Inequality and finance in a rent economy

Botta, Alberto; Caverzasi, Eugenio; Russo, Alberto; Gallegati, Mauro; Stiglitz, Joseph E.

doi:10.1016/j.jebo.2019.02.013

Cited by 42 publications

(26 citation statements)

References 59 publications

(60 reference statements)

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“…For previous agent-based models including this social dimension of consumption seeAversi et al (1999),Axtell (2006),Guerini et al (2018),Cardaci and Saraceno (2019),Botta et al (2019) andRengs and Scholz-Wäckerle (2019).6 This is in line with models with credit rationing based on either non-observability of labor input, i.e. moral hazard(Aghion and Bolton, 1997, Piketty, 1997, Aghion et al, 1999, physical output Newman, 1993, Galor andZeira, 1993), or individual ability(Jaffee and Stiglitz, 1990).…”

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confidence: 56%

An agent-based model of trickle-up growth and income inequality

et al. 2021

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Les opinions exprimées dans la série des Documents de travail GREDEG sont celles des auteurs et ne reflèlent pas nécessairement celles de l'institution. Les documents n'ont pas été soumis à un rapport formel et sont donc inclus dans cette série pour obtenir des commentaires et encourager la discussion. Les droits sur les documents appartiennent aux auteurs.The views expressed in the GREDEG Working Paper Series are those of the author(s) and do not necessarily reflect those of the institution. The Working Papers have not undergone formal review and approval. Such papers are included in this series to elicit feedback and to encourage debate. Copyright belongs to the author(s).

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confidence: 56%

An agent-based model of trickle-up growth and income inequality

et al. 2021

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“…We limit heterogeneity to the households sector only in order to make the interpretation of the results much clearer than in full-fledge AB models, but we maintain the network complexity ensuing from the structured financial products considered in this model. Botta, Caverzasi, Russo, Gallegati and Stiglitz (2018) and aims at representing the development of the financial system. As discussed in Section 2, this evolution is portrayed as a fourstage process.…”

Section: The Modelmentioning

confidence: 99%

When complexity meets finance: A contribution to the study of the macroeconomic effects of complex financial systems

2022

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In the last decade, complexity economics has emerged as a powerful approach to the understanding of the most relevant factors influencing economic development. The concept of economic complexity has been applied to the study of different economic issues such as economic growth, technological change and inequality. With this work we aim at extending the application of this concept to the study of the financial side of the economy, and, in particular, of the macroeconomic effects of rising financial complexity. In this paper, we present an agent-based model integrating an increasingly complex financial sector with a real side of the economy populated, among other sectors, by heterogeneous households. We test the systemic impact that the increasing complexity of both the financial system and the financial products it manufactures bear on economic growth, macroeconomic stability and inequality. We find mixed results with respect to the positive economic implications the existing literature ascribes to products complexity and deepening production capabilities. Despite higher financial complexity may lead to faster growth, our model suggests that this comes at the cost of heightened financial fragility, a more crisis-prone economic system, and increasing levels of income and wealth inequality. According to these findings, and consistently with pioneering insights from Minsky, we claim that rising complexity does not always entail positive consequences for the well-being of the economy. This is particularly true when it comes to financial innovations and financial complexity.

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“…The authors take inspiration from the traditional SFC models (Caverzasi & Godin, 2015; Godley, 1997; Godley & Lavoie, 2007; Godley & Zezza, 2006; Nikiforos & Zezza, 2017; Passarella, 2012), to overcome these drawbacks and develop a consistent decentralized AB‐SFC model. Since then, AB models framed in an SFC account structure, have been mostly used to study monetary and fiscal policy transmission (Caiani et al., 2018; Schasfoort et al., 2017), income inequality (Botta et al., 2019; Caiani et al., 2019; Cardaci & Saraceno, 2019; Russo et al., 2016; Willis, 2015), financial markets (Botta et al., 2020; Mazzocchetti et al., 2018; Riccetti et al., 2016, 2018), institutions and labor dynamics (Caiani et al., 2020; Dosi et al., 2018), climate change (Lamperti et al., 2018; Monasterolo & Raberto, 2018, 2019; Ponta et al., 2018).…”

Section: Agent‐based Modeling and Alternative Theoretical Approaches:...mentioning

confidence: 99%

Neoclassical influences in agent‐based literature: A systematic review

Brancaccio

Gallegati

Giammetti

2021

Journal of Economic Surveys

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Several studies highlight that the method and the object of analysis of Agent‐Based models are in various respects in stark contrast to the prevailing neoclassical paradigm and therefore should be included in the traditions of alternative economic thought. In this paper, however, we show that distinctive features of the neoclassical theory can be found in most of the AB literature. Through a systematic review of most cited academic papers published in the period 1996–2019 and dedicated to Agent‐Based models, we show that a “neoclassical influence” can be detected in 61% of papers, while only 39% do not have links with the mainstream neoclassical approach. We also note that after the “great recession” started in 2008 the neoclassical influences on AB literature decline to a significant extent.

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Inequality and finance in a rent economy

Cited by 42 publications

References 59 publications

An agent-based model of trickle-up growth and income inequality

An agent-based model of trickle-up growth and income inequality

When complexity meets finance: A contribution to the study of the macroeconomic effects of complex financial systems

Neoclassical influences in agent‐based literature: A systematic review

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