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Record W3210326786 · doi:10.1257/pol.20190131

Network Externality and Subsidy Structure in Two-Sided Markets: Evidence from Electric Vehicle Incentives

2021· article· en· W3210326786 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 · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSubsidyExternalityLiberian dollarCounterfactual thinkingIncentiveEconomicsNetwork effectElectric vehicleMicroeconomicsBusinessFinanceMarket economy

Abstract

fetched live from OpenAlex

This paper uses new, large-scale vehicle registry data from Norway and a two-sided market framework to show nonneutrality of different subsidies and estimate their impact on electric vehicle adoption when network externalities are present. Estimates suggest a strong positive connection between electric vehicle purchases and both consumer price and charging station subsidies. Counterfactual analyses suggest that between 2010 and 2015, every dollar spent on station subsidies resulted in more than twice as many additional electric vehicle purchases than the same amount spent on price subsidies. However, this relation inverts with increased spending, as station subsidies’ impact tapers off faster. (JEL D12, D62, D85, H25, L62, Q54)

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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