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Record W4413073932 · doi:10.18235/0013598

Supply chain networks and the macroeconomic expectations of firms

2025· other· en· W4413073932 on OpenAlexfundno aff
Ina Hajdini, Saten Kumar, Samreen Malik, J.R. Norris, Mathieu Pedemonte

Bibliographic record

VenueEconstor (Econstor) · 2025
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Policies and Impacts
Canadian institutionsnot available
FundersTamkeenYork UniversityNew York University Abu DhabiAuckland University of Technology, New ZealandUniversité Laval
KeywordsSpillover effectUpstream (networking)Downstream (manufacturing)Supply chainEconomicsMonetary economicsAggregate (composite)Investment (military)Inflation (cosmology)MicroeconomicsBusinessMarketingTelecommunicationsOperations management

Abstract

fetched live from OpenAlex

Using a randomized control trial of approximately 1,000 firm pairs in New Zealand that have a customer-supplier relationship, we provide an information treatment to analyze both the direct effects on expectations and actions of firms receiving this information and the spillover effects on connected firms that did not directly receive information. In a follow-up three months later, we find direct and spillover effects on expectations and actions that are both significant and of comparable magnitude. An increase in expected GDP growth increases prices and employment; an increase in expected GDP uncertainty reduces prices, investment, and employment. We provide evidence that it is communication between the firms, as opposed to observable actions, driving the spillover effect on the expectations of connected firms. This is consequential as we find communication to be symmetric upstream vs downstream, while propagation via actions is asymmetric. We embed firm-to-firm communication along the supply chain in a New Keynesian pricing problem and discuss its implications for the transmission of aggregate uncertainty to firms pricing decisions and aggregate 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 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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.010
GPT teacher head0.206
Teacher spread0.197 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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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