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We study the informational content of factor structures in discretetriangular systems. Factor structures have been employed in a variety ofsettings in cross sectional and panel data models, and in this paper weformally quantify their identifying power in a bivariate system often employedin the treatment effects literature. Our main findings are that imposing afactor structure yields point identification of parameters of interest, such asthe coefficient associated with the endogenous regressor in the outcomeequation, under weaker assumptions than usually required in these models. Inparticular, we show that a "non-standard" exclusion restriction that requiresan explanatory variable in the outcome equation to be excluded from thetreatment equation is no longer necessary for identification, even in caseswhere all of the regressors from the outcome equation are discrete. We alsoestablish identification of the coefficient of the endogenous regressor inmodels with more general factor structures, in situations where one has accessto at least two continuous measurements of the common factor.

Informational Content of Factor Structures in Simultaneous Binary Response Models