TI Complexity in Economics Seminars

Jaromír Kovářík (University of the Basque Country, Spain)
Wednesday, 19 April 2017

Empirical network studies typically analyze partial samples of the population of interest. However, partially sampled network data  bias systematically the properties of observed networks and suffer from non-classical measurement-error problem if applied as regressors. This paper analyzes statistical issues arising from examining non-randomly sampled networks. Combining theory, numerical experiments, and empirical applications, we illustrate the biases in both network statistics and the estimates of network effects under non-random sampling. We then propose a methodology that adapts post-stratification weighting approaches to networked contexts, which allows to recover several network-level statistics and reduces the biases these statistics exert on individual and network-level outcomes. The proposed methodology outperforms the corrections based on randomness proposed in the literature.

Joint work with Chih-Sheng Hsieh (Chinese University of Hong Kong), Stanley I.M. Ko (University of Macau), Trevon Logan (Ohio State University & NBER)