ESSAYS ON UNDERSTANDING MACROECONOMIC FLUCTUATIONS: AN INPUT-OUTPUT NETWORK APPROACH

Abstract

This dissertation includes three chapters. The first chapter studies the impact of financial shocks and financial frictions on business cycle dynamics in China's economy. The second and third chapters focus on the driving force of structural change and its impact on aggregate fluctuations using an input-output network approach. In the second chapter, I study two questions: (i) How has the U.S. production network structure changed from 1970 to 2017? (ii) What impact does that have on aggregate fluctuations? This paper shows that a few industries, like Finance and Insurance and Professional Services, have become much more central input suppliers over time, while others, like Paper Manufacturing, have become far less important. Therefore, the third chapter considers the driving force behind such structural change. In particular, I study the question of what determines the size of an industry in a production network. China has been one of the world's fastest-growing economies over the past several decades and emerged quickly from the global financial crisis of 2008. Chapter 1, titled DO FINANCIAL SHOCKS DRIVE REAL BUSINESS FLUCTUATIONS IN CHINA, investigates to what extent financial shocks can shape business cycle fluctuations in China. Specifically, I document the cyclical properties of China's macroeconomy and financial market and show the procyclicality of dividend payout and the countercyclicality of debt repurchases with real GDP. To account for these features, I use the real business cycle model incorporating debt and equity financing developed by Jermann and Quadrini (2012) to study how the dynamics of macroeconomic and financial variables are affected by financial shocks in China. This paper finds that financial shocks contribute significantly to business cycle fluctuations in China and can account for over 60% of the variations in the growth rate of output, investment, hours worked, and debt repurchases. Hulton's Theorem states that the impact of an industry-specific shock on the aggregate economy is entirely captured by the size of this industry, regardless of its position in the production network. Chapter 2, titled THE IMPORTANCE OF INPUT-OUTPUT NETWORK STRUCTURE IN THE US ECONOMY, proposes the idea that the network structure in isolation plays an essential role in shaping GDP growth and growth volatility. First, I introduce a new measure of network structure named centrality dispersion and document that the U.S. production network has become sparsely connected from 1970 to 2017, where many industries relied on a few central input suppliers for production. Such changes are associated with slower GDP growth and higher volatility. To account for this evidence, I embed input-output linkages into a multisector real business cycle model and provide a nonlinear characterization of the impact of network structure quantified using centrality dispersion on the macroeconomy. Finally, I study model-implied relationships between production network structure, GDP growth, and growth volatility. The calibrated model accounts for approximately one-quarter of the variation in real GDP growth and 40% of GDP volatility, as observed in the data. Chapter 3, titled THE NETWORK ORIGIN OF INDUSTRY SIZE VARIATIONS, quantifies the origin of industry size variations using the features of a production network. In the analysis, I perform an exact variance decomposition of industry total sales into the supplier, buyer, and final demand components. The findings suggest that matching with many buyers in the network, especially many large buyers is essential in understanding industry size variations. More importantly, these buyer characteristics have become increasingly important in contributing to industry size variations over the 1967-2012 period. Finally, I provide new empirical evidence related to the decomposition results. The evidence reveals a strengthening negative correlation between industry size and the concentration of customer networks in the long run.