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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 inducefirms 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 sectorswith 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 citationpanel 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 topfirm’s growth rate predicts future GDP growth, and investment leads hiring over the business cycle.
This dissertation is an empirical study of economic growth in India over the period of 1960-2004.The objective of the first chapter is to provide robust and reproducible period-wise growth estimates for India. Detailed growth accounting shows that without accounting for human capital, total factor productivity (TFP)differences over time account for 48% to 69% of the output variation. If we include the role of education, TFP growth accounts for 35% to 70% of the total GDP growth between 1960 and 2004. Starting from a modest rate in the 1960s, productivity growth dipped and became negative in the 1970s.This productivity growth rate began accelerating during the 1980s and it grew at an average rate of around 3% in the 1990s.Chapter 2 calculates a large set of productivity growth estimates using the Annual Survey of Industries data. The results show that even though the net-value-added for all registered manufacturing grew at around 4.4% per year, the average yearly TFP growth rate was only 2.2%. In the sub-period of 1991-1997, input growth jumped but TFP growth became negative. But after 1998, the trend is reversed and output grows because of positive and large TFP growth in spite of the moderating input growth. Production function estimates show that in gross output the share of materials is 0.6, much larger than the capital and the labor shares. “Public corporations” experienced significant TFP growth after the reforms.The last chapter provides an explanation for the sluggish performance of Indian manufacturing before the reforms. The interaction of quantitativerestriction policies and inflexible labor laws distorted the allocation of resources between intermediate inputs and labor inputs. Moreover, the combination of high inflation and the unavailability of credit exacerbated this factor distortion and lowered productivity growth further. Using panel data on Indian industries, this chapter finds underutilization of materials compared to labor until recently. The productivity growth is negatively related to labor growth and positively related to materials growth. Real wagesand labor productivity are negatively related to materials inflation and this relationship breaks down after the capital market reforms in the 1990s.