Essays on knowledge spillovers

• Main Author: Cai, Jie
• Language: English
• Published: University of British Columbia 2011
• Online Access: http://hdl.handle.net/2429/30825

This thesis studies three issues involving knowledge diffusion across firms. The first chapter explains two data facts related to firm size distribution. First, it uses sector-specific inter-firm knowledge spillovers to explain sectoral differences in firm size heterogeneity. Greater inter-firm knowledge spillovers in a sector induce firms in that sector to invest relatively more in imitation. Greater imitation also causes faster catch-up by lagging firms and declining firm growth rate with firm size. Hence, the sectoral firm size distribution becomes more homogeneous in sectors with greater knowledge spillovers. Second, in a multi-sector version of this environment, I use inter-sector knowledge spillovers to explain the observed dependent Pareto size distributions in every subset of the economy. I test the model using patent citation data and find support for both its sectoral and aggregate predictions. The second chapter rationalizes firms’ motivation to build directed links with each other and formalizes the dynamic formation process that generates the observed network structure, including triple Power-law degree distributions, in the patent citation networks. Networks allow firms to become more specialized without losing customers, because having more firms in the market results not only in competitors but also in potential partner who redirect customers. Using firm citation panel data from the NBER Patent Citation Database, I estimate the model’s parameters and simulate networks that exhibit similar structure features as corresponding real networks. The third chapter documents a new empirical fact that larger firms update information faster than smaller firms in patent citation data and address its macroeconomic implications. In a model with size-dependent reaction time lag and Pareto firm size distribution, the gradual spread of a firm-level technology shock generates a persistent and hump-shaped aggregate output growth rate. Greater information heterogeneity across firms de-synchronizes the co-movement among firms of different sizes, and hence causes a less volatile, smoother and longer aggregate business cycle. The model is well suited to explaining several timing relations of the business cycle. For example, productivity dispersion is pro-cyclical, the top firm’s growth rate predicts future GDP growth, and investment leads hiring over the business cycle. === Arts, Faculty of === Vancouver School of Economics === Graduate