This paper investigates the endogenous formation of production networks while exploring the role of learning in a network model of production. It also provides the theoretical underpinnings of the mechanisms through which production networks amplify firm-level productivity. After finding strong evidence in favor of firm-level production networks' asymmetry, I build a model that lays a theoretical foundation between productivity and the formation of production networks. This paper also exploits a rich micro-level data set of Turkish firms to understand the model's relation to the data. Empirical evidence reveals the close relationship between the expansion of a firm’s production network and its productivity. The productivity changes in each firm studied through the sophistication of a network while proposing a learning-by-networking hypothesis with other firms in their production network. 

We apply the Diebold-Yilmaz Connectedness Index (DYCI) methodology to monthly producer price

inflation (1947-2020) and industrial production growth (1976-2020) series for 17 U.S. manufacturing industries to

analyze supply and demand shock propagation across industries. Test results indicate that both at the aggregate

and pairwise connectedness level input-output network Granger-causes the inflation connectedness measures.

The Granger causality from the input-output network to the inflation connectedness is stronger during periods

of global supply shocks, in the form of significant global metal price hikes. Similarly, we show that the aggregate

and pairwise input-output network measures Granger-cause the industrial production connectedness, during U.S.

recessions since 1980s, including the latest Covid-19 recession. In summary, we find strong evidence showing

that supply shocks propagate downstream through changes in producer prices while demand shocks propagate

upstream through production changes. of production/input use decreases.

This paper investigates the relationship between input-output networks and the transmission of inflation shocks across manufacturing industries in South Korea, an economy that is open to external shocks. Using the dynamic inflation connectedness measures for 1971-2020, we show that production networks are responsible for the amplification of inflation shocks during times of supply shocks, such as the oil price shocks of 1973-74 and 1979-80. On the contrary, production networks are weakly associated with inflation transmission across sectors if the shocks originate from the demand-side such as the East Asian Financial Crisis of 1997.

This paper provides insights for policymakers to evaluate the impact of staying at home and lockdown policies by investigating possible links between individual mobility and the spread of the COVID-19 virus in Italy. By relying on the daily data, the empirical evidence suggests that an increase in the number of visits to public spaces such as workspaces, parks, retail areas, and the use of public transportation is associated with an increase in the positive COVID-19 cases in a subsequent week. On the contrary, the increased intensity of staying in residential spaces is related to a decrease in the confirmed cases of COVID-19 significantly. Results are robust after controlling for the lockdown period. Empirical evidence underlines the importance of the lockdown decision. Further, there is substantial regional variation among the twenty regions of Italy. Individual presence in public vs. residential spaces has a more significant effect on the number of COVID-19 cases in the Lombardy region.