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ABSTRACT
The paper examines different real-monetary dynamics and policy responses during the COVID-19 pandemic (C19P) and the Global Financial Crisis (GFC) in the United States through the lens of the Sraffian Supermultiplier model and the endogenous money approach. On the one hand, the C19P triggered a relatively deeper contraction in GDP, but its recovery was relatively faster and more robust. The ‘V’ shaped recovery from the C19P significantly contrasted the ‘L’ shaped trajectory of the post-GFC period. On the other hand, the money supply (M2) grew relatively more, and the monetary base expanded relatively less during the C19P. To explain these different real-monetary dynamics, a theoretical framework is developed and analyzed empirically for the United States. The theoretical model and the empirical analysis suggest that money supply is endogenously determined by creditworthy demand derived from the multiplier-accelerator effect of non-capacity generating autonomous demand, in which government spending plays a key role. Therefore, the absence of full hysteresis after the C19P shock had nothing to do with a traditional neoclassical trend reversal mechanism but with the fiscal supermultiplier effect of the extraordinary pandemic-related government spending.
Acknowledgements
I would like to use this opportunity to acknowledge and thank the reviewers who reviewed this article and aided in its publication. Moreover, I extend my gratitude to Franklin Serrano, Marc Lavoie, Mark Setterfield and Ricardo Summa for their direct or indirect contributions to this paper.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
Notes
1 The unemployment rate is an indicator that should be taken with caution. For example, it excludes discouraged and involuntary part-time workers unless explicitly added. Also, it does not consider the people who give up and stop looking for a job ‘actively’ and thus are excluded from the ‘labor force.’ Alternatively, the employment-population ratio is an indicator that provides additional information on the state of the labor market. For example, this ratio has diminished in each recession and has never returned to the pre-crisis level since 2000.
2 See Lavoie (Citation2016) for a discussion on ‘hysteresis’ in recent mainstream economics.
3 The vector of supply-side variables can include tax incentives, saving propensities, intertemporal discount rates, and/or human capital accumulation depending on the modeling version (see Blecker and Setterfield Citation2019, ch. 1.4)
4 Note that the population growth rate could eventually be endogenized in an open economy model. Considering Adam Smith’s ‘perfect liberty’ which implies free mobility of capital and labor, immigration could be a solution for potential labor constraints (see Foley Citation2011).
5 The M2 expansion referred to during the pandemic has nothing to do with the sudden increase of the aggregate M1 in May 2020. The latter was caused by changes in ‘Regulation D,’ which specifies how banks must classify deposit accounts. After specific deregulation during the C19P, the US Federal Reserve established that savings accounts must be computed as part of the M1 since they became more liquid. In summary, those changes affected the composition of M2 but not its level.
6 See the inflation debate in the Monetary Policy Institute Blog, or the debate between Franklin Serrano, Marc Lavoie, Louis-Phillipe Rochon, and Ettore Gallo during the ‘4th International Workshop on Demand-Led Growth’ at the Federal University of Rio de Janeiro (Rio de Janeiro, Brazil) on July 27, 2023.
7 It is worth noting that if public investment eventually generates capacity, capital accumulation would not be limited to the private sector. Therefore, the model would need to be adapted to these assumptions.
8 Considering the growth rate of the capacity utilization rate where
is the growth rate of capital accumulation (and
the technical-capital-to-capacity-output ratio), it follows that
. Therefore, assuming that
in the long-run, it follows that
when
and
.
9 One of the hot topics that central bankers worldwide are currently discussing is the possibility of issuing central bank digital currencies to replace physical notes and coins.
10 The stability of the velocity of money has been extensively debated in monetary economics. For example, take the debate at the Radcliffe Commission in England in the 50s between the academics who claimed that velocity was a constant or a predictive variable and the central bankers, supported by the post-Keynesians Kaldor and Kahn, who claimed that it was unstable (see Lavoie Citation2022, chapter 4).
11 In the post-pandemic, however, the Fed initiated a new rate hike cycle to reduce the inflation rate that accelerated during 2021 and the first semester of 2022. Although the Fed had initially claimed that inflationary pressures were ‘transitory’ derived from pandemic-related supply-chain factors, later, it returned to a more mainstream view by pointing out that the economy was ‘overheated’.
12 Similar to Girardi and Pariboni (Citation2016)’s analyses for the US case, Gallo and Barbieri Goes (Citation2023) also run a vector-error correction model for Euro Area.
13 As recommended for HP filtering, the lambda value was set at 1600 for quarterly frequency.
14 Note that it is not the objective to precisely estimate the level of the normal capacity utilization rate but simply in approximating the direction of the reaction function of capacity-creating expenditures. Also note that the use of a data-smoothing technique, such as the HP filter, does not necessarily imply a theoretical endogenous normal rate of capacity utilization as suggested by some neo-Kaleckians (see Lavoie Citation2022, ch. 6; and Setterfield Citation2017). Recall that the Sraffian Supermultiplier model assumes an exogenous normal rate determined by historical conventions that can eventually change over time but not with respect to the business cycle. Even so, Serrano, Summa, and Freitas (Citation2022) have already claimed that in the case in which ‘there is indeed a definite function that relates the normal degree of capacity utilization directly to the rate of growth of demand (…) this relation in the model would have the consequence of basically making the propensity to invest become somewhat less sensitive to increases in the actual growth rate of demand as less new capacity would now be necessary to meet it. This would, ceteris paribus, reduce the propensity to invest both during the adjustment process and in the fully adjusted position and, in its turn, make the model’s equilibrium more prone to be dynamically stable and compatible with higher rates of demand-led growth (…) Therefore, if such endogeneity of the normal degree of utilization were to be established as theoretically and empirically relevant, the main conclusions of the Supermultiplier model would only be reinforced’ (pp. 13–14). Finally, also consider that there are alternative methods to the HF filter if the objective would be to estimate the normal rate of capacity utilization (see Botte Citation2020 and Gallo and Barbieri Goes Citation2023, for example). Moreover, eventually, the so-called ‘capacity controversy’ on using the FRED index as a measure of actual utilization should be considered (see Gahn and Gonzalez Citation2020; Nikiforos Citation2016).
15 Note, however, that it is possible that one component, even if it is smaller than another, could have a relatively larger supermultiplier effect. Theoretically, each component has a particular supermultiplier effect.