Network Connectedness and Entrepreneurship

Columbia University, New York NY

Investigators

Abstract

This project leverages a variety of large data sources to assess how network connectedness within and among neighborhoods affects rates of neighborhood entrepreneurship. Informed by recent advances in artificial intelligence (AI) and machine learning, researchers are developing new, network-based indicators of neighborhood connections. They are using these measures and AI tools to generate a nation-wide, neighborhood-level dataset that includes indicators of connectedness, entrepreneurship rates, and other variables for every U.S. census tract. A key scientific contribution of the project is its use of machine-learning/AI methods and new techniques in causal analysis to generate and test propositions about the effect of network connectedness on rates of entrepreneurship. An important broader impact of the project is the resulting publicly available nation-wide database that will inform scholars and practitioners and aid future research on how neighborhood factors affect entrepreneurship and other critical economic outcomes. This project creates measures of network connectedness metrics using data that capture acquaintance and contact ties within neighborhoods and data that measure the anonymous flows of people between two neighborhoods. The two measures are used to assess the extent to which people are acquainted with within and between their zip codes and are in direct contact with people (via visits) within and between zip codes. The resulting measures are computed for all neighborhoods in the continental United States. Supplementing the data are yearly entrepreneurship data and control on neighborhood characteristics from multiple sources including the American Community Survey and EPA’s Smart Location Database. This project is jointly funded by the Sociology Program and the Secure and Trustworthy Cyberspace (SaTC) Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.