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Record W4412891203 · doi:10.26509/frbc-wp-202517

Supply Chain Networks and the Macroeconomic Expectations of Firms

2025· report· en· W4412891203 on OpenAlexfundno aff
Ilir Hajdini, Saten Kumar, Samreen Malik, J.R. Norris, Mathieu Pedemonte

Bibliographic record

VenueWorking paper · 2025
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
FundersTamkeenNew York University Abu DhabiUniversité Laval
KeywordsSupply chainChain (unit)BusinessEconomicsIndustrial organizationMonetary economicsMarketing

Abstract

fetched live from OpenAlex

In a randomized control trial of customer-supplier firm pairs in New Zealand, we treat with information one firm in a pair and analyze the treatment's effects on the expectations and actions of both the directly treated firms (direct effect) and connected firms that did not directly receive information (spillover effect). The direct and spillover effects on expectations and actions are significant and of comparable magnitude. Higher expected future real GDP growth increases prices and employment, while greater uncertainty about it reduces prices, investment, and employment. We show that spillover effects on the connected firms' expectations are driven by inter-firm communication, as opposed to observable actions. This matters as we find communication to be symmetric upstream vs downstream, while propagation via actions is asymmetric. We embed inter-firm communication along the supply chain in a New Keynesian pricing problem and discuss implications for the transmission of aggregate uncertainty to prices and inflation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.049
GPT teacher head0.240
Teacher spread0.191 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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