MétaCan
Menu
Back to cohort
Record W2910677818 · doi:10.34989/swp-2018-59

The Role of Corporate Saving over the Business Cycle: Shock Absorber or Amplifier?

2021· preprint· en· W2910677818 on OpenAlexaff
Xiaodan Gao, Shaofeng Xu

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsBank of Canada
Fundersnot available
KeywordsBusiness cycleShock (circulatory)Dynamic stochastic general equilibriumBusinessMonetary economicsEconomicsMonetary policyMacroeconomics

Abstract

fetched live from OpenAlex

We document countercyclical corporate saving behavior with the degree of countercyclicality varying nonmonotonically with firm size. We then develop a dynamic stochastic general equilibrium model with heterogeneous firms to explain the pattern and study its implications for business cycles. In the presence of financial frictions and fixed operating costs, a persistent negative productivity shock signals low future income and prompts firms to hold more cash in order to preserve financial flexibility and maintain normal operations. This countercyclicality exhibits a hump-shaped relation to firm size. Compared with medium-sized firms, small firms have a higher marginal product of capital and thus better investment opportunities, which compete for resources with cash, while large firms have more pledgeable assets and demand less cash. We find that, on average, firms accumulate cash by cutting investment and employment in recessions, which reduces aggregate output and increases economic fluctuations. Corporate saving, therefore, amplifies aggregate shocks.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.217
Teacher spread0.190 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEconstor (Econstor)Same topicEconomic theories and modelsFrench-language works237,207