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Record W2026295518 · doi:10.1080/0269217042000186697

Similitudes and Discrepancies in Post‐Keynesian and Marxist Theories of Investment: A Theoretical and Empirical Investigation

2004· article· en· W2026295518 on OpenAlexaffabout
Marc Lavoie, Gabriel Rodrı́guez, Mario Seccareccia

Bibliographic record

VenueInternational Review of Applied Economics · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsUniversity of Ottawa
FundersUniversity of Missouri-Kansas City
KeywordsMarxist philosophyInvestment functionEconomicsPost-Keynesian economicsRate of profitInvestment (military)Keynesian economicsInflation (cosmology)Neoclassical economicsEconometricsProfit (economics)MacroeconomicsProduction (economics)Law

Abstract

fetched live from OpenAlex

There has been a substantial amount of convergence between post‐Keynesian and Marxist economics, the writings of Kalecki being common ground for both traditions. Still, some differences remain. While authors in both traditions seem to agree to a large extent on short‐period issues, long‐period matters relating to the role of saving, the rate of profit, inflation, crowding out, excess money supply, are still contentious. All this seems to depend on the exact form taken by the investment function, more specifically the role of capacity utilization. Four different equations are set up to be tested, two of which correspond to two variants of the Marxist view, while the other two equations correspond to a naive and a sophisticated Kaleckian view, the latter being based on hysteresis. The equations are tested on three sets of annual Canadian data. Various statistical tests are applied to all four equations in an effort to rank them, notably information and encompassing tests. The Kaleckian equation with hysteresis generally comes out empirically with the preferred statistical properties, when manufacturing data on actual rates of capital accumulation are considered separately or when both realized and intended rates of investment for the total industrial sector are used.

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)0.014
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0010.007
Scholarly communication0.0050.008
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations93
Published2004
Admission routes2
Has abstractyes

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