickets—a natural nudge target—react least to letters. These low-response types, who incur significant late penalties, disproportionately come from already disadvantaged groups. They do react strongly to traditional, incentive-based interventions. We discuss how accounting for response heterogeneity might change one’s approach to policy and how one might use our analysis to target interventions at low-response types. (JEL D04, D12, D91, H71)

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)0.002
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.178
GPT teacher head0.272
Teacher spread0.094 · 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