Beyond the Full-Employment Equilibrium: A Forward-Looking Econometric Analysis of Capitalist Dynamics

09 September 2026, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

This paper develops and empirically evaluates a reduced-form econometric framework examining a specific tension associated with sustaining full employment in capitalist economies. Motivated by Kalecki’s political-economy analysis of permanent full employment, the framework integrates a bilateral autoregressive representation of real national revenue, a proportional full-employment benchmark mapping, a cumulative forward-looking expectation mechanism, and a terminal-horizon real-wage equation. The empirical application uses quarterly data for France, Spain, Italy, and the United Kingdom over 1996Q1–2025Q3, with country-specific adjustments for unemployment-based regime classification. Bilateral Student-t specifications select forward GDP-growth terms in all four countries; however, the innovation degrees of freedom reach the imposed lower boundary and residual diagnostics indicate limited model adequacy, requiring cautious interpretation. Using a Hodrick-Prescott statistical output benchmark, the admissibility condition 0≤θt<1 is frequently violated, while average θt is negative during identified sustained-full-employment episodes. The cumulative expectation coefficient is negative and statistically insignificant across countries, and Spain exhibits unstable out-of-sample forecasting performance. The wage block does not support the imposed positive-β restriction, while Driscoll-Kraay panel local projections reveal no significant common effect of full-employment duration on subsequent adjustment or real-wage growth. The findings do not validate the integrated mechanism but reveal substantial cross-country heterogeneity and motivate further theoretical refinement.

Keywords

Full employment
Kaleckian economics
capitalist economies
national revenue
noncausal autoregression

Supplementary materials

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Python Model Coding (Reproducible)
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The Python Codes that serve as the main tool for modeling and testing the econometrical models in all the case studies.
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