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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Notes
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.
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.
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.
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.
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.
The representative household is an analytical simplification that allows us to focus on the macroeconomic consequences of inter-sectoral flows.
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].
For ease of analysis, we assume that the ‘normal’ steady-state level of inventory is zero.
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.
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.
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.
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.
References
Acemoglu D, Carvalho VM, Ozdaglar A, Tahbaz-Salehi A (2012) The network origins of aggregate fluctuations. Econometrica 80(5):1977–2016
ADB (2020) India input–output table 2017. Asian Development Bank
Antras P, Chor D, Fally T, Hillberry R (2012) Measuring the upstreamness of production and trade flows. Am Econ Rev 102(3):412–416
Arthur WB (2006) Out-of-equilibrium economics and agent-based modeling. Handb Comput Econ 2:1551–1564
Axtell R (2000) Why agents?: On the varied motivations for agent computing in the social sciences. In: The Brookings Institution working paper 17. Center on Social and Economic Dynamics
Baqaee DR, Farhi E (2020)Supply and demand in disaggregated Keynesian economies with an application to the Covid-19 crisis. In: Discussion paper DP14743. Centre for Economic Policy Research
Barrot JN, Grassi B, Sauvagnat J (2020) Sectoral effects of social distancing. COVID Econ 3:85–102
Bonet-Morón J, Ricciulli-Marín D, Pérez-Valbuena GJ, Galvis-Aponte LA, Haddad EA, Araújo IF, Perobelli FS (2020) Regional economic impact of COVID-19 in Colombia: an input–output approach. Reg Sci Policy Pract 12:1123–1150
Borrill PL, Tesfatsion L (2011) Agent-based modeling: the right mathematics for the social sciences?. In: Davis JB, Wade Hands D (eds) The Elgar companion to recent economic methodology, chapter 11. Edward Elgar Publishing, pp 228–258
Dev M, Sengupta R (2020) Covid-19: impact on the Indian economy. In: Indira Gandhi Institute of development research working paper no. 2020-013
Epstein JM (1999) Agent-based computational models and generative social science. Complexity 4(5):41–60
Fadinger H, Schymik J (2020) The effects of working from home on covid-19 infections and production: a macroeconomic analysis for Germany. In: Working paper
Giammetti R, Papi L, Teobaldelli D, Ticchi D (2020) The Italian value chain in the pandemic: the input–output impact of Covid-19 lockdown. J Ind Bus Econ 47:483–497
Goyal A (2020) Post Covid-19: recovering and sustaining India’s growth. Indian Econ Rev 55:161–181
Gualdi S, Mandel A (2016) On the emergence of scale-free production networks. J Econ Dyn Control 73:61–77
Gualdi S, Mandel A (2018) Endogenous growth in production networks. J Evol Econ 29(1):1–27
IFO-Institute (2020) The economic costs of the coronavirus shutdown for selected European countries: a scenario calculation
Inoue H, Todo Y (2020) The propagation of the economic impact through supply chains: the case of a mega-city lockdown against the spread of COVID-19. Covid Econ 2:43–59
Inoue H, Murase Y, Todo Y (2021) ‘Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains?. In: Working paper
Kanitkar T (2020) The COVID-19 lockdown in India: impacts on the economy and the power sector. Glob Transit 2:150–156
Livan G, Novaes M, Perpaolo V (2017) Introduction to random matrices, theory and practice. Springer, Berlin
Mahajan K, Tomar S (2021) COVID-19 and supply chain disruption: evidence from food markets in India. Am J Agric Econ 103(1):35–52
Malinvaud E (1977) The theory of unemployment reconsidered. Blackwell, Oxford
Malinvaud E (1981) Econometrics faced with the needs of macroeconomic policy. Econom J Econom Soc 49:1363–1375
Malinvaud E (1982) An econometric model for macro-disequilibrium analysis. Springer, Berlin, pp 239–256
Mamgain RP (2021) Understanding labour market disruptions and job losses amidst COVID-19. J Soc Econ Dev 23:301–319
Mandel A, Veetil VP (2020a) Disequilibrium propagation of quantity constraints: application to the COVID lockdowns. In: SSRN working paper no
Mandel A, Veetil VP (2020b) The economic cost of COVID lockdowns: an out-of-equilibrium analysis. Econ Disaster Clim Change Extreme Events 4:431–451
Mandel A, Veetil V (2021) Monetary dynamics in a network economy. J Econ Dyn Control 125:104
Mandel A, Taghawi-Nejad D, Veetil VP (2019) The price effects of monetary shocks in a network economy. J Econ Behav Organ 164:300–316
McCann F, Myers S (2020) COVID-19 and the transmission of shocks through domestic supply chains. In: Working paper
Miller RE, Temurshoev U (2017) Output upstreamness and input downstreamness of industries/countries in world production. Int Reg Sci Rev 40(5):443–475
Osotimehin S, Popov L (2020) Sectoral impact of COVID-19: cascading risks. In: Opportunity and inclusive growth institute working paper no. 30
Richiardi M, Bronka P, Collado D (2020) The economic consequences of COVID-19 lock-down in the UK: an input–output analysis using consensus scenarios. In: Working paper
Sahoo P, Ashwani (2020) COVID-19 and Indian economy: impact on growth, manufacturing, trade and MSME sector. Glob Bus Rev 21(5):1159–1183
Sengupta S (2020) Coronavirus, population and the economy: a long-term perspective. Indian Econ J 68(3):323–340
Taylor L (1983) Structuralist macroeconomics. Basic Books, New York
Vidya CT, Prabheesh KP (2020) Implications of COVID-19 pandemic on the global trade networks. Emerg Mark Finance Trade 56(10):2408–2421
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Mandel, A., Veetil, V.P. Transient dynamics of the COVID lockdown on India’s production network. J Econ Interact Coord (2024). https://doi.org/10.1007/s11403-024-00409-z
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DOI: https://doi.org/10.1007/s11403-024-00409-z