Economy-Finance-Environment-Society Interconnections In a Stock-Flow Consistent Dynamic Model

Abstract

ABSTRACTThis work takes inspiration from four theoretical strands: recent developments in ecological macroeconomics; the Schumpeterian framework of evolutionary economics that emphasises the entrepreneurial role of the State; the stock-flow consistent approach to macroeconomic modelling; and the supermultiplier model. Building upon these approaches, we develop a formal model that reproduces key interactions between the economy, the financial sector, the ecosystem and the society. We test and assess the effects of several fiscal policies. We find that, in principle, mission-oriented innovation policies are the most effective option in supporting innovation and growth, while reducing income inequality. However, lacking a ‘green’ and progressive taxation system, they are unlikely to reverse the current trend in atmospheric temperature.KEYWORDS: Supermultipliermission-oriented policystock-flow consistent modellingclimate changerebound effectJEL CODES: B51B52E12Q57 AcknowledgementsWe would like to use this opportunity to acknowledge and thank the editor and the reviewers for their feedback, which helped us improve our original draft and publish the article.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 There are a few exceptions, notably, Naqvi and Stockhammer (Citation2018) and Dafermos and Nikolaidi (Citation2019).2 Examples of MOIS policies include the Apollo Program (EC Citation2018a) and the Energiewende Programme (EC Citation2018b; Mazzucato Citation2018). The former is the US human spaceflight program carried out by the NASA, which led to the first manned landing on the moon in 1969. The latter is a German program aiming at reducing CO2 emissions by developing a low-carbon energy system at the national level. The main purpose of the Energiewende Programme is to allow Germany to stop energy production from nuclear plants by 2022, and to rely on renewable energy resources only by 2050. The program is expected to create favourable conditions for the private sector, offering new opportunities to undertake green technological innovation (thanks to government-financed investment activities).3 More precisely, ‘[p]ublic expenditure, exports, household residential investment, and consumption financed out of debt are considered autonomous components of aggregate demand in the literature and are the proximate cause of economic growth in the supermultiplier model. These components have two characteristics: they do not increase the (private) productive capacity of the economy and they are neither caused nor funded by domestic income’ (Morlin, Passos, and Pariboni Citation2022, p. 5).4 Recently, supermultiplier-like models have been used by economists with different theoretical backgrounds (e.g., Allain Citation2015; Lavoie Citation2016; Hein Citation2018; Fazzari, Ferri, and Variato Citation2020; Nomaler, Spinola, and Verspagen Citation2021).5 See, e.g., Freitas and Christianes (Citation2020); Morlin (Citation2022); Deleidi and Mazzucato (Citation2019, Citation2021); Nomaler, Spinola, and Verspagen (Citation2021).6 While firms' investment behaviour is more complex, we provide a stylized representation of capital accumulation in this article based on empirical literature that consistently finds a strong accelerator effect. A detailed analysis of this process is beyond the scope of this article.7 We maintain the same numbering of Appendix A, where the complete set of equations is reported.8 Adaptive expectations are assumed in our model. See equation (158) in Appendix A. For the sake of simplicity, we assume that ψ=0 in our simulations, so that: E(pw)=pw,−1.9 See Brochier and Macedo e Silva (Citation2019) for a similar approach.10 See equations (131) to (136) in Appendix A.11 Notice that hybrid technologies — say, an engine using both fossil fuel and wind as energy sources — are simply considered as involving a third type of capital (with respect to fossil fuel — and wind-based engines).12 Equations (87) and (88) demonstrate that our model incorporates two distinct sources of autonomous demand. In this way, our contribution aligns with recent works that analyse the sub-components of autonomous demand, rather than treating it as a single entity. For example, Hein and Woodgate (Citation2021) explore government spending and autonomous consumption, while Morlin (Citation2022) examines exports and government spending. In Allain (Citation2022) the two types of expenditure are not specified, while Freitas and Christianes (Citation2020) focus on government and autonomous capitalists' consumption, and Pedrosa, Brochier, and Freitas (Citation2021) analyze government and household consumption.13 We refer to Moretti, Steinwender, and Van Reenen (Citation2015), Deleidi, Mazzucato, and Semieniuk (Citation2020), Ciaffi and Deleidi (Citation2021), and Deleidi and Mazzucato (Citation2021) for empirical analyses of the impact of government MOIS on private R&D spending. Furthermore, we refer to Ciaffi and Deleidi (Citation2021) and Deleidi and Mazzucato (Citation2021) for the empirical literature showing that MOIS produces larger multiplicative effects on GDP than more standard government expenditures.14 The socio-economic stock is made up of capital goods and durable (or yet-to-be-discarded) consumption goods.15 Private investment is not directly influenced by matter and energy prices instead. However, there is an indirect effect, for changes in prices affect the rates of extraction (or use) of natural reserves. These rates, in turn, affect the price level, real output, hence investment decisions.16 See equations (154) to (157) in Appendix A.17 Simulations have been performed using EViews. We are happy to provide the programming code.18 We refer to Carnevali et al. (Citation2021), who use an open-economy ecological SFC model prototype to analyse cross-border policy coordination problems.19 Notice also that LC households are usually assumed to have a higher propensity to consume relative to UC households. However, their consumption is usually greener, as green intentions of the upper classes are crowded out by wealth. In other words, ecological impacts are best predicted by people’s income level (e.g., Moser and Kleinhückelkotten Citation2018). As a result, the change in emissions due to policy (b) relative to policy (a) is ambiguous.20 See notes under Table 4 for further details.21 We refer the reader to equations (34) and (35), defining household debt level and ratio, respectively.22 Income inequality is measured as UC household income (including capital gains) to total income after taxes. Similarly, wealth inequality is measured as UC household net wealth to total net wealth.23 The same experiment can be replicated using alternative variables and/or composite indices for the four main spheres of our artificial world.