Abstract

Startup growth requires local resources. Economic geography has emphasized the role of agglomeration in accessing these resources. Sociology and urban planning have instead emphasized the role of social embeddedness. For many startups, this creates a trade-off between their home location, in which they are socially embedded, and a higher agglomeration location, where resources are more available. I present a parsimonious model that jointly explains the role of agglomeration and embeddness on startup migration, location choice, and performance. Then, using all Delaware jurisdiction firms in 26 US states, I use machine learning and firm fixed-effects models to estimate selection into Silicon Valley (a high agglomeration location) and the impact of moving on movers. Consistent with the model, higher quality firms are more likely to move, and movers leave low agglomeration areas for higher agglomeration areas. Moving to Silicon Valley increases performance under a variety of dimensions. Movers are more likely to be acquired and IPO, raise more financing, patent more, introduce more products, and have higher sales. The benefits appear to be (at least in part) driven by the benefit of knowledge spillovers in Silicon Valley. Looking at movers from 1996 to 2005, the financial benefits are only present before 2001, but non-financial benefits persist after the dot-com bust. The results imply that agglomeration is more important than embeddedness (for these high growth movers), and that the benefits are driven by access to localized knowledge and its influence on both financial and non-financial outcomes.

Full Text
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