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

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.