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When making decisions, agents tend to make use of decisions others have made in similar situations. Ignoring this behavior in empirical models can be interpreted as a problem of omitted variables. We suggest a possibility of integrating such outside influences into models of discrete choice by defining an abstract social space in which agents with similar characteristics are neighbors who possibly influence each other. Monte Carlo simulations show the small sample properties of the proposed spatial binary choice model. When applying the estimator to innovation decisions data of German firms, we find evidence for the existence of neighborhood effects.

Estimating the Neighborhood Influence on Decision Makers: Theory and an Application on the Analysis of Innovation Decisions