Üretim ağlarında denemeler, verimlilik ve enflasyon bağlantılılık

This thesis consists of three chapters on the firm- and industry-level production networks and their role in firm-level productivity and performance over time, as well as the amplification of inflation shocks across sectors. The first chapter analyzes the interaction between firm heterogeneity, production networks, and productivity. A detailed analysis of 2006-2017 data shows that input-output linkages across Turkish manufacturing firms provide an amplification mechanism for shocks. After finding strong evidence that supports the asymmetry of the firm-level production networks, I build a simple model that captures the interaction between productivity and firm performance in production networks. Empirical results demonstrate the close relationship between the sophistication of a firm's production network, its productivity, and its entry decision to export markets. This study provides evidence on how firms become more productive if they are part of a sophisticated production network while proposing a hypothesis of learning-by-networking with other firms in their production network. It is shown that productivity gains correlated with the firm's position in the supply chain, industry class, and the diversity of its export destinations. The second chapter applies the Diebold-Yilmaz Connectedness methodology to producer price inflation (1947-2018) and industrial production growth series (1976-2018) for 17 U.S. manufacturing sectors to analyze how supply and demand shocks propagated in the manufacturing industry. The empirical results show that supply (demand) shocks are transmitted downstream (upstream) through the input-output network in the form of price/cost increases (production/input use decreases). Going into further detail, the paper shows that the aggregate and granular input-output network measures Granger-cause the system-wide and pairwise producer price inflation connectedness measures, respectively. The Granger causality from the input-output network to the inflation connectedness is stronger during periods of major supply-side shocks, such as the global oil and metal price hikes. Similarly, it is shown that the input-output network also Granger cause the industrial production connectedness during times of major aggregate demand shocks, such as the Volcker disinflation of 1981-84 and the Great Recession of 2008. The third chapter investigates the relationship between input-output networks and the transmission of inflation shocks across manufacturing industries in South Korea, an economy that is more open to external shocks than the United States. 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.