Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Identification conditions for binary choice errors-in-variables models are explored. Conditions for the consistency and asymptotic normality of the maximum likelihood estimators of binary choice models with unbounded explanatory variables are given. Two- or three-step estimators to simplify computation are also suggested.