Endogenous money and the theory of long-period effective demand

Sergio Cesaratto () and Stefano Di Bucchianico

Bulletin of Political Economy, 2020, vol. 14, issue 1, 1-38

Abstract: The paper is a contribution to a monetary theory of demandled growth with elements from Sraffian supermultiplier analysis, monetary circuit theory, Modern Monetary Theory and endogenous money theory. The Sraffian supermultiplier is indicated as the most promising heterodox approach to growth and instability in capitalism. This approach allows consideration of such non-capacity-creating autonomous components of aggregate demand as autonomous consumption, public spending and exports, as the ultimate sources of growth and instability in modern capitalism. Following Steindl, capital gains are included among these components. Autonomous demand and investment are typically fed by endogenous money creation. The paper articulates the relation between autonomous demand and investment on one hand, and endogenous finance on the other, in the light of Keynes’s distinction between initial and final finance.

Keywords: Supermultiplier; endogenous money; monetary circuit theory; modern monetary theory; autonomous demand (search for similar items in EconPapers)
JEL-codes: B51 E11 E12 E42 E5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://serialsjournals.com/abstract/38226_1-cewsaratto_di_bucchianico.pdf (application/pdf)
https://www.bulletinofpe.com/sergio-cesaratto-stefano-di-bucchianico-20201 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:awu:journl:v:14:y:2020:i:1:p:1-38

Access Statistics for this article

More articles in Bulletin of Political Economy from Bulletin of Political Economy
Bibliographic data for series maintained by Maria Cristina Barbieri Goes ().