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Record W2768935815 · doi:10.31389/eco.56

Short-Run Market Access and the Construction of Better Transportation Infrastructure in Mexico

2017· article· en· W2768935815 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

VenueEconomía · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsWestern University
Fundersnot available
KeywordsWelfareMarket accessBusinessTransportation infrastructureTransport engineeringGeographyAgricultural economicsEconomicsEngineeringMarket economy

Abstract

fetched live from OpenAlex

We calculate the short-run effect that the construction of the Durango-Mazatlán highway in late 2013 and the Mexico City-Tuxpan highway in early 2014 produced on welfare in every municipality and on market access in every location of Mexico. Our estimates suggest that the former highway produced benefits not only in the region where the new highway is located, but in vast areas in the north of the country. Analogous estimates show that the latter highway mostly benefited regions near Tuxpan, but these focalized benefits were larger than any of the benefits derived from the construction of the Durango-Mazatlán highway. The municipalities in the south of the country have net short-run losses from the infrastructure construction due to losses in competitiveness. Our model is consistent with the observed sectoral growth in Sinaloa, Durango, and Veracruz in 2014. Qualitatively, market access and welfare change in the same direction and magnitudes. We thus recommend using the market access approach for shortrun analysis of infrastructure, because it is much less computationally intensive.
 
 JEL Codes: R1, R4, F15

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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