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19.03.2024 | Regular Article

Transient dynamics of the COVID lockdown on India’s production network

verfasst von: Antoine Mandel, Vipin P. Veetil

Erschienen in: Journal of Economic Interaction and Coordination

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Abstract

In the wake of the COVID-19 pandemic, the Government of India imposed production restrictions on various sectors of the economy. Prima facie there is reason to believe that the cost of the quantity constraints may be greater than their simple sum. This is because quantity constraints percolate through the production network forcing some sectors to reduce output because of the non-availability of inputs. This paper uses an input–output network model (IO-NET model) to study the impact of the lockdown on the Indian economy. We calibrate our IO-NET model to the Indian economy using data on sectoral linkages. We then examine the impact of the lockdown using sector-based computational experiments. Such experiments allow us to examine the out-of-equilibrium time dynamics that emerge in response to the lockdown. The transient dynamics reveal certain counterintuitive phenomena. The first of which is that the supply of output of some sectors increases during and immediately after the lockdown. Second, recovery after the relaxation of the lockdown entails the overshooting of GDP above its normal levels. And the size of the overshooting depends on the stickiness of prices. These counterintuitive phenomena are intimately related to the network interaction between firms as buyers and sellers of intermediate inputs. The paper also measures the network effect of the lockdown across different sectors. There is sizeable heterogeneity among sectors in how their network position amplifies the quantity constraints imposed on sectors distantly related to them as buyers–sellers of intermediate inputs. Ultimately, models like our own can serve as testbeds for policy experiments, especially when the model is calibrated to granular data on buyer–seller linkages in the economy.

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Fußnoten
1
Note that our model embeds a coordination problem that is subtly different from the equilibrium coordination problem models described by Foley. Unlike the social coordination problem described by Foley where each agent cares about the decisions of all others captured by some aggregate variable, our model studies a social coordination problem that emerges from the fact that each agent makes decisions based on the decisions of a small set of agents from the population of all agents. More specifically, each firm/sector within our model is directly influenced only the prices set by its input seller and the demands given by its output buyers. The coordination problem arises because of the web of inter-relations between the decisions of many agents, each tied to a few other agents, thereby through long chains related to everyone in the economic system.
 
2
For studies using input–output tables to under the impact of the COVID lockdowns on other economies, see Bonet-Morón et al. (2020), Fadinger and Schymik (2020), McCann and Myers (2020), Giammetti et al. (2020) and Richiardi et al. (2020).
 
3
From an empirical point of view, one of the primary shortcomings of equilibrium models of COVID lockdowns is their inability to generate the sizeable fluctuations in sectoral and subsectoral outputs, along with their complex nonlinear time dynamics. Figure 1 of the paper presents these complex time dynamics of subsectoral levels for the Indian economy. Our model is able to generate some aspects of the nonlinear time dynamics of sectoral outputs, with the outputs of some sectors rising in response to the lockdown shock as observed in the data (Fig. 4). See the following papers for an equilibrium treatment of the COVID lockdown shocks.
 
4
It is worth mentioning some of the India specific papers. Dev and Sengupta (2020) note the state of the Indian economy before the pandemic began and the constraints on policy options available to respond to it. While Goyal (2020) discusses possible policy responses to the COVID lockdown using old style simple aggregate Keynesian thinking. Kanitkar (2020) studies the impact on the energy sector, Sahoo and Ashwani. (2020) note the impact on MSME and trade, and Mamgain (2021) examines the effect on the labor market. Lastly, Sengupta (2020) studies the impact on output as a decline in labor today reduces capital formation and therefore future output. And Vidya and Prabheesh (2020) study the impact of the pandemic on the global trade network with particular emphasis on India.
 
5
The closet models to our own in this sense are Inoue and Todo’s (2020) account of the Japanese economy and Asian Development Bank’s MIROT model. While these models do not assume equilibrium, nor do they explicitly study the out-of-equilibrium dynamics that emerge when firms/sectors respond to quantity constraints.
 
6
For more simulation results on the model’s convergence to equilibrium see Mandel et al. (2019, pp. 9–10). For results on theoretical bounds of the convergence to equilibrium see Mandel and Veetil (2021, Lemma 1 and Proposition 2).
 
7
See Borrill and Tesfatsion (2011) and Axtell et al. (2000) for an introduction to agent-based models. See Epstein (1999) for a discussion about how the ‘generative’ approach ingrained in agent-based models is distinct from both the deductive and inductive methods. And see Arthur (2006) for a discourse on how agent-based models can be used to study out-of-equilibrium dynamics.
 
8
The representative household is an analytical simplification that allows us to focus on the macroeconomic consequences of inter-sectoral flows.
 
9
The following modification were made to IFO Scenario 1, the IFO lockdown production in brackets preceded by our Scenario A: ’Coke and refined petroleum products’ 0.5 [IFO 0.2], ‘Sale, maintenance, and repair of motor vehicles and motorcycles; retail sale of fuel’ 0.5 [IFO 0.2], ‘Post and telecommunications’ 0.8 [IFO 0.2], ‘Education’ 0.5 [IFO 1].
 
10
For ease of analysis, we assume that the ‘normal’ steady-state level of inventory is zero.
 
11
For example, the March 2020 value is the ratio of GVA in March 2020 to GVA in March 2019, the June 2021 value is the ratio of GVA in June 2021 to GVA in June 2019.
 
12
The supply chain index \(\hat{\textbf{v}}\) is also related to measures of Total Forward Linkages developed by Antras et al. (2012) and Miller and Temurshoev (2017).
 
13
The linear regression line plotted in Fig. 6 has a positive slope of 0.23 with a p value of 0.19 and r-value of 0.23. There is little reason to presume a linear relation between the two variables, both of which involve nonlinear transformations of the network of relations between firms. A linear regression is merely a starting point to examine such complex relations.
 
14
Non-market-clearing prices generate nonzero excess demands. In case of positive excess demand, goods are rationed in proportion to the nominal demand from different buyers. In case of negative excess demand, firms carry inventory over to the next time step. The inventory so carried is treated no differently from the output produced at the next time step. In other words, the inventory is added to the output produced to determine the price.
 
15
No one has so far studied the influence of the stickiness of prices in a multi-market setting, wherein price stickiness in one market can amplify the effect of price stickiness in another market. The super-linearity of the overshooting of GDP suggests that price stickiness interacts across markets related to each other as suppliers of intermediate inputs. Our impression is that macroeconomic dynamics is influenced by the ‘network structure of price stickiness’ by which we mean the distribution of price stickiness across markets related to each other via their input–output relations. Consider two economies, \(\mathcal {E}_1\) and \(\mathcal {E}_2\), each with n sectors. Assume that the network of buyer–seller relations between sectors in two economies is given by adjacency matrices \(M_1\) and \(M_2\). Suppose further that the distribution of price stickiness in both economies is given by \(\phi \). It may well be that the time dynamics of aggregate variables in response to fiscal and monetary shocks differ in two economies because \(\mathcal {E}_1\)’s time dynamics is driven by the relation between \(M_1\) and \({\phi,} \) whereas \(\mathcal {E}_2\)’s time dynamics is driven by the relation between \(M_2\) and \({\phi} \). Most workhorse macroeconomic models with price stickiness implicitly assume that the way in which \(\phi \) is embedded on \(M_1\) and \(M_2\) does not matter in the propagation of fiscal and monetary shocks. This seems to be far too heroic an assumption.
 
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Metadaten
Titel
Transient dynamics of the COVID lockdown on India’s production network
verfasst von
Antoine Mandel
Vipin P. Veetil
Publikationsdatum
19.03.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Economic Interaction and Coordination
Print ISSN: 1860-711X
Elektronische ISSN: 1860-7128
DOI
https://doi.org/10.1007/s11403-024-00409-z