Networks and Business Cycles
The speed at which the US economy has recovered from different recessions varies greatly, ranging from months to years. An important question is what drives this slow recovery process. Put differently, what links the short-term business cycles and the long-term growth trend? In this paper, we argue, theoretically and empirically, that the underlying network of knowledge flow on technology and its interactions with production networks and cross-sectional shocks explains the large variations in the speed of recovery across recessions in U.S.
Theoretically, we develop a dynamic general equilibrium incorporating two networks – production network where firms are linked through input-output, and innovation network where firms are linked through technology. We examine how cross-sectional shocks interact with these networks. In general, we show that these interactions allow us to decompose the effects of shocks, even idiosyncratic shocks, on future growth into several components. Each component includes its persistence and amplification. The persistence can be fully captured by the eigenvalue distribution of the adjacency matrix for the innovation network. When the innovation network is low rank (i.e., the leading eigenvalue is much larger than the rest), the direction of the current cross-sectional shock will reveal useful information on the economy’s future recovery process. Furthermore, when the leading eigenvalue is large enough, the impact of the shock would become extremely persistent.
The amplification can be fully captured by two sufficient statistics - the correlation between the centrality in innovation network and shocks, and the correlation between centralities in innovation and production networks. The slow recovery occurs when the amplification on the persistent component increases sharply.
To evaluate the importance of the channel, we construct a new and comprehensive patent dataset of U.S back to 1911 – patent issuance, transaction, and citation, and the production network back to 1950. We first document a set of new stylized facts in U.S. First, the innovation network is very stable and takes a low rank structure; Second, the structure of the innovation network is special such that the effect of the shock becomes very persistent and significantly amplified when sectors in the center of the innovation network are severely hit. Finally, there is a large variation in the sectors’ exposure to adversarial shocks across recessions in U.S.
Networks, Macroeconomics, Finance, and Machine Learning
Under this theme, my work can be divided into three branches:
i) Innovation network and its implication on business cycle, asset pricing, and investment;
ii) Equity-holding network and its implication on corporate finance, governance, and monetary policy;
iii) Machine learning in networks.